{"id": "2507.15584", "title": "We Need to Rethink Benchmarking in Anomaly Detection", "abstract": "Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms. In this position paper, we argue that this stagnation is due to limitations in how we evaluate anomaly detection algorithms. In current benchmarks, a trivial algorithm that only checks for extreme values in individual features performs competitively with state-of-the-art deep learning methods, despite failing on simple cases such as anomalies within an annulus of normal points. Moreover, existing benchmarks do not adequately reflect the diversity of anomaly detection applications, making it difficult for practitioners to reliably select algorithms for their applications. Consequently, we need to rethink benchmarking in anomaly detection. In our opinion, anomaly detection should be studied using scenarios that group applications sharing relevant characteristics, defined through a common taxonomy. Benchmarking within scenarios enables scenario-specific choices for preprocessing, metrics, and model selection, clarifying which advances transfer across similar applications and providing practitioners with reliable guidance for their specific contexts.", "date": "2025-07-21", "categories": []}
{"id": "2604.06265", "title": "SMT-AD: a scalable quantum-inspired anomaly detection approach", "abstract": "Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and the number of additional components in the matrix product operators structure. We demonstrate successful anomaly detection when applied to standard datasets, including credit card transactions, and find that, even with minimal configurations, it achieves competitive performance against established anomaly detection baselines. Furthermore, it provides a straightforward way to reduce the weight of the model and even improve the performance by highlighting the most relevant input features.", "date": "2026-04-07", "categories": []}
{"id": "2606.20055", "title": "PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection", "abstract": "Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains. Current Transformer- and large-model-based detection approaches incur excessive computational overhead, while existing lightweight alternatives are constrained by insufficient feature extraction and inadequate modeling of dependencies across multivariate variables. To mitigate the above drawbacks, this study develops a lightweight, efficient anomaly detection model, dubbed PaAno, within the patch-oriented representation learning paradigm. In the encoder module, a multiscale feature-extraction backbone is constructed using convolutional kernels with differentiated receptive fields to capture hierarchical temporal characteristics; subsequent cross-scale adaptive attention aggregation, combined with residual connection optimization, further stabilizes feature representation learning. A cross-variable fusion attention module is embedded to explicitly characterize inter-variable correlations, empowering the model to identify anomalous patterns amid intricate operational conditions. Moreover, a novel pretext task based on temporal patch-window sorting is customized to uncover intrinsic structural properties of time series, and triplet loss is leveraged to optimize the patch embedding space for enhanced feature discrimination. Extensive experiments on the TSB-AD benchmark demonstrate that the proposed PaAno achieves state-of-the-art detection accuracy on both univariate and multivariate tasks, yielding significant performance gains across evaluation metrics, including VUS-PR, relative to the original PaAno. Leveraging a compact network design, the presented model achieves favorable computational efficiency, enabling deployment on resource-limited terminals for real-time anomaly inference.", "date": "2026-06-18", "categories": []}
{"id": "2606.20300", "title": "CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection", "abstract": "Few-shot anomaly detection remains challenging due to limited training data. Multi-modal anomaly detection (MAD) offers a viable solution, leveraging 3D geometric cues to enrich 2D RGB representations and compensate for this scarcity. However, existing MAD methods apply spatially uniform feature processing, conflating stable macroscopic structures with high-frequency localized defect signals, exacerbating cross-modal misalignment and inflating false-positive rates. To overcome this, we present CMDS-AD, a Cross-Modal Dual-Stream Anomaly Detection framework. A LoRA-guided diffusion model generates diverse RGB samples to mitigate extreme data scarcity. For 3D normal augmentation, we employ a pre-trained diffusion model as a normal estimator. Crucially, this estimator inherently acts as a non-linear low-pass filter, directly extracting low-frequency normal representations from RGB inputs. This establishes an auxiliary estimated stream of purely low-frequency information, anchoring robust structural templates and assisting the uncompressed real stream, containing coupled high- and low-frequency components, to precisely isolate micro-defects. A Coordinate-Aware Hierarchical Feature Mapper adaptively aligns cross-modal semantics, while a multiplicative scoring mechanism filters modality-specific noise. Under the extreme 1-shot setting, CMDS-AD achieves absolute performance gains of 5.7% (I-AUROC) and 2.0% (AUPRO) on MVTec 3D-AD, alongside 7.7% and 5.6% improvements on EyeCandies, establishing a new state-of-the-art.", "date": "2026-06-18", "categories": []}
{"id": "2606.20312", "title": "Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection", "abstract": "Pose-flow video anomaly detectors are attractive for one-class surveillance because they provide likelihood-based rankings for tracked skeleton windows. However, a single likelihood score may hide multimodal normal behavior and be sensitive to pose-observation noise. We study a frozen-detector setting in which the pose-flow backbone, cached skeleton tracks, and evaluation pipeline are fixed. Reliability-Aware Prototype Calibration (RPC) is a post-hoc score calibration method for this setting. It adds a standardized nearest-prototype deviation in the frozen latent space to the standardized flow score, and uses keypoint confidence only to gate this added geometric evidence. Thus, RPC preserves the original density signal while correcting the ranking with empirical normal-mode structure under pose reliability. Across two frozen pose-flow backbones and four datasets, RPC improves frame-level AUROC in all eight backbone-dataset pairs, with gains ranging from 0.34 to 4.49 percentage points and averaging 2.03 points. Ablation and reliability analyses show that prototype deviation is the main corrective signal, while reliability gating is most useful when pose observations are less trustworthy. These results suggest that lightweight post-hoc calibration can strengthen cached pose-flow systems when retraining or reproducing the full pose pipeline is impractical.", "date": "2026-06-18", "categories": []}
{"id": "2606.18621", "title": "Towards Anomaly Detection on Relational Data", "abstract": "Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-explored. The key challenges lie in the intrinsic complexity of relational data: multi-table attributes are high-dimensional and heterogeneous, making sparse abnormal clues easy to overwhelm by normal or irrelevant information; and anomalies may further manifest as abnormal connection patterns across different foreign-key relations, which existing tabular and graph anomaly detection methods are ill-suited to capture. To address them, we propose RelAD, a reconstruction-based framework that captures anomalies from both attribute and relational edge reconstruction. RelAD contains two core modules: conditional sparse-gated attribute reconstruction, which suppresses redundant multi-table attributes and emphasizes abnormal semantic blocks, and dual-view multi-relational edge reconstruction, which detects relation-specific abnormal connections from both intrinsic and behavioral entity profiles. The resulting attribute and relational signals are integrated through a lightweight fusion module to produce the final anomaly score. We further construct 6 benchmark datasets with systematic anomalies, on which extensive experiments show that RelAD consistently outperforms other baselines while achieving competitive efficiency.", "date": "2026-06-17", "categories": []}
{"id": "2606.18749", "title": "Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models", "abstract": "Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changing patient populations, and pathologies for which annotated training data may be unavailable. Most existing zero-shot anomaly detection methods are designed for 2D images, and their direct extension to 3D medical volumes is limited by the scarcity of large-scale volumetric foundation models or by the difficulty of utilizing volumetric context. We propose CS3F, a training-free batch-based framework for ZSAD in 3D medical images using 2D foundation models. Each volume is decomposed along multiple anatomical axes and encoded slice-wise by a 2D vision transformer. These are then converted into localized volumetric tokens by pooling neighboring slice features. Anomaly scores are obtained from cross-subject mutual similarity: tokens that lack close analogues in other subjects are assigned higher anomaly scores. To reduce the attenuation of focal lesion signals caused by depth pooling, we introduce a coarse-to-fine tokenization strategy that enables fine-resolution volumetric scoring without exhaustive matching. CS3F is evaluated on brain MRI across metastases, glioma, and stroke, as well as validated on lung CT to test generalizability beyond atlas-aligned brain MRI. The results show that frozen 2D foundation models can support anomaly localization in 3D medical images, and that the benefit of fine tokenization depends strongly on lesion contrast and imaging modality.", "date": "2026-06-17", "categories": []}
{"id": "2606.18898", "title": "Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs", "abstract": "Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare. Real-world data is often sparse, irregularly sampled or partially observed, yet existing methods assume uniformly sampled time series. We propose a generative approach based on Latent SDEs that projects the observed time series on a continuous-time stochastic dynamical system, directly being able to handle missing observations and irregular sampling, while also naturally capturing possible cyclic behavior that many real-world use cases inherently possess. Experiments on six anomaly benchmark datasets show that our proposed method ranks first among state-of-the-art baselines. We further demonstrate that our method remains robust under severe data sparsity, while performance significantly degrades for the tested baseline methods. These results highlight latent SDEs as a natural inductive bias for anomaly detection in multivariate time series, especially in presence of real-world irregularities.", "date": "2026-06-17", "categories": []}
{"id": "2606.19255", "title": "SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering", "abstract": "Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generalization problems, which are challenging to balance. To address this, we introduce multi-scale clustering to enhance reconstruction-based methods. At the representation level, we integrate the cluster center representations of normal patterns to constrain the model to target representative normal patterns for reconstruction, preventing dominance of powerful capacity and representation capability. At the anomaly criterion level, we derive anomaly confidence score based on cluster membership probability and combine it with reconstruction error, providing dual criteria for detection. Furthermore, the effectiveness of the cluster center representations and anomaly confidence score depends on the clustering performance. Accordingly, we extract neighborhood-centered representations for multi-view clustering to improve clustering performance. Extensive experiments on multiple real-world datasets from diverse application domains demonstrate the state-of-the-art performance of SCAN.", "date": "2026-06-17", "categories": []}
{"id": "2505.03509", "title": "AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning", "abstract": "Anomaly detection in large datasets is essential in astronomy and computer vision. However, due to a scarcity of labelled data, it is often infeasible to apply supervised methods to anomaly detection. We present AnomalyMatch, an anomaly detection framework combining the semi-supervised FixMatch algorithm using EfficientNet classifiers with active learning. AnomalyMatch is tailored for large-scale applications and integrated into the ESA Datalabs science platform. In this method, we treat anomaly detection as a binary classification problem and efficiently utilise limited labelled and abundant unlabelled images for training. We enable active learning via a user interface for verification of high-confidence anomalies and correction of false positives. Evaluations on the GalaxyMNIST astronomical dataset and the miniImageNet natural-image benchmark under severe class imbalance display strong performance. Starting from five to ten labelled anomalies, we achieve an average AUROC of 0.96 (miniImageNet) and 0.89 (GalaxyMNIST), with respective AUPRC of 0.82 and 0.77. After three active learning cycles, anomalies are ranked with 76% (miniImageNet) to 94% (GalaxyMNIST) precision in the top 1% of the highest-ranking images by score. We compare to the established Astronomaly software on selected 'odd' galaxies from the 'Galaxy Zoo- The Galaxy Challenge' dataset, achieving comparable performance with an average AUROC of 0.83. Our results underscore the exceptional utility and scalability of this approach for anomaly discovery, highlighting the value of specialised approaches for domains characterised by severe label scarcity", "date": "2025-05-06", "categories": []}
{"id": "2604.17616", "title": "Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection", "abstract": "Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states. Instead of using marginal or randomly sampled baselines, our method retrieves representative normal instances conditioned on the anomalous observation, enabling dependency-preserving and operationally meaningful explanations. To support high-dimensional time-series data, contextual retrieval is performed in learned low-dimensional representations using both variational autoencoder latent spaces and UMAP manifold embeddings. By grounding the retrieval process in the system's learned manifold, this strategy avoids out-of-distribution artifacts and ensures attribution fidelity while maintaining computational efficiency. We further introduce confidence-aware and temporal evaluation metrics for assessing explanation reliability and responsiveness. Experiments on the SWaT and MSDS benchmarks demonstrate that the proposed approach consistently improves root-cause identification accuracy, temporal localization, and robustness across multiple anomaly detection models. These results highlight the practical utility of conditional attribution for explainable anomaly diagnosis in complex time-series systems. Code and models are available at: https://github.com/dfki-av/Conditional-Attribution-for-Root-Cause-Analysis-in-Time-Series-Anomaly-Detection.", "date": "2026-04-19", "categories": []}
{"id": "2605.29526", "title": "Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection", "abstract": "Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: \\textit{adversarial pattern evolution by malicious actors} and \\textit{the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains}. To address these challenges, we propose a novel framework termed \\textbf{TE}mporal \\textbf{M}otif-aware \\textbf{G}raph \\textbf{T}est-\\textbf{T}ime \\textbf{A}daptation (\\textbf{TEMG-TTA}). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed \\textbf{TEMG-TTA} outperforms \\textit{state-of-the-art} GAD approaches by an average of 54.88\\%. A further case study on interpretable motif patterns reveals that \\textbf{TEMG-TTA} explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.", "date": "2026-05-28", "categories": []}
{"id": "2510.24043", "title": "Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection", "abstract": "This paper presents Two-Stage LKPLO, a novel multi-stage outlier detection framework that overcomes the coexisting limitations of conventional projection-based methods: their reliance on a fixed statistical metric and their assumption of a single data structure. Our framework uniquely synthesizes three key concepts: (1) a generalized loss-based outlyingness measure (PLO) that replaces the fixed metric with flexible, adaptive loss functions like our proposed SVM-like loss; (2) a global kernel PCA stage to linearize non-linear data structures; and (3) a subsequent local clustering stage to handle multi-modal distributions. Comprehensive 5-fold cross-validation experiments on 10 benchmark datasets, with automated hyperparameter optimization, demonstrate that Two-Stage LKPLO achieves state-of-the-art performance. It significantly outperforms strong baselines on datasets with challenging structures where existing methods fail, most notably on multi-cluster data (Optdigits) and complex, high-dimensional data (Arrhythmia). Furthermore, an ablation study empirically confirms that the synergistic combination of both the kernelization and localization stages is indispensable for its superior performance. This work contributes a powerful new tool for a significant class of outlier detection problems and underscores the importance of hybrid, multi-stage architectures.", "date": "2025-10-28", "categories": []}
{"id": "2602.03293", "title": "Anomaly Detection via Mean Shift Density Enhancement", "abstract": "Unsupervised anomaly detection stands as an important problem in machine learning. Existing unsupervised anomaly detection algorithms rarely perform well across different anomaly types, often excelling only under specific structural assumptions. This lack of robustness also becomes particularly evident under noisy settings. We propose Mean Shift Density Enhancement (MSDE), a fully unsupervised framework that detects anomalies through their geometric response to density-driven manifold evolution. MSDE is designed as a general purpose anomaly detection framework, based on the principle that normal samples, being well supported by local density, remain stable under iterative density enhancement, whereas anomalous samples undergo large cumulative displacements as they are attracted toward nearby density modes. To operationalize this idea, MSDE employs a weighted mean-shift procedure with adaptive, sample-specific density weights derived from a manifold learning-based fuzzy neighborhood graph. We evaluate MSDE on an anomaly detection benchmark comprising 46 real-world tabular datasets, four realistic anomaly generation mechanisms, and six noise levels. Compared to 13 established unsupervised baselines, MSDE achieves consistently strong, balanced and robust performance for several standard classification metrics, at several noise levels and on average over several types of anomalies. These results demonstrate that displacement-based scoring provides a robust alternative to the existing state-of-the-art for unsupervised anomaly detection.", "date": "2026-02-03", "categories": []}
{"id": "2602.09329", "title": "MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection", "abstract": "Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) on tabular data underpins numerous real-world applications, yet existing OD benchmarks remain limited. The prominent OD benchmark AdBench is the de facto standard in the literature, yet comprises only 57 datasets. In addition to other shortcomings discussed in this work, its small scale severely restricts diversity and statistical power. We introduce MacrOData, a large-scale benchmark suite for tabular OD comprising three carefully curated components: OddBench, with 790 datasets containing real-world semantic anomalies; OvrBench, with 856 datasets featuring real-world statistical outliers; and SynBench, with 800 synthetically generated datasets spanning diverse data priors and outlier archetypes. Owing to its scale and diversity, MacrOData enables comprehensive and statistically robust evaluation of tabular OD methods. Our benchmarks further satisfy several key desiderata: We provide standardized train/test splits for all datasets, public/private benchmark partitions with held-out test labels for the latter reserved toward an online leaderboard, and annotate our datasets with semantic metadata. We conduct extensive experiments across all benchmarks, evaluating a broad range of OD methods comprising classical, deep, and foundation models, over diverse hyperparameter configurations. We report detailed empirical findings, practical guidelines, as well as individual performances as references for future research. All benchmarks containing 2,446 datasets combined are open-sourced, along with a publicly accessible leaderboard hosted at https://huggingface.co/MacrOData-CMU.", "date": "2026-02-10", "categories": []}
{"id": "2605.05372", "title": "Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models", "abstract": "Diffusion models are rapidly redefining 3D anomaly detection in point cloud data. As 3D sensing becomes integral to modern manufacturing, reliable anomaly detection is essential for high-throughput quality assurance and process control. Yet practical deployment on resource-constrained, latency-critical systems remains limited. Existing methods are often computationally prohibitive or unreliable in complex, unmasked regions, and diffusion pipelines are inherently bottlenecked by iterative denoising. In this work, we address this bottleneck by reformulating reconstructionbased anomaly detection through consistency learning, enabling direct prediction of anomaly-free geometry in one or two network evaluations. We further introduce a novel hybrid loss formulation that explicitly enforces reconstruction toward clean data. This design substantially reduces inference cost, achieving up to 80x faster runtime than the current state-of-the-art method, without GPU acceleration, while preserving strong detection performance. It outperforms R3D-AD on Anomaly-ShapeNet with 76.20% I-AUROC and remains competitive on Real3DAD with 72.80% I-AUROC, enabling efficient, low-latency anomaly detection on resource-constrained platforms, including drones, smart industrial cameras, and other edge devices.", "date": "2026-05-06", "categories": []}
{"id": "2606.02670", "title": "Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate", "abstract": "Many recent multivariate time series anomaly detection (MTSAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels. We evaluate this assumption on eight widely used public benchmarks by introducing a per-segment diagnostic framework that flags, for each labeled anomaly, whether at least one channel deviates individually from its normal history, whether the cross-channel correlation structure changes, or both. The framework shows that no cross-channel rupture occurs without an accompanying univariate deviation across a range of reasonable thresholds. A complementary metric also reveals that on six of the eight benchmarks, at least half of the labeled anomaly segments deviate univariately on 89% to 100% of their timesteps, reaching 100% on three of these datasets. To verify that our framework captures cross-channel structure when present, we construct synthetic data of phase-shifted sinusoidal channels with shared noise. Each anomalous segment is altered through one of two channel-wise corruptions that preserve the per-channel marginal distribution while breaking cross-channel structure, and our framework correctly characterizes these segments as cross-channel-only. On these data, channel-dependent (CD) models successfully exploit the cross-channel signal whereas channel-independent (CI) ones fail. The CI/CD comparison of a recent SOTA detector on real benchmarks further confirms that CD modeling brings no measurable gain. We conclude that current MTSAD benchmarks are unsuitable for validating cross-channel modeling capabilities, and we call for the development of more structurally diverse evaluation sets. The code for this study is publicly available.", "date": "2026-06-01", "categories": []}
{"id": "2606.08270", "title": "An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response", "abstract": "University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations.\n  This paper presents an AI-based security agent for ACMIS that combines supervised anomaly detection, behavioural analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework.\n  A modular architecture allows the core engine to be extended to other institutional systems. Experiments on a simulated ACMIS event log dataset of 147,922 sessions demonstrate a threat detection macro-average F1 of 0.966, compared to 0.156 for a rule-based baseline and 0.836 for a sequence-only (LSTM) baseline, with end-to-end critical-tier automated response latency under 1 ms on a single-node prototype.\n  The integrated recovery chatbot achieves 97.1 percent identity verification accuracy and an 87.3 percent mass-reset attack detection rate with zero false positives on legitimate high volume recovery periods.", "date": "2026-06-06", "categories": []}
{"id": "2606.14724", "title": "VigilFormer: Deformable Attention for Video Anomaly Detection with Causal Risk Inference", "abstract": "Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both. We present VigilFormer, a unified framework that combines deformable spatio-temporal attention with causal temporal modeling to detect anomalies in untrimmed surveillance video. The proposed Deformable Spatio-Temporal Encoder (DSTE) attends to a sparse set of informative locations across frames, avoiding the quadratic cost of dense attention while retaining the ability to capture irregular motion patterns. A Causal Anomaly Classifier (CAC) applies dilated causal convolutions over snippet-level features and optimizes a contrastive multiple-instance learning objective that separates anomalous and normal representations without frame-level labels. To meet deployment constraints, an Adaptive Confidence Scheduler (ACS) dynamically skips low-information frames at inference time, reducing redundant computation in static scenes. Evaluated on UCF-Crime, ShanghaiTech, and CUHK Avenue, VigilFormer achieves AUC scores of 87.83%, 97.21%, and 89.74% respectively, at 41.5 FPS on a single GPU, outperforming recent weakly-supervised methods in both accuracy and speed.", "date": "2026-05-31", "categories": []}
{"id": "2606.15280", "title": "Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators", "abstract": "Most existing anomaly detection methods rely on estimating a probability density or learning an enclosing decision boundary, implicitly assuming that normal data occupies a region of non-zero volume in the ambient space. In contrast, structural anomaly detection considers data that lies near a low-dimensional manifold, creating a mismatch between the inductive bias of existing methods and the structure of the data, often resulting in degraded performance. To address this mismatch, we introduce a geometric perspective. Specifically, we learn a projection operator onto the manifold of normal samples and define a sample as anomalous if it is altered by this projection. This formulation naturally integrates the inductive bias of manifold-supported data and reframes anomaly detection in terms of a projection residual, thereby resolving issues arising from modeling degenerate distributions. Notably, it provides a unifying interpretation of reconstruction-based methods by explaining their success and failure in terms of projection quality. In particular, it explains the strong generalization ability of projection-aligned models as a consequence of contraction behavior toward the manifold. Moreover, by decoupling anomaly detection from probabilistic modeling, it reduces the tendency to misclassify rare but normal samples, a widely recognized limitation of existing approaches. Empirically, we demonstrate that projection-aligned methods achieve strong performance, outperforming boundary-based methods while improving upon existing reconstruction-based approaches.", "date": "2026-06-13", "categories": []}
{"id": "2606.15856", "title": "Early Anomaly-Onset Detection based on Wigner--Ville Distribution Slice Spectra: A Transmission-Grid Test Case", "abstract": "Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event. This study evaluates full-vector Wigner--Ville Distribution Slice (WVDS) spectra for sequential anomaly-onset detection in high-voltage grid-voltage waveforms. The approach keeps the bilinear midpoint interaction structure of the Wigner--Ville distribution and represents each 128-sample voltage window by a 128-dimensional slice spectrum, avoiding manually selected fault-frequency markers. WVDS is used with a baseline-normalized deviation (BND) score and is compared against the BND of Fast Fourier Transform (FFT-BND), raw-window autoencoders, FFT autoencoders, and WVDS autoencoders under the same thresholding and three-window persistence rule. A synthetic autoencoder--clustering teacher is used to select RTE fault records that start from an initially normal region and then transition to anomalous behavior. On the filtered test set, FFT-BND achieves the highest sensitivity, whereas WVDS-BND provides the lowest false-alarm operating point, reducing record-level pre-onset false alarms to 0.69%. The autoencoder comparison follows the same selectivity pattern: WVDS reconstruction decreases false alarms relative to FFT reconstruction but misses more examples. The results indicate that preserved WVD cross-term information can form a selective representation for online grid-waveform anomaly monitoring when false alarms are costly.", "date": "2026-06-14", "categories": []}
{"id": "2606.16119", "title": "EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices", "abstract": "Industrial inspection needs zero-shot anomaly detection (ZSAD) that remains useful under edge deployment constraints. Recent methods often rely on ViT-L foundation backbones (~300M parameters), which exceed the memory and operator budget of typical embedded hardware. We study this regime through EdgeZSAD, a compact reference system built around a TinyViT-21M-512 backbone, an asymmetric global-local readout (EdgeGLR), and a reproducible source-side training recipe (Real-IAD-DR). We train a single checkpoint in a source-trained, target-unseen protocol and evaluate it across six industrial benchmarks. Across three independent runs, the resulting model reaches an average image AUROC of 91.6 on MVTec-AD and 88.2 on VisA, while remaining directly deployable on Jetson Orin Nano Super (TensorRT FP16) and RB5 Gen2 (QNN GPU FP16). Across the six device-rescored benchmarks, image-AUROC drift stays below 0.2 points, indicating that the exported graph preserves host-side ranking behavior in the evaluated deployment setting.", "date": "2026-06-15", "categories": []}
{"id": "2606.16421", "title": "Beer-Lambert Guided Representation Learning for Unsupervised Anomaly Detection in Sub-THz Food Inspection Images", "abstract": "Food manufacturing requires reliable inspection systems to detect foreign material contamination and maintain product safety. Sub-THz transmission imaging provides material-dependent attenuation characteristics that are useful for detecting low-density contaminants in food products. However, existing unsupervised anomaly detection methods mainly rely on RGB-pretrained visual representations, which may not adequately capture the transmission behavior of Sub-THz images. This paper proposes a Beer-Lambert guided representation learning framework for unsupervised anomaly detection in Sub-THz food inspection images. The proposed method introduces an attenuation decomposition module as an auxiliary regularization module that constrains student representations through attenuation reconstruction during training. In addition to the conventional one-class setting, we introduce a Leave-One-Food-Out protocol to evaluate generalization capability under unseen food categories. Experimental results on the Inline-Food-Inspection-THz dataset show that the proposed method improves overall anomaly detection performance over the baseline method.", "date": "2026-06-15", "categories": []}
{"id": "2606.16524", "title": "Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier", "abstract": "Engineered robust losses such as Huber, Student-$t$, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupted. We introduce Neural Bayesian Anomaly Mitigation (NBAM), a general-purpose drop-in loss derived from a Bayesian latent-switch mixture model: the marginal likelihood defines a robust supervised loss, and the associated posterior defines an unsupervised contamination classifier. Like Huber or Student-$t$, NBAM can replace the standard training loss in any supervised pipeline; unlike them, it additionally learns a structured contamination model and returns a calibrated per-sample contamination posterior. A learned input-dependent prior $\\pi_\\phi(x)$ captures the spatial locality of contamination, so that samples near known corruptions are more likely to be flagged, while an Occam penalty emerges automatically and regularises against over-flagging. On CIFAR-10 with asymmetric label contamination, NBAM recovers the structure of the corruption process without supervision: the contamination posterior separates clean from corrupted samples, and the learned anomaly head identifies the direction of every label-flip pair. Alongside these capabilities, NBAM outperforms the four robust-loss baselines considered here at contamination rates 0.2-0.6.", "date": "2026-06-15", "categories": []}
{"id": "2606.16601", "title": "DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models", "abstract": "Industrial anomaly localization aims to accurately identify and localize abnormal regions in industrial products, addressing the critical challenge of detecting unseen defect categories in real-world scenarios. Traditional closed-set methods often suffer from poor cross-scenario generalization, while existingMultimodal Large Language Model (MLLM)-based approachesface two core limitations: they either adopt QA-style paradigmsmisaligned with the practical demands of localization, or relyon standard optimization techniques such as Group RelativePolicy Optimization (GRPO), which fails to deliver effectivelearning signals for subtle defects. To tackle these issues, thispaper proposes DifferAD-R1, an MLLM-augmented reinforcement learning framework tailored for industrial anomaly localization. We design a Difference-Guided dual-image paradigm,which reformulates the localization task as a one-shot difference grounding problem to effectively explore cross-scenarioanomalies. A Dual-Consistency Localization Reward is developedfor hard-to-detect anomalies, enhancing optimization stabilityand robustness. Additionally, we integrate a difficulty-awarestrategy with adaptive reweighting and group-wise resamplingto prioritize learning on challenging instances. To facilitateevaluations in real-world industrial settings, we construct theAD-DualDiff dataset, comprising 13K paired images across 20categories. Experimental results demonstrate that DifferADR1 significantly outperforms existing baselines and achievescompetitive performance compared to large-scale models likeQwen3-VL (235B parameters). Our code is publicly availableat: https://github.com/Rong2026/work-1.", "date": "2026-06-15", "categories": []}
{"id": "2402.16388", "title": "Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors", "abstract": "The need for uncertainty quantification in anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates without inflating Type II error rates in these systems can build trust and reduce costs associated with false discoveries. The field of conformal anomaly detection emerges as a promising approach for providing respective statistical and finite-sample validity guarantees through model calibration. However, reliance on calibration data imposes practical limitations, especially in low-data regimes. In this work, we formally define and evaluate leave-one-out-, bootstrap-, and cross-conformal methods for conformal anomaly detection, building on methods from the field of conformal prediction. Looking beyond the classical split-conformal approach, we show that derived methods for calculating resampling-conformal $p$-values offer a practical compromise between the data efficiency of full-conformal (transductive) approaches and the computational efficiency of split-conformal (inductive) methods. We validate derived methods and quantify their improvements for a range of one-class classifiers and datasets.", "date": "2024-02-26", "categories": []}
{"id": "2501.15196", "title": "A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges", "abstract": "Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality. In response to this limitation, self-supervised techniques for time series have garnered attention as a potential solution to undertake this obstacle and enhance the performance of anomaly detectors. This paper presents a comprehensive review of the recent methods that make use of self-supervised learning for time series anomaly detection. A taxonomy is proposed to categorize these methods based on their primary characteristics, facilitating a clear understanding of their diversity within this field. The information contained in this survey, along with additional details that will be periodically updated, is available on the following GitHub repository: https://github.com/Aitorzan3/Awesome-Self-Supervised-Time-Series-Anomaly-Detection.", "date": "2025-01-25", "categories": []}
{"id": "2605.24609", "title": "Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning", "abstract": "Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly detection for pelvic magnetic resonance imaging (MRI) remains largely unexplored, and transparent evaluation of its feasibility for full automation is limited. We developed and evaluated a fully automated, unsupervised anomaly-detection framework for pelvic and brain MRI. A two-stage framework was trained on reference images from public datasets: LUND-PROBE for pelvic MRI, and IXI, fastMRI, and fastMRI+ for brain MRI. In the first stage, MRI slices were compressed into discrete tokens; in the second, the distribution of normal tokens was modeled. Anomaly evidence was estimated by combining perceptual image differences with token-surprisal scores based on negative log-likelihood. Automated detection was evaluated on pelvic MRI with synthetic global and real clinical anomalies, and on brain MRI with clinically annotated fastMRI+ abnormalities. Sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and false-positive behavior in held-out normal cases were assessed. The framework achieved robust detection across hidden evaluation cohorts, with AUCs of 0.97 (95% CI, 0.95-0.98) and 0.81 (95% CI, 0.74-0.87) for pelvic and brain MRI, respectively. Heatmap analysis showed strong spatial agreement between detected anomalies and ground-truth locations, supporting localization accuracy and interpretability. These results support the potential of unsupervised anomaly detection as an automated MRI quality-control layer for radiotherapy workflows, with transparent visualization of image regions likely to compromise downstream AI-based tasks.", "date": "2026-05-23", "categories": []}
{"id": "2606.13754", "title": "D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection", "abstract": "Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments. Although conventional machine learning and deep learning methods have demonstrated effectiveness in identifying anomalies, they often rely on large labeled datasets, incur high computational costs, and face scalability challenges in edge and high-dimensional settings. This paper presents D2H-AD, a novel anomaly detection framework based on Hyperdimensional Computing (HDC), a brain-inspired paradigm that represents information using high-dimensional distributed vectors. Unlike existing HDC-based methods, D2H-AD integrates distance-based similarity and density-aware encoding within a unified framework, improving anomaly representation and detection performance. Ablation studies show that hyperdimensional encoding alone yields up to 5.4% higher ROC-AUC than applying the same density-distance scoring directly in the original feature space. Furthermore, D2H-AD consistently outperforms five established baselines, namely HDAD, ODHD, One-Class SVM, Isolation Forest, and Autoencoders, across all evaluated datasets. The framework is lightweight, interpretable, and computationally efficient, making it suitable for resource-constrained and real-time applications. We validate D2H-AD on five benchmark datasets and demonstrate superior F1-score and ROC-AUC performance, together with robustness to class imbalance, noise, and data complexity. In addition to improved accuracy, D2H-AD offers scalability, a small memory footprint, and low-latency operation enabled by binary computations and a compact design. These properties make it particularly attractive for TinyML and edge AI deployments. The proposed framework highlights the potential of HDC for accurate, interpretable, and energy-efficient anomaly detection in dynamic environments.", "date": "2026-06-11", "categories": []}
{"id": "2606.13780", "title": "Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches", "abstract": "Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a model that scans many regions inflates the look-elsewhere effect, and the asymptotic significances the field relies on are blind to the background mismodelling that anomaly detectors are especially prone to. We propose a calibration layer, built on conformal prediction, that turns any anomaly score into a defensible significance with distribution-free, finite-sample guarantees. Conformal prediction converts scores into valid local p-values, weighted and Mondrian variants repair the sideband-to-signal-region exchangeability failures that resonant searches suffer, and a Gross-Vitells step carries the result through to a look-elsewhere-aware global significance. The layer does two things at once. It exposes miscalibration that the standard pipeline cannot see, and it corrects it without retraining the detector. On public LHC Olympics data, a classifier develops a substructure-mass correlation that makes sideband-calibrated background p-values anti-conservative. Taken at face value, this manufactures a $\\sim 46\\sigma$ excess from background sculpting alone, which the label-free weighted correction removes, restoring an honest null. When run as a blind wide-mass bump hunt, the standard asymptotic and unweighted procedures fabricate $\\gtrsim10\\sigma$ excesses and $\\approx5\\sigma$ excesses even in signal-free windows, while the conformal layer raises no false alarms and its global false-positive rate is verified on background-only pseudoexperiments. The result is an auditable, detector-agnostic path from an uncalibrated score to a trials-factor-aware significance, ready to be folded into experimental anomaly searches.", "date": "2026-06-11", "categories": []}
{"id": "2606.14129", "title": "BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection", "abstract": "Reconstruction-based anomaly detection is attractive for industrial inspection, but scaling it from category-specific training to a one-for-all setting is challenging. A single model must reconstruct diverse normal appearances without copying abnormal details, which exposes two coupled failure modes: identical shortcut, where anomalies pass through the reconstruction path, and mis-reconstruction, where normal categories are confused with one another. We propose \\textbf{BoRAD}, a label-free training framework that treats this as a representation-capacity allocation problem. BoRAD uses a shared learnable prototype bank to impose two complementary regularizers: spatial prototype alignment contracts local within-prototype variation to suppress anomaly copying, while prototype-relative global alignment preserves between-prototype structure and improves sensitivity to abnormal angular deviations. The prototype bank and prediction heads are used only during training; inference remains a standard teacher-student feature discrepancy pass, with no class labels, negative pairs, memory retrieval, or prototype lookup. BoRAD achieves competitive one-for-all anomaly detection performance, including 86.2\\% mAD on MVTec AD, 80.7\\% mAD on VisA and 73.1\\% mAD on Real-IAD. Diagnostic analyses further show reduced anomaly leakage, improved normal-category separability, and stronger anomaly-normal score separation.", "date": "2026-06-12", "categories": []}
{"id": "2606.14475", "title": "Value-order Decomposition for Generalist Anomaly Detection", "abstract": "Industrial anomaly detection suffers from limited data, making cross-domain generalization particularly challenging. Generalist Anomaly Detection (GAD) aims to train a unified model on a source domain that can effectively detect anomalies in unseen target domains. In the initial semantic feature space, strong entanglement between anomalies and object categories or defect types hinders effective generalization across domains. Recent works address this issue by projecting features into a residual space; however, such methods primarily increase cross-domain overlap for normal features, while anomalous features remain specific to object categories, defect types and data domains, leading to poor alignment and generalization. To address this limitation, we propose Value-order Decomposition (VOD), a simple yet effective technique that bridges \\textbf{three types of generalization gaps} across object categories, defect types (including real and synthetic defects), and data domains. VOD disentangles and suppresses object-category-, defect-type-, and domain-specific information, promoting alignment within normal and abnormal samples while preserving their separability, thereby enabling robust generalization across the three gaps. Leveraging the strong alignment between real and synthetic defects within the same object, we perform anomaly detection using only normal and synthetic-abnormal reference, and effectively generalize to unseen real defect types. Experiments on diverse industrial and medical benchmarks demonstrate that our method, using a simple cut-and-paste anomaly simulation strategy, achieves strong generalization across the three gaps.", "date": "2026-06-12", "categories": []}
{"id": "2603.14407", "title": "Towards One-for-All Anomaly Detection for Tabular Data", "abstract": "Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods follow a ``one model for one dataset (OFO)'' paradigm, which relies on dataset-specific training and thus incurs high computational cost and yields limited generalization to unseen domains. To address these limitations, we propose OFA-TAD, a generalist one-for-all (OFA) TAD framework that only requires one-time training on multiple source datasets and can generalize to unseen datasets from diverse domains on-the-fly. To realize one-for-all tabular anomaly detection, OFA-TAD extracts neighbor-distance patterns as transferable cues, and introduces multi-view neighbor-distance representations from multiple transformation-induced metric spaces to mitigate the transformation sensitivity of distance profiles. To adaptively combine multi-view distance evidence, a Mixture-of-Experts (MoE) scoring network is employed for view-specific anomaly scoring and entropy-regularized gated fusion, with a multi-strategy anomaly synthesis mechanism to support training under the one-class constraint. Extensive experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance and strong cross-domain generalizability under the strict OFA setting. The source code is available at https://github.com/Shiy-Li/OFA-TAD.", "date": "2026-03-15", "categories": []}
{"id": "2603.27393", "title": "K-Means Based TinyML Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)", "abstract": "This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feature extraction, clustering, and threshold estimation to identify abnormal appliance behavior. To avoid retraining models on every device, we introduce the Distributed Internet of Learning (DIoL), which enables a model trained on one MCU to be exported as a portable, text-based representation and reused directly on other devices. A two-device prototype demonstrates the feasibility of the \"Train Once, Share Everywhere\" (TOSE) approach using a real-world appliance case study, where Device A trains the model and Device B performs inference without retraining. Experimental results show consistent anomaly detection behavior, negligible parsing overhead, and identical inference runtimes between standalone and DIoL-based operation. The proposed framework enables scalable, low-cost TinyML deployment across fleets of embedded devices.", "date": "2026-03-28", "categories": []}
{"id": "2604.08581", "title": "Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data", "abstract": "This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the proposed system performs both model training and inference directly on a resource-constrained microcontroller without external computation or connectivity. The system continuously samples current consumption, computes Root Mean Square (RMS) values on-device, and derives statistical parameters during an initial training phase. Anomalies are detected using lightweight Z-Score thresholds, enabling interpretable and computationally efficient inference suitable for embedded deployment. The architecture was implemented on an STM32-based platform and evaluated using a 14-day dataset collected from a household mini-fridge under normal operation and controlled anomaly conditions. Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash. These results confirm that robust and fully autonomous TinyML anomaly detection can be achieved on low-cost microcontrollers. Future work includes extending the framework to incorporate additional lightweight models and multi-device learning scenarios.", "date": "2026-03-28", "categories": []}
{"id": "2604.13924", "title": "ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection", "abstract": "Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data. This scarcity makes unsupervised approaches predominant, yet existing methods often rely on reconstruction or forecasting, which struggle with complex data, or on embedding-based approaches that require domain-specific anomaly synthesis and fixed distance metrics. We propose ASTER, a framework that generates pseudo-anomalies directly in the latent space, avoiding handcrafted anomaly injections and the need for domain expertise. A latent-space decoder produces tailored pseudo-anomalies to train a Transformer-based anomaly classifier, while a pre-trained LLM enriches the temporal and contextual representations of this space. Experiments on three benchmark datasets show that ASTER achieves state-of-the-art performance and sets a new standard for LLM-based TSAD.", "date": "2026-04-15", "categories": []}
{"id": "2606.12483", "title": "Scalable anomaly detection via a univariate Christoffel function", "abstract": "Anomaly detection plays a critical role in identifying unusual patterns across domains such as fraud detection, network intrusion, and system fault diagnosis. Recently, Christoffel function-based methods, rooted in polynomial optimization, have emerged as promising alternatives to deep learning due to their strong mathematical foundations and computational frugality. However, their practical applicability is hindered by the need to invert a matrix whose size grows exponentially with the data dimension, rendering the method intractable even for moderate-dimensional datasets. This paper addresses the dimensionality limitations of Christoffel function-based anomaly detection while preserving its key theoretical properties, i.e., the on-off support dichotomy behavior and the accurate support shape capture. We introduce UCF, a univariate Christoffel function which is based on the squared distance between the query point and the support points. Extensive experiments on the ADBench benchmark demonstrate that UCF consistently outperforms 14 state-of-the-art baselines in terms of Average Precision. By resolving the scalability bottleneck of the Christoffel Function, this work expands the toolkit of anomaly detection methods with a robust, theoretically grounded, and universally applicable approach.", "date": "2026-06-10", "categories": []}
{"id": "2606.12673", "title": "A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction", "abstract": "Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.", "date": "2026-06-10", "categories": []}
{"id": "2606.13277", "title": "ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization", "abstract": "Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation-specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they provide limited insight into the characteristics of flagged anomalies. To address this limitation, we propose ProtoX-AD, a prototype-based self-explainable framework for self-supervised TSAD. ProtoX-AD learns transformation-aware latent representations alongside interpretable prototypes, enabling both accurate anomaly detection and the identification of distinct anomalous profiles through prototype-based explanations. Additionally, it allows for systematic analysis of how transformation design impacts detection performance and explainability. Experimental results on synthetic and real-world datasets demonstrate that ProtoX-AD achieves detection performance comparable to its black-box counterparts while offering more consistent and semantically meaningful explanations than existing explainable baselines. Our code is publicly available at https://github.com/Aitorzan3/ProtoX-AD.", "date": "2026-06-11", "categories": []}
{"id": "2606.13311", "title": "Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection", "abstract": "Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information. Under such frequency bias, context-conditioned models can produce unstable decisions and excessive false alarms in rare contexts. We propose Rarity-Gated Feature-wise Linear Modulation (RGFiLM), a rarity-aware conditioning module that combines feature-wise modulation (i.e., context-conditioned scaling and shifting of hidden features) with a gate controlled by a data-driven rarity score. The rarity score is estimated from the empirical distribution of context variables and regulates how strongly context modulates intermediate representations: the gate becomes more decisive under rare contexts while remaining conservative under frequent contexts. We evaluate RGFiLM on maritime trajectory anomaly detection using AIS motion sequences with ERA5 environmental context in an environment-sensitive detour scenario. When instantiated in a sequential anomaly scoring pipeline, RGFiLM achieves the best mean F1--False Positive Rate (FPR) trade-off among the compared context-agnostic and context-conditioned methods. These results suggest that explicitly accounting for context rarity is an effective approach for reducing false alarms in context-sensitive anomaly detection.", "date": "2026-06-11", "categories": []}
{"id": "2606.13486", "title": "CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection", "abstract": "Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types -- point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) -- each requiring different feature representations. Most unsupervised methods target only one or two types and provide limited interpretability. We present CRAFTIIF (Cross-Resolution Analytic Four-Type Interpretable Isolation Forest), a fully unsupervised framework targeting all four types without dataset-specific tuning. CRAFTIIF generates K=500 random analytic wavelet feature draws across four families (Morlet, DOG, Haar, Coiflet), each targeting a specific anomaly type, feeding five structured Isolation Forests -- one per type plus a meta-IF for compound anomalies. An adaptive Otsu/MAD threshold calibrates detection automatically across anomaly rates from 0.1% to 69.2%. Because each IF is trained exclusively on type-specific features, branch firing provides direct anomaly-type attribution by construction, without post-hoc explanation. Evaluated on all 19 datasets of the mTSBench benchmark (Zhou et al., TMLR 2026), CRAFTIIF achieves mean F1=0.228 (all 19 datasets) and F1=0.322 (13 detectable datasets), ranking first among all 25 evaluated methods on VUS-PR (0.463 vs. previous best 0.329, +40.7%). A diagnostic framework -- oracle F1, detectability limits, and branch separation ratios -- identifies 6 of 19 datasets as fundamentally undetectable by any unsupervised method. Ablation over 11 conditions confirms adaptive thresholding (+38% F1), four-branch structure (+20%), and meta-IF (+23%) are each essential. Code: https://github.com/smitswil/craftiif", "date": "2026-06-11", "categories": []}
{"id": "2507.21164", "title": "OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection", "abstract": "Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available. Most state-of-the-art methods fall into two categories: reconstruction-based approaches, which often reconstruct anomalies too well, and decoupled representation learning with density estimators, which can suffer from suboptimal feature spaces. While some recent methods attempt to couple feature learning and anomaly detection, they often rely on surrogate objectives, restrict kernel choices, or introduce approximations that limit their expressiveness and robustness. To address this challenge, we propose a novel method that couples representation learning with an analytically solvable One-Class SVM (OCSVM), through a custom loss formulation that directly aligns latent features with the OCSVM decision boundary. The model is evaluated on two tasks: a \\deleted{new} benchmark based on MNIST-C, and a challenging brain MRI \\deleted{subtle} lesion detection task. Unlike most methods that focus on large, hyperintense lesions at the image level, our approach succeeds to target small, non-hyperintense lesions, while we evaluate voxel-wise metrics, addressing a more clinically relevant scenario. Both experiments evaluate a form of robustness to domain shifts, including corruption types in MNIST-C and texture or population age variations in MRI. Results demonstrate performance and robustness of our proposed model, highlighting its potential for general UAD and real-world medical imaging applications. The source code is available at https://github.com/Nicolas-Pinon/uad_ocsvm_guided_repr_learning.", "date": "2025-07-25", "categories": []}
{"id": "2606.11844", "title": "TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data", "abstract": "Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance. In many real-world applications, data arrive sequentially from diverse domains, rendering conventional continual learning methods ineffective due to their reliance on a fixed input space. We propose a continual learning (CL) method, which can overcome these challenges and continually learn from different tasks. Our method consists of three main parts: our AGF model, Taskfusion augmentation, and outlier exposure. The AGF-model maps task-specific features into a shared space, then aligns distributions to reduce representation drift, and learns anomaly decision boundaries in the aligned space. To improve stability, we introduce Taskfusion augmentation, combining boundary-aware interpolation within tasks to refine the model anomaly boundaries and cross-task mixing to transfer anomaly structure across datasets. To handle class imbalance and memory constraints, we employ tabular dataset distillation to store compact synthetic replay samples, which are jointly used with augmented data in an outlier exposure objective for robust anomaly detection. We evaluate the approach on 21 heterogeneous datasets across multiple domains. Results show that our approach substantially improves continual anomaly detection performance over sequential fine-tuning and other CL baselines while reducing catastrophic forgetting and maintaining stable detection across heterogeneous datasets.", "date": "2026-06-10", "categories": []}
{"id": "2410.22967", "title": "Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector", "abstract": "The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network Anomaly Detection framework for security domains. A two-layer anomaly detection strategy is proposed to generate reliable high-confidence pseudo-labels. Then, an online training scheme is introduced to update Adaptive NAD by a novel threshold calculation technique. Experimental results demonstrate that Adaptive NAD achieves the lowest false alarm rate (1.33%, 0.71%, and 0.08%) and has a more than 3 times faster online inference latency compared with state-of-the-art solutions on the CIC-Darknet2020, NSL-KDD, and Edge-IIoTset datasets, respectively. The code is released at https://github.com/MyLearnCodeSpace/Adaptive-NAD.", "date": "2024-10-30", "categories": []}
{"id": "2601.22763", "title": "Is Task-Specific Training Necessary for Anomaly Detection?", "abstract": "Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-free features. However, we argue that such task-specific training is costly under distribution shifts, and that reconstruction-based residual scoring further faces a fidelity--stability dilemma. Existing training-free alternatives, in turn, remain prone to cross-category and cross-region mismatches in MUAD. Motivated by these limitations, we propose Retrieval-based Anomaly Detection (RAD), a task-specific training-free framework that stores anomaly-free features in a memory and detects anomalies through multi-level retrieval, matching test patches against the memory. Experiments demonstrate that RAD achieves state-of-the-art performance across four established benchmarks (MVTec-AD, VisA, Real-IAD, 3D-ADAM) under both standard and few-shot settings. On MVTec-AD, RAD reaches 96.7% Pixel AUROC with just a single anomaly-free image compared to 98.5% of RAD's full-data performance. Collectively, these findings overturn the assumption that MUAD requires task-specific training, showing that state-of-the-art anomaly detection is feasible with training-free memory-based retrieval. Our code is available at https://github.com/longkukuhi/RAD.", "date": "2026-01-30", "categories": []}
{"id": "2602.13807", "title": "AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning", "abstract": "Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative prediction task with fixed feature representations, rather than an evidence-driven diagnostic process. As a result, they often struggle when anomalies exhibit strong context dependence, diverse patterns, or domain shifts across datasets. To address these challenges, we propose AnomaMind, an agentic time series anomaly detection framework that reformulates anomaly detection as a sequential decision-making process. AnomaMind operates through a coarse-to-fine workflow that first localizes suspicious intervals, then constructs diagnostic evidence through tool interaction, and finally refines anomaly decisions through self-reflection. The workflow is supported by a toolkit box that combines knowledge memory and numerical diagnostics: visual anomaly patterns mined from training data and domain knowledge provide contextual guidance, while statistical, value-based, change-based, and region-level operators provide measurable evidence for verification. AnomaMind further adopts a hybrid inference mechanism in which general-purpose models handle flexible reasoning, tool invocation, and refinement, while a detection-specific policy is optimized with rule-based rewards for parsable outputs, F1-score alignment, and false-positive control. Extensive experiments under both in-domain and cross-domain settings demonstrate that AnomaMind consistently improves anomaly detection performance and enhances generalization across heterogeneous anomaly patterns, validating the effectiveness of tool-augmented reasoning for anomaly detection. The code is available at https://github.com/Xiaoyu-Tao/AnomaMind-TS.", "date": "2026-02-14", "categories": []}
{"id": "2606.09874", "title": "Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection", "abstract": "Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors. However, reported results are often hard to compare due to heterogeneous evaluation practices and underspecified inference procedures. In this paper, we revisit reconstruction-based anomaly detection in the univariate offline setting and study the role of the inference stride, which controls whether subsequences are processed as disjoint windows or with overlap. We propose a unified training, tuning, and multi-seed evaluation protocol on the curated TSB-AD benchmark, and study how overlapping inference affects anomaly detection performance for a range of reconstruction models, including PCA-based baselines, DLinear, an AutoEncoder, TimesNet, and Transformer variants. The results show that across all models, overlapping windows yield consistent improvements, with average relative gain up to +28%, and can alter method rankings. We further analyze variability across datasets, random seeds, and hyperparameter configurations. Finally, we complement the benchmark study with an evaluation on the full UCR archive using localization criteria aligned with sliding-window reconstruction. Overall, our results highlight that reconstruction-based anomaly detection performance depends not only on model architecture and training, but also on inference choices, motivating a clear and reproducible protocol. Our results show that reconstructionbased baselines achieve strong performance on both TSB-AD and UCR benchmarks, supporting them as competitive and practical approaches for univariate time series anomaly detection.", "date": "2026-06-02", "categories": []}
{"id": "2606.09942", "title": "Anomaly Detection and Root Cause Analysis for Microservice Systems", "abstract": "Microservice systems are widely used to build cloud applications, yet their complexity makes failures inevitable, degrading user experience and causing economic loss. Automated anomaly detection and root cause analysis (RCA) are now active research areas, but existing techniques share five limitations. First, most treat anomaly detection and RCA separately, assuming anomalies are detected correctly, and falter when detection is imprecise due to noise or delay. Second, they focus on metrics, logs, and traces, leaving event data such as API calls and configuration changes underexplored. Third, many require a given service call graph and cannot diagnose without one. Fourth, the field lacks standardised datasets and evaluation frameworks, so methods are hard to compare fairly. Fifth, although causal inference-based RCA has become dominant, its effectiveness, efficiency, and robustness remain unclear.\n  This thesis addresses these limitations through two groups of contributions. The first introduces methods that exploit observability data both independently and collectively. BARO is an end-to-end anomaly detection and RCA approach for metric data. EventADL is an end-to-end framework for event data. TORAI is a multimodal RCA framework that requires no service call graph. Extensive experiments on real microservice systems demonstrate their effectiveness and robustness. The second group delivers benchmarking datasets, an evaluation framework, and systematic evaluation efforts. RCAEval is a comprehensive benchmark providing ready-to-use datasets and reproducible baselines for future research. A systematic evaluation of existing RCA methods, especially causal inference-based approaches, offers insights that guide future directions. This thesis thereby advances automated anomaly detection and RCA for microservice failures, enabling future research on incident mitigation and remediation.", "date": "2026-06-08", "categories": []}
{"id": "2606.10216", "title": "A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport", "abstract": "Advanced Persistent Threats (APTs) are stealthy, multi-stage cyberattacks whose detection is difficult due to scarce labeled traces, severe class imbalance, and the challenge of generating realistic malicious behavior. These challenges are amplified in cross-operating-system (cross-OS) settings, where a detector trained on one source platform must be deployed on an unlabeled target platform without access to target-domain labels. We study this source-only cross-OS APT detection problem using system-level provenance traces and propose a transport-based framework for ranking anomalous target processes under zero target supervision. The framework abstracts process behavior into structured natural-language descriptions, embeds them using pretrained language models, and constructs a source-normal reference for target scoring. It combines three evidence channels: semantic deviation from source-normal prototypes, structural deviation captured by graph autoencoding, and geometric deviation measured through Optimal Transport (OT). The main contribution is an OT-based barycentric anomaly score that projects target embeddings onto the source-normal manifold and quantifies residual transport mismatch. We further introduce entropy-weighted, angle-aware, and density-aware OT variants to capture uncertainty, directional drift, and sparse-support behavior. Evaluation on DARPA Transparent Computing data spanning Linux, Windows, BSD, and Android, across two APT scenarios and twelve cross-OS transfer pairs, shows that the proposed framework improves ROC-AUC and nDCG over source-only anomaly-detection baselines. The results demonstrate that source-only provenance modeling, combined with semantic abstraction and OT-based anomaly scoring, can support practical cross-platform APT detection without target-domain supervision.", "date": "2026-06-08", "categories": []}
{"id": "2606.10827", "title": "A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks", "abstract": "A multi-similarity Siamese neural network unifies zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy and instant adaptability across lightpaths and unseen anomaly types without any retraining.", "date": "2026-06-09", "categories": []}
{"id": "2410.00713", "title": "RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations", "abstract": "Anomaly detection is a core capability for robotic perception and industrial inspection, yet most existing benchmarks are collected under controlled conditions with fixed viewpoints and stable illumination, failing to reflect real deployment scenarios. We introduce RAD (Realistic Anomaly Detection), a robot-captured, multi-view dataset designed to stress pose variation, reflective materials, and viewpoint-dependent defect visibility. RAD covers 13 everyday object categories and four realistic defect types--scratched, missing, stained, and squeezed--captured from over 60 robot viewpoints per object under uncontrolled lighting. We benchmark a wide range of state-of-the-art approaches, including 2D feature-based methods, 3D reconstruction pipelines, and vision-language models (VLMs), under a pose-agnostic setting. Surprisingly, we find that mature 2D feature-embedding methods consistently outperform recent 3D and VLM-based approaches at the image level, while the performance gap narrows for pixel-level localization. Our analysis reveals that reflective surfaces, geometric symmetry, and sparse viewpoint coverage fundamentally limit current geometry-based and zero-shot methods. RAD establishes a challenging and realistic benchmark for robotic anomaly detection, highlighting critical open problems beyond controlled laboratory settings.", "date": "2024-10-01", "categories": []}
{"id": "2502.09194", "title": "XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection", "abstract": "Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance. In the realm of open radio access networks (O-RAN), characterized by their disaggregated architecture and heterogeneous components from multiple vendors, the deployment of generative models offers significant advantages for network management such as traffic analysis, traffic forecasting and anomaly detection. However, the complex and dynamic nature of O-RAN introduces challenges that necessitate not only accurate detection mechanisms but also reduced complexity, scalability, and most importantly interpretability to facilitate effective network management. In this study, we introduce the XAInomaly framework, an explainable and interpretable Semi-supervised (SS) Deep Contractive Autoencoder (DeepCAE) design for anomaly detection in O-RAN. Our approach leverages the generative modeling capabilities of our SS-DeepCAE model to learn compressed, robust representations of normal network behavior, which captures essential features, enabling the identification of deviations indicative of anomalies. To address the black-box nature of deep learning models, we propose reactive Explainable AI (XAI) technique called fastshap-C.", "date": "2025-02-13", "categories": []}
{"id": "2510.02014", "title": "Normality Calibration in Semi-supervised Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subset of annotated normal nodes available during training, is among the most widely explored application settings. However, the normality learned by existing semi-supervised GAD methods is limited to the labeled normal nodes, often inclining to overfitting the given patterns. These can lead to high detection errors, such as high false positives. To overcome this limitation, we propose GraphNC , a graph normality calibration framework that leverages both labeled and unlabeled data to calibrate the normality from a teacher model (a pre-trained semi-supervised GAD model) jointly in anomaly score and node representation spaces. GraphNC includes two main components, anomaly score distribution alignment (ScoreDA) and perturbation-based normality regularization (NormReg). ScoreDA optimizes the anomaly scores of our model by aligning them with the score distribution yielded by the teacher model. Due to accurate scores in most of the normal nodes and part of the anomaly nodes in the teacher model, the score alignment effectively pulls the anomaly scores of the normal and abnormal classes toward the two ends, resulting in more separable anomaly scores. Nevertheless, there are inaccurate scores from the teacher model. To mitigate the misleading by these scores, NormReg is designed to regularize the graph normality in the representation space, making the representations of normal nodes more compact by minimizing a perturbation-guided consistency loss solely on the labeled nodes.", "date": "2025-10-02", "categories": []}
{"id": "2511.06644", "title": "UniADC: A Unified Framework for Anomaly Detection and Classification", "abstract": "In this paper, we introduce a novel task termed unified anomaly detection and classification, which aims to simultaneously detect anomalous regions in images and identify their specific categories. Existing methods typically treat anomaly detection and classification as separate tasks, thereby neglecting their inherent correlations and limiting information sharing, which results in suboptimal performance. To address this, we propose UniADC, a model designed to effectively perform both tasks with only a few or even no anomaly images. Specifically, UniADC consists of two key components: a training-free Controllable Inpainting Network and an Implicit-Normal Discriminator. The inpainting network can synthesize anomaly images of specific categories by repainting normal regions guided by anomaly priors, and can also repaint few-shot anomaly samples to augment the available anomaly data. The implicit-normal discriminator addresses the severe challenge of the imbalance between normal and anomalous pixel distributions by implicitly modeling the normal state, achieving precise anomaly detection and classification by aligning fine-grained image features with anomaly-category embeddings. We conduct extensive experiments on four anomaly detection and classification datasets, including MVTec-FS, MTD, WFDD and Real-IAD, and the results demonstrate that UniADC consistently outperforms existing methods in anomaly detection, localization, and classification. The code is available at https://github.com/cnulab/UniADC.", "date": "2025-11-10", "categories": []}
{"id": "2603.21511", "title": "Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection", "abstract": "Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training data. Existing approaches render 3D point clouds into 2D images and leverage pre-trained Vision-Language Models (VLMs) for anomaly detection. However, such strategies inevitably discard geometric details and exhibit limited sensitivity to local anomalies. In this paper, we revisit intrinsic 3D representations and explore the potential of pre-trained Point-Language Models (PLMs) for ZS 3D anomaly detection. We propose BTP (Back To Point), a novel framework that effectively aligns 3D point cloud and textual embeddings. Specifically, BTP aligns multi-granularity patch features with textual representations for localized anomaly detection, while incorporating geometric descriptors to enhance sensitivity to structural anomalies. Furthermore, we introduce a joint representation learning strategy that leverages auxiliary point cloud data to improve robustness and enrich anomaly semantics. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that BTP achieves superior performance in ZS 3D anomaly detection. Code will be available at \\href{https://github.com/wistful-8029/BTP-3DAD}{https://github.com/wistful-8029/BTP-3DAD}.", "date": "2026-03-23", "categories": []}
{"id": "2605.02439", "title": "Anomaly-Preference Image Generation", "abstract": "Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to reconcile fidelity and diversity, often hampered by distribution misalignment and overfitting, respectively.To mitigate this, we introduce Anomaly Preference Optimization,a novel paradigm that reformulates anomaly generation as a preference learning problem.Central to our approach is an implicit preference alignment mechanism that leverages real anomalies as positive references, deriving optimization signals directly from denoising trajectory deviations without requiring costly human annotation. Furthermore, we propose a Time-Aware Capacity Allocation module that dynamically distributes model capacity along the diffusion timeline,prioritizing structural diversity during highnoise phases while enhancing fine-grained fidelity in low-noise stages. During inference, a hierarchical sampling strategy modulates the coherencealignment trade-off, enabling precise control over generation. Extensive experiments demonstrate that significantly outperforms existing baselines,achieving state-of-the-art performance in both realism and diversity.", "date": "2026-05-04", "categories": []}
{"id": "2606.07669", "title": "MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios", "abstract": "Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision-Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge-cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance.", "date": "2026-06-04", "categories": []}
{"id": "2606.08153", "title": "LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection", "abstract": "Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a log anomaly detector built on EleutherAI's GPT-Neo (1.3B parameters) and fine-tuned with a novel partial-credit, exponentially decaying position-aware reward scheme combined with cross-entropy regularisation via Proximal Policy Optimisation (PPO). The position-aware reward explicitly models prediction difficulty: early positions receive higher rewards for correct predictions, while later positions incur stronger penalties for errors. LogNEO attains F1-scores of 0.927, 0.913, and 0.984 on the HDFS, BGL, and Thunderbird benchmarks, improving recall by up to 6 percentage points over the prior state-of-the-art LogGPT while maintaining comparable precision. A production microservice deployment over Apache Kafka, Redis, and TensorRT-accelerated inference demonstrates 45 ms end-to-end latency at 15,000 events per second.", "date": "2026-06-06", "categories": []}
{"id": "2606.08649", "title": "Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning", "abstract": "Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present CEF-Log, a context-enhanced few-shot chain-of-thought prompting strategy for Large Language Models that addresses this dual requirement. CEF-Log embeds expert investigative methodology through a structured five-step reasoning template, enabling the model to learn \\textit{how} to analyze logs rather than \\textit{what} patterns to memorize. Experimental evaluation demonstrates that CEF-Log achieves an F1-score of 0.99 on the CSIC 2010 dataset using only four examples while providing a $10\\times$ improvement in sample efficiency compared to other prompting-based methods. We also introduce ForenWebLog, a new dataset that incorporates real-world attacks and multi-step attack sequences for comprehensive evaluation. Qualitative analysis confirms that CEF-Log generates traceable, accurate explanations suitable for forensic documentation, addressing the critical \"black-box\" limitation of traditional machine learning approaches.", "date": "2026-06-07", "categories": []}
{"id": "2606.08935", "title": "PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection", "abstract": "Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks. However, we notice that they suffer from a major limitation in our evaluation - their learned embeddings are often amplitude-agnostic. Losing amplitude information can degrade performance on amplitude related anomalies, and this failure is prevalent across all existing representation-based methods. To address aforementioned issues, we propose a new anomaly scoring scheme named PAI. PAI consists of two complementary modules, a diagnostic module and a final score augmentation function. The diagnostic module compares cosine and Euclidean scoring on the same representation bank to test whether amplitude information is already captured in the learned representation. Then in final score augmentation function, PAI computes a point-wise median and MAD deviation score and a local mean-shift score-which are fused with the representation score to produce the final anomaly score. On the TSB-AD-U-Eva and TAB UV datasets, PAI improves all four evaluated representation-based methods across every reported metric, achieving average VUS-PR gains of 98.4% and 36.8%, respectively. Among all evaluated combinations, PaAno + PAI achieves the best performance, outperforming the state-of-the-art method by 15%. Further evaluation on bootstrap confidence intervals, anomaly-type breakdowns, and a TS2Vec input-normalization ablation further support the proposed scheme. These results suggest that explicitly retaining amplitude information is important for representation-based time-series anomaly detection, which has been underemphasized in existing scoring schemes. Code is available at: https://github.com/pantheon5100/PAI", "date": "2026-06-08", "categories": []}
{"id": "2606.09111", "title": "Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds", "abstract": "Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however, their efficacy is significantly compromised by severe illumination heterogeneity caused by vegetation shadows. To address this, we propose a prior-free anomaly detection framework capable of robustly handling lighting variations. First, we formulate solar angle estimation as an inverse optimization problem. By coupling spectral indices with a ray-tracing model, this strategy achieves Prior-Free Shadow Extraction without relying on flight metadata, effectively distinguishing dark objects from true shadows. Second, to mitigate spectral distortions, we introduce an Illumination-Consistent Sparse Representation mechanism. Unlike standard reconstruction methods, we construct a background dictionary strictly from neighbors sharing the same illumination state. This constraint effectively disentangles spectral reflectance from lighting variations, ensuring that targets are represented solely by physically consistent background points. Experimental results indicate that the proposed method significantly improves the separability between anomalies and background in complex forest environments, demonstrating superior performance over state-of-the-art baselines. This framework is particularly suited for identifying camouflaged military targets, mapping fallen tree trunks, and uncovering archaeological ruins hidden beneath dense foliage.", "date": "2026-06-08", "categories": []}
{"id": "2606.09670", "title": "Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision", "abstract": "Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated. Those variations that occur render anomaly detection methods unusable in many real-world scenarios. To address these limitations, we introduce three key contributions: (1) a visual prompting pipeline that isolates objects using foreground-background masking; (2) a mechanism for unfreezing the teacher in student-teacher models to improve domain adaptability; and (3) a data augmentation strategy leveraging diffusion-generated synthetic images to enhance anomaly detection performance. We achieve a 3.5 percentage point improvement over the previous state-of-the-art on the challenging AeBAD dataset by using the Masked Multiscale Reconstruction (MMR) model as our backbone.", "date": "2026-06-08", "categories": []}
{"id": "2505.21285", "title": "Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection", "abstract": "This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effectively capturing both structural patterns and semantic variations while maintaining theoretical guarantees. Combining graph kernels and kernel density estimation (KDE) is a standard approach to graph density estimation, but has unsatisfactory performance due to the handcrafted and fixed features of kernels. Our method LGKDE leverages graph neural networks to represent each graph as a discrete distribution and utilizes maximum mean discrepancy to learn the graph metric for multi-scale KDE, where all parameters are learned by maximizing the density of graphs relative to the density of their well-designed perturbed counterparts. The perturbations are conducted on both node features and graph spectra, which helps better characterize the boundary of normal density regions. Theoretically, we establish consistency and convergence guarantees for LGKDE, including bounds on the mean integrated squared error, robustness, and generalization. We validate LGKDE by demonstrating its effectiveness in recovering the underlying density of synthetic graph distributions and applying it to graph anomaly detection across diverse benchmark datasets. Extensive empirical evaluation shows that LGKDE demonstrates superior performance compared to state-of-the-art baselines on most benchmark datasets.", "date": "2025-05-27", "categories": []}
{"id": "2605.25806", "title": "An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?", "abstract": "Women's safety and security are paramount for a modern society. Crimes against women occur in daylight as well as in low-light conditions. Often, such events are captured through real-world surveillance cameras that operate at lower resolutions. Despite substantial progress in CV-related research, video anomaly detection (VAD) focused on women's safety has not yet been adequately addressed. Existing video anomaly datasets contain well-lit, high-resolution, close-shot videos, and fail to represent women-centric anomalies such as chain snatching, stalking, inappropriate touch, and other subtle forms of crime against women. To address these problems, we propose the ExtrAnom dataset, a new multi-modal benchmark containing 1001 videos with textual descriptions, 500 normal and 501 anomalous, classified into 5 different types of women-centric crimes. The dataset comprises low-light (8%), low-resolution videos (13%), long-shot (15%), along with daylight (64%) anomalous videos. And it covers anomalous events like stalking (3.9%), chain snatching (17.6%), kidnapping (7.3%), assassinations (2.3%), harassment (18.9%), and normal (50%). Each video is supplemented with 4 textual annotations, including one human-generated and three LLM-generated descriptions, enabling cross-modal and VLM-based validations. The aim of creating a women-centric dataset is to accurately detect the women-centric anomaly patterns, which are possible to observe visually. The dataset supplements the VLMs to accurately generate video-level descriptions. ExtrAnom has been benchmarked against popular unimodal and multi-modal VAD datasets (e.g., XD-Violence, UCF-Crime, and UCA) and SOTA methods. Experiments reveal that the existing datasets are insufficient to train models for detecting women-centric anomalies.", "date": "2026-05-25", "categories": []}
{"id": "2506.00188", "title": "Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series", "abstract": "Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections. While MLP-based mixer models have shown promise in time-series analysis, they do not maintain temporal causality during data processing. Moreover, real-world multivariate time series often contain numerous channels with diverse inter-channel correlations. Spurious correlations in the reconstructed time series lead to noisy representations, resulting in inaccurate anomaly detection. In addition, anomaly scoring methods that ignore temporal continuity can mislead sequential detection. To address these challenges, we propose a cluster-aware causal mixer for multivariate time-series anomaly detection. Channels are grouped into clusters based on their correlations, and each cluster is embedded through a dedicated embedding layer. A causal mixer is introduced to integrate information while maintaining temporal causality. We further develop a sequential anomaly-scoring method that accumulates evidence over time and refines anomaly boundaries. Our proposed model operates in an online fashion, making it suitable for real-time time-series anomaly detection. Experimental evaluations across six public benchmark datasets demonstrate that the proposed approach consistently achieves superior performance.", "date": "2025-05-30", "categories": []}
{"id": "2606.05274", "title": "Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder", "abstract": "Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation. However, the large volume and high sampling frequency of EHA sensor data pose challenges for accurate and efficient anomaly detection. Conventional statistical and classical machine-learning methods such as Z-score, Interquartile Range (IQR), Median Absolute Deviation (MAD), Isolation Forest, Gaussian Mixture, and k-means often fail to capture the temporal dependencies inherent in EHA signals, resulting in limited detection accuracy and elevated false-alarm rates. Furthermore, systematic evaluations of data-driven anomaly detection approaches for EHA systems remain scarce, particularly under varying operational conditions. This study presents an offline anomaly-detection framework for univariate EHA sensor signals, focusing on temperature and pressure data collected from a controlled test bench. The method employs a reconstruction-based Long Short-Term Memory (LSTM) autoencoder, calibrated and evaluated using validation-set reconstruction-error distributions. Performance is assessed across multiple fault-injection scenarios using accuracy, precision, recall, and F1-score, complemented by sensitivity analyses under varying operating conditions. The LSTM autoencoder achieved an average accuracy of 99.0\\%, precision up to 100\\%, recall between 90.2\\% and 99.6\\%, and F1-scores from 93.1\\% to 99.8\\%, demonstrating high detection sensitivity and a very low false-alarm rate across all evaluated sensors. These results highlight the feasibility of data-driven offline anomaly detection for EHAs. Future work will focus on adapting the developed framework for an online (real-time) environment.", "date": "2026-06-03", "categories": []}
{"id": "2606.05700", "title": "T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction", "abstract": "We present T-SAR-JEPA, a self-supervised framework for temporal anomaly detection in SAR amplitude stacks via latent prediction. A ViT-Base/16 encoder from SAR-JEPA is domain-adapted on 39,300 Capella patches using local masked reconstruction with gradient feature prediction. A temporal transformer with sinusoidal time encoding forecasts future latent states from K=7 acquisitions, with progressive unfreezing substantially reducing validation loss. The model operates on amplitude alone; InSAR coherence serves exclusively as independent pseudo-ground-truth. On the DFC 2026 dataset (300 time-series, three AOIs), T-SAR-JEPA achieves ROC-AUC of 77.0% on the Hawaii eruption window, outperforming RX, PaDiM, Linear AR, and LSTM baselines (~50%). Spatial coherence of 99.9% (p < 0.001, permutation test) confirms structured detections. Code: https://github.com/TerraLatent/t-sar-jepa", "date": "2026-06-04", "categories": []}
{"id": "2606.06261", "title": "DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN", "abstract": "O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces. The same openness that enables multi-vendor composition also expands the attack surface across logically decoupled tiers that make up the compute continuum. Among these threats, Denial-of-Service and performance-degradation attacks, which account for the majority of catalogued O-RAN threats, are particularly difficult to detect. Traditional Time-Series Anomaly Detection (TSAD) methods fail in this new regime where labelled baselines are scarce, threats evolve faster than detectors can be retrained, and the high-dimensional multivariate telemetry overwhelms monolithic inference models. To address these challenges, we present DAST, a zero-shot multi-agent framework for cross-interface anomaly detection in O-RAN that chains a three-stage VLM $\\rightarrow$ LLM $\\rightarrow$ VLM pipeline. DAST converts multivariate KPI streams into visual representations, scores textual per-interface descriptions against O-RAN domain knowledge, and verifies suspects on high-resolution heatmaps to output the problematic interfaces, the anomalous time intervals, an indicative O-RAN WG11-aligned operational impact rating and the decision rationale. We evaluate DAST on real network traces collected from an O-RAN testbed under representative performance degradation scenarios, achieving 0.910 F1-Score and 0.843 Accuracy, outperforming state-of-the-art TSAD baselines.", "date": "2026-06-04", "categories": []}
{"id": "2606.04073", "title": "TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection", "abstract": "This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\\textbf{T}wo-stage \\textbf{P}seudo \\textbf{A}nomaly-guided \\textbf{A}nomaly \\textbf{D}etection, \\textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training. The method first generates pseudo-anomalous windows near the normal boundary using a reconstruction model and per-feature target-error control. It then learns anomaly-sensitive representations through contrastive learning between normal and pseudo-anomalous windows, and finally produces window-level and point-level anomaly scores using k-nearest neighbors (KNN). Compared with existing methods that rely on known fault categories, real anomaly priors, or random anomaly injection, TPA-AD improves the separability of the normal boundary by constructing pseudo-anomalies in boundary neighborhoods and can jointly handle continuous and discrete features in mixed-variable scenarios. The main experiments are conducted on bearing fault detection datasets and degradation-process datasets, with an additional exploratory extension on $13$ public TSAD datasets. The results show that the proposed method yields relatively stable anomaly responses, is sensitive to degradation evolution, and demonstrates a certain degree of broader applicability on public TSAD benchmarks and real high-speed-train-related bearing data.", "date": "2026-06-02", "categories": []}
{"id": "2606.04369", "title": "VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment", "abstract": "Few-shot cross-category 3D anomaly detection aims to determine whether an unknown point cloud belongs to a target normal category using only a few normal references. Existing training-based methods usually require category-wise optimization, while recent training-free methods based on multi-view CLIP visual features mainly rely on visual similarity and may be confused by geometrically similar categories. In this paper, we propose VT-3DAD, a training-free framework for cross-category 3D anomaly detection via Visual-Text Normal Space Alignment. Given few-shot normal references and a test point cloud, VT-3DAD first generates realistic multi-view depth maps and extracts view-wise features using a frozen CLIP visual encoder. The visual branch measures reference-test deviation in the multi-view feature space. In parallel, depth-aware and 3D-aware prompts are encoded by the frozen CLIP text encoder to construct textual normal anchors, which provide semantic normality constraints for the target category. The final anomaly score is obtained by fusing visual deviation from normal references and semantic deviation from the textual normal space. Experiments on the ShapeNetPart dataset demonstrate that VT-3DAD achieves state-of-the-art performance. In particular, VT-3DAD improves the one-shot average AUC-ROC from 92.49% to 94.80% compared with the visual-only baseline, while also reducing the average standard deviation from 5.64 to 3.41.", "date": "2026-06-03", "categories": []}
{"id": "2606.04599", "title": "Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection", "abstract": "Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a \"Plan First, Judge Later\" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.", "date": "2026-06-03", "categories": []}
{"id": "2606.04957", "title": "NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewriting", "abstract": "System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog (Natural-Language Log), a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools them with term-frequency-inverse-document-frequency weighting, classifies sessions with tree ensembles, and back-projects evidence with TreeSHAP for analyst review. On Hadoop Distributed File System (HDFS) and Blue Gene/L (BGL) corpora, NLLog exceeds two reproduced matched-protocol baselines; across HDFS, BGL, and the AIT Alert Data Set, it sustains low false-positive rates with commodity-hardware latency suitable for security operations center triage. Coverage, sparse-versus-dense, faithfulness, and adversarial ablations show that fallback sufficiency is corpus-dependent, that an enrollment-time coverage check can surface refinement requirements before deployment, and that an auditable deterministic rewrite combined with lightweight dense encoding provides a measurable representation layer for log-anomaly detection and triage.", "date": "2026-06-03", "categories": []}
{"id": "2606.02601", "title": "Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection", "abstract": "Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection. We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space. In this regime, anomaly scores may collapse toward chance or even invert, and the preferred score direction can depend on the unknown anomaly class. We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette, in both pixel and VAE latent spaces. Our results suggest that class-split AD benchmarks should be treated as geometry-dependent stress tests rather than unconditional evidence of anomaly-detection ability.", "date": "2026-05-23", "categories": []}
{"id": "2606.03112", "title": "Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data", "abstract": "With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing. To reduce operation and maintenance costs and enhance the reliability of wind turbine and system operation data before reaching catastrophic failures, monitoring the operating status of the equipment and detecting failures at an early stage is crucial. It is of great practical significance to utilize the working condition data for abnormal assessment of the operating status of wind turbines to realize abnormal monitoring of the operating status of wind turbines. However, the existing anomaly detection methods can neither perform effective relational modeling in data filled with a large amount of redundant information nor reasonably utilize the valuable anomaly data. For this reason, this paper proposes an anomaly detection model that fuses a Transformer and a generative adversarial network. Firstly, it reduces the leakage detection rate of minor deviation anomalies by amplifying the reconstruction error. Secondly, it uses autoregressive inference to extract multimodal features to enhance the stability and generalization ability of training. Finally, the temporal feature extraction module is constructed to promote the interactive learning between features of different time scales and effectively reduce the time redundancy. The results of multiple sets of experiments conducted on real WTG datasets show that TransGAN-WT achieves an average F1 score of 96.10% across multiple wind turbine datasets, which is 5.84% and 2.89% higher than several other state-of-the-art baseline methods. It also realizes a false positive rate (FPR) of 0.06%, and is verified by the Wilcoxon signed-rank test to have achieved a statistically significant performance enhancement compared to the state-of-the-art baseline methods, effectively ensuring the stable operation of wind turbines.", "date": "2026-06-02", "categories": []}
{"id": "2512.06906", "title": "MINES: Explainable Anomaly Detection through Web API Invariant Inference", "abstract": "Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing reliable web services. Many modern web applications operate on web APIs (e.g., RESTful, SOAP, and WebSockets), their exposure invites intended attacks or unintended illegal visits, causing abnormal system behaviors. However, such anomalies can share very similar logs with normal logs, missing crucial information (which could be in database) for log discrimination. Further, log instances can be also noisy, which can further mislead the state-of-the-art log learning solutions to learn spurious correlation, resulting superficial models and rules for anomaly detection. In this work, we propose MINES which infers explainable API invariants for anomaly detection from the schema level instead of detailed raw log instances, which can (1) significantly discriminate noise in logs to identify precise normalities and (2) detect abnormal behaviors beyond the instrumented logs. Technically, MINES (1) converts API signatures into table schema to enhance the original database shema; and (2) infers the potential database constraints on the enhanced database schema to capture the potential relationships between APIs and database tables. MINES uses LLM for extracting potential relationship based on two given table structures; and use normal log instances to reject and accept LLM-generated invariants. Finally, MINES translates the inferred constraints into invariants to generate Python code for verifying the runtime logs. We extensively evaluate MINES on web-tamper attacks on the benchmarks of TrainTicket, NiceFish, Gitea, Mastodon, and NextCloud against baselines such as LogRobust, LogFormer, and WebNorm. The results show that MINES achieves high recall for the anomalies while introducing almost zero false positives, indicating a new state-of-the-art.", "date": "2025-12-07", "categories": []}
{"id": "2602.03018", "title": "From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection", "abstract": "Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selection notoriously hard. Foundation models (FMs) have transformed ML, and OD is no exception: Shen et. al. (2025) introduced FoMo-0D, the first FM for OD, achieving remarkable performance against numerous baselines. This work introduces OUTFORMER, which advances FoMo-0D with (1) a mixture of synthetic priors and (2) self-evolving curriculum training. OUTFORMER is pretrained solely on synthetic labeled datasets and infers test labels of a new task by using its training data as in-context input. Inference is fast and zero-shot, requiring merely forward pass and no labeled outliers. Thanks to in-context learning, it requires zero additional work-no OD model training or bespoke model selection-enabling truly plug-and-play deployment. OUTFORMER achieves state-of-the-art performance on the prominent AdBench, as well as two new large-scale OD benchmarks that we introduce, comprising over 1,500 datasets, while maintaining speedy inference.", "date": "2026-02-03", "categories": []}
{"id": "2602.08868", "title": "AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection", "abstract": "Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional, detailed reasoning, which is vital for understanding complex time-series data. We present AnomSeer to address this by reinforcing the model to ground its reasoning in precise, structural details of time series, unifying anomaly classification, localization, and explanation. At its core, an expert chain-of-thought trace is generated to provide a verifiable, fine-grained reasoning from classical analyses (e.g., statistical measures, frequency transforms). Building on this, we propose a novel time-series grounded policy optimization (TimerPO) that incorporates two additional components beyond standard reinforcement learning: a time-series grounded advantage based on optimal transport and an orthogonal projection to ensure this auxiliary granular signal does not interfere with the primary detection objective. Across diverse anomaly scenarios, AnomSeer, with Qwen2.5-VL-3B/7B-Instruct, outperforms larger commercial baselines (e.g., GPT-4o) in classification and localization accuracy, particularly on point- and frequency-driven exceptions. Moreover, it produces plausible time-series reasoning traces that support its conclusions.", "date": "2026-02-09", "categories": []}
{"id": "2602.20019", "title": "Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision", "abstract": "Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervised or semi-supervised: unsupervised methods avoid the need for labeled anomalies but often produce ambiguous boundary, whereas semi-supervised methods can overfit to the limited labeled anomalies and generalize poorly to unseen anomalies. To address this gap, we consider a largely underexplored problem: learning a discriminative boundary from normal/unlabeled data, while leveraging limited labeled anomalies \\textbf{when available} without sacrificing generalization to unseen anomalies. In this paper, we propose an effective, generalizable, and model-agnostic framework with three main components: (i) residual representation encoding that capture deviations between current interactions and their historical context, providing anomaly-relevant signals; (ii) a restriction loss that constrain the normal representations within an interval bounded by two co-centered hyperspheres, ensuring consistent scales while keeping anomalies separable; (iii) a bi-boundary optimization strategy that learns a discriminative and robust boundary using the log-likelihood distribution modeled by a normalizing flow. Extensive experiments demonstrate the superiority of our framework across diverse evaluation settings.", "date": "2026-02-23", "categories": []}
{"id": "2605.23231", "title": "Beyond Normal References: Discriminative Few-Shot Anomaly Detection", "abstract": "This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormality as deviations from normality. IDEAL decomposes the learning process into two novel components: 1) a Normal Variation Eraser to suppress nuisance normal variations that may lead to noisy deviations from normality, thereby highlighting anomaly-relevant deviation representations; 2) an Intrinsic Deviation Encoder to decompose these denoised deviation representations into intrinsic deviation vectors capturing the most discriminative orthogonal deviation directions. At inference, IDEAL scores query-to-normal deviations preserved after projection onto the learned intrinsic deviation vectors, enabling generalization for both seen and unseen anomalies. Extensive experiments on eight real-world datasets show that IDEAL generalizes effectively to unseen anomalies and consistently outperforms existing state-of-the-art FSAD methods. Code and data will be available at \\href{https://github.com/mala-lab/IDEAL}{https://github.com/mala-lab/IDEAL}.", "date": "2026-05-22", "categories": []}
{"id": "2605.26068", "title": "Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark", "abstract": "Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whether they address unique challenges or share fundamental mechanisms. This paper introduces WSADBench, the first benchmark that unifies evaluation across distinct weakly supervised scenarios, benchmarking diverse approaches from specialized WSAD methods to advanced tabular foundation models. WSADBench establishes standardized protocols to evaluate 36 algorithms across 4 modalities by systematically varying label quantity, granularity, and quality, revealing the performance boundaries of various methods. Based on over 700K experiments, WSADBench reveals four critical insights: (i) Strong intrinsic correlations exist between these weak supervision scenarios, challenging the isolation of current research directions. (ii) Specialized WSAD algorithms excel only in extreme label-scarcity regimes but are quickly dominated by tabular foundation models and general classification methods as supervision increases or in OOD scenarios. (iii) Unlabeled data shows inconsistent utility across settings, with marginal gains compared to label refinement. (iv) Models exhibit asymmetric sensitivity to different types of label noise. We release WSADBench as an open-source benchmark with code and datasets to facilitate future WSAD research: https://github.com/SUFE-AILAB/WSADBench.", "date": "2026-05-25", "categories": []}
{"id": "2605.31487", "title": "Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems", "abstract": "Deploying computer vision models in Warehouse Facilities traditionally requires extensive resources for camera mounting, image collection, annotation, training, and deployment - a process often needing repetition in each new environment due to camera mounting constraints and environmental variability. This paper explores an innovative approach to streamline this process by conducting the standard procedure solely in a laboratory setting, focusing on vertical material handling systems and anomaly detection in forks of the systems. Through extensive experimentation, we have found that combining optimal camera placement, strategic image triggering, careful model selection and model ensemble enables effective generalization from laboratory conditions to diverse warehouse facilities environments, potentially transforming warehouse automation implementation by simplifying warehouse facilities deployment to just camera mounting, image collection, and model deployment, thereby saving significant resources and time typically spent on image annotation and model retraining. This is an experimental research study and not a production deployment.", "date": "2026-05-29", "categories": []}
{"id": "2606.00052", "title": "Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems", "abstract": "As Industry 4.0 accelerates the integration of Cyber-Physical Systems (CPS) in manufacturing, robust anomaly detection has become critical for ensuring process safety and security. Current data-driven approaches typically employ \"product-agnostic\" or global models trained on the aggregate of all normal operating data. However, modern industrial facilities frequently operate under diverse product grades. While computationally simple, these global models inherently expand their decision boundaries to accommodate the variance of multiple modes, creating a \"blind spot\" where subtle anomalies or targeted cyber-physical attacks may be masked by the wide acceptance region of the model. In this work, we first demonstrate that the vulnerability described above is present in global-agnostic models operating across multiple product grades. We then present a Product-Aware Autoencoder as a principled mitigation that restricts the learning domain to grade-specific distributions. While this approach reduces the identified blind-spot risk, we do not claim it as the optimal mitigation among all possible alternatives. We rigorously validate this approach against a Global Agnostic baseline using the Extended Tennessee Eastman Process (TEP) benchmark. Our empirical results indicate that the Product-Aware framework performs comparably to the global baseline on standard detection metrics, while offering improved robustness to product-grade-specific operating modes. Most critically, stress tests simulating our hypothetical attack scenarios reveal that while the global model fails to detect operational deviations in 77.8% of the scenarios, the product-aware system achieves 100% detection accuracy. These findings suggest that, in flexible manufacturing environments, generalized anomaly detectors can pose non-trivial security risks, motivating a shift toward mode-aware diagnostic architectures.", "date": "2026-05-13", "categories": []}
{"id": "2606.00304", "title": "Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection", "abstract": "Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectral energy distributions, they overlook those that result in decreased variation, i.e., camouflaged anomalies that appear normal. We show that this type of anomaly persists across multiple datasets and remains undetectable by existing spectral approaches. To address this limitation, we propose a node-level spectral energy formulation that is fully compatible with message passing and enables the detection of camouflaged anomalies. Building on this formulation, we introduce an energy-aware graph learning framework that models spectral shifts through energy-driven message passing in both static and time-series graphs. Besides, our unified architecture extends to temporal settings without introducing specialized sequence modules, enabling efficient learning under long sliding windows. Extensive experiments on large-scale benchmarks demonstrate the effectiveness and scalability of our approach.", "date": "2026-05-29", "categories": []}
{"id": "2606.00867", "title": "Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers", "abstract": "Recent publications have suggested using the Shap- ley value for sensor anomaly/attack localization. We study the performance of such an approach by using mathematically de- fined optimum binary classifiers in the Shapley value calculation. To judge localization performance, we study the ability of the Shapley value of a given sensor observation to determine if that observation is anomalous. First, we prove that for cases with independent sensor observations, an optimized anomaly test using the Shapley value is equivalent to an optimized lower-complexity anomaly test using a single term in the Shapley value calculation, yielding the exact same probability of error. For some popular dependent observation cases involving two sensors, including correlated bivariate Gaussian/Laplacian probability density functions and constant/Gaussian at- tacks/anomalies, we prove that these two tests are fundamentally different, yielding different decision regions and error probabil- ities. Further, we prove that the Shapley value test is sometimes strictly inferior to the other (single term in Shapley calculation) test in certain statistically dependent bivariate Gaussian scenarios with large correlation magnitude and additive attacks/anomalies, while it is strictly superior in others, depending on the sign of the correlation. One can combine these two approaches to obtain a strictly better approach in these cases. These results, which provide the first theoretical statistical analysis of Shapley-based localization, seem very interesting based on the wide acceptance of the Shapley value by many researchers and should encourage further research on this topic. Numerical results are provided which illustrate our findings.", "date": "2026-05-30", "categories": []}
{"id": "2606.01300", "title": "ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection", "abstract": "Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, composed of Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Attention, refines these embeddings to capture temporal dependencies and highlight salient patterns. Unlike previous approaches, our model requires minimal task-specific tuning and demonstrates robust generalization across a wide range of domains, including industrial, medical, cyber-physical, and automotive systems. Extensive experiments on 11 benchmarks show that ChronosAD outperforms existing methods by 4.72% in AUC and 6.60% in AP on average. The source code is available at https://github.com/intelligolabs/ChronosAD.", "date": "2026-05-31", "categories": []}
{"id": "2606.01691", "title": "IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems", "abstract": "Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhibit limited effectiveness in real-time anomaly detection due to the complex dependencies among sensors and actuators. To tackle this, we present IstGPT, the first industrial anomaly detection tool based on LLMs and graph learning to provide real-time protection against a wide range of ICS attacks. IstGPT achieves fine-grained and precise modeling on spatial-temporal dependencies in industrial cyber-physical systems. It first leverages industrial multi-modal knowledge, including operational data, technical documents, and system diagrams, to extract sensor-actuator dependency graphs via multi-stage prompt engineering. Then, LLM-Optimation iteratively refines the graph based on node accuracy, edge consistency, and logical coherence. Finally, IstGPT integrated improved graph neural networks with an encoder-decoder architecture to detect anomalies via reconstruction errors. We evaluate IstGPT against 12 state-of-the-art baselines on 9 datasets, including 2 public, 6 simulated, and a real-world robotic arm dataset. IstGPT achieves the best F1-scores and eTaF1 (a newer time-aware metric) across nine datasets. We further discuss the feasibility of deploying IstGPT in real-world industrial scenarios.", "date": "2026-06-01", "categories": []}
{"id": "2606.01992", "title": "A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision", "abstract": "Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual input alongside the image and are presented as enabling text-guided zero- and few-shot inspection. Yet these methods are evaluated with protocols inherited from unimodal benchmarks that hold the textual condition constant and therefore cannot measure whether language conditions the decision; whether reported gains reflect text guidance or strong pretrained visual features remains open. We introduce Text-Guided Anomaly Detection (TGAD), a structured benchmark that progressively increases the functional role of language across three scenarios: a controlled prompt-sensitivity setting on MVTec AD; a component-tagged extension of MVTec AD that requires the model to restrict its assessment to an instructed part; and the new Assembled Panel Dataset (APD), a realistic industrial setting that requires both defect-type and component-location knowledge. We evaluate one representative model per paradigm: generative large vision-language, training-free discriminative, and embedding-adaptive discriminative. In all three, the textual interface conditions the decision only superficially: prompt content is absorbed unless the object noun is removed (the generative model's I-AUROC drops from 97.4 to 82.6); component-level instructions do not constrain the decision once defects outside the instructed part are admitted as normal (from 90.3 to 66.3); and when both combine on APD, image-level discrimination collapses below the MVTec level, in one case below chance (71.2, 50.5, 31.5). These results suggest that standard benchmarks overstate the text-guided capabilities of current multimodal anomaly detection systems, and that a protocol of this kind is a prerequisite for models that can be reliably controlled through language for industrial deployment.", "date": "2026-06-01", "categories": []}
{"id": "2606.02042", "title": "Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks", "abstract": "Continual industrial anomaly detection with diffusion models suffers from historical normality prior drift and catastrophic forgetting. Existing continual diffusion methods preserve previous knowledge through replay or constrained optimization, but they lack an explicit mechanism for isolating and protecting category-specific normality priors during sequential adaptation. Although low-rank adaptation provides modular residual updates, standard LoRA neither freezes historical normality subspaces nor prevents new adapters from interfering with previous ones. To address this issue, we propose a normality-preserving continual anomaly detection framework based on two modules: History Frozen Orthogonal LoRA Bank (HF-OLB) and Hierarchical Novelty Adaptive Bank Growth module (HNABG). HF-OLB freezes both the pre-trained U-Net backbone and the learned LoRA banks, and constrains new task-specific normality residuals to the orthogonal complement of historical LoRA subspaces. HNABG further allocates layer-dependent residual capacity and expands the bank only when the residual normality novelty exceeds the expressive capacity of existing banks. Extensive experiments on MVTec and VisA demonstrate the effectiveness of the proposed method. On the challenging VisA 2x6 setting, our method achieves 83.6/91.8 image and pixel level A-AUROC with 3.8/3.9 FM, improving pixel level A-AUROC over the state of the art by 3.2 points while reducing pixel level FM by 1.3. These results show that our method effectively preserves historical normality priors in long horizon continual category sequences.", "date": "2026-06-01", "categories": []}
{"id": "2509.21190", "title": "Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy", "abstract": "Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data in a zero-shot manner remains challenging. Existing foundation models for TSAD often rely on reconstruction-error scoring at inference time, which can miss subtle anomalies that are well reconstructed and can falsely flag complex but normal patterns in unseen domains. We introduce TimeRCD, a foundation model for TSAD built on Relative Context Discrepancy (RCD), a pre-training paradigm that trains the model to detect anomalies by comparing a query pattern with its surrounding context. This relational formulation, implemented with a standard Transformer architecture, enables the model to infer normality from the input context rather than relying on fixed global normal patterns. We further construct a large-scale synthetic corpus with context-dependent anomaly labels to provide supervised pre-training signals for RCD. Experiments across diverse benchmarks show that TimeRCD outperforms existing general-purpose and anomaly-specific foundation models in most zero-shot TSAD settings, while remaining competitive with dataset-specific full-shot baselines. These results provide empirical evidence that RCD is an effective direction for building robust and generalizable TSAD models.", "date": "2025-09-25", "categories": []}
{"id": "2509.21561", "title": "Unsupervised Defect Detection for Surgical Instruments", "abstract": "Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applying or fine-tuning these approaches leads to issues: false positive detections arising from textured backgrounds, poor sensitivity to small, subtle defects, and inadequate capture of instrument-specific features due to domain shift. To address these challenges, we propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments. By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations, enabling the reliable detection of fine-grained defects in surgical instrument imagery.", "date": "2025-09-25", "categories": []}
{"id": "2510.02060", "title": "ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection", "abstract": "In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descriptions and domain knowledge that experts rely on in practice. This limitation restricts research flexibility and prevents models from fully leveraging domain knowledge for detection. ReTabAD addresses this gap by restoring textual semantics to enable context-aware tabular AD research. We provide (1) 20 carefully curated tabular datasets enriched with structured textual metadata, together with implementations of state-of-the-art AD algorithms including classical, deep learning, and LLM-based approaches, and (2) a zero-shot LLM framework that leverages semantic context without task-specific training, establishing a strong baseline for future research. Furthermore, this work provides insights into the role and utility of textual metadata in AD through experiments and analysis. Results show that semantic context improves detection performance and enhances interpretability by supporting domain-aware reasoning. These findings establish ReTabAD as a benchmark for systematic exploration of context-aware AD.", "date": "2025-10-02", "categories": []}
{"id": "2603.12916", "title": "Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection", "abstract": "Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering command might be internally consistent but decouple from the resulting lateral acceleration. Residual-based detectors can miss such anomalies when flexible sequence models still reconstruct signals plausibly despite altered coordination. We introduce AxonAD, an unsupervised detector that treats multi-head attention query evolution as a short horizon predictable process. A gradient-updated reconstruction pathway is coupled with a history-only predictor that forecasts future query vectors from past context. This is trained via a masked predictor-target objective against an exponential moving average (EMA) target encoder. At inference, reconstruction error is combined with a tail-aggregated query mismatch score, which measures cosine deviation between predicted and target queries on recent timesteps. This dual approach provides sensitivity to structural dependency shifts while retaining amplitude-level detection. On proprietary in-vehicle telemetry with interval annotations and on the TSB-AD multi-variate suite (17 datasets, 180 series) with threshold-free and range-aware metrics, AxonAD improves ranking quality and temporal localization over strong baselines. Ablations confirm that query prediction and combined scoring are the primary drivers of the observed gains. Code is available at the URL https://github.com/iis-esslingen/AxonAD.", "date": "2026-03-13", "categories": []}
{"id": "2605.19233", "title": "Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets", "abstract": "Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive anomaly detectors. We present a leakage-free evaluation of quantum machine learning for UAV anomaly detection on the multi-sensor TLM:UAV benchmark. Three contributions support the study. (i) A group-aware temporal protocol (B2) partitions the dataset into ten contiguous TimeUS blocks and evaluates over ten seeds, eliminating the inflation produced by random stratified splits that mix neighbouring samples. (ii) A three-mode feature audit (full/loose/strict) quantifies how much accuracy stems from instantaneous physical signals versus contextual proxies (cumulative energy, battery state, GPS trajectory). (iii) A hybrid XGBoost + Data Reuploading (DRU) classifier is benchmarked against five paired non-linear controls (raw, PCA, polynomial-2, random-RBF, and an untrained DRU map) under identical budgets. The standalone DRU does not consistently match the strongest classical baseline across seeds; however, the trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05), a directional signal that the per-seed standard deviations prevent from being interpreted as a statistically established difference. The trained-DRU hybrid also records the lowest mean false-alarm rate under proxy-free evaluation, subject to the inter-seed variance reported. We frame this as an incremental, reproducible quantum-enhanced hybrid benefit, and provide an open Qiskit 2.x implementation as a benchmark for cybersecurity analytics in NISQ-era aerospace systems.", "date": "2026-05-19", "categories": []}
{"id": "2605.30388", "title": "A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI", "abstract": "This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, time gaps, and turn angles. Although unsupervised learning algorithms such as Isolation Forest are widely used for detecting anomalous vessel movements, they often lack systematic and meaningful evaluation measures. To address this limitation, we propose a novel quality metric called Maritime Anomaly Detection Quality Index (MADQI). The prosed MADQI is a composite index designed to evaluate the anomaly detection performance of machine learning models without requiring labelled data. The proposed framework uses Haversine distance calculations to analyse AIS datasets and identify anomalies based on their spatial and behavioural characteristics. The proposed MADQI evaluation framework integrates four interconnected metrics: Anomaly Rate Consistency (ARC), Physical Plausibility Score (PPS), Score Distribution Separation (SDS), and Extreme Case Evidence (ECE). These metrics are combined through automatic normalisation using multi-chunk evaluation and adaptive scaling techniques. Experimental results on the AIS dataset show that the proposed framework achieved a MADQI score of 80.37%, demonstrating its effectiveness for unsupervised anomaly detection. In particular, the algorithm performed strongly in identifying abnormal vessel behaviour. Among the individual MADQI components, ECE and ARC achieved scores of 0.907 and 1.000, respectively, indicating excellent capability in detecting extreme anomalies and maintaining anomaly rate consistency. Overall, these results are encouraging and demonstrate that the proposed framework provides a reliable and meaningful approach for evaluating unsupervised anomaly detection in maritime AIS data.", "date": "2026-05-28", "categories": []}
{"id": "2605.31007", "title": "DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks", "abstract": "Anomaly detection in physiological sensor data from Wireless Body Area Networks (WBANs) can be caused by sensor faults, network disruptions, or missing data, leading to false alarms. Hence, it demands both high predictive accuracy and clinically interpretable explanations. Existing approaches rely either on black-box models that achieve strong performance but offer no transparency, or on post-prediction explanation methods such as SHAP and LIME. In this paper, we propose the Distilled Explanation Model (DEM), a three-stage glass-box framework that distills the non-linear knowledge of a gradient boosting expert into an interpretable decision tree operating on residuals relative to a linear baseline, so that the explanation is not an approximation but the prediction itself. DEM introduces a novel distillation fidelity metric that quantifies how faithfully the explanation tree captures the expert model's non-linear contribution, providing a principled measure of explanation trustworthiness absent from prior interpretable models. Evaluated across four physiological datasets, including MIMIC-IV, WESAD, eICU, and an in-house SmartNet WBAN corpus, DEM achieves an AUC of 0.9964 on clinical contextual anomaly detection and 0.9047 on wearable stress detection while producing human-readable if-then rules at a controllable depth. Inference requires 0.17ms per 1000 samples, rendering DEM 1235x faster than SHAP-based post-hoc explanation and suitable for real-time physiological monitoring. Ablation studies confirm that the XGBoost distillation step provides measurable gains over naive residual fitting, and depth-sensitivity analysis demonstrates an explicit, user-controlled accuracy-interpretability trade-off unique to DEM among existing intrinsically interpretable models.", "date": "2026-05-29", "categories": []}
{"id": "2605.31259", "title": "Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source", "abstract": "Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities. At the Spallation Neutron Source (SNS), the High Voltage Converter Modulators (HVCMs) are consistently the second-largest contributor to lost beam time. Each HVCM pulse is recorded across sensor channels spanning currents, voltages, and magnetic fluxes, whose mutual interactions encode the operating state of the system. Fault precursors do not manifest uniformly across these channels: depending on fault type, they may alter the temporal structure of individual signals, change the statistical dependencies among channels, or both. Existing deep-learning approaches typically process multi-channel signals with standard convolutional pipelines that entangle temporal and cross-channel operations from the first layer, giving the model no explicit mechanism to represent channel independence or structured inter-channel interaction. We hypothesise that architectural inductive bias, specifically the ordering of temporal filtering and cross-channel mixing, plays a central role in detection performance on this class of data. To test this, we vary the order in which these two operations are applied, and examine whether per-pulse adaptive channel reweighting further improves sensitivity. Evaluated on the public HVCM dataset across all four SNS subsystems (RFQ, DTL, CCL, SCL), our best variant achieves a pooled AUC-PR of 0.816 and AUC-ROC of 0.934, outperforming the state of the art on most subsystems and five of the six fault families. Ablations identify three dominant input channels and link per-fault-family performance to whether precursors manifest as amplitude shifts in individual channels or as subtler patterns requiring joint channel representations to surface.", "date": "2026-05-29", "categories": []}
{"id": "2411.00278", "title": "KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks", "abstract": "Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to prevent costly failures. Most TSAD methods driven by forecasting models tend to overfit by emphasizing minor fluctuations. Our analysis reveals that effective TSAD should focus on modeling \"normal\" behavior through smooth local patterns. To achieve this, we reformulate time series modeling as approximating the series with smooth univariate functions. The local smoothness of each univariate function ensures that the fitted time series remains resilient against local disturbances. However, a direct KAN implementation proves susceptible to these disturbances due to the inherently localized characteristics of B-spline functions. We thus propose KAN-AD, replacing B-splines with truncated Fourier expansions and introducing a novel lightweight learning mechanism that emphasizes global patterns while staying robust to local disturbances. On four popular TSAD benchmarks, KAN-AD achieves an average 15% improvement in detection accuracy (with peaks exceeding 27%) over state-of-the-art baselines. Remarkably, it requires fewer than 1,000 trainable parameters, resulting in a 50% faster inference speed compared to the original KAN, demonstrating the approach's efficiency and practical viability.", "date": "2024-11-01", "categories": []}
{"id": "2512.10659", "title": "DCFO: Density-Based Counterfactuals for Outliers -- Additional Material", "abstract": "Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for understanding the underlying factors that contribute to their detection, validating their significance, and identifying potential biases or errors. Effective explanations provide actionable insights, facilitating preventive measures to avoid similar outliers in the future. Counterfactual explanations clarify why specific data points are classified as outliers by identifying minimal changes required to alter their prediction. Although valuable, most existing counterfactual explanation methods overlook the unique challenges posed by outlier detection, and fail to target classical, widely adopted outlier detection algorithms. Local Outlier Factor (LOF) is one the most popular unsupervised outlier detection methods, quantifying outlierness through relative local density. Despite LOF's widespread use across diverse applications, it lacks interpretability. To address this limitation, we introduce Density-based Counterfactuals for Outliers (DCFO), a novel method specifically designed to generate counterfactual explanations for LOF. DCFO partitions the data space into regions where LOF behaves smoothly, enabling efficient gradient-based optimisation. Extensive experimental validation on 50 OpenML datasets demonstrates that DCFO consistently outperforms benchmarked competitors, offering superior proximity and validity of generated counterfactuals.", "date": "2025-12-11", "categories": []}
{"id": "2602.20316", "title": "Inspectorch: Efficient rare event exploration in solar observations", "abstract": "The Sun is observed in unprecedented detail, enabling studies of its activity on very small spatiotemporal scales. However, the large volume of data collected by our telescopes cannot be fully analyzed with conventional methods. Popular machine learning methods identify general trends from observations, but tend to overlook unusual events due to their low frequency of occurrence. We study the applicability of unsupervised probabilistic methods to efficiently identify rare events in multidimensional solar observations and optimize our computational resources to the study of these extreme phenomena. We introduce Inspectorch, an open-source framework that utilizes flow-based models: flexible density estimators capable of learning the multidimensional distribution of solar observations. Once optimized, it assigns a probability to each sample, allowing us to identify unusual events. We apply this approach by applying it to observations from the Hinode Spectro-Polarimeter, the Interface Region Imaging Spectrograph, the Microlensed Hyperspectral Imager at Swedish 1-m Solar Telescope, the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and the Extreme Ultraviolet Imager on board Solar Orbiter. We find that the algorithm assigns consistently lower probabilities to spectra that exhibit unusual features. For example, it identifies profiles with very strong Doppler shifts, uncommon broadening, and temporal dynamics associated with small-scale reconnection events, among others. As a result, Inspectorch demonstrates that density estimation using flow-based models offers a powerful approach to identifying rare events in large solar datasets. The resulting probabilistic anomaly scores allow computational resources to be focused on the most informative and physically relevant events. We make our Python package publicly available at https://github.com/cdiazbas/inspectorch.", "date": "2026-02-23", "categories": []}
{"id": "2605.11723", "title": "CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating", "abstract": "In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it first conducts a global temporal scan to anchor anomalous time windows, then performs fine-grained spatial grounding within the localized interval, and finally derives robust judgments via structured spatiotemporal Chain-of-Thought reasoning. To equip the model with these capabilities, we construct the first large-scale generated video anomaly dataset with per-frame bounding-box annotations, temporal anomaly windows, and fine-grained attribution labels. Building on this dataset, we design a three-stage progressive training paradigm. The model initially learns spatial and temporal anchoring through single- and multi-frame supervised fine-tuning, and then is optimized by a reinforcement learning strategy based on two-turn Group Relative Policy Optimization (GRPO). Beyond conventional accuracy rewards, we introduce Temporal and Spatial IoU rewards to supervise the intermediate localization process, effectively guiding the model toward more grounded and interpretable spatiotemporal reasoning. Extensive experiments demonstrate that CaC can stably concentrate on subtle anomalies, achieving a 25.7% accuracy improvement on fine-grained anomaly benchmarks and, when used as a reward signal, CaC reduces generated-video anomalies by 11.7% while improving overall video quality.", "date": "2026-05-12", "categories": []}
{"id": "2605.26193", "title": "Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection", "abstract": "Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the effectiveness of popular deep learning methods for TSAD, suggesting their failure in detecting subtle and prolonged anomalies. Outlier Exposure (OE) and Masked Autoencoder (MAE) emerge as two promising paradigms (classification and reconstruction) for solving the above problems. However, OE-based methods are constrained by poor generalization, while MAE-based methods are limited by masking misalignment issues. To address these limitations, this paper proposes a novel framework, CoAD, which unifies the two paradigms to leverage their complementary strengths while mitigating their respective weaknesses. In this framework, the classification module generates probability-informed soft masks for the reconstruction module, which in turn alleviates the generalization problem of the classification module. This cooperative design enables CoAD to effectively detect subtle and complex anomalies that are often overlooked by existing methods. Additionally, the classification module is carefully designed to resolve issues related to improper classification granularity and the neglect of frequency information. Extensive experiments on high-quality benchmark datasets, conducted under rigorous evaluation protocols, demonstrate that CoAD significantly outperforms both state-of-the-art deep learning and traditional data mining methods, highlighting the potential of deep learning in TSAD. Moreover, CoAD is lightweight and substantially faster than existing SOTA methods, demonstrating its practical value for large-scale, real-time applications.", "date": "2026-05-25", "categories": []}
{"id": "2605.29455", "title": "Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection", "abstract": "Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. When shifting to a unified paradigm that handles diverse classes simultaneously, detection accuracy often degrades due to inter-class interference and feature manifold confusion. To overcome these challenges, we propose a Unified Reference guided Cross-modal Mapping framework, named Uni-RCM. At its core, we propose a reference guide block to dynamically filter out category-specific noise by introducing a learnable reference feature, which captures the commonalities across different modalities. Besides, an offline residual quantizer is proposed to characterize the normal distribution by multiple cascaded codebooks. Extensive evaluations on the MVTec-3D AD dataset demonstrate the state-of-the-art performance in the challenging multi-class setting and in terms of image-level detection and pixel-level localization.", "date": "2026-05-28", "categories": []}
{"id": "2605.30046", "title": "Masked Diffusion Modeling for Anomaly Detection", "abstract": "Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. However, developing effective anomaly detection methods for categorical, mixed-type, and discrete sequence data remains challenging and relatively underexplored. Masked diffusion models provide a natural way to model such data by learning to recover masked values from the remaining visible context. In this paper, we propose Masked Diffusion for Anomaly Detection (MaskDiff-AD), a forward-only method based on masked diffusion models trained only on nominal data. Given a test sample, MaskDiff-AD constructs anomaly scores from the difficulty of reconstructing randomly masked coordinates, yielding a content-sensitive score that operates directly on discrete state spaces while avoiding reverse-time sampling. We also develop a non-parametric variant of MaskDiff-AD and provide theoretical guarantees by characterizing Type-I and Type-II errors under a fixed detection threshold. Experiments on fourteen categorical and mixed-type tabular datasets from ADBench and UADAD, as well as four text anomaly detection datasets from NLP-ADBench, show that MaskDiff-AD achieves competitive performance against classical, diffusion-based, and recent tabular/text anomaly detection baselines. Notably, MaskDiff-AD achieves the best overall average rank, outperforming all twelve tabular baseline methods.", "date": "2026-05-28", "categories": []}
{"id": "2605.30140", "title": "AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection", "abstract": "Benefiting from generalizability of vision-language models (VLMs) such as CLIP, many zero-/few-shot anomaly detection (AD) approaches have achieved impressive detection performance across various datasets. Nevertheless, they require substantial training on large auxiliary datasets to adapt VLMs to anomaly detection, and their inference largely relies on visual-text embedding similarity-based anomaly scores, lacking reasoning abilities to detect complex anomalies that require in-depth contextual understanding. To address this limitation, we propose \\textbf{AnomalyAgent}, a novel training-free, agentic framework that leverages the advanced reasoning and generalization capabilities of multimodal large language models (MLLMs) for anomaly detection. The key ingredients include \\textbf{1)} a comprehensive anomaly-centric toolset that enables adaptive MLLM-driven, agentic anomaly reasoning in zero-shot settings, and \\textbf{2)} a customized memory module that grounds anomaly reasoning with few-shot, in-context reference examples. We extend evaluation beyond the detection of simple anomalies (e.g., surface defects like cracks and dents and clear lesions) in widely used benchmarks to more diverse types of anomalies such as logical/contextual anomalies in logistics and manufacturing settings. Extensive experiment results demonstrate that our AnomalyAgent achieves substantially better performance compared to training-free VLM-based AD and generic agentic methods, highlighting its superior generalization capability in both zero-shot and few-shot anomaly detection settings. The code implementation can be find at this address.", "date": "2026-05-28", "categories": []}
{"id": "2605.30344", "title": "Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection", "abstract": "Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large language or multimodal models to finding abnormal patterns in sequential data. Public anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, making it difficult to fine-tune VLMs to produce grounded, interpretable decisions. To address this gap, we construct VisAnomBench, a curated benchmark built from public time-series datasets and augmented with high-quality anomaly explanations selected from multiple large VLMs using fine-grained, task-specific rewards. Through fine-tuning on this benchmark, we develop VisAnomReasoner, a parameter-efficient VLM for time-series anomaly detection. Experimental results on VisAnomBench show that VisAnomReasoner achieves more accurate anomaly localization and consistently outperforms all baselines, with improvements of at least 21.23 and 23.87 percentage points in precision and F1, respectively. Additional experiments on the TSB-AD-U benchmark demonstrate strong cross-benchmark generalization, with VisAnomReasoner improving precision and F1 by 9.57 and 13.39 percentage points, respectively.", "date": "2026-05-28", "categories": []}
{"id": "2605.27470", "title": "Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection", "abstract": "Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection methods still face two key challenges: 1) fixed pipelines, which restrict their adaptability across different graph tasks under limited supervision; 2) weak evidence, which prevents them from explicitly incorporating contextual and structural anomaly signals into the detection process. In this paper, we propose a novel framework, self-designing agentic workflows for few-shot graph anomaly detection (SignGAD). Specifically, we propose a novel paradigm that reformulates graph anomaly detection task from training a fixed anomaly detector to designing task-conditioned detection workflows. By constructing detection workflows, SignGAD selects suitable graph encodings and detector designs to exploit task-specific anomaly evidence. Meanwhile, we introduce a guarded final refit strategy to refine the selected workflow by calibrating refit acceptance, enhancing reliability under limited supervision. Extensive experiments conducted on several real-world datasets demonstrate that SignGAD achieves strong performance against state-of-the-art methods, highlighting its effectiveness on graph anomaly detection tasks.", "date": "2026-05-26", "categories": []}
{"id": "2605.27486", "title": "Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation", "abstract": "Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection methods within FL paradigm poses data-centric challenges. The existing datasets do not counteract these challenges since they do not simultaneously provide sufficient scale, accurate labels, and freedom from common flaws. In addition, the role of cyclic process behavior, which is common in discrete industrial automation, remains underexplored for MTSAD for the current state of research. This paper aims to shed more light on the literature and address these gaps by introducing a dataset designed with cyclic dynamics arising from the repetitive nature of discrete automation processes and evaluates selected MTSAD methods on both the proposed dataset and a public benchmark dataset.", "date": "2026-05-26", "categories": []}
{"id": "2605.27748", "title": "Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection", "abstract": "Industrial visual anomaly detection is usually one-class: normal images are abundant, while defects are rare, heterogeneous, and often unavailable during system design. PatchCore-style retrieval suits this setting because it scores test images from a memory bank of normal patch features, but the standard Euclidean geometry ignores feature correlations and its offline construction materialises the full patch pool before subsampling.\n  We introduce Mahalanobis PatchCore, a covariance-aware, streaming-compatible extension of PatchCore. Its artificial intelligence contribution is a retrieval detector that estimates a regularised covariance model in reduced feature space and whitens embeddings, so Euclidean nearest-neighbour search after transformation implements Mahalanobis retrieval. A bounded-memory, re-iterable training pipeline builds the memory bank without storing all normal patches at once, using incremental dimensionality reduction, online covariance estimation, and streaming aggregation.\n  The engineering application is automated industrial inspection, where visual anomaly detection must remain accurate under practical memory limits. We evaluate the method on a public 15-category industrial anomaly-detection benchmark and three industrial datasets covering blow-fill-seal strip-ampoule meniscus inspection, amber-glass-ampoule bottom inspection, and lyophilised-cake vial inspection. Mahalanobis PatchCore preserves most offline PatchCore image-level performance on the public benchmark while reducing peak memory from 5.41 to 2.78 GB, and improves the selected industrial mean image area under the receiver operating characteristic curve from 0.981 to 0.986.", "date": "2026-05-26", "categories": []}
{"id": "2605.27992", "title": "Patched-DeltaNet: Token-Level Event-Driven Memory for Linear-Time Anomaly Detection", "abstract": "Time series anomaly detection is critical for maintaining the reliability of mission-critical systems. While Transformer-based models like PatchTST have shown remarkable performance, their $\\mathcal{O}(L^2)$ computational complexity severely limits deployment in resource-constrained environments. In this paper, we propose Patched-DeltaNet, a novel architecture combining time-series patching with Gated Delta Networks. By integrating these paradigms, we hypothesize and demonstrate the emergence of token-level event-driven memory, whereby the patching mechanism extracts local semantic chunks, while the error-driven DeltaNet updates its recurrent state exclusively when significant physical changes, defined as deltas, occur. This synergy effectively filters out background noise and captures sudden anomalous drifts. Our rigorous experiments on the Server Machine Dataset (SMD) benchmark demonstrate the structural superiority and sample efficiency of Patched-DeltaNet. By strictly outperforming recent architectures under unified evaluation constraints and identical compute budgets, our model yields an ROC-AUC of 0.957 and PA-F1 of 0.822, while drastically reducing computational complexity to the theoretical minimum of $\\mathcal{O}(L/P)$.", "date": "2026-05-27", "categories": []}
{"id": "2605.28103", "title": "Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector", "abstract": "We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors -- spanning statistical, reconstruction, association, frequency, and generic-transformer families -- are evaluated on five datasets (SMD, MSL, SMAP, PSM, and MSDS) under effectiveness, efficiency, robustness, and cross-dataset generalisation. All methods share the same windowing, scoring, hardware, and metric protocols. Effectiveness, ablation, and robustness use three random seeds; cross-dataset transfer uses seed~0 because each extra seed requires $250$ source-target evaluations. The benchmark yields three method-independent findings: no single-bias baseline dominates; absolute perturbation VUS-ROC is more informative than retention ratios; and MSDS behaves as an event-dense deployment workload rather than a sparse point-anomaly benchmark. Under this protocol we also introduce \\ours{}, an adaptive detector family combining a NOTEARS-constrained directed channel-graph view with optional patch-attention and temporal-association views. \\ours{} achieves the best macro-average VUS-ROC ($0.675$, $+5.1$~pt over the second-best LSTM-AE), ranks first overall, and reaches the top-3 on all five datasets. Its wins on MSL and MSDS are narrow, while its average and robustness gains are larger: under the same three-seed robustness protocol for every method, it obtains the strongest absolute VUS-ROC across noise, channel dropout, and time-shift perturbations. We release the MSDS preprocessing protocol, configurations, scripts, and seed-level metric dumps.", "date": "2026-05-27", "categories": []}
{"id": "2605.28428", "title": "Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization", "abstract": "Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by the cost of Non-Conformity of a query patch to align with a fixed normal manifold. For each query patch, we construct a bipartite query to normal graph weighted by cosine affinity, explicitly removing query-query and normal-normal edges to prevent evidence dilution. We formulate anomaly scoring as a convex Laplacian energy with anchored normal nodes, and solve in closed form. In particular, we do not use the optimized features themselves-the anomaly score is the magnitude of the update required to satisfy normality constraints, reframing the graph Laplacian as a non-conformity operator rather than a smoothing prior. The proposed method introduces no learnable parameters, message passing, or sampling, and has complexity comparable to a single linear solve. Across standard benchmarks, it delivers strong image-level AUROC, stable localization maps, and improved robustness over prior methods, demonstrating the effectiveness of using optimization-induced feature drift as anomaly measure.", "date": "2026-05-27", "categories": []}
{"id": "2605.28630", "title": "EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection", "abstract": "Zero-Shot Anomaly Detection (ZSAD) aims to detect anomalies in unseen domains without target-domain adaptation. Recent CLIP-based methods have shown promising performance by leveraging prompt learning and visual-text alignment. However, most existing approaches rely on a single adaptation pathway, which may be insufficient for heterogeneous anomaly patterns across domains. In practice, anomalies exhibit vastly different characteristics, ranging from salient, localized structural disruptions to subtle, diffuse, and irregular variations. To address this challenge, we propose EntroAD, a structural entropy-guided zero-shot anomaly detection framework. Unlike previous methods, EntroAD introduces a dynamic routing mechanism to process different types of anomalies with specialized adaptation strategies. Specifically, we estimate patch-level structural entropy from self-attention-induced patch relations and use it as a proxy for relational uncertainty to guide anomaly-aware token routing. Based on this routing signal, we construct anomaly-aware routed tokens to better capture anomaly cues with different structural characteristics. We further introduce a confidence-aware dual-branch prompt adaptation module to stabilize visual-text alignment while preserving CLIP's transferable prior. Extensive experiments on 10 industrial and medical benchmarks show that EntroAD achieves state-of-the-art performance in challenging cross-dataset ZSAD settings.", "date": "2026-05-27", "categories": []}
{"id": "2504.02775", "title": "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection", "abstract": "We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a model is robust against pixel noise, then its performance deteriorates on tail class samples, and vice versa. To mitigate the issue, we handle the tail class and noise samples independently. To this end, we propose TailSampler, a novel class size predictor that estimates the class cardinality of samples based on a symmetric assumption on the class-wise distribution of embedding similarities. TailSampler can be utilized to sample the tail class samples exclusively, allowing to handle them separately. Based on these facets, we build a memory-based anomaly detection model TailedCore, whose memory both well captures tail class information and is noise-robust. We extensively validate the effectiveness of TailedCore on the unsupervised long-tail noisy anomaly detection setting, and show that TailedCore outperforms the state-of-the-art in most settings.", "date": "2025-04-03", "categories": []}
{"id": "2509.18919", "title": "Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset", "abstract": "The pretraining-finetuning paradigm is a crucial strategy in metallic surface defect detection for mitigating the challenges posed by data scarcity. However, its implementation presents a critical dilemma. Pretraining on natural image datasets such as ImageNet, faces a significant domain gap. Meanwhile, naive self-supervised pretraining on in-domain industrial data is often ineffective due to the inability of existing learning objectives to distinguish subtle defect patterns from complex background noise and textures. To resolve this, we introduce Anomaly-Guided Self-Supervised Pretraining (AGSSP), a novel paradigm that explicitly guides representation learning through anomaly priors. AGSSP employs a two-stage framework: (1) it first pretrains the model's backbone by distilling knowledge from anomaly maps, encouraging the network to capture defect-salient features; (2) it then pretrains the detector using pseudo-defect boxes derived from these maps, aligning it with localization tasks. To enable this, we develop a knowledge-enhanced method to generate high-quality anomaly maps and collect a large-scale industrial dataset of 120,000 images. Additionally, we present two small-scale, pixel-level labeled metallic surface defect datasets for validation. Extensive experiments demonstrate that AGSSP consistently enhances performance across various settings, achieving up to a 10\\% improvement in mAP@0.5 and 11.4\\% in mAP@0.5:0.95 compared to ImageNet-based models. All code, pretrained models, and datasets are publicly available at https://clovermini.github.io/AGSSP-Dev/.", "date": "2025-09-23", "categories": []}
{"id": "2602.19206", "title": "GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning", "abstract": "Zero-shot 3D Anomaly Detection is an emerging task that aims to detect anomalies in a target dataset without any target training data, which is particularly important in scenarios constrained by sample scarcity and data privacy concerns. While current methods adapt CLIP by projecting 3D point clouds into 2D representations, they face challenges. The projection inherently loses some geometric details, and the reliance on a single 2D modality provides an incomplete visual understanding, limiting their ability to detect diverse anomaly types. To address these limitations, we propose the Geometry-Aware Prompt and Synergistic View Representation Learning (GS-CLIP) framework, which enables the model to identify geometric anomalies through a two-stage learning process. In stage 1, we dynamically generate text prompts embedded with 3D geometric priors. These prompts contain global shape context and local defect information distilled by our Geometric Defect Distillation Module (GDDM). In stage 2, we introduce Synergistic View Representation Learning architecture that processes rendered and depth images in parallel. A Synergistic Refinement Module (SRM) subsequently fuses the features of both streams, capitalizing on their complementary strengths. Comprehensive experimental results on four large-scale public datasets show that GS-CLIP achieves superior performance in detection. Code can be available at https://github.com/zhushengxinyue/GS-CLIP.", "date": "2026-02-22", "categories": []}
{"id": "2605.26135", "title": "SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection", "abstract": "Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular classical methods due to its scalability and ease of deployment. We propose SilIF, an augmentation of Isolation Forest that adds a silhouette-based scoring layer computed in a representation space induced by the trees of the forest. For each point, we extract a vector of per-tree path lengths, cluster these \"fingerprints\" into structural groups, and compute a silhouette score that measures how well the point fits its assigned group versus the nearest alternative. The silhouette signal is combined with the base IF score via a single hyperparameter alpha. On the IEEE-CIS Fraud Detection benchmark (~590K transactions, 3.5% fraud), SilIF with alpha=1.0 improves over plain Isolation Forest by +0.0080 AUC-PR on average across five seeds, with SilIF winning on all five seeds (paired t-test p=0.046). We also report results on a synthetic credit-card dataset (Sparkov) where the silhouette augmentation does not improve over plain IF, and we characterize the conditions that distinguish the two outcomes. The paper presents SilIF as a tunable, easy-to-deploy enhancement to Isolation Forest with honest reporting of when it helps and when it does not. Code at https://github.com/venkat15vk/silif-anomaly-detection.", "date": "2026-05-21", "categories": []}
{"id": "2605.26171", "title": "When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection", "abstract": "Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply preconditions, and events satisfy temporal or relational regularities. We study anomaly detection in this setting, where constraints are given as logical rules over learned visual concepts, but real rule violations are rare or absent during training. We propose a neural rule evaluator that compiles each constraint into a directed acyclic graph and learns feature-aware subtree MLP gates for its internal logical operators. Each gate maps child features and edge-level negations to a parent representation and a rule-satisfaction probability, with intermediate supervision obtained from exact Boolean propagation over ground-truth concept labels. The key difficulty is that same-image training data often provide insufficient coverage of informative truth configurations and also allow shortcut solutions. To address this, we introduce chimera training: an operand-level counterfactual construction at the feature level. Instead of mixing input images, we concatenate subtree features from different samples; each operand keeps the hard truth label of the sample it came from, and the chimera target is obtained by applying the node's logical operator to those inherited labels. This supplies supervised logical counterexamples without requiring real anomalous images. Across CLEVRER, OpenImages, and VidOR, the resulting evaluator improves rule-level anomaly AUROC over independent-events and same-image semantic-training baselines, especially for compositional and relational rules. The method yields both scalar anomaly scores and rule-level attributions.", "date": "2026-05-25", "categories": []}
{"id": "2605.26446", "title": "DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) aims to identify nodes or substructures whose behavior or attributes deviate significantly from the overall pattern in graph-structured data, with critical applications in financial risk control, social network analysis, and cybersecurity. However, existing GCN-based methods suffer from the fundamental problem of contamination propagation, where anomalous nodes pollute the representations of their neighbors through message passing, leading to degraded detection performance. In this paper, we propose DDGAD, a novel diffusion-based graph anomaly detection framework that leverages trajectory dynamics to distinguish normal and anomalous nodes. Our key insight is that normal nodes exhibit consistent and stable representation trajectories under the coupled effects of diffusion regularization and reliability-aware neighborhood consensus, while anomalous nodes exhibit unstable and conflicting dynamics due to the directional disagreement between the global manifold prior and locally contaminated message passing. To mitigate contamination propagation, we introduce a distributed reliability-aware consensus refinement mechanism and define three complementary anomaly signals: neighbor inconsistency, reliability weight, and dynamical conflict energy. We further provide a preliminary theoretical analysis on normal node stability under the coupled dynamics. These signals collectively characterize anomalous behaviors from the perspectives of local inconsistency, consensus reliability, and dynamical instability. Extensive experiments on five real-world datasets demonstrate the effectiveness of the proposed framework.", "date": "2026-05-26", "categories": []}
{"id": "2605.26468", "title": "Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection", "abstract": "Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion Transformer. Raw test measurements are first compressed by an autoencoder, then reshaped into a structured token sequence enriched with sinusoidal and per-device wafer-position embeddings. Anomaly scores are derived from the noise-prediction error over mid-range diffusion timesteps, enabling fast wafer-scale screening without any labeled defects or manual feature engineering. Our approach achieves state-of-the-art performance on industrial 16nm IC test data under extreme class imbalance, offering interpretable failure localization through latent-space reconstruction residuals.", "date": "2026-05-26", "categories": []}
{"id": "2605.26676", "title": "Memory-Distilled Selection for Noise-Robust Anomaly Detection", "abstract": "Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is impractical. However, existing methods are sensitive to contamination, suffering significant performance degradation as the noise ratio increases. In this paper, we propose Memory-Distilled Selection (MeDS), a training algorithm based on data selection. MeDS constructs an ensemble of partial memories via random subsampling, where the resulting sparsity acts as a low-pass filter that captures nominal patterns across a wide range of noise ratios, enabling coarse-level identification of contaminated samples. The aggregated distances to the bootstrapped memories are then distilled into a reconstruction score network, which is subsequently fine-tuned on clean data filtered using scores from the distilled model, enabling fine-grained localization of anomalies. MeDS is robust across a wide range of noise ratios without requiring noise-ratio-specific hyperparameter tuning, achieving 99.16\\% image-level AUROC on MVTecAD at a 40\\% noise ratio, and attaining state-of-the-art performance on both VisA and Real-IAD under noisy settings. We thoroughly verify the efficacy of MeDS on industrial AD benchmarks under noisy data scenarios, accompanied by in-depth empirical analyses.", "date": "2026-05-26", "categories": []}
{"id": "2605.26857", "title": "Generalist Graph Anomaly Detection via Prototype-Based Distillation", "abstract": "Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs, has recently gained growing attention. However, existing methods often rely on scarce and costly annotations for training and sometimes even require few-shot support at inference, which limits their robustness to diverse and unseen anomaly patterns. To address this limitation, we introduce ProMoS, the first unsupervised generalist GAD framework, which detects anomalies by modeling the abundant normality in unlabeled data. ProMoS adopts a knowledge-distillation paradigm to distill normality priors from a frozen self-supervised graph neural network (GNN) teacher to a mixture-of-students model with shared global and lightweight personalized branches, enabling efficient and expressive normality modeling without learning from scratch. We further propose prototype-guided soft-label distillation to align teacher and student in a shared prototype space, enhancing cross-graph generalizability. During inference, ProMoS performs zero-shot anomaly detection on unseen graphs via distillation bias and prototype geometric deviation. Extensive experiments show the effectiveness and efficiency of ProMoS, charting a practical path toward label-free, zero-shot generalist GAD.", "date": "2026-05-26", "categories": []}
{"id": "2409.17608", "title": "Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) often learns the distribution of normal samples and detects the anomaly through measuring significant deviations, but the undesired generalization may reconstruct a few anomalies thus suppressing the deviations. Meanwhile, most VADs cannot cope with cross-dataset validation for new target domains, and few-shot methods must laboriously rely on model-tuning from the target domain to complete domain adaptation. To address these problems, we propose a novel VAD method with a motion-guided memory module to achieve cross-dataset validation with zero-shot. First, we add Gaussian blur to the raw appearance images, thereby constructing the global pseudo-anomaly, which serves as the input to the network. Then, we propose multi-scale residual channel attention to deblur the pseudo-anomaly in normal samples. Next, memory items are obtained by recording the motion features in the training phase, which are used to retrieve the motion features from the raw information in the testing phase. Lastly, our method can ignore the blurred real anomaly through attention and rely on motion memory items to increase the normality gap between normal and abnormal motion. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method. Compared with cross-domain methods, our method achieves competitive performance without adaptation during testing.", "date": "2024-09-26", "categories": []}
{"id": "2501.18196", "title": "GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection", "abstract": "Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly points. The existing methods are mainly based on reconstruction error or association divergence, which are both confined to isolated subsequences with limited horizons, hardly promising unified series-level criterion. In this paper, we propose the Global Dictionary-enhanced Transformer (GDformer) with a renovated dictionary-based cross attention mechanism to cultivate the global representations shared by all normal points in the entire series. Accordingly, the cross-attention maps reflect the correlation weights between the point and global representations, which naturally leads to the representation-wise similarity-based detection criterion. To foster more compact detection boundary, prototypes are introduced to capture the distribution of normal point-global correlation weights. GDformer consistently achieves state-of-the-art unsupervised anomaly detection performance on five real-world benchmark datasets. Further experiments validate the global dictionary has great transferability among various datasets.", "date": "2025-01-30", "categories": []}
{"id": "2601.18135", "title": "Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection", "abstract": "As a crucial element of public security, video anomaly detection (VAD) aims to measure deviations from normal patterns for various events in real-time surveillance systems. However, most existing VAD methods rely on large-scale models to pursue extreme accuracy, limiting their feasibility on resource-limited edge devices. Moreover, mainstream prediction-based VAD detects anomalies using only single-frame future prediction errors, overlooking the richer constraints from longer-term temporal forward information. In this paper, we introduce FoGA, a lightweight VAD model that performs Forward consistency learning with Gated context Aggregation, containing about 2M parameters and tailored for potential edge devices. Specifically, we propose a Unet-based method that performs feature extraction on consecutive frames to generate both immediate and forward predictions. Then, we introduce a gated context aggregation module into the skip connections to dynamically fuse encoder and decoder features at the same spatial scale. Finally, the model is jointly optimized with a novel forward consistency loss, and a hybrid anomaly measurement strategy is adopted to integrate errors from both immediate and forward frames for more accurate detection. Extensive experiments demonstrate the effectiveness of the proposed method, which substantially outperforms state-of-the-art competing methods, running up to 155 FPS. Hence, our FoGA achieves an excellent trade-off between performance and the efficiency metric.", "date": "2026-01-26", "categories": []}
{"id": "2604.18970", "title": "Mechanistic Anomaly Detection via Functional Attribution", "abstract": "We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produced by normal or anomalous internal mechanisms. Mechanistic anomaly detection (MAD) aims to flag these cases, but existing methods either depend on latent space analysis, which is vulnerable to obfuscation, or are specific to particular architectures and modalities. We reframe MAD as a functional attribution problem: asking to what extent samples from a trusted set can explain the model's output, where attribution failure signals anomalous behavior. We operationalize this using influence functions, measuring functional coupling between test samples and a small reference set via parameter-space sampling. We evaluate across multiple anomaly types and modalities. For backdoors in vision models, our method achieves state-of-the-art detection on BackdoorBench, with an average Defense Effectiveness Rating (DER) of 0.93 across seven attacks and four datasets (next best 0.83). For LLMs, we similarly achieve a significant improvement over baselines for several backdoor types, including on explicitly obfuscated models. Beyond backdoors, our method can detect adversarial and out-of-distribution samples, and distinguishes multiple anomalous mechanisms within a single model. Our results establish functional attribution as an effective, modality-agnostic tool for detecting anomalous behavior in deployed models.", "date": "2026-04-21", "categories": []}
{"id": "2605.24251", "title": "Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions", "abstract": "Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints. We introduce a unified benchmark combining discrete-task evaluation on structural and logical anomalies, a novel continuous drift protocol, the first head-to-head comparison of all published CAD methods, and computational efficiency profiling on edge hardware. Our results reveal that existing CAD methods do not consistently outperform traditional approaches with simple experience replay. Thus motivated, we propose DINOSaur, a training-free method combining a frozen DINOv3 backbone with spatially-indexed coreset memory and neighborhood-restricted anomaly scoring. DINOSaur achieves zero forgetting by construction, outperforms all evaluated methods across all five protocols, and runs at sub-100\\,ms inference on an NVIDIA Jetson Orin Nano, with on-device adaptation to new tasks in under 30 seconds.", "date": "2026-05-22", "categories": []}
{"id": "2605.24402", "title": "Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces", "abstract": "Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to increasingly complex and heterogeneous normal distributions. To address this challenge, we propose DPDiff-AD, a Dual Prototype-conditioned Diffusion model for large-scale multi-class Anomaly Detection. DPDiff-AD models heterogeneous normal distributions through complementary local and global prototypes. Local prototypes capture representative fine-grained structural patterns via nearest-prototype aggregation, while global prototypes regulate holistic feature geometry through optimal transport regularization. Together, these dual-scale representations define a structured normality space. This space is refined through diffusion-based reconstruction conditioned on both local and global prototypes via prototype-aware attention. By jointly leveraging dual prototypes during generation, DPDiff-AD achieves precise normality modeling, preserves structured separability as category cardinality grows, and enables scalable anomaly discrimination. Extensive experiments across five benchmarks demonstrate the effectiveness and scalability of DPDiff-AD. On the 160-category large-scale dataset, it improves image- and pixel-level AUROC by 5.3 and 2.9 points over the previous state-of-the-art method Dinomaly+, while maintaining stable performance as category cardinality increases.", "date": "2026-05-23", "categories": []}
{"id": "2605.25135", "title": "ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems", "abstract": "Anomaly detection in Industrial Internet of Things (IIoT) environments is essential to protect the Industrial Control Systems (ICS) and Cyber-Physical Systems (CPS) from occuring run time false data injection and other malicious attacks. The increasing complexity of sensor networks and interconnected control loops makes it difficult to identify anomalous behavior hidden within high-dimensional and time-dependent signals. To address these challenges, this article introduces Adaptive Spatio-Temporal Reinforcement Optimization ASTRO (ASTRO), a novel anomaly detection framework that pioneers the use of reinforcement learning for dynamic threshold optimization. By integrating a Deep Q-Network (DQN) with Graph Neural Networks (GNNs), temporal modelling and a Multi-Head Attention mechanism, ASTRO continuously adapts its decision boundaries to improve detection accuracy. The GNN component models the spatial relations among sensors, Temporal model captures time series dependencies and the attention layer highlights most informative time steps. The model generates continuous anomaly scores, which are transformed into binary decisions using an adaptive threshold, optimized via a Deep Q-Network (DQN). The ASTRO approach is evaluated on two real world industrial benchmarks: the Secure Water Treatment (SWaT) and Water Distribution (WADI) datasets. The proposed model achieves an exceptional performance on the SWaT with F1 score of 0.990. Moreover, on highly complex 127 end devices WADI dataset, it secures F1 score of 0.788, outperforming state-of-the-art baselines by nearly 14%. Results across multiple runs confirm consistent generalization and stability. These experiments demonstrate that the ASTRO framework is highly practical and scalable method for strengthening the large scale cyber physical infrastructures", "date": "2026-05-24", "categories": []}
{"id": "2605.25407", "title": "Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection", "abstract": "The deployment of zero-shot anomaly detection (AD) in embodied industrial inspection is severely bottlenecked by its reliance on passive, fixed-viewpoint 2D imagery. Such formulations inherently fail to accommodate the active, dynamic observations required in real-world environments. To break this limitation, we introduce Real-to-Twin Anomaly Detection, a novel task that evaluates physical observations directly against geometrically matched CAD Digital Twins. To tackle this new task, we propose AVATAR, a framework designed to learn robust semantic alignment between Real and Digital Twins. By bridging benign Sim2Real domain gaps using only defect-free pairs, AVATAR effectively transforms CAD priors into dynamic, anomaly-free references. This elegant formulation enables the model to localize diverse anomalies in a zero-shot manner as unalignable deviations, eliminating the need for defect annotations. Extensive experiments demonstrate that AVATAR substantially outperforms adapted state-of-the-art baselines, exhibiting exceptional robustness to severe viewpoint variations. The code and dataset will be made publicly available.", "date": "2026-05-25", "categories": []}
{"id": "2605.25429", "title": "Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach", "abstract": "Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning heterogeneous features across different data domains via PCA-based projection, which harmonizes feature dimensions ignores feature semantics. As a result, GAD models fail to learn transferable semantic knowledge, and even exhibit negative transfer on unseen graphs. To address this issue, we propose a Relational Fingerprint-based generalist GAD approach (ReFi-GAD for short), aligning heterogeneous raw features with a universal and semantics-aware Relational Fingerprint (ReFi) that encodes anomaly-indicative cues from both contextual and structural perspectives. Building on ReFi, we design a fingerprint-grounded generalist GAD model, which combines a transformer-based encoder to capture domain-invariant knowledge with an SNR-guided refinement module for domain-specific adaptation. Extensive experiments on 14 datasets demonstrate that ReFi-GAD significantly outperforms state-of-the-art methods.", "date": "2026-05-25", "categories": []}
{"id": "2605.23070", "title": "Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models", "abstract": "We propose Flow Mismatching, an unsupervised anomaly detection method that deliberately avoids reconstruction-based paradigms. Instead, we treat flow matching as geometric dynamics and leverage a key insight: anomalies occur at places where the learned normal flow disagrees with the geometric path toward a test image.\n  Given a flow matching model trained only on normal images, we probe its learned velocity field along affine paths from Gaussian noise to a target image. Along each path, we compare the model-predicted velocity, which follows normal generative dynamics, with the geometric velocity toward the target, which includes any anomalous content. Anomalies induce strong local disagreement between these velocities. Aggregating the mismatch over different time steps and multiple paths yields pixel-wise heatmaps and image-level scores without test-time optimization, feature memories, or additional calibration.\n  Our analysis shows that the population mismatch decomposes into an irreducible denoising term and a Fisher-divergence term between the test-path and normal-path score functions, which identifies the score-gap component that drives anomaly separation and explains the effectiveness of robust path aggregation. Extensive experiments on MVTec-AD and VisA demonstrate superior performance compared with SOTA reconstruction-based and recent flow matching-based approaches.", "date": "2026-05-21", "categories": []}
{"id": "2605.23116", "title": "CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection", "abstract": "Existing Video Anomaly Detection (VAD) methods typically rely on task-specific training, leading to strong domain dependency and high training costs. Moreover, most existing methods output only scalar anomaly scores, providing limited insight into why specific events are considered abnormal. Recent advances in Vision-Language Models (VLMs) have enabled both anomaly detection and human-interpretable reasoning. However, many VLM-based approaches still require additional training steps (e.g., instruction tuning or verbalized learning) or external Large Language Models (LLMs), incurring further training costs and inference overhead. To address these challenges, we propose CoReVAD, a contextual reasoning framework for training-free video anomaly detection that operates with a single frozen VLM. CoReVAD directly generates anomaly scores and temporal descriptions from the VLM. To mitigate noise in generative outputs, we introduce a Local Response Cleaning (LRC) module based on local vision-text alignment. Furthermore, global temporal context and progression are incorporated through softmax-based refinement, Gaussian smoothing, and position weighting. Experiments on UCF-Crime and XD-Violence demonstrate that CoReVAD achieves competitive performance among training-free methods while providing reliable and interpretable explanations. Our official code is available at: https://github.com/Muk-00/CoReVAD", "date": "2026-05-22", "categories": []}
{"id": "2605.23139", "title": "CALAD: Channel-Aware contrastive Learning for multivariate time series Anomaly Detection", "abstract": "Multivariate time series anomaly detection has become increasingly important in real-world applications, where labeled data are often scarce. Many existing approaches rely on unsupervised learning to model normal patterns, but they often treat all channels equally. This design can dilute anomaly-relevant signals, since not all channels contribute equally to anomaly detection. In this paper, we propose CALAD, a channel-aware contrastive learning framework for multivariate time series anomaly detection. CALAD governs the construction of contrastive samples using estimated channel relevance, allowing the learning process to reflect anomaly semantics rather than generic similarity. Channel relevance is estimated from reconstruction errors of a transformer-based autoencoder and is used to distinguish channels that are more influential to anomalous behaviors. Using this information, we design a channel-wise augmentation strategy in which positive and negative samples are constructed based on whether anomaly-relevant channels are preserved or perturbed. This encourages invariance to changes in irrelevant channels while being sensitive to changes in anomaly-relevant channels. Furthermore, CALAD combines contrastive learning and an auxiliary reconstruction head, allowing the model to learn discriminative representations while retaining normal structures. Experiments on multiple real-world datasets shows that CALAD consistently outperforms existing methods, particularly under distribution shift scenarios. We provide the code for reproducibility at https://github.com/hirundo1218/CALAD", "date": "2026-05-22", "categories": []}
{"id": "2605.23504", "title": "VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection", "abstract": "Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central challenge is learning a characterisation of normality precise enough to flag deviations. Representation self-supervised learning, typically through contrastive approaches, addresses this by embedding temporal patches into a latent space where normality occupies a well-defined region, with anomalies detected by geometric deviation. However, contrastive approaches shape this space indirectly through pair-sampling heuristics, providing no explicit control over the geometric structure that distance-based scoring requires. This means how tightly normal representations are grouped, and whether distances are directionally meaningful. We present VACE (Velocity-Aligned Channel Embeddings), a self-supervised anomaly detection method that represents normality as a compact, directionally coherent region in the embedding space. To this end, VACE trains a channel-aware encoder through a velocity-consistency objective, with no negatives and no synthetic anomalies, so that normal trajectories are locally smooth and aligned. At test time, a Mahalanobis positional score and a velocity-bank directional score are combined multiplicatively, flagging points that are simultaneously off-distribution and dynamically atypical. Despite its simplicity, VACE achieves state-of-the-art performance on TSB-AD-M under rigorous evaluation, significantly outperforming more complex methods trained on substantially larger budgets.", "date": "2026-05-22", "categories": []}
{"id": "2605.23744", "title": "Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series", "abstract": "Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, and in practice, is further complicated by the absence of anomaly labels. Existing reconstruction-based detectors tend to recover anomalies as faithfully as normal patterns, while prevailing graph contrastive methods enforce invariance across views and thus assume a stationary relational structure, an assumption that breaks under structural drift in real systems. We propose ContrastAD, an unsupervised framework that turns structural evolution itself into a learning signal rather than suppressing it. A Multi-Perspective Embedder encodes inputs from temporal, attribute, and structural perspectives. A Frequency-Aware Attention Mixer then performs spectral top-K filtering before attention, preventing noise from leaking into query-key similarities. The core component, a Dynamic Graph Contrastive Learner, builds power-law-inspired sparse graph snapshots from batch-level DTW distances and contrasts the most divergent pair against a stable anchor, regularizing the latent space without imposing rigid invariance. Across five real-world benchmarks, ContrastAD attains the highest mean F1 on all five datasets and the highest AUC on three (SWaT 93.60, SMD 98.66, PSM 97.79), with statistically significant F1 and AUC margins over the strongest baseline on SWaT and PSM. On MSL and SMAP, it trails the AUC leader by under 0.7 points while still leading on F1. Ablation and sensitivity studies further confirm that the contrastive objective works best as a soft regularizer, supporting our claim that strict invariance is suboptimal under non-stationary dynamics.", "date": "2026-05-22", "categories": []}
{"id": "2605.21957", "title": "Bounding-Box Trajectories Matter for Video Anomaly Detection", "abstract": "Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in appearance, viewpoint, and scene dynamics. Among existing approaches, human pose-based methods have emerged as a major line of research, showing strong performance since many anomalies in public datasets involve humans and pose representations are robust to appearance changes while providing compact motion descriptions. However, these methods often overlook bounding-box trajectories, although such information is inherently available in pose-based pipelines. In this paper, we explicitly leverage these trajectories as a primary anomaly cue. We present TrajVAD, a framework that models multi-class bounding-box trajectories using normalizing flows to learn normal kinematic patterns. Its trajectory-only variant (TrajVAD-T) eliminates pose estimation and surpasses all compared pose-based methods on ShanghaiTech in AP (87.7%), while achieving the best results on MSAD. An extended version (TrajVAD-P) incorporates pose information and further improves performance to 88.6% AUROC and 90.9% AP on ShanghaiTech, highlighting bounding-box trajectories as an effective yet underexplored modality for video anomaly detection.", "date": "2026-05-21", "categories": []}
{"id": "2605.22112", "title": "Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection", "abstract": "We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised spatio-temporal deep learning. We generate a ten-year synthetic Universe with gtobssim and process the simulated events into daily all-sky maps of counts and exposure, obtaining a time-ordered sequence that mirrors the structure of Fermi-LAT observations. To model the nominal evolution of the sky, we employ a Convolutional Long Short-Term Memory (ConvLSTM) network that operates directly on map sequences, preserving spatial locality while learning temporal dependencies. The model is trained to reconstruct expected emission, and departures from the learned baseline are quantified through pixel-wise mean-squared residual maps. We then define statistically motivated anomaly criteria by estimating per-pixel thresholds from the residual distribution on the training set, and we enforce spatial coherence via local filtering to suppress isolated fluctuations. The ConvLSTM is then deployed as trained predictor on Fermi-LAT daily maps, where the sky can depart from the nominal behavior because of genuine astrophysical variability and instrumental non-stationarities. The resulting pipeline flags localized, time-dependent excesses consistent with high-variable sources or transient events (e.g., flares or GRBs) and provides a benchmark for evaluating anomaly-detection strategies on long-duration, Fermi-LAT-like datasets.", "date": "2026-05-21", "categories": []}
{"id": "2605.22779", "title": "FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection", "abstract": "Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible. This coarse granularity forces operators to inspect many routine lines per alert. Message-level detection offers finer granularity, but remains challenging. A single event template may correspond to both normal and anomalous messages, failures arise from heterogeneous subsystems, and line-level labeling at scale is impractical. Although large language models (LLMs) can reason over log semantics, applying them to every line is too costly for continuous monitoring. We present FAME (Failure-Aware Mixture-of-Experts), a label-efficient message-level mixture-of-experts framework that uses an LLM only once offline. We annotate at most K labeled lines per template to derive binary normal/anomaly indicators and representative examples. The LLM proposes a partition of templates into failure domains, and a certification step validates the proposal before training. FAME trains a lightweight router and domain experts that run on-premise and output anomaly predictions and failure-domain labels. On BGL, FAME achieves F1 = 98.16 at K = 100 reducing annotation effort by 76x and detects 86.3% of anomalies from unseen EventIDs. On Thunderbird, FAME reaches F1 = 99.95 with perfect recall.", "date": "2026-05-21", "categories": []}
{"id": "2603.23890", "title": "Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis", "abstract": "As the modern microservice architecture for cloud applications grows in popularity, cloud services are becoming increasingly complex and more vulnerable to misconfiguration and software bugs. Traditional approaches rely on expert input to diagnose and fix microservice anomalies, which lacks scalability in the face of the continuous integration and continuous deployment (CI/CD) paradigm. Microservice rollouts, containing new software installations, have complex interactions with the components of an application. Consequently, this added difficulty in attributing anomalous behavior to any specific installation or rollout results in potentially slower resolution times. To address the gaps in current diagnostic methods, this paper introduces Praxium, a framework for anomaly detection and root cause inference. Praxium aids administrators in evaluating target metric performance in the context of dependency installation information provided by a software discovery tool, PraxiPaaS. Praxium continuously monitors telemetry data to identify anomalies, then conducts root cause analysis via causal impact on recent software installations, in order to provide site reliability engineers (SRE) relevant information about an observed anomaly. In this paper, we demonstrate that Praxium is capable of effective anomaly detection and root cause inference, and we provide an analysis on effective anomaly detection hyperparameter tuning as needed in a practical setting. Across 75 total trials using four synthetic anomalies, anomaly detection consistently performs at >0.97 macro-F1. In addition, we show that causal impact analysis reliably infers the correct root cause of anomalies, even as package installations occur at increasingly shorter intervals.", "date": "2026-03-25", "categories": []}
{"id": "2603.29183", "title": "IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection", "abstract": "Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unseen anomalies. Despite being promising in image data, these methods are found to be ineffective in time series data due to the failure to preserve its sequential nature, resulting in trivial or unrealistic anomaly patterns. They are further plagued when the training data is contaminated with unlabeled anomalies. This work introduces $\\textbf{IMPACT}$, a novel framework that leverages $\\underline{\\textbf{i}}$nfluence $\\underline{\\textbf{m}}$odeling for o$\\underline{\\textbf{p}}$en-set time series $\\underline{\\textbf{a}}$nomaly dete$\\underline{\\textbf{ct}}$ion, to tackle these challenges. The key insight is to $\\textbf{i)}$ learn an influence function that can accurately estimate the impact of individual training samples on the modeling, and then $\\textbf{ii)}$ leverage these influence scores to generate semantically divergent yet realistic unseen anomalies for time series while repurposing high-influential samples as supervised anomalies for anomaly decontamination. Extensive experiments show that IMPACT significantly outperforms existing state-of-the-art methods, showing superior accuracy under varying OSAD settings and contamination rates. Code is available at https://github.com/mala-lab/IMPACT.", "date": "2026-03-31", "categories": []}
{"id": "2605.20284", "title": "JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA", "abstract": "Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually grounded reasoning for better image understanding. However, LMMs lack domain-specific knowledge, which limits their ability to generate accurate responses in complex industrial scenarios. In this work, we present JUDO, Juxtaposed Domain-Oriented Multimodal Reasoner, a framework that efficiently incorporates domain knowledge and context in visual and textual reasoning. Through visual reasoning, our model segments the defect region by juxtaposing query images with normal images as visual domain context, enabling a fine-grained visual comparative inspection. Furthermore, we inject domain knowledge through supervised fine-tuning (SFT) to enhance context understanding and subsequently guide domain reasoning through reinforcement learning (GRPO) with tailored rewards, opting for a domain-oriented reasoning process. Experimental results demonstrate that JUDO achieves superior performance on the MMAD benchmark, surpassing models such as Qwen2.5-VL-7B and GPT-4o. These results highlight the importance of enhancing domain knowledge and context for effective reasoning in anomaly understanding.", "date": "2026-05-19", "categories": []}
{"id": "2605.20391", "title": "Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks", "abstract": "Traditional anomaly detection marks events when measured signals cross predefined thresholds. This captures the moment of transition but not the structural pressure that precedes it. We propose treating large behavioral populations as geometric energy landscapes whose deformation can be measured before and during major transitions. The central thesis is that structure precedes geometry: the structural organization of the population is the signal, and geometric metrics are instruments for measuring it. Applied to the Tor anonymity network across 67 consecutive daily observation windows, the dual-observer pipeline identifies a stable nine-dimensional load-bearing subspace invariant across the observation period and validates this structure by Monte Carlo simulation at 16.8 sigma above the noise floor. Primary detection gates achieve 0.0% false positive rate on 24 confirmed stable windows. Forensic analysis of the February 20, 2026 confirmed infrastructure event formally falsifies the relay-departure hypothesis, identifying connectivity degradation without topology change as a detectable network failure mode. The result is a candidate structural-monitoring framework for behavioral populations with sufficient telemetry.", "date": "2026-05-19", "categories": []}
{"id": "2605.20682", "title": "IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools", "abstract": "Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot understanding across diverse industrial scenarios. However, their performance in open-vocabulary industrial anomaly detection (IAD) is often limited by domain-misaligned reasoning and hallucinated structural inferences. To address these challenges, we propose \\textbf{IndusAgent}, a tool-augmented agentic framework for open-vocabulary IAD. Specifically, we first construct \\textbf{Indus-CoT}, a structured dataset that integrates global visual observations, high-resolution local patches, and expert normalcy priors, providing supervision for fine-tuning the model on rigorous industrial inspection trajectories. Building on this, IndusAgent dynamically orchestrates a set of external tools, including dynamic region cropping, high-frequency feature enhancement, and prior retrieval, thus enabling the agent to actively resolve visual ambiguities and disentangle subtle anomalies. Furthermore, we introduce a gated reinforcement learning objective that jointly optimizes anomaly classification, localization accuracy, anomaly type reasoning, and efficient tool usage, ensuring that tool invocation occurs only when beneficial. Extensive evaluations on five industrial anomaly benchmarks, including MVTec-AD, VisA, MPDD, DTD, and SDD, demonstrate that IndusAgent achieves state-of-the-art zero-shot performance among all existing methods, validating our robustness and generalization capacity.", "date": "2026-05-20", "categories": []}
{"id": "2605.20879", "title": "NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity", "abstract": "Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain \"one-for-all\" detection, but existing GGAD methods predominantly rely on the neighbor consistency principle, falling into the \\textbf{Node-to-Neighbor Consistency Paradigm} for anomaly quantification. These methods suffer from complex training pipelines, heavy training data dependency, high computational costs, and unstable cross-domain generalization. To address these limitations, we propose NeighborDiv, a training-free generalist graph anomaly detection framework based on neighbor diversity. Departing from the dominant Node-to-Neighbor Consistency Paradigm, we shift the focus to the \\textbf{Neighbor-to-Neighbor Diversity Paradigm}, and uncover that the internal structural dispersion of a node's neighbor set is a powerful, independently discriminative anomaly signal. We quantify neighbor diversity via the variance of inter-neighbor feature similarities, which captures how a node organizes its local graph environment, and operates independently of conventional node-to-neighbor consistency frameworks. Extensive experiments under two standard GGAD evaluation paradigms show NeighborDiv achieves state-of-the-art performance, with relative gains of 10.25% in average AUC and 17.78% in average AP over the second-best baseline under Single-Domain Independent Training (SDIT), and 6.89%/9.58% in AUC/AP under Unified Multi-Domain Training (UMDT), respectively. Notably, NeighborDiv yields zero performance volatility across all datasets, eliminating training-set dependency and establishing a lightweight and highly practical GGAD framework.", "date": "2026-05-20", "categories": []}
{"id": "2502.20981", "title": "Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection", "abstract": "In Open-set Supervised Anomaly Detection (OSAD), the existing methods typically generate pseudo anomalies to compensate for the scarcity of observed anomaly samples, while overlooking critical priors of normal samples, leading to less effective discriminative boundaries. To address this issue, we propose a Distribution Prototype Diffusion Learning (DPDL) method aimed at enclosing normal samples within a compact and discriminative distribution space. Specifically, we construct multiple learnable Gaussian prototypes to create a latent representation space for abundant and diverse normal samples and learn a Schr\\\"odinger bridge to facilitate a diffusive transition toward these prototypes for normal samples while steering anomaly samples away. Moreover, to enhance inter-sample separation, we design a dispersion feature learning way in hyperspherical space, which benefits the identification of out-of-distribution anomalies. Experimental results demonstrate the effectiveness and superiority of our proposed DPDL, achieving state-of-the-art performance on 9 public datasets.", "date": "2025-02-28", "categories": []}
{"id": "2507.10492", "title": "BenchReAD: A systematic benchmark for retinal anomaly detection", "abstract": "Retinal anomaly detection plays a pivotal role in screening ocular and systemic diseases. Despite its significance, progress in the field has been hindered by the absence of a comprehensive and publicly available benchmark, which is essential for the fair evaluation and advancement of methodologies. Due to this limitation, previous anomaly detection work related to retinal images has been constrained by (1) a limited and overly simplistic set of anomaly types, (2) test sets that are nearly saturated, and (3) a lack of generalization evaluation, resulting in less convincing experimental setups. Furthermore, existing benchmarks in medical anomaly detection predominantly focus on one-class supervised approaches (training only with negative samples), overlooking the vast amounts of labeled abnormal data and unlabeled data that are commonly available in clinical practice. To bridge these gaps, we introduce a benchmark for retinal anomaly detection, which is comprehensive and systematic in terms of data and algorithm. Through categorizing and benchmarking previous methods, we find that a fully supervised approach leveraging disentangled representations of abnormalities (DRA) achieves the best performance but suffers from significant drops in performance when encountering certain unseen anomalies. Inspired by the memory bank mechanisms in one-class supervised learning, we propose NFM-DRA, which integrates DRA with a Normal Feature Memory to mitigate the performance degradation, establishing a new SOTA. The benchmark is publicly available at https://github.com/DopamineLcy/BenchReAD.", "date": "2025-07-14", "categories": []}
{"id": "2605.19738", "title": "TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection", "abstract": "Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-rich approaches typically integrate structural context into the data representation pipeline using raw textual features, they often neglect the structural context of nodes. This limitation hinders their ability to detect sophisticated anomalies arising from inconsistencies between a node's inherent content and its topological role. To bridge this gap, we propose TERGAD (Structure-aware Text-enhanced Representations for Graph Anomaly Detection), A novel data augmentation framework that enriches structural semantics for GAD via the semantic reasoning capabilities of Large Language Models (LLMs). Specifically, TERGAD translates node-level topological properties into descriptive natural language narratives, which are subsequently processed by an LLM to derive high-level semantic embeddings. These embeddings are then adaptively fused with original node attributes through a gated dual-branch autoencoder to jointly reconstruct both graph structure and node features. The anomaly score is computed based on the integrated reconstruction error, effectively capturing deviations in both observable attributes and LLM-informed semantic expectations. Extensive experiments on six real-world datasets demonstrate that TERGAD consistently outperforms state-of-the-art baselines. Furthermore, our ablation studies validate the indispensable role of structural semantic guidance and the efficacy of the gated fusion mechanism. Code is available at https://github.com/Kantorakitty/TERGAD-main.", "date": "2026-05-19", "categories": []}
{"id": "2605.19744", "title": "Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection", "abstract": "Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challenging. Existing anomaly detection methods are highly specialized and rely on normality as defined by the abstract semantic Cityscapes classes, making it difficult to adapt to diverse real-world scenarios. We propose an adaptable real-time anomaly detection method that leverages foundation models in the form of pretrained vision transformer embeddings to detect deviations via nearest-neighbor similarity in the latent semantic feature space. Based on patch-wise processing, the algorithm produces dense anomaly masks, allowing for the localization of detected anomalies. The method robustly models normality through a single reference image. This formulation avoids explicit supervision and dataset-specific training, making it suitable for real-world deployment. We evaluate the method on standard benchmarks and on an automated vehicle in real-world scenarios. Despite its simplicity, the method achieves good performance on the Road Anomaly benchmark and demonstrates consistent qualitative behavior in practice, successfully highlighting semantically unusual objects in diverse scenes. These results suggest that simple, reference-based methods can provide useful anomaly signals under realistic operating conditions.", "date": "2026-05-19", "categories": []}
{"id": "2409.15980", "title": "Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection", "abstract": "Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accuracy. These systems typically require expensive camera, computing equipment and significant machine learning expertise, which can substantially burden small and medium-sized enterprises. This study explores leveraging unsupervised learning methods with pre-trained models and low-cost hardware to create a cost-effective visual anomaly detection system. The research aims to develop a low-cost visual anomaly detection solution that uses minimal data for model training while maintaining generalizability and scalability. The system utilises unsupervised learning models from Anomalib and is deployed on affordable Raspberry Pi hardware through openVINO. The results show that this cost-effective system can complete anomaly defection training and inference on a Raspberry Pi in just 90 seconds using only 10 normal product images, achieving an F1 macro score exceeding 0.95. While the system is slightly sensitive to environmental changes like lighting, product positioning, or background, it remains a swift and economical method for factory automation inspection for small and medium-sized manufacturers. The code is available at https://github.com/Yunbo-max/Cost-Effective-Visual-Anomaly-Detection-using-Unsupervised-Learning.", "date": "2024-09-24", "categories": []}
{"id": "2603.04727", "title": "Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild", "abstract": "Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for real-world Video Anomaly Detection (VAD) remains largely unexplored. Unlike conventional pipelines relying on reconstruction or pose-based cues, MLLMs enable a paradigm shift: treating anomaly detection as a language-guided reasoning task. In this work, we systematically evaluate state-of-the-art MLLMs on the ShanghaiTech and CHAD benchmarks by reformulating VAD as a binary classification task under weak temporal supervision. We investigate how prompt specificity and temporal window lengths (1s--3s) influence performance, focusing on the precision--recall trade-off. Our findings reveal a pronounced conservative bias in zero-shot settings; while models exhibit high confidence, they disproportionately favor the 'normal' class, resulting in high precision but a recall collapse that limits practical utility. We demonstrate that class-specific instructions can significantly shift this decision boundary, improving the peak F1-score on ShanghaiTech from 0.09 to 0.64, yet recall remains a critical bottleneck. These results highlight a significant performance gap for MLLMs in noisy environments and provide a foundation for future work in recall-oriented prompting and model calibration for open-world surveillance, which demands complex video understanding and reasoning.", "date": "2026-03-05", "categories": []}
{"id": "2605.16567", "title": "Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version", "abstract": "Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve detection robustness. However, composing an ensemble without labeled data is challenging. Naively composed ensembles can suffer from ensemble saturation, where redundant or unreliable detection models degrade performance and incur unnecessary computation. We propose MetaEns, an automatic unsupervised framework for selecting ensembles of outlier detection models. Using labeled meta-datasets, MetaEns learns a model that predicts marginal ensemble gains, estimating the expected improvement from adding a candidate model to a partially constructed ensemble. At test time, this learned signal is combined with a submodular-inspired proxy objective that enforces diminishing returns through diversity-aware discounting and family-level risk regularization, thereby enabling greedy sequential selection with adaptive early stopping. As a result, MetaEns constructs compact, high-quality ensembles without access to ground-truth labels. Experiments on 39 real-world datasets show that MetaEns consistently outperforms state-of-the-art unsupervised selectors and ensemble baselines, achieving higher average precision while using fewer models.", "date": "2026-05-15", "categories": []}
{"id": "2605.17256", "title": "Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids", "abstract": "This work introduces a latency-aware benchmarking framework for evaluating deep learning models in power system anomaly detection using high-fidelity, time-domain signals generated from an industry-grade electromagnetic transient simulator. Eight neural network architectures, ranging from MLPs to Transformers, were systematically evaluated on streaming datasets representing both physical faults and cyber-attacks in inverter-dominated networks. All models successfully classified two representative multi-event sequences in real time with sub-cycle response times below 15 ms. However, although classification decisions occurred within one cycle, the end-to-end inference latency consistently exceeded three cycles, ranging from 50 to 90 ms. These results highlight a critical gap between algorithmic capability and protection-grade deployment, pointing to the need for further optimization and hardware acceleration. The findings establish a reproducible benchmark for sub-cycle anomaly detection and provide guidance for transitioning machine learning methods from research prototypes to real-world protection applications.", "date": "2026-05-17", "categories": []}
{"id": "2605.17334", "title": "Causal Anomaly Detection for Lithium-Ion Battery Degradation", "abstract": "Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce \\textsc{CausalHealth}, a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) -- with Isolation Forest reported separately as it falls below the bundle reliability threshold -- to characterise detection sensitivity across ten commissioning fractions (5--30\\,\\%). The Magnitude-shift class achieves 100\\,\\% detection across all seven tested cells spanning LFP (MIT--Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) -- a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation -- improves lead time by 15--52 cycles over the class median on long-lived cells while maintaining 100\\,\\% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy $\\mathrm{TE}(R \\!\\to\\! V)$ correlates with charge-transfer resistance $R_{\\mathrm{ct}}$ (pooled $r = +0.990$; temperature-controlled partial $r = +0.898$), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.", "date": "2026-05-17", "categories": []}
{"id": "2605.18128", "title": "POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection", "abstract": "Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence models to capture complex spatio-temporal dependencies. However, less attention is paid to the spatial over-generalization problem, where unconstrained structural modeling indiscriminately reconstructs anomalies, inevitably degrading detection recall. To tackle this problem, we propose a novel framework that unifies spatio-temporal modeling through a joint prior-observation adversarial learning paradigm. In the spatial dimension, the model alternately learns adjacency matrices as structural prior and models the association discrepancy between prior and data-driven observation in a minimax manner during training. Such adversarial optimization not only improves the model sensitivity for time-wise detection, but also enables the model to localize anomalies to specific channels. To systematically evaluate this anomaly localization capability, we further construct a synthetic benchmark equipped with precise channel-wise annotations. Extensive experiments across public datasets and our dedicated benchmark demonstrate that the proposed framework establishes a new state-of-the-art in both time-wise detection and spatial localization tasks. Our code, pre-trained models, and benchmark are publicly available at https://github.com/anocodetest1/POST.", "date": "2026-05-18", "categories": []}
{"id": "2605.18188", "title": "UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction", "abstract": "Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on single-modality sensor data. This work introduces UTOPYA (Unified Temporal Observation for Physics-Informed Anomaly Detection and Time-Series Prediction), a 15.2M-parameter multimodal framework that jointly addresses anomaly detection, time-series prediction, and phase classification in batch distillation by fusing eight data modalities through Feature-wise Linear Modulation (FiLM) conditioned cross-modal attention and gated fusion. A physics-informed regularisation scheme introduced in this work enforces temporal smoothness and thermodynamic monotonicity, while curriculum learning introduces training samples in order of physical difficulty. On the 119-experiment multimodal batch distillation dataset of Arweiler et al. (2026), UTOPYA achieves a window-level test AUROC of 0.832 and 0.874 under multi-signal experiment-level scoring, substantially outperforming four external baselines (PCA, autoencoder, Isolation Forest, and LSTM autoencoder) evaluated under identical conditions (+0.147 window-level AUROC over the best baseline). A multimodal ablation over 15~architectural configurations shows that static context via FiLM conditioning is the key enabler, lifting experiment-level multi-signal AUROC by +0.145 over the unimodal baseline (0.729 to 0.874). Separately, a training ablation across 14 design choices reveals that several widely-adopted techniques, including instance normalisation, Mixup, ensembling, test-time augmentation, and stochastic weight averaging, fail to improve or actively degrade generalisation in this data-scarce setting. These negative results expose a fundamental tension between smoothing-based regularisation and anomaly detection, providing practical guidance for multimodal process monitoring deployment.", "date": "2026-05-18", "categories": []}
{"id": "2503.23927", "title": "Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics", "abstract": "Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal events, regime changes, or model mismatch. We introduce EagleEye, a method that pinpoints local over- and under-densities in multivariate feature spaces. EagleEye assigns each point an anomaly score by encoding its ordered k-nearest-neighbour list as a binary membership sequence and testing whether the cumulative number of successes in this sequence is consistent with a binomial (coin-flipping) null model. In the presence of a genuine local anomaly, neighbours will preferentially belong to one of the two datasts, yielding an excess of ``successes'' relative to the binomial null model. These local, pointwise detections are consolidated into interpretable anomaly sets through a deterministic refinement procedure that can also estimate the irreducible background and local density anomaly purity. We demonstrate EagleEye's efficacy in three scenarios. We first consider an artificial data example with known localized over- and under-densities. Second, we demonstrate how EagleEye may be used for new physics searches at particle collider experiments in the presence of systematic background modelling differences. Finally, we conduct a climate analysis study that reveals localized changes in spatiotemporal temperature-pattern recurrence.", "date": "2025-03-31", "categories": []}
{"id": "2605.15242", "title": "Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity", "abstract": "The reliability of Healthcare Information Systems (HIS) is frequently compromised by human-induced data entry errors, which existing statistical anomaly detection methods fail to distinguish from legitimate clinical extremes. This paper proposes Logic-GNN, a novel neuro-symbolic framework that treats clinical records as a structured ``private language'' governed by latent logical games. By integrating Temporal Graph Neural Networks (TGNN) with Graph Kolmogorov Complexity, we induce a symbolic grammar that represents the underlying logic of medical interactions. We define anomalies as ``grammatical violations'' that cause a significant expansion in the Minimum Description Length (MDL) of the clinical graph. Evaluated on the Sina System dataset (2M+ records), Logic-GNN achieves an F1-score of 0.94, outperforming state-of-the-art baselines by 12\\% in distinguishing between life-threatening medical outliers and data corruption. Our approach introduces a self-healing mechanism that suggests logical corrections to maintain data integrity in real-time HIS environments.", "date": "2026-05-14", "categories": []}
{"id": "2605.15325", "title": "COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection", "abstract": "Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existing VLM-based VAD methods suffer from a fundamental mismatch between training and inference in both data distribution and model configuration. First, most approaches rely on static post-training adaptation, limiting generalization under distribution shifts such as unseen environments or anomaly types. Second, they train VLMs on sparse frames from long videos, but perform inference on densely sampled short segments, creating inconsistencies between training and testing. To address these limitations, we propose COPRA, a conditional parameter adaptation framework for VLM-based VAD. Instead of fixed prompts or shared parameter updates, COPRA generates input-specific parameter updates to dynamically adapt a frozen VLM for each video segment during both training and inference. Experiments show strong performance on standard VAD benchmarks, consistently outperforming static baselines in both in-domain and cross-domain settings. Moreover, COPRA generalizes beyond VAD to unseen tasks such as multiple-choice Video Question Answering and Dense Captioning. These results highlight COPRA as an effective weight-space generation framework for scalable, adaptive, and context-aware video understanding. The code will be released at https://github.com/THE-MALT-LAB/COPRA", "date": "2026-05-14", "categories": []}
{"id": "2605.15874", "title": "Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems", "abstract": "Industrial Water Treatment Systems (IWTS) are safety critical cyber-physical infrastructures and due to increased connectivity, these systems are exposed to cyber threats that can manipulate process behaviour without creating obvious devices outliers. In particular, logic-layer deception anomalies can preserve numerically plausible measurements while breaking expected cause-and-effect relationships in the control process. These attacks are difficult to detect using threshold-based monitoring or require heavy server-oriented anomaly detection models. This paper explores the potential of Tiny Deep Learning (TinyDL) to provide lightweight on-device logic-level anomaly detection for resource constrained Programmable Logic Controllers (PLCs). We propose a novel framework, TinyDL-based incremental LSTM (Ti-iLSTM) which optimises the memory and space foot print of Long Short-Term Memory (LSTM), to detect logic-layer inconsistencies in Programmable Logic Controller (PLC) based Industrial Water Treatment Systems (IWTS). Experiments on the publicly available SWaT dataset show that the optimised model achieves high detection performance (F1-score=0.983 and ROC-AUC=0.998). A deployment-style validation on the WADI dataset confirms that the proposed light-weight framework remains applicable beyond a single dataset. The research demonstrates that combining logic-aware supervision with Tiny Deep Learning (TinyDL) sequence learning creates an efficient and accurate anomaly detection suitable for resource constrained Programmable Logic Controllers (PLCs) in industrial environments.", "date": "2026-05-15", "categories": []}
{"id": "2605.16171", "title": "Res$^2$CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment", "abstract": "Few-shot Generalist Anomaly Detection requires models to generalize to novel categories without retraining, posing significant challenges in real-world scenarios with scarce samples and rapidly changing categories. Existing CLIP-based methods face two major challenges: coarse-grained unified text prompts struggle to adapt to fine-grained foreground-background differences, causing cross-granularity mismatch; and fine-tuning on auxiliary datasets disrupts CLIP's inherent open-world generalization due to domain shift, leading to cross-category generalization degradation. To address these, we propose to shift multimodal alignment entirely into a unified residual space, where residual representations naturally eliminate fine-grained normal feature differences across regions and class-specific biases, simultaneously resolving both problems. Based on this insight, Res$^2$CLIP, the first residual-to-residual alignment framework that symmetrically bridges visual and text modalities within CLIP's residual space, is designed. The framework is developed from a residual perspective into three branches: a text prompt-based branch, a visual prompt-based branch, and a novel residual-to-residual alignment branch. All learnable optimizations are constrained within the residual domain, and the residual alignment optimization objectives are designed to force the model to focus on relative anomaly deviations rather than optimizing class-specific features. Experiments on multiple datasets demonstrate the effectiveness of our architecture. The code is available at https://github.com/hito2448/Res2CLIP.", "date": "2026-05-15", "categories": []}
{"id": "2507.21023", "title": "On Using the Shapley Value for Anomaly Localization: A Statistical Investigation", "abstract": "Recent publications have suggested using the Shapley value for anomaly localization for sensor data systems. Using a reasonable mathematical anomaly model for full control, experiments indicate that using a single fixed term in the Shapley value calculation achieves a lower complexity anomaly localization test, with the same probability of error, as a test using the Shapley value for all cases tested. A proof demonstrates these conclusions must be true for all independent observation cases. For dependent observation cases, no proof is available.", "date": "2025-07-28", "categories": []}
{"id": "2511.18739", "title": "A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection", "abstract": "Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metric assumptions. This study introduces a problem-oriented framework that reinterprets existing metrics based on the specific evaluation challenges they are designed to address, rather than their mathematical forms or output structures. We categorize over twenty commonly used metrics into six dimensions: 1) basic accuracy-driven evaluation; 2) timeliness-aware reward mechanisms; 3) tolerance to labeling imprecision; 4) penalties reflecting human-audit cost; 5) robustness against random or inflated scores; and 6) parameter-free comparability for cross-dataset benchmarking. Comprehensive experiments are conducted to examine metric behavior under genuine, random, and oracle detection scenarios. By comparing their resulting score distributions, we quantify each metric's discriminative ability -- its capability to distinguish meaningful detections from random noise. The results show that while most event-level metrics exhibit strong separability, several widely used metrics (e.g., NAB, Point-Adjust) demonstrate limited resistance to random-score inflation. These findings reveal that metric suitability must be inherently task-dependent and aligned with the operational objectives of IoT applications. The proposed framework offers a unified analytical perspective for understanding existing metrics and provides practical guidance for selecting or developing more context-aware, robust, and fair evaluation methodologies for time series anomaly detection.", "date": "2025-11-24", "categories": []}
{"id": "2602.01359", "title": "PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection", "abstract": "Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios. Moreover, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series anomaly detection. PaAno extracts short temporal patches from time-series training data and uses a 1D convolutional neural network to embed each patch into a vector representation. The model is trained using a combination of triplet loss and pretext loss to ensure the embeddings capture informative temporal patterns from input patches. During inference, the anomaly score at each time step is computed by comparing the embeddings of its surrounding patches to those of normal patches extracted from the training time-series. Evaluated on the TSB-AD benchmark, PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.", "date": "2026-02-01", "categories": []}
{"id": "2605.02438", "title": "Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection", "abstract": "Open-set supervised anomaly detection (OSAD) aims to identify unseen anomalies using limited anomalous supervision. However, existing prototype-based methods typically model normal data via a unimodal Gaussian prior, failing to capture inherent multi-modality and resulting in blurred decision boundaries. To address this, we propose Mixture Prototype Flow Matching (MPFM), a framework that learns a continuous transformation from normal feature distributions to a structured Gaussian mixture prototype space. Departing from traditional flow-based approaches that rely on a single velocity vector, MPFM explicitly models the velocity field as a Gaussian mixture prior where each component corresponds to a distinct normal class. This design facilitates mode-aware and semantically coherent distribution transport. Furthermore, we introduce a Mutual Information Maximization Regularizer (MIMR) to prevent prototype collapse and maximize normal-anomaly separability. Extensive experiments demonstrate that MPFM achieves state-of-the-art performance across diverse benchmarks under both single- and multi-anomaly settings.", "date": "2026-05-04", "categories": []}
{"id": "2605.13863", "title": "Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks", "abstract": "Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper introduces ASTDP-GAD, a novel Adaptive Spiking Temporal Dynamics Plasticity framework for Graph Anomaly Detection that integrates spiking graph neural networks with STDP learning for energy-efficient neuromorphic detection in dynamic networks. Our framework unifies spiking neural computation, STDP learning, and graph-based anomaly detection through the following key innovations: temporal spike graph encoding with adaptive Leaky Integrate-and-Fire (LIF) dynamics; LIF-based graph attention with lateral inhibition; event-driven hypergraph memory with STDP-inspired prototype updates; spike rate contrast pooling based on spiking irregularity; adaptive STDP layers capturing causal temporal relationships; and multi-scale temporal convolution with multi-factor anomaly fusion. Theoretical analysis provides rigorous guarantees: spike encoding preserves input information with resolution scaling linearly in simulation steps and hidden dimension; LIFGAT approximates any continuous attention function; hypergraph memory converges to optimal prototypes; contrast pooling achieves provable anomaly selection bounds; STDP learning converges stably; and multi-factor fusion produces calibrated scores with up to $5\\times$ variance reduction. Extensive experiments on nine datasets on both dynamic and static graphs demonstrate superior anomaly detection accuracy while maintaining biological plausibility and energy efficiency for neuromorphic deployment.", "date": "2026-04-29", "categories": []}
{"id": "2605.14808", "title": "SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track", "abstract": "Visual anomaly detection (AD) for industrial inspection is a highly relevant task in modern production environments. The problem becomes particularly challenging when training and deployment data differ due to changes in acquisition conditions during production. In the VAND 4.0 Industrial Track, models must remain robust under distribution shifts such as varying illumination and their performance is assessed on the MVTec AD 2 dataset. To address this setting, we propose a training-free and class-agnostic anomaly detection pipeline based on the work of SuperAD. Our approach improves generalization through several modifications designed to enhance robustness under distribution shifts. These adaptations include using a DINOv3 backbone, overlapping patch-wise processing, intensity-based augmentations, improved memory-bank subsampling for better coverage of the data distribution, and iterative morphological closing for cleaner and more spatially consistent anomaly maps. Unlike methods that rely on class-specific architectures or per-class hyperparameter tuning, our method uses a single architecture and one shared hyperparameter configuration across all object classes. This makes the approach well suited for industrial deployment, where product variants and appearance changes must be handled with minimal adaptation effort. We achieve segmentation F1 scores of $62.61\\%$, $57.42\\%$, and $54.35\\%$ on test public, private, and private mixed of MVTec AD 2 respectively, thereby outperforming SuperAD and other state-of-the-art methods. Code is available at https://github.com/LukasRoom/SuperADD.", "date": "2026-05-14", "categories": []}
{"id": "2605.15054", "title": "LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection", "abstract": "Vision-language models (VLMs) have recently emerged as a promising paradigm for video anomaly detection (VAD) due to their strong visual reasoning ability and natural language-based explainability. In this paper, we aim to address a key limitation of such pipelines, which perform segment-level inference independently owing to token constraints and reason without structured temporal context, allowing VLMs to interpret anomalies as deviations from evolving video dynamics rather than producing fragmented predictions and explanations. To specify, we propose a context-aware framework named LATERN, which reformulates VAD as a temporal evidence aggregation process. LATERN consists of two complementary modules: Context-Aware Anomaly Scoring (CEA) and Recursive Evidence Aggregation (REA). CEA introduces a novel image-grounded memory mechanism, which selectively chooses historical content via frame diversity and visual-textual alignment as expanded context to help generate reliable anomaly scores. Building upon these scores, REA performs recursive temporal aggregation to identify coherent anomaly intervals and produce event-level decisions and explanations grounded in visual-textual evidence. Extensive experiments on challenging benchmarks, including UCF-Crime and XD-Violence, show that LATERN enhances detection accuracy and explanation consistency for frozen VLMs during test time, while generating temporally coherent and semantically grounded event-level explanations.", "date": "2026-05-14", "categories": []}
{"id": "2601.22868", "title": "Conditional Compatibility Learning for Context-Dependent Anomaly Detection", "abstract": "Anomaly detection usually assumes that abnormality is an intrinsic property of an observation. A defect is a defect, and a rare object is rare, regardless of where it appears. Many real-world anomalies do not work this way. A runner on a track is normal, but the same runner on a highway is not. The subject is unchanged; only the context makes it anomalous. This setting, long recognized as contextual anomaly detection, remains largely underexplored in modern vision-language systems. The difficulty is not merely empirical; it is formal. When anomaly labels depend on the relation between a subject and its context, any detector reasoning from a global representation that conflates subject and context is provably non-identifiable: two different subject-context configurations can map to the same embedding while requiring opposite labels, and no such detector can be correct on both. This impossibility motivates a different formulation: instead of asking whether an observation deviates from a global notion of normality, the model should ask whether subjects are compatible with their surrounding context. We define this as conditional compatibility learning. We instantiate this framework in CC-CLIP, a vision-language architecture that learns disentangled subject- and context-aware representations from a single image and fuses visual evidence through text-conditioned attention. CC-CLIP achieves state-of-the-art results on real-world contextual anomaly detection, substantially outperforming all existing CLIP-based and context-reasoning baselines. A single-branch variant of CC-CLIP also achieves competitive performance on structural anomaly benchmarks.", "date": "2026-01-30", "categories": []}
{"id": "2602.23013", "title": "SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling", "abstract": "Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achieve strong results employing foundation-model features, but typically rely on memory banks, auxiliary datasets, or multi-modal tuning of vision-language models. We therefore question whether such complexity is necessary given the feature representations of vision foundation models. To answer this question, we introduce SubspaceAD, a training-free method, that operates in two simple stages. First, patch-level features are extracted from a small set of normal images by a frozen DINOv2 backbone. Second, a Principal Component Analysis (PCA) model is fit to these features to estimate the low-dimensional subspace of normal variations. At inference, anomalies are detected via the reconstruction residual with respect to this subspace, producing interpretable and statistically grounded anomaly scores. Despite its simplicity, SubspaceAD achieves state-of-the-art performance across one-shot and few-shot settings without training, prompt tuning, or memory banks. In the one-shot anomaly detection setting, SubspaceAD achieves image-level and pixel-level AUROC of 97.1% and 97.5% on the MVTec-AD dataset, and 93.2% and 98.2% on the VisA dataset, respectively, surpassing prior state-of-the-art results. Code and demo are available at https://github.com/CLendering/SubspaceAD.", "date": "2026-02-26", "categories": []}
{"id": "2605.12725", "title": "Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models", "abstract": "Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scenes. While this focus has led to improvements in scalability and multi-scene generalization, it has also shifted the field away from modeling the scene-specific and context-dependent nature of normal behavior. Contemporary approaches frequently rely on video-level weak supervision and opaque pretrained representations from multi-modal large language models (MLLMs), which encourage models to respond to familiar semantic anomaly categories rather than to deviations from the normal patterns of a particular environment. This trend suppresses spatial localization, introduces semantic bias, and reduces anomaly detection to a form of action recognition. In this paper, we examine whether these prevailing formulations align with the core requirements of real-world VAD, which is typically performed within a single scene where normality is determined by local geometry, semantics, and activity patterns. Through targeted visual analyses and empirical evaluations, we demonstrate the practical consequences of these limitations and show that meaningful progress in VAD requires renewed focus on single-scene, spatially-aware, and explainable formulations that capture the nuanced structure of normality within individual environments.", "date": "2026-05-12", "categories": []}
{"id": "2605.13642", "title": "Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'", "abstract": "Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation. Conformal anomaly detection addresses this limitation by converting anomaly scores into calibrated p-values that are valid under the statistical assumption of data exchangeability, with a growing literature extending this idea beyond that setting. We present 'nonconform', a Python package for applying conformal anomaly detection within existing machine-learning workflows, and use it as the basis for an implementation-grounded introduction to the field. The package integrates with 'scikit-learn', 'pyod', and custom anomaly detectors, and provides a unified interface for calibration, p-value generation, and false discovery rate control. It supports several conformalization strategies, ranging from simple split-conformal calibration to more data-efficient and shift-aware extensions. Through a progression from foundational concepts to advanced conformalization strategies, complemented by code examples, the paper connects the statistical ideas behind conformal anomaly detection to their practical use in 'nonconform'. Empirical results demonstrate that the implemented methods enable statistically principled anomaly detection. Together, the package and exposition aim to make core conformal anomaly detection workflows more accessible and reproducible in experimental and production-oriented settings.", "date": "2026-05-13", "categories": []}
{"id": "2605.13746", "title": "Weakly-Supervised Spatiotemporal Anomaly Detection", "abstract": "In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-level labels during training. This means that given a video, we know that it is either normal or contains an anomaly, but no further annotations are used to train the network. Features are extracted from video clips that are either normal or anomalous. These features are used to determine anomaly scores for spatiotemporal regions of the clips based on a classifier and the implementation of a multiple instance ranking loss (MIL). We represent both anomalous and normal video clips as positive and negative bags, respectively, to apply MIL. Furthermore, since anomalies are usually localized to a part of a frame rather than the whole frame, we chose to explore temporal as well as spatial anomaly detection. We show our results on the UCF Crime2Local Dataset, which contains spatiotemporal annotations for a portion of the UCF Crime Dataset.", "date": "2026-05-13", "categories": []}
{"id": "2509.00931", "title": "Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection", "abstract": "We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets, struggle with time series of irregular sampling frequencies and varying sequence lengths. To address these challenges, we extend conditional Generative Adversarial Networks (GANs) for targeted data augmentation, integrate Bayesian inference to obtain predictive distributions and quantify uncertainty, and leverage log-signatures for robust feature encoding of transaction histories. We introduce a novel Wasserstein distance-based loss to align generated and real unlabeled samples while simultaneously maximizing classification accuracy on labeled data. Our approach is evaluated on the BankSim dataset, a widely used simulator for credit card transaction data, under varying proportions of labeled samples, demonstrating consistent improvements over benchmarks in both global statistical and domain-specific metrics. These findings highlight the effectiveness of GAN-driven semi-supervised learning with log-signatures for irregularly sampled time series and emphasize the importance of uncertainty-aware predictions.", "date": "2025-08-31", "categories": []}
{"id": "2605.10988", "title": "Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation", "abstract": "Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at massive scale while instance-level annotations are prohibitively expensive, posing great difficulties to fine-grained anomaly localization. To address this challenge, we propose LogMILP (Log anomaly localization based on Multi-Instance Learning enhanced by prototypes and Perturbation), a weakly supervised framework that enables both bag-level anomaly detection and instance-level anomaly localization using only bag-level labels. Our method guides the model to pinpoint the critical log entries using prototype-guided structural modeling with counterfactual perturbation consistency regularization, thereby improving localization reliability and interpretability under coarse-grained supervision. Experimental results on three public datasets demonstrate that LogMILP achieves competitive detection performance while yielding significantly more reliable instance-level localization. Our code is open-sourced at https://github.com/YUK1207/LogMILP.", "date": "2026-05-09", "categories": []}
{"id": "2605.11749", "title": "Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection", "abstract": "Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra-class variance among normal data while remaining highly sensitive to anomalies. Although recent works have applied self-supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain-specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real-world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two-phase learning strategy: an initial warm-up phase using only synthetic samples, followed by a full-training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj-zhou/SAWGAD.", "date": "2026-05-12", "categories": []}
{"id": "2605.12069", "title": "Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection", "abstract": "Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feature transformation to all samples, treating normal and anomalous data uniformly despite their fundamentally asymmetric distributions, compact normals versus diverse anomalies. We instead exploit this natural asymmetry by proposing AVA-DINO, an anomaly-aware vision-language adaptation framework with dual specialized branches for normal and anomalous patterns that adapt frozen DINOv3 visual features. During training on auxiliary data, the two branches are learned jointly with a text-guided routing mechanism and explicit routing regularization that encourages branch specialization. At test time, only the input image and fixed, predefined language descriptions are used to dynamically combine the two branches, enabling an asymmetric activation. This design prevents degenerate uniform routing and allows context-specific feature transformations. Experiments across nine industrial and medical benchmarks demonstrate state-of-the-art performance, achieving 93.5% image-AUROC on MVTec-AD and strong cross-domain generalization to medical imaging without domain-specific fine-tuning. https://github.com/aqeeelmirza/AVA-DINO", "date": "2026-05-12", "categories": []}
{"id": "2507.20051", "title": "$K^4$: Online Log Anomaly Detection Via Unsupervised Typicality Learning", "abstract": "Existing Log Anomaly Detection (LogAD) methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. We introduce $K^4$, an unsupervised and parser-independent framework for high-performance online detection. $K^4$ transforms arbitrary log embeddings into compact four-dimensional descriptors (Precision, Recall, Density, Coverage) using efficient k-nearest neighbor (k-NN) statistics. These descriptors enable lightweight detectors to accurately score anomalies without retraining. Using a more realistic online evaluation protocol, $K^4$ sets a new state-of-the-art (AUROC: 0.995-0.999), outperforming baselines by large margins while being orders of magnitude faster, with training under 4 seconds and inference as low as 4 $\\mu$s.", "date": "2025-07-26", "categories": []}
{"id": "2508.12776", "title": "Randomized PCA Forest for Unsupervised Outlier Detection", "abstract": "We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in approximate K-Nearest Neighbor (KNN) search, we develop a novel unsupervised outlier detection method that utilizes RPCA Forest for unsupervised outlier detection by deriving an outlier score from its intrinsic properties. Experimental results showcase the superiority of the proposed approach compared to the classical and state-of-the-art methods in performing the outlier detection task on several datasets while performing competitively on the rest. The extensive analysis of the proposed method reflects its robustness and its computational efficiency, highlighting it as a good choice for unsupervised outlier detection.", "date": "2025-08-18", "categories": []}
{"id": "2511.06894", "title": "COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing", "abstract": "Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean Squared Error (MSE) loss results in statistically flawed reconstruction residuals. This fundamental weakness leads to noisy, unstable anomaly scores, hindering reliable detection. To address this, we propose Constrained Gaussian-Noise Optimization and Smoothing (COGNOS), a universal, model-agnostic enhancement framework that tackles this issue at its source. COGNOS introduces a novel Gaussian-White Noise Regularization strategy during training, which directly constrains the model's output residuals to conform to a Gaussian white noise distribution. This engineered statistical property creates the ideal precondition for our second contribution: Adaptive Residual Kalman Smoother that operates as a statistically robust estimator to denoise the raw anomaly scores. Extensive experiments on multiple benchmarks demonstrate that COGNOS consistently enhances the performance of state-of-the-art backbones significantly, validating the efficacy of coupling statistical regularization with adaptive filtering.", "date": "2025-11-10", "categories": []}
{"id": "2601.18823", "title": "VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space", "abstract": "Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can hope to detect out-of-distribution (abnormal) latent vectors, but several issues arise when the latent space is high dimensional. This includes an exponential growth of the hypervolume with the dimension, which severely affects the generative capacity of the VAE. In this paper, we draw insights from high dimensional statistics: in these regimes, the latent vectors of a standard VAE are distributed on the `equators' of a hypersphere, challenging the detection of anomalies. We propose to formulate the latent variables of a VAE using hyperspherical coordinates, which allows compressing the latent vectors towards a given direction on the hypersphere, thereby allowing for a more expressive approximate posterior. We show that this improves both the fully unconditional-OOD and conditional-OOD anomaly detection ability of the VAE, achieving the best performance on the datasets we considered, outperforming existing methods. For the unconditional-OOD and conditional-OOD modalities, respectively, these are: i) detecting unusual landscape from the Mars Rover camera and unusual Galaxies from ground based imagery (complex, real world datasets); ii) standard benchmarks like Cifar10 and subsets of ImageNet as the in-distribution (ID) class.", "date": "2026-01-25", "categories": []}
{"id": "2601.23026", "title": "Root Cause Analysis of Measurement and Mechanistic Anomalies", "abstract": "Root cause analysis of anomalies aims to identify how and why a sample deviates from the normal process. Existing methods primarily focus on telling which features are responsible, ignoring that anomalies can arise through two fundamentally different processes: measurement errors, where the sample is generated normally but one or more values is recorded incorrectly, and mechanism shifts, where the causal process that generated the sample was changed. While measurement errors can often be safely corrected, mechanistic anomalies require careful consideration. In this paper, we formally define a causal model that explicitly captures both types by treating outliers as latent interventions on latent (\"true\") and observed (\"measured\") variables and show under which conditions the distinction is possible. Based on this model, we develop an efficient inference procedure for localizing root causes and distinguishing anomaly types. Experiments on synthetic and real-world data show that our method provides state-of-the-art and highly robust performance in both root cause localization and classification of anomaly types.", "date": "2026-01-30", "categories": []}
{"id": "2602.09524", "title": "HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection", "abstract": "Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detection is required. In this paper, we propose HLGFA, a high-low resolution guided feature alignment framework that learns normality by modeling cross-resolution feature consistency between high-resolution and low-resolution representations of normal samples, instead of relying on pixel-level reconstruction. Dual-resolution inputs are processed by a shared frozen backbone to extract multi-level features, and high-resolution representations are decomposed into structure and detail priors to guide the refinement of low-resolution features through conditional modulation and gated residual correction. During inference, anomalies are naturally identified as regions where cross-resolution alignment breaks down. In addition, a noise-aware data augmentation strategy is introduced to suppress nuisance-induced responses commonly observed in industrial environments. Extensive experiments on standard benchmarks demonstrate the effectiveness of HLGFA, achieving 97.9% pixel-level AUROC and 97.5% image-level AUROC on the MVTec AD dataset, outperforming representative reconstruction-based and feature-based methods.", "date": "2026-02-10", "categories": []}
{"id": "2605.06681", "title": "A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry", "abstract": "A hierarchical ensemble pipeline is introduced to address anomaly detection in multivariate telemetry data provided by European Space Agency (ESA). The method integrates shapelet-based and statistical feature extraction, per-channel modeling, intra-channel stacking, and a final cross-channel aggregation. The pipeline is trained and validated using time-series cross-validation and two-level masking strategies to prevent information leakage. Results on the European Space Agency Anomaly Detection Benchmark (ESA-ADB) challenge demonstrate strong generalization, highlighting the effectiveness of hierarchical modeling in detecting subtle anomalies in realistic satellite telemetry.", "date": "2026-04-22", "categories": []}
{"id": "2605.08651", "title": "Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection", "abstract": "Video anomaly detection (VAD) systems often prioritize accuracy while overlooking privacy concerns, limiting their suitability for real-world deployment. We propose the Orthogonal Projection Layer (OPL), a lightweight module that removes task-irrelevant variations to produce representations focused on anomaly-relevant cues. To address privacy risks in human-centered scenarios, we introduce Guided OPL (G-OPL), which suppresses facial attributes using weak supervision from face-presence signals while preserving non-identifying features such as pose and motion. A cosine alignment objective enforces consistent capture and removal of facial information without identity labels or adversarial training. We further present a privacy-aware evaluation framework that jointly assesses detection performance and privacy preservation, and enables analysis of how sensitive information is filtered. Experiments show that embedding privacy constraints into model design reduces sensitive information while maintaining or improving detection accuracy, supporting projection-based architectures as a principled approach for privacy-aware VAD.", "date": "2026-05-09", "categories": []}
{"id": "2605.08914", "title": "Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation", "abstract": "This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencoder with local attention, which leverages the powerful pattern identification capabilities of transformers complemented by traditional data cleaning and normalization methods. It efficiently captures relevant patterns within irregular sequences suffering from sparse data collection, benefiting from the discriminative ability of the local attention mechanism. The proposed framework is applied to a real-world case study, on the risk estimation of non-technical losses in electrical power systems in a wide area in Greece. Non-technical losses in electrical power systems, primarily stemming from electricity theft, pose significant economic and operational challenges. Detecting these anomalies is particularly challenging due to the inherent sparse and irregular nature of real-world data collection practices. Traditional risk estimation methods struggle with effectively capturing long-range dependencies and robustly handling such data characteristics. We demonstrate that our approach effectively yields highly discriminative latent features, which results in more consistent risk estimation compared with existing state-of-the-art and widely used methods. It achieves high recall and precision, meeting the critical objectives of the problem. As such, our solution offers a robust and effective tool for risk detection in irregular time series datasets.", "date": "2026-05-09", "categories": []}
{"id": "2605.08955", "title": "Outlier detection for patient monitoring and alerting", "abstract": "We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\\% to 66\\% for a variety of patient-management actions, with 66\\% corresponding to the strongest outliers.", "date": "2026-05-09", "categories": []}
{"id": "2605.09222", "title": "Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation", "abstract": "Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hindering effective anomaly diagnosis. To address this, Krone introduces a novel hierarchical log abstraction that transforms flat log sequences into semantically coherent units across entity, action, and status levels. Building on this abstraction, Krone introduces a hierarchical orchestration framework that decomposes flat log sequences into hierarchical execution units and performs modular detection over them. It executes and optimizes the modular detection tasks across levels, enabling precise anomaly detection, localization, and explanation with selective invocation of LLM-based reasoning. In this work, we present Krone-viz, an interactive visualization system based on Krone, which makes hierarchical log analysis interpretable and actionable for software engineers and system operators. Demonstrated on the widely used HDFS benchmark dataset, Krone-viz supports: 1) examining hierarchical decompositions of flat log sequences, 2) inspecting detection results and abnormal segments identified by Krone with LLM-generated explanations, and 3) reusing, reviewing, and revising knowledge generated by LLMs with human-in-the-loop guardrails. The code of Krone-viz is available at https://github.com/LeiMa0324/KRONE_Demo_official, and we deploy a live demo at https://leima0324.github.io/KRONE_Demo_official.", "date": "2026-05-09", "categories": []}
{"id": "2605.09428", "title": "FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection", "abstract": "Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering the privacy concerns in distributed scenarios, federated graph-level anomaly detection (FedGLAD) has emerged as a promising solution to enable collaborative detection without sharing raw data. However, existing methods suffer from poor generalization due to the reliance on unrealistic synthetic anomalies and insufficient personalization capabilities under data heterogeneity. To address these challenges, we propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR). Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity. Extensive experiments demonstrate that FedCIGAR achieves superior performance and robustness in contrast to state-of-the-art methods.", "date": "2026-05-10", "categories": []}
{"id": "2605.09685", "title": "Learning Unified Representations of Normalcy for Time Series Anomaly Detection", "abstract": "The core challenge in unsupervised anomaly detection is identifying abnormal patterns without prior knowledge of their characteristics. While existing methods have addressed aspects of this problem, they often struggle to learn a robust representation of the normal data distribution that is distinct from anomalous patterns. In this paper, we present a novel framework, Unified Unsupervised Anomaly Detection ($\\text{U}^2\\text{AD}$), that comprehensively addresses anomaly detection in multivariate time series. Our approach learns the underlying data distribution of normal samples by utilizing score-based generative modeling. We introduce a novel time-dependent score network and a unified training objective that together delineate the manifold of normal data while considering both local and global temporal contexts. Reconstruction is then performed via a deterministic sampling process using an ordinary differential equation solver. Our extensive experimental evaluations demonstrate that $\\text{U}^2\\text{AD}$ not only outperforms current state-of-the-art methods in detection accuracy but also identifies anomalies at significantly earlier stages of their occurrence.", "date": "2026-05-10", "categories": []}
{"id": "2605.09790", "title": "Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale", "abstract": "Detecting orbital anomalies, such as maneuvers, atmospheric decay, and attitude upsets, across the rapidly growing population of low-Earth-orbit (LEO) satellites is a prerequisite for collision avoidance, decay forecasting, and conjunction screening. The bottleneck is not modeling capacity but labels: there is no public ground-truth corpus of orbital anomalies, manual review does not scale to approximately 10^4 active satellites, and pure rule-based detectors trade recall for precision so aggressively that they are blind to most behavioral anomalies. We present a multi-tier labeling cascade that composes three weak supervision sources of increasing fidelity: a fast physics rule set (rule_v1), an Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) bank, and a supplemental-element calibration step (supGP), to produce labels at a scale unavailable from any single source. Applied to 232M Two-Line Element (TLE) records spanning 60 years, the cascade yields 8.6M labeled sequences of length 50 (430M timesteps) over 11 features that include explicit time encoding and full mean-element state. On overlapping satellites, IMM-UKF surfaces 42.6x more anomalies than rule_v1 alone. We train a 6.5M-parameter Transformer in two stages, achieving a maneuver recall of 55.4% and decay recall of 62.8% on a held-out test set. An ablation on the time-delta feature alone yields a 107% relative improvement in decay recall. We frame the resulting model as a high-recall triage classifier whose role is to surface candidate events for downstream filtering, not to issue final attributions, and discuss the path toward a Neural-ODE-based orbital world model.", "date": "2026-05-10", "categories": []}
{"id": "2605.10242", "title": "When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection", "abstract": "Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples that deviate from these patterns as anomalies during testing. However, in practical scenarios, the limited scale and diversity of training data often lead to an incomplete characterization of normal patterns. While test-time adaptation offers a remedy, its isolated focus on test-time optimization ignores the critical synergy with training-phase learning. Furthermore, indiscriminate adaptation to unlabeled test data inevitably triggers anomaly contamination, preventing the model from fully realizing its discriminative capability between normal and anomalous samples. To address these issues, we propose RTTAD, a Risk-aware Test-time adaptation method for unsupervised Tabular Anomaly Detection. RTTAD holistically tackles normality shifts via a synergistic two-stage mechanism. During training, collaborative dual-task learning captures multi-level representations to establish a robust normal prior. During testing, a Test-Time Contrastive Learning (TTCL) module explicitly accounts for adaptation risk by selectively updating the model using high-confidence pseudo-normal samples while constraining anomalous ones. Additionally, TTCL incorporates a k-nearest neighbor-based contrastive objective to refine embedding distributions, thereby further enhancing the model's discriminative capacity. Extensive experiments on 15 tabular datasets demonstrate that RTTAD achieves state-of-the-art overall detection performance.", "date": "2026-05-11", "categories": []}
{"id": "2605.10397", "title": "AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation", "abstract": "Visual anomaly detection (VAD) is crucial in many real-world fields, such as industrial inspection, medical imaging, infrastructure monitoring, and remote sensing. However, the specific anomaly definitions, data modalities, and annotation standards across different domains make it difficult to transfer single-domain trained VAD models. Vision-language models (VLMs), pre-trained on large-scale cross-domain data, can perform visual perception under task instructions, offering a promising solution for cross-domain VAD. However, single-inference VLM judgments are unreliable, since they rely more on prior knowledge than on normal-sample references or fine-grained feature evidence. We therefore present AnomalyClaw, a training-free VAD agent that turns anomaly judgment into a multi-round refutation process. In each round, the agent proposes candidate anomalies and refutes each against normal-sample references, drawing on a 13-tool library for visual verification, reference parsing, and frozen expert probing. On the CrossDomainVAD-12 benchmark (12 datasets), AnomalyClaw achieves consistent macro-AUROC improvements over single-step direct inference with +6.23 pp on GPT-5.5, +7.93 pp on Seed2.0-lite, and +3.52 pp on Qwen3.5-VL-27B. We further introduce an optional verbalized self-evolution extension. It builds an online rulebook from internal-branch disagreement without oracle labels. On Qwen3.5-VL-27B, it delivers a +2.09 pp mean gain, comparable to a K = 10 oracle-label supervised baseline (+1.99 pp). These results show that agentic refutation improve anomaly understanding and reasoning of VLMs, rather than merely aggregating tool outputs.", "date": "2026-05-11", "categories": []}
{"id": "2605.10628", "title": "Hypergraph-Enhanced Training-Free and Language-Free Few-Shot Anomaly Detection", "abstract": "Few-shot anomaly detection (FSAD) has made significant strides, yet existing methods still face critical challenges: (i) dependence on task- or dataset-specific training/fine-tuning, (ii) reliance on language supervision or carefully hand-crafted prompts, and (iii) limited robustness across domains. In this paper, we introduce HyperFSAD, a novel FSAD framework that is training-free, language-free, and robust across domains, offering a powerful solution to these challenges. Built upon DINOv3 and a hypergraph-based inference mechanism, our approach performs inference without any task-specific optimization or text prompts, while remaining competitive. Specifically, we replace sensitive nearest-neighbor / top-$n$ matching with \\textbf{Sparse Hyper Matching}: \\textit{sparsemax} first selects the most relevant support patches, which are then aggregated into a \\textit{hyperedge} as compact normal evidence to suppress background noise and distractors. We further introduce \\textbf{Dual-Branch Image Scoring}, which fuses \\emph{spatial anomaly evidence} from the patch-grid anomaly map with \\emph{global semantic deviation} captured by support-aware CLS matching, yielding a robust image-level anomaly score in a strictly visual manner. Notably, all components of HyperFSAD are purely visual, eliminating the need for labor-intensive hand-crafted text prompts. Under the stringent training-free and language-free setting, HyperFSAD achieves state-of-the-art performance across six datasets spanning four industrial datasets (MVTecAD, VisA, MPDD, BTAD) and two medical datasets (RESC, BraTS).", "date": "2026-05-11", "categories": []}
{"id": "2605.10833", "title": "MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection", "abstract": "Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which do not fully reflect continuous inspection processes in real industrial scenarios. We introduce MMVIAD (Multi-view Multi-task Video Industrial Anomaly Detection), to the best of our knowledge the first continuous multi-view video dataset for industrial anomaly detection and understanding, together with a benchmark for multi-task evaluation. MMVIAD contains object-centric 2-second inspection clips with approximately 120 degrees of camera motion, covering 48 object categories, 14 environments, and 6 structural anomaly types. It supports anomaly detection, defect classification, object classification, and anomaly visible-time localization. Systematic evaluations on MMVIAD show that current commercial and open-source video MLLMs remain far below human performance, especially for fine-grained defect recognition and temporal grounding. To improve transferable anomaly understanding, we further develop a two-stage post-training pipeline where PS-SFT (Perception-Structured Supervised Fine-Tuning) initializes perception-structured reasoning and VISTA-GRPO (Visibility-grounded Industrial Structured Temporal Anomaly Group Relative Policy Optimization) refines the model with semantic-gated defect reward and visibility-aware temporal reward, producing the final model VISTA. On MMVIAD-Unseen, VISTA improves the base model's average score across the four tasks from 45.0 to 57.5, surpassing GPT-5.4. Source code is available at https://github.com/Georgekeepmoving/MMVIAD.", "date": "2026-05-11", "categories": []}
{"id": "2605.10847", "title": "Conditional anomaly detection methods for patient-management alert systems", "abstract": "Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods rely on the distance metric to identify examples in the dataset that are most critical for detecting the anomaly. We investigate various metrics and metric learning methods to optimize the performance of the instance-based anomaly detection methods. We show the benefits of the instance-based methods on two real-world detection problems: detection of unusual admission decisions for patients with the community-acquired pneumonia and detection of unusual orders of an HPF4 test that is used to confirm Heparin induced thrombocytopenia - a life-threatening condition caused by the Heparin therapy.", "date": "2026-05-11", "categories": []}
{"id": "2305.18593", "title": "On Diffusion Modeling for Anomaly Detection", "abstract": "Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.", "date": "2023-05-29", "categories": []}
{"id": "2602.09850", "title": "Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning", "abstract": "Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain data, often struggle to capture category-specific anomalies, thereby limiting both detection accuracy and interpretability. To address these limitations, we propose Reason-IAD, a knowledge-guided dynamic latent reasoning framework for explainable industrial anomaly detection. Reason-IAD comprises two core components. First, a retrieval-augmented knowledge module incorporates category-specific textual descriptions into the model input, enabling context-aware reasoning over domain-specific defects. Second, an entropy-driven latent reasoning mechanism conducts iterative exploration within a compact latent space using optimizable latent think tokens, guided by an entropy-based reward that encourages confident and stable predictions. Furthermore, a dynamic visual injection strategy selectively incorporates the most informative image patches into the latent sequence, directing the reasoning process toward regions critical for anomaly detection. Extensive experimental results demonstrate that Reason-IAD consistently outperforms state-of-the-art methods across multiple tasks. The code will be publicly available at https://github.com/chenpeng052/Reason-IAD.", "date": "2026-02-10", "categories": []}
{"id": "2605.06955", "title": "Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection", "abstract": "Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the gradient of the log density, from noise-corrupted samples. Once trained, the score magnitude at a test point reflects how consistent that point is with the learned distribution, making it a natural anomaly signal. The key practical challenge is selecting the perturbation scale: too little noise yields unstable score estimates in sparse regions, while too much erases local structure and weakens anomaly sensitivity. This is compounded by the difficulty of hyperparameter tuning when anomalies are unknown and no validation set is available. We introduce kurtosis-based noise scaling (K-DSM), a per-feature scheme that sets noise levels from the shape of each marginal distribution, improving coverage of low-density regions and precision in high-density regions without extra model complexity. Contrary to prior claims that multi-scale or noise-conditioned training is necessary, we find that a carefully trained single-scale model is already a strong anomaly detector. On standard tabular anomaly detection benchmarks, K-DSM achieves state-of-the-art performance in the semi-supervised setting. When combined with a lightweight EMA-teacher filtering rule that removes low-density training points before each gradient step, it also achieves strong performance in the fully unsupervised (contaminated) setting, suggesting that simple, data-adaptive noise scaling enables robust anomaly detection while reducing reliance on hyperparameter tuning.", "date": "2026-05-07", "categories": []}
{"id": "2605.07133", "title": "GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges", "abstract": "Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform governance. However, existing GAD benchmarks are often restricted to small-scale, curated graphs with relatively balanced anomaly ratios, leaving a substantial gap between academic evaluation and real-world deployment. To bridge this gap, we present a multi-dimensional benchmark that systematically evaluates GAD models under three deployment-relevant challenges: million-scale graphs, extreme anomaly scarcity, and missing node attributes. We derive a family of controlled benchmark variants from five diverse graphs, including two native industrial-scale datasets with over 3.7 million nodes. Our extensive evaluation of nine representative GAD models reveals three major limitations: (1) most GNN-based methods fail to scale to million-node graphs due to prohibitive memory requirements; (2) detection performance drops sharply under realistic anomaly ratios (e.g., 0.1\\%), often resulting in zero recall; and (3) reconstruction-based models are highly sensitive to attribute imputation strategies. Our findings suggest that strong performance in laboratory settings does not guarantee robustness in production environments. We release this benchmark and empirical evaluation as a diagnostic testbed to promote the development of robust and scalable GAD systems for large-scale, imperfect graphs encountered in practice. Code is available at https://anonymous.4open.science/r/Benchmark_GAD-E7A3.", "date": "2026-05-08", "categories": []}
{"id": "2605.07149", "title": "Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection", "abstract": "Industrial Anomaly Detection (IAD) is critical for quality control, but existing methods struggle with subtle, geometric defects. Standard 2D (RGB) images are sensitive to texture and lighting but often miss fine geometric anomalies. While 3D point clouds capture macro-shape, they are typically too sparse to detect micro-defects like scratches or pits. We address this fundamental data limitation by introducing Real-IAD-MVN (Multi-View Normal), a large-scale industrial dataset. By upgrading our acquisition system, Real-IAD-MVN captures high-fidelity surface normal maps from five distinct viewpoints, replacing sparse 3D data entirely. This provides a comprehensive geometric representation at a micro-detail level, making previously invisible side-wall and occluded defects explicitly detectable. Our experiments, conducted on this new dataset, first provide evidence that incorporating dense, multi-view pseudo-3D (surface normals) yields significantly better detection performance than using sparse 3D point cloud data. To further validate the dataset and provide a strong benchmark, we introduce a baseline method based on reconstruction, which learns to extract cross-modal unified prototypes from the image and normal map streams. We demonstrate that this unified prototype approach surpasses existing state-of-the-art multimodal fusion methods, highlighting the rich potential of our new dataset for advancing geometric anomaly detection.", "date": "2026-05-08", "categories": []}
{"id": "2605.07690", "title": "Fortifying Time Series: DTW-Certified Robust Anomaly Detection", "abstract": "Time-series anomaly detection is critical for ensuring safety in high-stakes applications, where robustness is a fundamental requirement rather than a mere performance metric. Addressing the vulnerability of these systems to adversarial manipulation is therefore essential. Existing defenses are largely heuristic or provide certified robustness only under $\\ell_p$-norm constraints, which are incompatible with time-series data. In particular, $\\ell_p$-norm fails to capture the intrinsic temporal structure in time series, causing small temporal distortions to significantly alter the $\\ell_p$-norm measures. Instead, the similarity metric \\emph{Dynamic Time Warping} (DTW) is more suitable and widely adopted in the time-series domain, as DTW accounts for temporal alignment and remains robust to temporal variations. To date, however, there has been no certifiable robustness result in this metric that provides guarantees. In this work, we introduce the first \\emph{DTW-certified robust defense} in time-series anomaly detection by adapting the randomized smoothing paradigm. We develop this certificate by bridging the $\\ell_p$-norm to DTW distance through a lower-bound transformation. Extensive experiments across various datasets and models validate the effectiveness and practicality of our theoretical approach. Results demonstrate significantly improved performance, e.g., up to 18.7\\% in F1-score under DTW-based adversarial attacks compared to traditional certified models.", "date": "2026-05-08", "categories": []}
{"id": "2605.07812", "title": "GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification", "abstract": "Advanced persistent threat (APT) attacks remain difficult to detect due to their stealth, adaptability, and use of legitimate system components. Provenance-based intrusion detection systems (PIDS) offer a promising defense by capturing detailed relationships between system components and actions. However, current PIDS rely on predefined or subset-determined thresholds, which limit detection stability and the ability to detect any anomalous behavior in general. Furthermore, related work often neglects the role of process executables, which describe system activity by interacting through a process with files, network components, and other processes. We introduce GRASP, a PIDS based on masked self-supervised classification. GRASP masks the executable information of processes and learns to infer it from their two-hop provenance graph neighborhood, marking misclassified processes as anomalies. It captures behavior patterns for the learned executables without thresholding, making it robust against interference and unknown activities. Evaluations on the DARPA TC and OpTC datasets demonstrate that GRASP consistently detects anomalous behavior, including known attack-related activities, outperforming existing systems. Our PIDS identifies all documented attacks on datasets where the behavior of executables is learnable. In addition, compared to existing systems, GRASP uncovers potentially malicious anomalous behavior not labeled as an attack in the documentation.", "date": "2026-05-08", "categories": []}
{"id": "2605.08003", "title": "SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere", "abstract": "Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large-scale annotations or task-specific training procedures, severely limiting their rapid deployment to novel scenes. We observe that intermediate-layer features of pre-trained multimodal large language models (MLLMs) already encode rich anomaly semantics, yet existing approaches rely on the language output pathway and fail to exploit the geometric discriminability latent in these representations. Based on this finding, we propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises-Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere, unleashing latent discriminability through principled geometric reasoning rather than learning new representations. Specifically, SphereVAD first applies Frechet mean centering to unfold feature distributions and eliminate domain biases, then employs Holistic Scene Attention (HSA) to reinforce feature consistency using cross-video priors, and finally performs vMF-guided Spherical Geodesic Pulling (SGP) to align ambiguous segments with directional prototypes on the spherical manifold. This training-free pipeline requires only minimal synthetic images for calibration. SphereVAD establishes new state-of-the-art results among training-free approaches on three major benchmarks and remains competitive with fully supervised baselines. Code will be available upon acceptance.", "date": "2026-05-08", "categories": []}
{"id": "2602.17419", "title": "EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models", "abstract": "Multimodal large language models (MLLMs) can enrich industrial anomaly detection with semantic descriptions and anomaly reasoning, but they still lag specialist anomaly detectors in binary detection accuracy. Existing approaches address this gap by fine-tuning MLLMs or training bridging modules to align expert outputs with MLLM inputs, limiting flexibility across backbones. We propose EAGLE, a tuning-free framework that integrates expert anomaly detectors with frozen MLLMs. EAGLE consists of Threshold-Guided Prompt Selection (TGPS), which estimates a decision threshold from expert model statistics and selects textual and visual prompts, and Confidence-Aware Attention Sharpening (CAAS), which shifts MLLM attention toward visual evidence when expert confidence is low. Beyond improving accuracy, we analyze MLLM attention and find that correct anomaly predictions are associated with stronger focus on ground-truth defect regions; EAGLE consistently strengthens this alignment. On MVTec-AD and VisA, EAGLE improves five MLLM backbones without parameter updates, reaching up to 94.4\\% and 88.1\\% in anomaly discrimination accuracy, respectively, and achieving performance competitive with fine-tuning-based methods while largely preserving MLLM semantic reasoning ability.", "date": "2026-02-19", "categories": []}
{"id": "2603.12278", "title": "Unsupervised Anomaly Detection in Wearable Foot Sensor Data: A Baseline Feasibility Study Towards Diabetic Foot Ulcer Prevention", "abstract": "Diabetic foot ulcers (DFUs) are a severe complication of diabetes associated with significant morbidity, amputation risk, and healthcare burden. Developing effective continuous monitoring frameworks requires first establishing reliable baseline models of normal foot biomechanics. This paper presents a feasibility study of an anomaly detection framework applied to time-series data from wearable foot sensors, specifically NTC thin-film thermocouples for temperature and FlexiForce A401 pressure sensors for plantar load monitoring. Data were collected from healthy adult subjects across 312 capture sessions on an instrumented pathway, generating 93,790 valid multi-sensor readings spanning September 2023 to June 2024. Two unsupervised algorithms, Isolation Forest and K-Nearest Neighbors using Local Outlier Factor (KNN/LOF), were applied to detect statistical deviations in foot temperature and pressure signals. Results show that Isolation Forest is more sensitive to subtle, distributed anomalies, while KNN/LOF identifies concentrated extreme deviations but flags a higher proportion of sessions not corroborated by Isolation Forest. Since no clinical ground truth is available, this difference is interpreted as lower specificity under the shared 5 percent contamination assumption rather than a confirmed false-positive rate. A mild positive correlation (0.41-0.48) between pressure and temperature features supports the case for combined multi-modal monitoring. These findings establish a validated baseline analytical pipeline and provide a methodological foundation for future clinical validation studies involving diabetic patients, where the relationship between detected anomalies and DFU-related pathophysiology can be directly assessed.", "date": "2026-02-27", "categories": []}
{"id": "2605.05725", "title": "Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers", "abstract": "Recent studies have explored large language models for time-series anomaly detection, yet existing approaches often rely on a single general-purpose model to directly infer anomaly indices or intervals, limiting controllability, interpretability, and reliability for complex anomaly patterns. We propose SAGE (Specialized Analyzer Group for Expert-like Detection), a multi-agent framework for structured anomaly diagnosis in univariate time series. It decomposes anomaly analysis into four specialized Analyzers for point, structural, seasonal, and pattern anomalies. Each Analyzer applies family-specific numerical tools and diagnostic visualizations to generate evidence, while an evidence-grounded Detector consolidates the evidence into confidence-scored anomaly records with intervals and candidate types. A Supervisor then converts these structured records into analyst-facing diagnostic reports. SAGE further constructs synthetic in-context examples from normal-reference training segments, without using real anomalous segments or anomaly-type labels as in-context examples. Across three benchmarks, SAGE achieves the best average performance among strong ML/DL and language-model-based baselines. Ablation studies and human evaluation further show that the proposed framework improves detection reliability and the practical usefulness of diagnostic outputs.", "date": "2026-05-07", "categories": []}
{"id": "2605.05850", "title": "Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection", "abstract": "Zero-shot 3D anomaly detection aims to identify anomalies without access to training data from target categories. However, existing methods mainly rely on projecting 3D observations into multi-view representations that primarily capture geometric cues rather than realistic visual semantics and process them with vision encoders pretrained on RGB data, leading to a significant domain gap between the encoder and the projected representations. To address this issue, we propose Align3D-AD, a unified two-stage framework that leverages the RGB modality from auxiliary categories as cross-modal guidance for zero-shot 3D anomaly detection. First, we introduce a cross-modal feature alignment paradigm that maps rendering features into the RGB semantic space. Unlike prior works that implicitly rely on pretrained encoders, our method enables direct semantic transfer from RGB observations. A semantic consistency reweighting strategy is further introduced to refine feature alignment by reweighting local regions according to holistic semantic consistency. Second, we propose a modality-aware prompt learning framework with dual-prompt contrastive alignment. By assigning independent prompts to RGB-aligned and rendering features, our method captures complementary semantics across modalities, while the contrastive alignment further enhances prompt representations to improve discriminability. Extensive experiments on MVTec3D-AD, Eyecandies, and Real3D-AD demonstrate that Align3D-AD consistently outperforms existing zero-shot methods under both one-vs-rest and cross-dataset settings, highlighting its generalization capability and robustness. Code and the dataset will be made available once our paper is accepted.", "date": "2026-05-07", "categories": []}
{"id": "2605.05891", "title": "MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors", "abstract": "Anomaly detection in medical images is a challenging task, since anomalies are not typically available during training. Recent methods leverage a single pretext task coupled with a large-scale pre-trained model to reach state-of-the-art performance. Instead, we propose to learn multiple self-supervised and pseudo-labeling tasks from scratch, using a joint model based on Mixture-of-Experts (MoE). By carefully integrating multiple proxy tasks, the joint model effectively learns a robust representation of normal anatomical structures, so that anomaly scores can be derived based on how well the multi-task learner (MTL) solves each task during inference. We perform comprehensive experiments on BMAD, a recent benchmark that comprises a broad range of medical image modalities. The empirical results indicate that our multi-task learner is an effective anomaly detector, outperforming all state-of-the-art competitors on BMAD. Moreover, our model produces interpretable anomaly maps, potentially helping physicians in providing more accurate diagnoses.", "date": "2026-05-07", "categories": []}
{"id": "2602.06810", "title": "Calibrating Tabular Anomaly Detection via Optimal Transport", "abstract": "Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in distribution and scale, and exhibit diverse types. Consequently, each TAD method makes implicit assumptions about anomaly patterns that work well on some datasets but fail on others, and no method consistently outperforms across diverse scenarios. We present CTAD (Calibrating Tabular Anomaly Detection), a model-agnostic post-processing framework that enhances any existing TAD detector through sample-specific calibration. Our approach characterizes normal data via two complementary distributions, i.e., an empirical distribution from random sampling and a structural distribution from K-means centroids, and measures how adding a test sample disrupts their compatibility using Optimal Transport (OT) distance. Normal samples maintain low disruption while anomalies cause high disruption, providing a calibration signal to amplify detection. We prove that OT distance has a lower bound proportional to the test sample's distance from centroids, and establish that anomalies systematically receive higher calibration scores than normals in expectation, explaining why the method generalizes across datasets. Extensive experiments on 34 diverse tabular datasets with 7 representative detectors spanning all major TAD categories (density estimation, classification, reconstruction, and isolation-based methods) demonstrate that CTAD consistently improves performance with statistical significance. Remarkably, CTAD enhances even state-of-the-art deep learning methods and shows robust performance across diverse hyperparameter settings, requiring no additional tuning for practical deployment.", "date": "2026-02-06", "categories": []}
{"id": "2603.25956", "title": "ARTA: Adversarial-Robust Multivariate Time--Series Anomaly Detection via Sparsity-Constrained Perturbations", "abstract": "Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via sparsity-constrained perturbations), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, task-relevant temporal perturbations that maximally increase the detector's anomaly score, while the detector is optimized to remain stable under these structured perturbations. The resulting masks characterize the detector's sensitivity to adversarial temporal corruptions and can serve as explanatory signals for the detector's decisions. This adversarial training strategy exposes brittle decision pathways and encourages the detector to rely on distributed and stable temporal patterns rather than spurious localized artifacts. We conduct extensive experiments on the TSB-AD benchmark, demonstrating that ARTA consistently improves anomaly detection performance across diverse datasets and exhibits significantly more graceful degradation under increasing noise levels compared to state-of-the-art baselines.", "date": "2026-03-26", "categories": []}
{"id": "2605.03437", "title": "Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection", "abstract": "Detecting anomalies from 3D point clouds has received increasing attention in the field of computer vision, with some group-based or point-based methods achieving impressive results in recent years. However, learning accurate point-wise representations for 3D anomaly detection faces great challenges due to the large scale and sparsity of point clouds. In this study, a surface-based method is proposed for 3D anomaly detection, which learns a discriminative signed distance function using multi-scale level-of-detail features. We first present a Noisy Points Generation (NPG) module to generate different types of noise, thereby facilitating the learning of discriminative features by exposing abnormal points. Then, we introduce a Multi-scale Level-of-detail Feature (MLF) module to capture multi-scale information from a point cloud, which provides both fine-grained local and coarse-grained global feature information. Finally, we design an Implicit Surface Discrimination (ISD) module that leverages the extracted multi-scale features to learn an implicit surface representation of point clouds, which effectively trains a signed distance function to distinguish between abnormal and normal points. Experimental results demonstrate that the proposed method achieves an average object-level AUROC of 92.1\\% and 85.9\\% on the Anomaly-ShapeNet and Real3D-AD datasets, outperforming the current best approach by 2.1\\% and 3.6\\%, respectively. Codes are available at https://anonymous.4open.science/r/DLF-3AD-DA61.", "date": "2026-05-05", "categories": []}
{"id": "2605.04664", "title": "Evidence-based anomaly detection in clinical domains", "abstract": "Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that let us evaluate management decisions for a specific patient and identify those decisions that are highly unusual with respect to patients with the same or similar condition. The statistics used in this detection are derived from probabilistic models such as Bayesian networks that are learned from a database of past patient cases. We apply our methods to the problem of identifying unusual patient-management decisions in post-surgical cardiac patients.", "date": "2026-05-06", "categories": []}
{"id": "2605.05124", "title": "Conditional outlier detection for clinical alerting", "abstract": "We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.", "date": "2026-05-06", "categories": []}
{"id": "2605.05161", "title": "Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging", "abstract": "Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is fundamentally limited by the absence of healthy anatomical context. We reformulate zero-shot localisation as a comparative inference problem in which anomalies are identified through structured comparison against reference distributions of normal anatomy. We introduce WALDO, a training-free framework grounded in optimal transport theory that enables comparative reasoning through: (i) entropy-weighted Sliced Wasserstein distances for anatomically-aware reference selection from DINOv2 patch distributions, (ii) Goldilocks zone sampling exploiting the non-monotonic relationship between reference similarity and localisation accuracy, and (iii) self-consistency aggregation via weighted non-maximum suppression. We theoretically analyse the Goldilocks effect through distributional divergence, and show that references with moderate similarity minimize a bias-variance trade-off in comparative visual reasoning. On the NOVA brain MRI benchmark, WALDO with Qwen2.5-VL-72B achieves $43.5_{\\pm1.6}\\%$ mAP@30 (95\\% CI: [40.4, 46.7]), representing a 19\\% relative improvement over zero-shot baselines. Cross-model evaluation shows consistent gains: GPT-4o achieves $32.0_{\\pm6.5}\\%$ and Qwen3-VL-32B achieves $32.0_{\\pm6.6}\\%$ mAP@30. Paired McNemar tests confirm statistical significance ($p<0.01$). Source code is available at https://github.com/bkainz/WALDO_MICCAI26_demo .", "date": "2026-05-06", "categories": []}
{"id": "2605.02912", "title": "Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models", "abstract": "Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor precise spatial localization of anomalous events. While Vision-Language Models (VLMs) offer rich scene understanding, they struggle with reliable spatial grounding - often producing hallucinated or geometrically invalid bounding boxes when asked to localize objects. We propose VANGUARD (Video Anomaly Understanding through Reasoning and Grounding), a framework that unifies anomaly classification, spatial grounding, and chain-of-thought reasoning within a single VLM. VANGUARD introduces a three-stage curriculum that progressively layers training objectives: (1) classifier warmup on frozen backbone features, (2) LoRA-adapted spatial grounding, and (3) chain-of-thought generation. To overcome the sparse annotation typical of VAD benchmarks, we employ a teacher-student annotation pipeline in which a VLM (Qwen3-VL-4B) generates structured per-subclip reasoning trajectories based on manual annotations available from the UCA Dataset. Further, GroundingDINO provides bounding box supervision. On UCF-Crime, VANGUARD achieves 94% ROC-AUC with 84% F1 while simultaneously producing interpretable chain-of-thought explanations and spatial grounding of anomalous objects - capabilities absent from prior VAD methods. Ablations confirm that staged training outperforms monolithic optimization, and that structured reasoning acts as an implicit regularizer yielding more balanced predictions than classification-only fine-tuning. Zero-shot transfer to XD-Violence and ShanghaiTech demonstrates cross-domain generalization without target-domain adaptation.", "date": "2026-04-07", "categories": []}
{"id": "2605.02918", "title": "Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection", "abstract": "Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize the reconstruction error on normal samples. In this paper, we reveal a trade-off between reconstruction quality and anomaly detection among $\\beta$-VAE models. Models with constrained latent space reach higher detection metrics but lower reconstruction quality. We also assess the performance variability across random seeds and show it is linked to the distance between normal and abnormal latent distributions. From this analysis, we justify and investigate two methods to mitigate the reconstructiondetection tradeoff: beta-scheduling and the Sparse VAE. The latter especially shows an improvement in detection while maintaining high reconstruction quality.", "date": "2026-04-09", "categories": []}
{"id": "2605.02970", "title": "Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic", "abstract": "Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In response, image-based methods that model traffic as visual patterns have emerged as the dominant approach. However, this work pioneers the identification of a pervasive ``full-frequency'' characteristic and an associated limitation termed ``spectral mismatch'' within this paradigm. Specifically, while encrypted traffic exhibits prominent high-frequency components, mainstream reconstruction methods demonstrate an inherent bias toward learning low-frequency information. This fundamental mismatch results in incomplete representations that consequently degrade anomaly detection performance. To address this challenge, we propose FreeUp, a novel frequency-decoupled framework designed explicitly for encrypted traffic analysis. FreeUp decomposes traffic data into distinct low- and high-frequency bands, processing them through separate, dedicated branches along with a customized training strategy that ensures stable and independent frequency-specific learning. Furthermore, recognizing that simple reconstruction error proves inadequate for evaluating dual-branch architectures, we introduce an uncertainty-inspired fusion scoring mechanism. This mechanism quantifies the reconstruction uncertainty of the frequency-specific branches and dynamically integrates their outputs, yielding a more comprehensive and reliable anomaly score. Extensive experiments across multiple benchmarks demonstrate that FreeUp consistently outperforms state-of-the-art baselines. The code is available at https://github.com/ikun0124/FreeUp.", "date": "2026-05-03", "categories": []}
{"id": "2605.03328", "title": "LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing", "abstract": "Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse industries. Fused filament fabrication (FFF) has extended AM to laboratories, classrooms, and small production environments, but this accessibility shifts process-planning responsibility to users who may lack manufacturing expertise. A syntactically valid slicer profile can still encode thermally or geometrically harmful settings, and subtle G-code edits can alter extrusion, cooling, or adhesion before a print begins. Pre-print G-code screening catches accidental or adversarial machine-program errors before material or machine time is wasted. This paper proposes LLM-ADAM as a generalizable LLM framework for pre-print anomaly detection in AM. The framework decomposes the task into three roles: Extractor-LLM maps a G-code file to a structured process-parameter schema; Reference-LLM converts printer and material documentation into aligned operating ranges; and Judge-LLM interprets a deterministic deviation table and G-code evidence to decide whether a part is non-defective or belongs to an anomaly class. Printers, materials, and LLM backbones are interchangeable test conditions, not fixed assumptions. We evaluate the framework on an N=200 FFF G-code corpus spanning two desktop printer families, two materials, and five classes including non-defective, under-extrusion, over-extrusion, warping, and stringing. The best framework configuration reaches 87.5% accuracy, compared with 59.5% for the strongest engineered single-LLM baseline. The results show that structured decomposition, rather than backbone strength alone, is the dominant source of improvement, with defect classes identified at or near ceiling for leading configurations while residual errors concentrate on conservative false alarms for non-defective samples.", "date": "2026-05-05", "categories": []}
{"id": "2605.03495", "title": "Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning", "abstract": "We develop graph-based methods for semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We also present graph-based methods for detecting conditional anomalies and apply them to the identification of unusual clinical actions in hospitals. Our hypothesis is that patient-management actions that are unusual with respect to the past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe and isolated points. We devise novel nonparametric graph-based methods to tackle these problems. Our methods rely on graph connectivity analysis and soft harmonic solution. Finally, we conduct an extensive human evaluation study of our conditional anomaly methods by 15 experts in critical care.", "date": "2026-05-05", "categories": []}
{"id": "2409.15030", "title": "Anomaly Detection from a Tensor Train Perspective", "abstract": "We present a series of algorithms in tensor networks for anomaly detection in datasets, by using data compression in a Tensor Train representation. These algorithms consist of preserving the structure of normal data in compression and deleting the structure of anomalous data. The algorithms can be applied to any tensor network representation. We test the effectiveness of the methods with digits and Olivetti faces datasets and a cybersecurity dataset to determine cyber-attacks.", "date": "2024-09-23", "categories": []}
{"id": "2511.19474", "title": "Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks", "abstract": "Automatically detecting abnormal events in videos is crucial for modern autonomous systems, yet existing Video Anomaly Detection (VAD) benchmarks lack the scene diversity, balanced anomaly coverage, and temporal complexity needed to reliably assess real-world performance. Meanwhile, the community is increasingly moving toward Video Anomaly Understanding (VAU), which requires deeper semantic and causal reasoning but remains difficult to benchmark due to the heavy manual annotation effort it demands. In this paper, we introduce Pistachio, a new VAD/VAU benchmark constructed entirely through a controlled, generation-based pipeline. By leveraging recent advances in video generation models, Pistachio provides precise control over scenes, anomaly types, and temporal narratives, effectively eliminating the biases and limitations of Internet-collected datasets. Our pipeline integrates scene-conditioned anomaly assignment, multi-step storyline generation, and a temporally consistent long-form synthesis strategy that produces coherent 41-second videos with minimal human intervention. Extensive experiments demonstrate the scale, diversity, and complexity of Pistachio, revealing new challenges for existing methods and motivating future research on dynamic and multi-event anomaly understanding.", "date": "2025-11-22", "categories": []}
{"id": "2605.00929", "title": "PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs", "abstract": "Multivariate time series anomaly detection in ICS has attracted growing attention due to the increasing threat of cyber-physical attacks on critical infrastructure. State-of-the-art methods model inter-sensor relationships from raw time-domain amplitude values, using graph neural networks, Transformers. However, these methods discard the phase spectrum produced by time frequency transformations, We argue that phase information constitutes a complementary and previously overlooked detection modality for ICS anomaly detection. We present PhaseNet++, a frequency-domain autoencoder that operates on the Short-Time Fourier Transform (STFT) of sliding sensor windows, retaining both magnitude and phase spectra. A Phase Coherence Index (PCI), inspired by the Phase Locking Value from neuroscience, summarizes pairwise phase consistency across frequency bins into a continuous adjacency matrix. This matrix guides a graph attention network that propagates information preferentially among phase-synchronized sensors. A sensor-token Transformer encoder captures system-wide structure, and a dual-head decoder reconstructs magnitude and phase jointly via circular and coherence-aware objectives. Evaluated on the Secure Water Treatment (SWaT) benchmark, PhaseNet++ achieves an F1-score of 90.98%, ROC-AUC of 95.66%, and average precision of 91.51%. Ablation studies show that the phase-aware front-end and PCI graph module together add only 264,816 parameters, demonstrating that the phase inductive bias is lightweight. While the absolute F1-score is second best than that of all recent raw-value methods evaluated under different protocols, we position this work as the first systematic study of phase-domain anomaly detection for ICS.", "date": "2026-04-30", "categories": []}
{"id": "2605.00936", "title": "EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems", "abstract": "Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving event data unexplored. To address this gap, we propose EventADL, the first open-box event-based ADL framework for cloud-based service systems. To motivate the design of our framework, we conduct a systematic analysis on 520 real-world incidents, and provide insights into how anomalies and their root causes manifest through event data. EventADL has three phases: offline training, online anomaly detection, and root cause localization. During the training phase, EventADL first learns Event Semantic Patterns (ESPs), which capture normal interactions between system entities using historical event data, and then learns Event Frequency Patterns (EFPs), which capture the normal frequency of known ESPs. In the online anomaly detection phase, any data in the event stream that deviates significantly from either pattern is identified as anomalous. For localization, EventADL constructs an Intervention Graph that models the relationships between recent system interactions and the detected anomalies for automatic root cause localization. The framework is designed to operate efficiently with unlabeled data and to produce interpretable anomalies with their corresponding root causes. Our evaluation on three real cloud service systems and two real-world incidents demonstrates that EventADL outperforms existing methods, achieving F1-scores of at least 90% for anomaly detection and 100% top-3 accuracy in root cause localization.", "date": "2026-05-01", "categories": []}
{"id": "2605.02884", "title": "Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics", "abstract": "Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such as range edits, ratio checks, or univariate outlier detection, are effective for identifying extreme values in individual series but are less suited for detecting unusual combinations of indicators in high-dimensional settings. This paper proposes an unsupervised machine learning framework for identifying structurally atypical regional profiles within Europe using publicly available Eurostat data. We construct a cross-sectional dataset of NUTS2 regions (2022) covering four key indicators: GDP per capita in PPS, unemployment rate, tertiary educational attainment, and population density. We apply and compare five anomaly detection techniques, univariate z-scores, Mahalanobis distance, Isolation Forest, Local Outlier Factor, and One-Class SVM, and classify a region as a structural anomaly if it is flagged by at least three of the five methods. The findings show that machine learning methods identify a consistent set of regions whose multivariate profiles diverge substantially from the EU-wide pattern. These include both highly developed metropolitan economies (Brussels, Vienna, Berlin, Prague) and regions with persistent socio-economic disadvantages (Central and Western Slovakia, Northern Hungary, Castilla-La Mancha, Extremadura), as well as Istanbul, whose profile differs markedly from EU capital regions. Importantly, these anomalies do not necessarily signal data quality issues; rather, they reflect meaningful structural divergence that warrants analytical or policy attention. The proposed framework is fully reproducible, scalable, and compatible with existing validation workflows, offering a flexible tool for early detection of unusual regional configurations within the European Statistical System.", "date": "2026-05-04", "categories": []}
{"id": "2605.00482", "title": "Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks", "abstract": "Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing high-dimensional KPI time series. The scale and cost of incident labelling make supervised approaches impractical, motivating unsupervised anomaly detection robust to context shifts and nonstationarity.\n  We propose \\textbf{C-MTAD-GAT} (\\emph{Context-aware Multivariate Time-series Anomaly Detection with Graph Attention}), an anomaly detection framework designed to operate as a single shared model across large populations of network elements. The model combines temporal and feature-wise graph attention with lightweight static and dynamic context conditioning and a dual-head decoder for reconstruction and multi-step forecasting. It produces per-element, per-feature anomaly scores, converted to alerts via fully unsupervised thresholds calibrated from validation residuals.\n  On the TELCO dataset released with DC-VAE \\cite{garcia2023onemodel}, C-MTAD-GAT improves event-level affiliation and pointwise F1 while generating fewer alarms than prior graph-attention and VAE-based baselines. We then apply the same system to nation-scale radio access and evolved packet core control-plane counter data from a mobile network operator, where it is deployed. Operator feedback indicates the alerts are actionable and support daily monitoring, showing scalability across domains without relying on labelled incidents.", "date": "2026-05-01", "categories": []}
{"id": "2605.00490", "title": "Distance metric learning for conditional anomaly detection", "abstract": "Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods depend heavily on the distance metric that lets us identify examples in the dataset that are most critical for detecting the anomaly. To optimize the performance of such methods we study and devise a metric learning method that learns the distance metric to reflect best the conditional anomaly pattern.", "date": "2026-05-01", "categories": []}
{"id": "2511.14791", "title": "Enabling Predictive Maintenance in District Heating Substations: A Labelled Dataset and Fault Detection Evaluation Framework based on Service Data", "abstract": "Early detection of faults in district heating substations is imperative to reduce return temperatures and enhance efficiency. However, progress in this domain has been hindered by the limited availability of public, labelled datasets. We present an open-source framework combining a service report validated public dataset, an evaluation method based on accuracy, reliability, and earliness, and baseline results implemented with EnergyFaultDetector, an open-source Python framework developed for automated anomaly detection in operational data from energy systems.\n  The dataset contains time series of operational data from 93 substations across two manufacturers, annotated with a list of disturbances due to faults and maintenance actions, a set of normal-event examples and detailed fault metadata. We evaluate the EnergyFaultDetector using three metrics: accuracy for recognising normal behaviour, an eventwise F-score for reliable fault detection with few false alarms, and earliness for early detection. The framework also supports root cause analysis using ARCANA, a feature-attribution method for autoencoders. We demonstrate three use cases to assist operators in interpreting anomalies and identifying underlying faults. The models achieve high normal-behaviour accuracy (0.98) and eventwise F-score (beta = 0.5) of 0.83 and could detect 60% of the faults in the dataset before the customer reported a problem, with an average lead time of 3 to 5 days.\n  Integrating an open dataset, metrics, open-source code, and baselines establishes a reproducible, fault-centric benchmark with operationally meaningful evaluation, enabling consistent comparison and development of early fault detection and diagnosis methods for district heating substations.", "date": "2025-11-14", "categories": []}
{"id": "2604.27102", "title": "Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment", "abstract": "Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at unregulated waste disposal sites. This study applies an unsupervised machine learning framework to detect and characterise anomalous heavy metal contamination patterns in soils from twelve waste sites and residential controls in the Central Region, of Ghana. Concentrations of eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) were analysed alongside standard health risk indices, including the Hazard Index (HI) and Incremental Lifetime Cancer Risk (ILCR). Isolation Forest and PCA reconstruction error each identified $12$ anomalous samples ($15.4\\%$ of $78$ samples), while DBSCAN detected no density-isolated noise points. A consensus approach isolated six robust anomalies ($7.7\\%)$, all spatially concentrated at a single site (S3). Anomalies exhibited approximately $70$--$80\\%$ higher mean HI values than normal samples, with all consensus anomalies exceeding the HI$=1$ threshold. PCA reconstruction error showed a strong positive association with HI ($r \\approx 0.8$), indicating consistency between multivariate deviation and health risk. Three distinct anomaly types were identified: extreme Cu enrichment at S3, anomalously low Ni at S4/S5, and moderate multi-metal (Pb--Zn) co-elevation at S9--S12. The results demonstrate that unsupervised machine learning provides granular, objective insight beyond aggregate indices, enabling targeted site prioritisation and risk-informed environmental management.", "date": "2026-04-29", "categories": []}
{"id": "2604.27172", "title": "Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection", "abstract": "We propose C-MTAD-GAT, an \\emph{unsupervised}, \\emph{context-aware} graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MTAD-GAT combines graph attention with lightweight context embeddings, and uses a deterministic reconstruction head and multi-step forecaster to produce anomaly scores. Detection thresholds are calibrated \\emph{without labels} from validation residuals, keeping the pipeline fully unsupervised. On the public TELCO dataset, C-MTAD-GAT consistently outperforms MTAD-GAT and the Telco-specific DC-VAE, two state-of-the-art baselines, in both event-level and pointwise F1, while triggering substantially fewer alarms. C-MTAD-GAT is also deployed in the Core network of a national mobile operator, demonstrating its resilience in real industrial settings.", "date": "2026-04-29", "categories": []}
{"id": "2207.06229", "title": "Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection", "abstract": "Massive vector field datasets are common in multi-spectral optical and radar sensors, among many other emerging areas of application. We develop a novel stochastic functional (data) analysis approach for detecting anomalies based on the covariance structure of nominal stochastic behavior across a domain. An optimal vector field Karhunen-Loeve expansion is applied to such random field data. A series of multilevel orthogonal functional subspaces is constructed from the geometry of the domain, adapted from the KL expansion. Detection is achieved by examining the projection of the random field on the multilevel basis. A critical feature of this approach is that reliable hypothesis tests are formed, which do not require prior assumptions on probability distributions of the data. The method is applied to the important problem of degradation in the Amazon forest. Due to the complexity and high dimensionality of satellite imagery, it is not feasible to assume known distributions, nor to estimate them. In addition to providing reliable hypothesis tests, our approach shows the advantage of using multiple bands of data in a vectorized complex, leading to better anomaly detection. Furthermore, using simulated data, our approach is capable of detecting subtle anomalies that are impossible to detect with PCA-based methods.", "date": "2022-07-11", "categories": []}
{"id": "2507.02314", "title": "MAGIC: Few-Shot Mask-Guided Anomaly Inpainting with Prompt Perturbation, Spatially Adaptive Guidance, and Context Awareness", "abstract": "Few-shot anomaly generation is a key challenge in industrial quality control. Although diffusion models are promising, existing methods struggle: global prompt-guided approaches corrupt normal regions, and existing inpainting-based methods often lack the in-distribution diversity essential for robust downstream models. We propose MAGIC, a fine-tuned inpainting framework that generates high-fidelity anomalies that strictly adhere to the mask while maximizing this diversity. MAGIC introduces three complementary components: (i) Gaussian prompt perturbation, which prevents model overfitting in the few-shot setting by learning and sampling from a smooth manifold of realistic anomalies, (ii) spatially adaptive guidance that applies distinct guidance strengths to the anomaly and background regions, and (iii) context-aware mask alignment to relocate masks for plausible placement within the host object. Under consistent identical evaluation protocol, MAGIC outperforms state-of-the-art methods on diverse anomaly datasets in downstream tasks.", "date": "2025-07-03", "categories": []}
{"id": "2509.07523", "title": "RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection", "abstract": "Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations that correspond to anomalies. Convolutional Dictionary Learning (CDL) is a powerful tool for modeling local structures, but its adoption for this task has been limited by computational demands and sensitivity to outliers. We introduce RoseCDL, a novel CDL algorithm designed for robust and scalable modeling of signal pattern distribution. RoseCDL leverages stochastic windowing for efficient training and incorporates inline outlier detection to enhance robustness. This enables unsupervised identification of anomalous and rare patterns in long signals based on the local reconstruction loss. Experiments on real-world datasets show that RoseCDL delivers improved detection accuracy and computational efficiency, making CDL practical for challenging detection tasks in large-scale signal analysis.", "date": "2025-09-09", "categories": []}
{"id": "2509.11058", "title": "Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection", "abstract": "Zero-Shot Video Anomaly Detection (ZS-VAD) requires temporally localizing anomalies without target domain training data, which is a crucial task due to various practical concerns, e.g., data privacy or new surveillance deployments. Skeleton-based approach has inherent generalizable advantages in achieving ZS-VAD as it eliminates domain disparities both in background and human appearance. However, existing methods only learn low-level skeleton representation and rely on the domain-limited normality boundary, which cannot generalize well to new scenes with different normal and abnormal behavior patterns. In this paper, we propose a novel zero-shot video anomaly detection framework, unlocking the potential of skeleton data via action typicality and uniqueness learning. Firstly, we introduce a language-guided semantic typicality modeling module that projects skeleton snippets into action semantic space and distills LLM's knowledge of typical normal and abnormal behaviors during training. Secondly, we propose a test-time context uniqueness analysis module to finely analyze the spatio-temporal differences between skeleton snippets and then derive scene-adaptive boundaries. Without using any training samples from the target domain, our method achieves state-of-the-art results against skeleton-based methods on four large-scale VAD datasets: ShanghaiTech, UBnormal, NWPU, and UCF-Crime, featuring over 100 unseen surveillance scenes.", "date": "2025-09-14", "categories": []}
{"id": "2604.26216", "title": "Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships", "abstract": "This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised discriminant framework based on graph neural networks. This framework is used to mine stable correspondences between subjects and identify structural deviations from general ledger details and voucher entries. The method first abstracts accounting subjects as graph nodes, and the co-occurrence and debit/credit correspondence of subjects in the same business record are abstracted as weighted edges. The edge weights are characterized by statistical measures such as co-occurrence frequency or amount aggregation, thus forming a period-level accounting subject association graph. In the representation learning stage, a message passing mechanism is used to fuse the node's own attributes and neighborhood context to obtain node embeddings containing structural information. In the anomaly detection stage, the rationality of subject pair connections is estimated through a relation reconstruction decoder, and edge-level anomaly scores are defined based on the degree of deviation in reconstruction probabilities. These scores are then aggregated to obtain node-level risk ranking and local anomaly localization. This framework can simultaneously capture local substructure anomalies and cross-community anomaly connections without relying on anomaly labeling, outputting traceable subject pair risk clues. Comparative experiments demonstrate more stable comprehensive discriminant capabilities and higher top-ranking accuracy.", "date": "2026-04-29", "categories": []}
{"id": "2604.26868", "title": "Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection", "abstract": "Existing 3D anomaly detection methods are built on a rigid prior: normal geometry is pose-invariant and can be canonicalized through registration or alignment. This prior does not hold for articulated objects with hinge or sliding joints, where valid pose changes induce structured geometric variations that cannot be collapsed to a single canonical template, causing pose-induced deformations to be misidentified as anomalies while true structural defects are obscured. No existing benchmark addresses this challenge. We introduce ArtiAD, the first large-scale benchmark for articulated 3D anomaly detection, comprising 15,229 point clouds across 39 object categories with dense joint-angle variations and six structural anomaly types. Each sample is annotated with its joint configuration and part-level motion labels, enabling explicit disentanglement of pose-induced geometry from structural defects. ArtiAD also provides a seen/unseen articulation split to evaluate both interpolation and extrapolation to novel joint configurations. We propose Shape-Pose-Aware Signed Distance Field (SPA-SDF), a baseline that replaces the rigid prior with a continuous pose-conditioned implicit field, factorized into an articulation-independent structural prior and a Fourier-encoded joint embedding. At inference, the articulation state is recovered by minimizing reconstruction energy, and anomalies are identified as point-wise deviations from the learned manifold. SPA-SDF achieves 0.884 object-level AUROC on seen configurations and 0.874 on unseen configurations, substantially outperforming all rigid-based baselines. Our code and benchmark will be publicly released to facilitate future research.", "date": "2026-04-29", "categories": []}
{"id": "2604.07802", "title": "Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models", "abstract": "Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) performance remain poorly understood. Current methods predominantly treat VLMs as black-box feature extractors, assuming that anomaly-specific knowledge must be acquired through external adapters or memory banks. In this paper, we challenge this assumption by arguing that anomaly knowledge is intrinsically embedded within pre-trained models but remains latent and under-activated. We hypothesize that this knowledge is concentrated within a sparse subset of anomaly-sensitive neurons. To validate this, we propose latent anomaly knowledge excavation (LAKE), a training-free framework that identifies and elicits these critical neuronal signals using only a minimal set of normal samples. By isolating these sensitive neurons, LAKE constructs a highly compact normality representation that integrates visual structural deviations with cross-modal semantic activations. Extensive experiments on industrial AD benchmarks demonstrate that LAKE achieves state-of-the-art performance while providing intrinsic, neuron-level interpretability. Ultimately, our work advocates for a paradigm shift: redefining anomaly detection as the targeted activation of latent pre-trained knowledge rather than the acquisition of a downstream task.", "date": "2026-04-09", "categories": []}
{"id": "2604.17388", "title": "Back to Repair: A Minimal Denoising Network for Time Series Anomaly Detection", "abstract": "We introduce JuRe (Just Repair), a minimal denoising network for time series anomaly detection that exposes a central finding: architectural complexity is unnecessary when the training objective correctly implements the manifold-projection principle. JuRe consists of a single depthwise-separable convolutional residual block with hidden dimension 128, trained to repair corrupted time series windows and scored at inference by a fixed, parameter-free structural discrepancy function. Despite using no attention, no latent variable, and no adversarial component, JuRe ranks second on the TSB-AD multivariate benchmark (AUC-PR 0.404, 180 series, 17 datasets) and second on the UCR univariate archive by AUC-PR (0.198, 250 series), leading all neural baselines on AUC-PR and VUS-PR. Component ablation on TSB-AD identifies training-time corruption as the dominant factor ($\\Delta$AUC-PR $= 0.047$ on removal), confirming that the denoising objective, not network capacity, drives detection quality. Pairwise Wilcoxon signed-rank tests establish statistical significance against 21 of 25 baselines on TSB-AD. Code is available at the URL https://github.com/iis-esslingen/JuRe.", "date": "2026-04-19", "categories": []}
{"id": "2604.23724", "title": "Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference", "abstract": "Expressway video anomaly detection is essential for safety management. However, identifying anomalies across diverse scenes remains challenging, particularly for far-field targets exhibiting subtle abnormal vehicle motions. While Vision-Language Models (VLMs) demonstrate strong semantic reasoning capabilities, processing global frames causes attention dilution for these far-field objects and incurs prohibitive computational costs. To address these issues, we propose VIBES, an asynchronous collaborative framework utilizing VLMs guided by Bayesian inference. Specifically, to overcome poor generalization across varying expressway environments, we introduce an online Bayesian inference module. This module continuously evaluates vehicle trajectories to dynamically update the probabilistic boundaries of normal driving behaviors, serving as an asynchronous trigger to precisely localize anomalies in space and time. Instead of processing the continuous video stream, the VLM processes only the localized visual regions indicated by the trigger. This targeted visual input prevents attention dilution and enables accurate semantic reasoning. Extensive evaluations demonstrate that VIBES improves detection accuracy for far-field anomalies and reduces computational overhead, achieving high real-time efficiency and explainability while demonstrating generalization across diverse expressway conditions.", "date": "2026-04-26", "categories": []}
{"id": "2402.11789", "title": "Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference", "abstract": "Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image generation models in anomaly localization, where these models generate normal-looking counterparts of anomalous images, thereby allowing flexible and adaptive anomaly localization. However, these methods inherit the uncertainty and bias implicitly embedded in the employed generative model, raising concerns about the reliability. To address this, we propose a statistical framework based on selective inference to quantify the significance of detected anomalous regions. Our method provides $p$-values to assess the false positive detection rates, providing a principled measure of reliability. As a proof of concept, we consider anomaly localization using a diffusion model and its applications to medical diagnoses and industrial inspections. The results indicate that the proposed method effectively controls the risk of false positive detection, supporting its use in high-stakes decision-making tasks.", "date": "2024-02-19", "categories": []}
{"id": "2511.10047", "title": "MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples", "abstract": "Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, while anomalies remain diverse and isolated. To explicitly leverage this discriminative property, we propose a Mutual Scoring framework (MuSc-V2) for zero-shot AC/AS, which flexibly supports single 2D/3D or multimodality. Specifically, our method begins by improving 3D representation through Iterative Point Grouping (IPG), which reduces false positives from discontinuous surfaces. Then we use Similarity Neighborhood Aggregation with Multi-Degrees (SNAMD) to fuse 2D/3D neighborhood cues into more discriminative multi-scale patch features for mutual scoring. The core comprises a Mutual Scoring Mechanism (MSM) that lets samples within each modality to assign score to each other, and Cross-modal Anomaly Enhancement (CAE) that fuses 2D and 3D scores to recover modality-specific missing anomalies. Finally, Re-scoring with Constrained Neighborhood (RsCon) suppresses false classification based on similarity to more representative samples. Our framework flexibly works on both the full dataset and smaller subsets with consistently robust performance, ensuring seamless adaptability across diverse product lines. In aid of the novel framework, MuSc-V2 achieves significant performance improvements: a $\\textbf{+23.7\\%}$ AP gain on the MVTec 3D-AD dataset and a $\\textbf{+19.3\\%}$ boost on the Eyecandies dataset, surpassing previous zero-shot benchmarks and even outperforming most few-shot methods. The code will be available at The code will be available at \\href{https://github.com/HUST-SLOW/MuSc-V2}{https://github.com/HUST-SLOW/MuSc-V2}.", "date": "2025-11-13", "categories": []}
{"id": "2603.10926", "title": "ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection", "abstract": "Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism. Accuracy-only leaderboards can therefore misrepresent which methods remain feasible under deployment-relevant constraints.\n  We present ECoLAD (Efficiency Compute Ladder for Anomaly Detection), a deployment-oriented evaluation protocol instantiated as an empirical study on proprietary automotive telemetry (anomaly rate ${\\approx}$0.022) and complementary public benchmarks. ECoLAD applies a monotone compute-reduction ladder across heterogeneous detector families using mechanically determined, integer-only scaling rules and explicit CPU thread caps, while logging every applied configuration change. Throughput-constrained behavior is characterized by sweeping target scoring rates and reporting (i) coverage (the fraction of entities meeting the target) and (ii) the best AUC-PR achievable among measured ladder configurations satisfying the target. On constrained automotive telemetry, lightweight classical detectors sustain both coverage and detection lift above the random baseline across the full throughput sweep. Several deep methods lose feasibility before they lose accuracy.", "date": "2026-03-11", "categories": []}
{"id": "2604.02445", "title": "Matrix Profile for Time-Series Anomaly Detection: A Reproducible Open-Source Benchmark on TSB-AD", "abstract": "Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre-sorted multidimensional aggregation, efficient exclusion-zone-aware k-nearest-neighbor (kNN) retrieval for repeated anomalies, and moving-average post-processing. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset characteristics.The open-source implementation is available at https://github.com/mcyeh/mmpad_tsb.", "date": "2026-04-02", "categories": []}
{"id": "2604.22899", "title": "Text-Guided Multimodal Unified Industrial Anomaly Detection", "abstract": "Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer from two critical limitations: ambiguous cross-modal alignment caused by the lack of high-level semantic guidance and insufficient geometric modeling for RGB-to-3D feature mapping. To address these issues, we propose a unified multimodal industrial anomaly detection framework guided by text semantics. The framework consists of two core modules: a Geometry-Aware Cross-Modal Mapper to preserve geometric structure during modality conversion, and an Object-Conditioned Textual Feature Adaptor to align multimodal features with semantic priors. Furthermore, we establish a unified learning paradigm for multimodal industrial anomaly detection, which breaks the one-model-one-class constraint and enables accurate anomaly detection across diverse classes using a single model. Extensive experiments on the MVTec 3D-AD and Eyecandies datasets demonstrate that our method achieves state-of-the-art performance in classification and localization under unsupervised settings.", "date": "2026-04-24", "categories": []}
{"id": "2604.23604", "title": "Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation", "abstract": "Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial in real-world environments, as done in Anomaly Segmentation. However, research in the 3D field remains limited, with most existing approaches applying post-processing techniques from 2D vision. To cover this lack, we propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples. Moreover, the only publicly available 3D LiDAR anomaly segmentation dataset contains simple scenarios, with few anomaly instances, and exhibits a severe domain gap due to its sensor resolution. To bridge this gap, we introduce a set of mixed real-synthetic datasets for 3D LiDAR anomaly segmentation, built upon established semantic segmentation benchmarks, with multiple out-of-distribution objects and diverse, complex environments. Extensive experiments demonstrate that our approach achieves state-of-the-art and competitive results on the existing real-world dataset and the newly introduced mixed datasets, respectively, validating the effectiveness of our method and the utility of the proposed datasets. Code and datasets are available at https://simom0.github.io/lido-page/.", "date": "2026-04-26", "categories": []}
{"id": "2602.00208", "title": "Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity", "abstract": "Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods are often adopted to mitigate these challenges by combining multiple detectors, which can reduce individual biases and increase robustness. Yet building an ensemble that is genuinely complementary remains challenging, since many detectors rely on similar decision cues and end up producing redundant anomaly scores. As a result, the potential of ensemble learning is often limited by the difficulty of identifying models that truly capture different types of irregularities. To address this, we propose a methodology for characterizing anomaly detectors through their decision mechanisms. Using SHapley Additive exPlanations, we quantify how each model attributes importance to input features, and we use these attribution profiles to measure similarity between detectors. We show that detectors with similar explanations tend to produce correlated anomaly scores and identify largely overlapping anomalies. Conversely, explanation divergence reliably indicates complementary detection behavior. Our results demonstrate that explanation-driven metrics offer a different criterion than raw outputs for selecting models in an ensemble. However, we also demonstrate that diversity alone is insufficient; high individual model performance remains a prerequisite for effective ensembles. By explicitly targeting explanation diversity while maintaining model quality, we are able to construct ensembles that are more diverse, more complementary, and ultimately more effective for unsupervised anomaly detection.", "date": "2026-01-30", "categories": []}
{"id": "2603.18941", "title": "Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning", "abstract": "The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the dominant paradigm. To address the lack of systematization in the field, this study introduces a novel and unified taxonomy with eleven dimensions over three parts (Input, Output and Model) for the categorization of DL-based MTSAD methods. The dimensions were established in a two-fold approach. First, they derived from a comprehensive analysis of methodological studies. Second, insights from review papers were incorporated. Furthermore, the proposed taxonomy was validated using an additional set of recent publications, providing a clear overview of methodological trends in MTSAD. Results reveal a convergence toward Transformer-based and reconstruction and prediction models, setting the foundation for emerging adaptive and generative trends. Building on and complementing existing surveys, this unified taxonomy is designed to accommodate future developments, allowing for new categories or dimensions to be added as the field progresses. This work thus consolidates fragmented knowledge in the field and provides a reference point for future research in MTSAD.", "date": "2026-03-19", "categories": []}
{"id": "2604.21953", "title": "Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics", "abstract": "Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by short detection windows for many prohibited substances. These constraints leave large portions of athletes without regular testing, motivating complementary screening approaches that analyze routine competition results to identify suspicious performance patterns. We present a system that processes 1.6 million athletics performances from over 19,000 competitions (2010-2025) using eight detection methods ranging from statistical rules to machine learning and trajectory analysis. We validate all methods against publicly confirmed anti-doping violations to measure their effectiveness in identifying sanctioned athletes. Trajectory-based methods, which compare performances to expected career progression, achieve the best balance between detecting violations and limiting false alarms, though all methods face challenges from incomplete data and rare confirmed violations. The system provides an interactive interface for expert-driven investigation, emphasizing transparency and human judgment to support, rather than replace, established anti-doping processes.", "date": "2026-04-23", "categories": []}
{"id": "2604.21956", "title": "Conditional anomaly detection using soft harmonic functions: An application to clinical alerting", "abstract": "Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method in detecting unusual labels on a real-world electronic health record dataset and compare it to several baseline approaches.", "date": "2026-04-23", "categories": []}
{"id": "2604.22139", "title": "Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography", "abstract": "Reliable automated analysis of Optical Coherence Tomography (OCT) imaging is crucial for diagnosing retinal disorders but faces a critical barrier: the need for expensive, labor-intensive expert annotations. Supervised deep learning models struggle to generalize across diverse pathologies, imaging devices, and patient populations due to their restricted vocabulary of annotated abnormalities. We propose an unsupervised anomaly detection framework that learns the normative distribution of healthy retinal anatomy without lesion annotations, directly addressing annotation efficiency challenges in clinical deployment. Our approach leverages a discrete latent model trained on normal B-scans to capture OCT-specific structural patterns. To enhance clinical robustness, we incorporate retinal layer-aware supervision and structured triplet learning to separate healthy from pathological representations, improving model reliability across varied imaging conditions. During inference, anomalies are detected and localized via reconstruction discrepancies, enabling both image and pixel-level identification without requiring disease-specific labels. On the Kermany dataset (AUROC: 0.799), our method substantially outperforms VAE, VQVAE, VQGAN, and f-AnoGAN baselines. Critically, cross-dataset evaluation on Srinivasan achieves AUROC 0.884 with superior generalization, demonstrating robust domain adaptation. On the external RETOUCH benchmark, unsupervised anomaly segmentation achieves competitive Dice (0.200) and mIoU (0.117) scores, validating reproducibility across institutions.", "date": "2026-04-24", "categories": []}
{"id": "2502.15793", "title": "Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach", "abstract": "Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.", "date": "2025-02-18", "categories": []}
{"id": "2604.21462", "title": "Conditional anomaly detection with soft harmonic functions", "abstract": "In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method on several synthetic and UCI ML datasets in detecting unusual labels when compared to several baseline approaches. We also evaluate the performance of our method on a real-world electronic health record dataset where we seek to identify unusual patient-management decisions.", "date": "2026-04-23", "categories": []}
{"id": "2511.17113", "title": "AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders", "abstract": "Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very costly to obtain. Moreover, existing public datasets have limited and/or outdated attacks, and many of them suffer from mislabelled data. To reduce the reliance on labelled data, we propose AutoGraphAD, a novel unsupervised anomaly detection based on a Heterogeneous Variational Graph Autoencoder. AutoGraphAD operates on heterogeneous graphs, made from connection and IP nodes that represent network activity. The model is trained using unsupervised and contrastive learning, without relying on any labelled data. The model's losses are then weighted and combined in an anomaly score used for anomaly detection. Overall, AutoGraphAD yields the same, and in some cases better, results than Anomal-E, but without requiring costly downstream anomaly detectors. As a result, AutoGraphAD achieves around 1.18 orders of magnitude faster training and 1.03 orders of magnitude faster inference, which represents a significant advantage for operational deployment.", "date": "2025-11-21", "categories": []}
{"id": "2603.26842", "title": "VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection", "abstract": "Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. To address this limitation, foundation models have emerged as a promising direction. However, existing approaches either repurpose large language models (LLMs) or construct largescale time series datasets to develop general anomaly detection foundation models, and still face challenges caused by severe cross-modal gaps or in-domain heterogeneity. In this paper, we investigate the applicability of large-scale vision models to TSAD. Specifically, we adapt a visual Masked Autoencoder (MAE) pretrained on ImageNet to the TSAD task. However, directly transferring MAE to TSAD introduces two key challenges: overgeneralization and limited local perception. To address these challenges, we propose VAN-AD, a novel MAE-based framework for TSAD. To alleviate the over-generalization issue, we design an Adaptive Distribution Mapping Module (ADMM), which maps the reconstruction results before and after MAE into a unified statistical space to amplify discrepancies caused by abnormal patterns. To overcome the limitation of local perception, we further develop a Normalizing Flow Module (NFM), which combines MAE with normalizing flow to estimate the probability density of the current window under the global distribution. Extensive experiments on nine real-world datasets demonstrate that VAN-AD consistently outperforms existing state-of-the-art methods across multiple evaluation metrics.We make our code and datasets available at https://github.com/PenyChen/VAN-AD.", "date": "2026-03-27", "categories": []}
{"id": "2604.20122", "title": "Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring", "abstract": "We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value), facilitating transparent and actionable decision-making. It employs weighted quantile conformal prediction bounds and adaptively learns optimal weighting parameters from past predictions, enabling calibration under distribution shifts and stable false alarm control, while preserving out-of-sample guarantees. As a model-agnostic solution, it integrates seamlessly with foundation models and supports rapid deployment in resource-constrained environments. This approach addresses key industrial challenges such as limited data availability, lack of training expertise, and the need for immediate inference, while taking advantage of the growing accessibility of time series foundation models. Experiments on both synthetic and real-world datasets show that the proposed approach delivers strong performance, combining simplicity, interpretability, robustness, and adaptivity.", "date": "2026-04-22", "categories": []}
{"id": "2604.20255", "title": "uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN", "abstract": "Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cues and may miss anomalies caused by violations of complex feature dependencies. Dependency-based anomaly detection provides a principled alternative by identifying anomalies as violations of dependencies among features. However, existing methods often struggle to model such dependencies robustly and to scale to high-dimensional data with complex dependency structures. To address these challenges, we propose uLEAD-TabPFN, a dependency-based anomaly detection framework built on Prior-Data Fitted Networks (PFNs). uLEAD-TabPFN identifies anomalies as violations of conditional dependencies in a learned latent space, leveraging frozen PFNs for dependency estimation. Combined with uncertainty-aware scoring, the proposed framework enables robust and scalable anomaly detection. Experiments on 57 tabular datasets from ADBench show that uLEAD-TabPFN achieves particularly strong performance in medium- and high-dimensional settings, where it attains the top average rank. On high-dimensional datasets, uLEAD-TabPFN improves the average ROC-AUC by nearly 20\\% over the average baseline and by approximately 2.8\\% over the best-performing baseline, while maintaining overall superior performance compared to state-of-the-art methods. Further analysis shows that uLEAD-TabPFN provides complementary anomaly detection capability, achieving strong performance on datasets where many existing methods struggle.", "date": "2026-04-22", "categories": []}
{"id": "2508.04818", "title": "Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models", "abstract": "Generative models have demonstrated significant success in anomaly detection and segmentation over the past decade. Recently, diffusion models have emerged as a powerful alternative, outperforming previous approaches such as GANs and VAEs. In typical diffusion-based anomaly detection, a model is trained on normal data, and during inference, anomalous images are perturbed to a predefined intermediate step in the forward diffusion process. The corresponding normal image is then reconstructed through iterative reverse sampling.\n  However, reconstruction-based approaches present three major challenges: (1) the reconstruction process is computationally expensive due to multiple sampling steps, making real-time applications impractical; (2) for complex or subtle patterns, the reconstructed image may correspond to a different normal pattern rather than the original input; and (3) Choosing an appropriate intermediate noise level is challenging because it is application-dependent and often assumes prior knowledge of anomalies, an assumption that does not hold in unsupervised settings.\n  We introduce Reconstruction-free Anomaly Detection with Attention-based diffusion models in Real-time (RADAR), which overcomes the limitations of reconstruction-based anomaly detection. Unlike current SOTA methods that reconstruct the input image, RADAR directly produces anomaly maps from the diffusion model, improving both detection accuracy and computational efficiency. We evaluate RADAR on real-world 3D-printed material and the MVTec-AD dataset. Our approach surpasses state-of-the-art diffusion-based and statistical machine learning models across all key metrics, including accuracy, precision, recall, and F1 score. Specifically, RADAR improves F1 score by 7% on MVTec-AD and 13% on the 3D-printed material dataset compared to the next best model.\n  Code available at: https://github.com/mehrdadmoradi124/RADAR", "date": "2025-08-06", "categories": []}
{"id": "2603.13779", "title": "AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison", "abstract": "Multimodal Large Language Models (MLLMs) have achieved impressive success in natural visual understanding, yet they consistently underperform in industrial anomaly detection (IAD). This is because MLLMs trained mostly on general web data differ significantly from industrial images. Moreover, they encode each image independently and can only compare images in the language space, making them insensitive to subtle visual differences that are key to IAD. To tackle these issues, we present AD-Copilot, an interactive MLLM specialized for IAD via visual in-context comparison. We first design a novel data curation pipeline to mine inspection knowledge from sparsely labeled industrial images and generate precise samples for captioning, VQA, and defect localization, yielding a large-scale multimodal dataset Chat-AD rich in semantic signals for IAD. On this foundation, AD-Copilot incorporates a novel Comparison Encoder that employs cross-attention between paired image features to enhance multi-image fine-grained perception, and is trained with a multi-stage strategy that incorporates domain knowledge and gradually enhances IAD skills. In addition, we introduce MMAD-BBox, an extended benchmark for anomaly localization with bounding-box-based evaluation. The experiments show that AD-Copilot achieves 82.3% accuracy on the MMAD benchmark, outperforming all other models without any data leakage. In the MMAD-BBox test, it achieves a maximum improvement of $3.35\\times$ over the baseline. AD-Copilot also exhibits excellent generalization of its performance gains across other specialized and general-purpose benchmarks. Remarkably, AD-Copilot surpasses human expert-level performance on several IAD tasks, demonstrating its potential as a reliable assistant for real-world industrial inspection. All datasets and models will be released for the broader benefit of the community.", "date": "2026-03-14", "categories": []}
{"id": "2604.14726", "title": "Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation", "abstract": "Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining and rigid decision boundaries, limiting their ability to adapt both effectively and efficiently to concept drift in dynamic environments. To address these challenges, we propose DyMETER, a dynamic concept adaptation framework for OAD that unifies on-the-fly parameter shifting and dynamic thresholding within a single online paradigm. DyMETER first learns a static detector on historical data to capture recurring central concepts, and then transitions to a dynamic mode to adapt to new concepts as drift occurs. Specifically, DyMETER employs a novel dynamic concept adaptation mechanism that leverages a hypernetwork to generate instance-aware parameter shifts for the static detector, thereby enabling efficient and effective adaptation without retraining or fine-tuning. To achieve robust and interpretable adaptation, DyMETER introduces a lightweight evolution controller to estimate instance-level concept uncertainty for adaptive updates. Further, DyMETER employs a dynamic threshold optimization module to adaptively recalibrates the decision boundary by maintaining a candidate window of uncertain samples, which ensures continuous alignment with evolving concepts. Extensive experiments demonstrate that DyMETER significantly outperforms existing OAD approaches across a wide spectrum of application scenarios.", "date": "2026-04-16", "categories": []}
{"id": "2604.18606", "title": "Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data", "abstract": "Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities essential. Recent Earth Observation (EO) approaches have shown that thermal anomaly detection can be performed directly on decompressed Level-0 (L0) sensor data, avoiding computationally expensive preprocessing chains. However, direct exploitation of raw data remains challenging due to domain shift, sensor drift, radiometric inconsistencies, and the scarcity of labelled training samples. To address these challenges, this work proposes a Physics-Aware Neuromorphic Network (PANN) framework for onboard thermal anomaly detection. The proposed lightweight architecture, inspired by physical neural network principles and neuromorphic computing paradigms, is evaluated using two Sentinel-2 datasets: decompressed L0 with additional metadata (i.e. raw) and Level-1C (L1C). The PANN achieves a Matthews Correlation Coefficient (MCC) of $0.809$ on raw measurements, compared to $0.875$ when using ground-processed L1C products. The mean processing latency per L0 granule is $2.44 \\pm 0.09~\\mathrm{s}$, which is below the Sentinel-2 acquisition time of $3.6~\\mathrm{s}$, demonstrating the feasibility of real-time, onboard processing. Furthermore, the projected execution time for the corresponding neuromorphic hardware instantiation is substantially lower at $0.1290 \\pm 0.0002~\\mathrm{s}$. Memory usage, including all necessary programs and packages, remains within realistic onboard constraints, with requirements of $0.673 \\pm 0.007~\\mathrm{Gb}$ for the software PANN and $0.393 \\pm 0.004~\\mathrm{Gb}$ for the estimated hardware realisation. Overall, these results indicate that PANN offers a promising pathway toward low-latency and resource-efficient onboard EO processing for thermal event detection.", "date": "2026-04-10", "categories": []}
{"id": "2604.18611", "title": "Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems", "abstract": "Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stages of plant commissioning. When a conventional neural network is sequentially trained to monitor new subsystems, it catastrophically forgets previously learned anomaly patterns, a safety-critical failure mode. We present the first spiking neural network (SNN)-based anomaly detection system with continual learning for nuclear ICS, addressing both challenges simultaneously. Our approach introduces spike-encoded asynchronous sensor fusion, a delta-based encoding that converts heterogeneous sensor streams into sparse spike trains at rates dictated by each sensor's natural dynamics, achieving 92.7% input sparsity. We evaluate five continual learning strategies, including sequential fine-tuning, Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), experience replay, and a hybrid EWC+Replay approach, on the HAI 21.03 nuclear ICS security dataset across three sequentially deployed subsystems (boiler, turbine, water treatment). The hybrid EWC+Replay method achieves an average F1 score of 0.979 with near-zero average forgetting (AF = 0.000 single seed; 0.035 +/- 0.039 across three seeds), while requiring 12.6x fewer operations (an estimated 2.5x in energy based on published hardware specifications) than an equivalent artificial neural network. The system detects all tested attacks with a mean latency of 0.6 seconds. These results demonstrate that neuromorphic computing offers a viable path toward always-on, energy-efficient, and adaptable safety monitoring for next-generation nuclear facilities.", "date": "2026-04-13", "categories": []}
{"id": "2604.19118", "title": "DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs", "abstract": "Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world settings, these logs are often distributed across multiple organizations and cannot be centralized due to privacy and security constraints. Existing log anomaly detection methods, including recent large language model (LLM) based approaches, largely rely on centralized training and are not suitable for such environments. In this paper, we propose DP-FLogTinyLLM, a privacy preserving federated framework for log anomaly detection using parameter efficient LLMs. Our approach enables collaborative learning without sharing raw log data by integrating federated optimization with differential privacy. To ensure scalability in resource constrained environments, we employ low rank adaptation (LoRA) for efficient fine tuning of Tiny LLMs at each client. Empirical results on the Thunderbird and BGL datasets show that the proposed framework matches the performance of centralized LLM based methods, while incurring additional computational overhead due to privacy mechanisms. Compared to existing federated baselines, DP-FLogTinyLLM consistently achieves higher precision and F1-score, with particularly strong gains on the Thunderbird dataset, highlighting its effectiveness in detecting anomalies while minimizing false positives.", "date": "2026-04-21", "categories": []}
{"id": "2604.19191", "title": "Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement", "abstract": "Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limited. We propose a hybrid anomaly detection framework that integrates self-supervised representation learning with manifold-based density estimation, a combination that remains largely unexplored in this domain.\n  Medical images are first embedded into a latent feature space using pretrained, potentially domain-specific, backbones. These representations are then refined via Mean Shift Density Enhancement (MSDE), an iterative manifold-shifting procedure that moves samples toward regions of higher likelihood. Anomaly scores are subsequently computed using Gaussian density estimation in a PCA-reduced latent space, where Mahalanobis distance measures deviation from the learned normal distribution. The framework follows a one-class learning paradigm and requires only normal samples for training.\n  Extensive experiments on seven medical imaging datasets demonstrate state-of-the-art performance. MSDE achieves the highest AUC on four datasets and the highest Average Precision on five datasets, including near-perfect performance on brain tumor detection (0.981 AUC/AP). These results underscore the potential of the proposed framework as a scalable clinical decision-support tool for early disease detection, screening in low-label settings, and robust deployment across diverse imaging modalities.", "date": "2026-04-21", "categories": []}
{"id": "2604.19240", "title": "Industrial Surface Defect Detection via Diffusion Generation and Asymmetric Student-Teacher Network", "abstract": "Industrial surface defect detection often suffers from limited defect samples, severe long-tailed distributions, and difficulties in accurately localizing subtle defects under complex backgrounds. To address these challenges, this paper proposes an unsupervised defect detection method that integrates a Denoising Diffusion Probabilistic Model (DDPM) with an asymmetric teacher-student architecture. First, at the data level, the DDPM is trained solely on normal samples. By introducing constant-variance Gaussian perturbations and Perlin noise-based masks, high-fidelity and physically consistent defect samples along with pixel-level annotations are generated, effectively alleviating the data scarcity problem. Second, at the model level, an asymmetric dual-stream network is constructed. The teacher network provides stable representations of normal features, while the student network reconstructs normal patterns and amplifies discrepancies between normal and anomalous regions. Finally, a joint optimization strategy combining cosine similarity loss and pixel-wise segmentation supervision is adopted to achieve precise localization of subtle defects. Experimental results on the MVTecAD dataset show that the proposed method achieves 98.4\\% image-level AUROC and 98.3\\% pixel-level AUROC, significantly outperforming existing unsupervised and mainstream deep learning methods. The proposed approach does not require large amounts of real defect samples and enables accurate and robust industrial defect detection and localization.\n  \\keywords{Industrial defect detection \\and diffusion models \\and data generation \\and teacher-student architecture \\and pixel-level localization}", "date": "2026-04-21", "categories": []}
{"id": "2604.19259", "title": "Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection", "abstract": "Multi-class defect detection constitutes a critical yet challenging task in industrial quality inspection, where existing approaches typically suffer from two fundamental limitations: (i) the necessity of training separate models for each defect category, resulting in substantial computational and memory overhead, and (ii) degraded robustness caused by inter-class feature perturbation when heterogeneous defect categories are jointly modeled. In this paper, we present FPFNet, a Feature Perturbation Pool-based Fusion Network that synergistically integrates a stochastic feature perturbation pool with a multi-layer feature fusion strategy to address these challenges within a unified detection framework. The feature perturbation pool enriches the training distribution by randomly injecting diverse noise patterns -- including Gaussian noise, F-Noise, and F-Drop -- into the extracted feature representations, thereby strengthening the model's robustness against domain shifts and unseen defect morphologies. Concurrently, the multi-layer feature fusion module aggregates hierarchical feature representations from both the encoder and decoder through residual connections and normalization, enabling the network to capture complex cross-scale relationships while preserving fine-grained spatial details essential for precise defect localization. Built upon the UniAD architecture~\\cite{you2022unified}, our method achieves state-of-the-art performance on two widely adopted benchmarks: 97.17\\% image-level AUROC and 96.93\\% pixel-level AUROC on MVTec-AD, and 91.08\\% image-level AUROC and 99.08\\% pixel-level AUROC on VisA, surpassing existing methods by notable margins while introducing no additional learnable parameters or computational complexity.", "date": "2026-04-21", "categories": []}
{"id": "2604.19538", "title": "Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity", "abstract": "Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risks in human activity, including the persistent hazard of falls among elderly populations. Despite numerous approaches to fall mitigation through fall prediction and detection, existing systems have not yet functioned as universal solutions across care pathways and safety-critical environments. This is largely due to limitations in consistently handling real-world complexity, particularly poor context awareness, high false alarm rates, environmental noise, and data scarcity. We argue that fall detection and fall prediction can usefully be formulated as anomaly detection problems and more effectively addressed through an agentic AI system. More broadly, this perspective enables the early identification of subtle deviations in movement patterns associated with increased risk, whether arising from age-related decline, fatigue, or environmental factors. While technical requirements for immediate deployment are beyond the scope of this paper, we propose a conceptual framework that highlights potential value. This framework promotes a well-orchestrated approach to risk management by dynamically selecting relevant tools and integrating them into adaptive decision-making workflows, rather than relying on static configurations tailored to narrowly defined scenarios.", "date": "2026-04-21", "categories": []}
{"id": "2512.24290", "title": "Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC", "abstract": "Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rare-event searches, but whose size challenges real-time data selection. We present an unsupervised, reconstruction-based anomaly-detection strategy for fast Region-of-Interest (ROI) extraction that operates directly on minimally processed camera frames. A convolutional autoencoder trained exclusively on pedestal images learns the detector noise morphology without labels, simulation, or fine-grained calibration. Applied to standard data-taking frames, localized reconstruction residuals identify particle-induced structures, from which compact ROIs are extracted via thresholding and spatial clustering. Using real data from the CYGNO optical TPC prototype, we compare two pedestal-trained autoencoder configurations that differ only in their training objective, enabling a controlled study of its impact. The best configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with an inference time of approximately 25 ms per frame on a consumer GPU. The results demonstrate that careful design of the training objective is critical for effective reconstruction-based anomaly detection and that pedestal-trained autoencoders provide a transparent and detector-agnostic baseline for online data reduction in optical TPCs.", "date": "2025-12-30", "categories": []}
{"id": "2603.23868", "title": "MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection", "abstract": "In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are used directly for training and testing without any labels. This differs sharply from prior work that either requires extensive labeling (fully or weakly supervised) or depends on normal-only videos (one-class classification), which are vulnerable to distribution shifts and contamination. We propose an entropy-guided autoencoder that detects anomalies through reconstruction error by reconstructing normal frames well while making anomalies reconstruct poorly. The key idea is to combine the standard reconstruction loss with a novel Minimal Latent Entropy (MLE) loss in the autoencoder. Reconstruction loss alone maps normal and abnormal inputs to distinct latent clusters due to their inherent differences, but also risks reconstructing anomalies too well to detect. Therefore, MLE loss addresses this by minimizing the entropy of latent embeddings, encouraging them to concentrate around high-density regions. Since normal frames dominate the raw video, sparse anomalous embeddings are pulled into the normal cluster, so the decoder emphasizes normal patterns and produces poor reconstructions for anomalies. This dual-loss design produces a clear reconstruction gap that enables effective anomaly detection. Extensive experiments on two widely used benchmarks and a challenging self-collected driving dataset demonstrate that our method achieves robust and superior performance over baselines.", "date": "2026-03-25", "categories": []}
{"id": "2604.16575", "title": "Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic", "abstract": "Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assume a fixed traffic representation, either temporal or structural, without validating which feature space best matches the data. We propose a lightweight decision framework that prioritizes temporal or structural features before training, using two diagnostics: lag-1 autocorrelation of an aggregated flow signal and PCA cumulative explained variance. When the probes are inconclusive, the framework reserves a hybrid option as a future fallback rather than an empirically validated branch. Experiments on two statistically distinct datasets with Isolation Forest, One-Class SVM, and KMeans show that structural features consistently match or outperform temporal ones, with the performance gap widening as temporal dependence weakens.", "date": "2026-04-17", "categories": []}
{"id": "2604.17420", "title": "TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering", "abstract": "Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction-graph datasets suffer from two pervasive limitations: (i) they provide sparse node-level semantics beyond anonymized identifiers, and (ii) they rely on template-driven anomaly injection, which biases benchmarks toward static structural motifs and yields overly optimistic assessments of model robustness. We propose TransXion, a benchmark ecosystem for Anti-Money Laundering (AML) research that integrates profile-aware simulation of normal activity with stochastic, non-template synthesis of illicit subgraphs.TransXion jointly models persistent entity profiles and conditional transaction behavior, enabling evaluation of \"out-of-character\" anomalies where observed activity contradicts an entity's socio-economic context. The resulting dataset comprises approximately 3 million transactions among 50,000 entities, each endowed with rich demographic and behavioral attributes. Empirical analyses show that TransXion reproduces key structural properties of payment networks, including heavy-tailed activity distributions and localized subgraph structure. Across a diverse array of detection models spanning multiple algorithmic paradigms, TransXion yields substantially lower detection performance than widely used benchmarks, demonstrating increased difficulty and realism. TransXion provides a more faithful testbed for developing context-aware and robust AML detection methods. The dataset and code are publicly available at https://github.com/chaos-max/TransXion.", "date": "2026-04-19", "categories": []}
{"id": "2604.17949", "title": "ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection", "abstract": "Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a multimodal vision-language framework for zero-shot grounded industrial anomaly detection. Given RGB images, sensor images, and 3D point clouds, ZSG-IAD generates structured anomaly reports and pixel-level anomaly masks. ZSG-IAD introduces a language-guided two-hop grounding module: (1) anomaly-related sentences select evidence-like latent slots distilled from multimodal features, yielding coarse spatial support; (2) selected slots modulate feature maps via channel-spatial gating and a lightweight decoder to produce fine-grained masks. To improve reliability, we further apply Executable-Rule GRPO with verifiable rewards to promote structured outputs, anomaly-region consistency, and reasoning-conclusion coherence. Experiments across multiple industrial anomaly benchmarks show strong zero-shot performance and more transparent, physically grounded explanations than prior methods. We will release code and annotations to support future research on trustworthy industrial anomaly detection systems.", "date": "2026-04-20", "categories": []}
{"id": "2604.17998", "title": "Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection", "abstract": "Anomaly detection in multivariate time series is a central challenge in industrial monitoring, as failures frequently arise from complex temporal dynamics and cross-sensor interactions. While recent deep learning models, including graph neural networks and Transformers, have demonstrated strong empirical performance, most approaches remain primarily correlational and offer limited support for causal interpretation and root-cause localization. This study introduces a causally-constrained probabilistic forecasting framework which is a Causally Guided Transformer (CGT) model for multivariate time-series anomaly detection, integrating an explicit time-lagged causal graph prior with deep sequence modeling. For each target variable, a dedicated forecasting block employs a hard parent mask derived from causal discovery to restrict the main prediction pathway to graph-supported causes, while a latent Gaussian head captures predictive uncertainty. To leverage residual correlational information without compromising the causal representation, a shadow auxiliary path with stop-gradient isolation and a safety-gated blending mechanism is incorporated to suppress non-causal contributions when reliability is low. Anomalies are identified using negative log-likelihood scores with adaptive streaming thresholding, and root-cause variables are determined through per-dimension probabilistic attribution and counterfactual clamping. Experiments on the ASD and SMD benchmarks indicate that the proposed method achieves state-of-the-art detection performance, with F1-scores of 96.19% on ASD and 95.32% on SMD, and enhances variable-level attribution quality. These findings suggest that causal structural priors can improve both robustness and interpretability in detecting deep anomalies in multivariate sensor systems.", "date": "2026-04-20", "categories": []}
{"id": "2604.18266", "title": "Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation", "abstract": "Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning. However, unsupervised methods lack sufficient anomaly awareness, while current generation and contrastive approaches tend to compute anomalies globally, overlooking the localized anomaly patterns of tabular features, resulting in suboptimal detection performance. To address these limitations, we propose PLAG, a pseudo-label-guided anomaly generation method designed to enhance tabular anomaly detection. Specifically, by utilizing pseudo-anomalies as guidance signals and decoupling the overall anomaly quantification of a sample into an accumulation of feature-level abnormalities, PLAG not only effectively obviates the need for scarce ground-truth labels but also provides a novel perspective for the model to comprehend localized anomalous signals at a fine-grained level. Furthermore, a two-stage data selection strategy is proposed, integrating format verification and uncertainty estimation to rigorously filter candidate samples, thereby ensuring the fidelity and diversity of the synthetic anomalies. Ultimately, these filtered synthetic anomalies serve as robust discriminative guidance, empowering the model to better separate normal and anomalous instances. Extensive experiments demonstrate that PLAG achieves state-of-the-art performance against eight representative baselines. Moreover, as a flexible framework, it integrates seamlessly with existing unsupervised detectors, consistently boosting F1-scores by 0.08 to 0.21.", "date": "2026-04-20", "categories": []}
{"id": "2604.18393", "title": "One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection", "abstract": "Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under the common assumption that off-manifold anomalies are harder to generate, resulting in larger reconstruction errors in data space or lower probability densities in the tractable latent space. However, their iterative denoising and noising nature leads to slow inference. In this paper, we propose OSD-IRF, a novel one-step diffusion with inverse residual fields, to address this limitation for uIAD task. We first train a deep diffusion probabilistic model (DDPM) on normal data without any conditioning. Then, for a test sample, we predict its inverse residual fields (IRF) based on the noise estimated by the well-trained parametric noise function of the DDPM. Finally, uIAD is performed by evaluating the probability density of the IRF under a Gaussian distribution and comparing it with a threshold. Our key observation is that anomalies become distinguishable in this IRF space, a finding that has seldom been reported in prior works. Moreover, OSD-IRF requires only single step diffusion for uIAD, thanks to the property that IRF holds for any neighboring time step in the denoising process. Extensive experiments on three widely used uIAD benchmarks show that our model achieves SOTA or competitive performance across six metrics, along with roughly a 2X inference speedup without distillation.", "date": "2026-04-20", "categories": []}
{"id": "2602.07303", "title": "KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction", "abstract": "Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when stored as flat sequences. As a result, state-of-the-art methods often miss true dependencies within executions while learning spurious correlations across unrelated events. We propose KRONE, the first hierarchical anomaly detection framework that automatically derives execution hierarchies from flat logs to enable modular, multi-level anomaly detection. At its core, the KRONE Log Abstraction Model extracts application-specific semantic hierarchies, which are used to recursively decompose log sequences into coherent execution units, referred to as KRONE Seqs. This transforms sequence-level detection into a set of modular KRONE Seq-level detection tasks. For each test KRONE Seq, KRONE adopts a hybrid modular detection strategy that routes between an efficient level-independent Local-Context detector for rapid filtering and a Nested-Aware detector that captures cross-level semantic dependencies, augmented with LLM-based anomaly detection and explanation. KRONE further optimizes detection through cached result reuse and early-exit strategies along the hierarchy. Experiments on three public benchmarks and one industrial dataset from ByteDance Cloud demonstrate that KRONE achieves substantial improvements in accuracy (42.49% to 87.98%), F1 score, data efficiency (117.3x reduction), resource efficiency (43.7x reduction), and interpretability. KRONE improves F1-score by 10.07% (82.76% to 92.83%) over prior methods while reducing LLM usage to only 1.1% to 3.3% of the test data. Code: https://github.com/LeiMa0324/KRONE Demo: https://leima0324.github.io/KRONE_Demo_official/", "date": "2026-02-07", "categories": []}
{"id": "2604.15668", "title": "NK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection", "abstract": "Graph anomaly detection aims to identify irregular patterns in graph-structured data. Most unsupervised GNN-based methods rely on the homophily assumption that connected nodes share similar attributes. However, real-world graphs often exhibit attribute-level heterophily, where connected nodes have dissimilar attributes. Our analysis of attribute-level heterophily graphs reveals two phenomena indicating that current approaches are not practical for unsupervised graph anomaly detection: 1) attribute similarities between connected nodes show nearly identical distributions across different connected node pair types, and 2) anomalies cause consistent variation trends between the graph with and without anomalous edges in the low- and high-frequency components of the spectral energy distributions, while the mid-part exhibits more erratic variations. Based on these observations, we propose NK-GAD, a neighbor knowledge-enhanced unsupervised graph anomaly detection framework. NK-GAD integrates a joint encoder capturing both similar and dissimilar neighbor features, a neighbor reconstruction module modeling normal distributions, a center aggregation module refining node features, and dual decoders for reconstructing attributes and structures. Experiments on seven datasets show NK-GAD achieves an average 3.29\\% AUC improvement.", "date": "2026-04-17", "categories": []}
{"id": "2507.15066", "title": "Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback", "abstract": "Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. To address these limitations, we propose Time-series Reasoning for Anomaly (Time-RA), a novel task that reformulates TSAD from a discriminative into a generative, reasoning-intensive paradigm. To facilitate this, we introduce RATs40K, the first real-world large-scale multimodal benchmark with ~40,000 samples across 10 domains, integrating raw time series, textual context, and visual plots with structured reasoning annotations. Extensive benchmarking shows that while supervised fine-tuning and visual representations boost diagnostic accuracy and reasoning consistency, performance varies across complex scenarios. Notably, fine-tuned models demonstrate strong \"plug-and-play\" transferability, outperforming traditional baselines on unseen real-world datasets. Our work establishes a foundation for interpretable, multimodal time series analysis. All code (https://github.com/yyysjz1997/Time-RA) and the RATs40K dataset (https://huggingface.co/datasets/Time-RA/RATs40K) are fully open-sourced to facilitate future research.", "date": "2025-07-20", "categories": []}
{"id": "2512.13671", "title": "AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation", "abstract": "Industrial anomaly detection (IAD) is challenging due to the subtle and highly localized nature of many defects, which single-pass vision--language models (VLMs) often fail to capture. Moreover, existing approaches lack mechanisms to actively acquire complementary evidence during inference. We propose AgentIAD, an agentic vision--language framework that enables iterative industrial inspection through a unified action space. The agent dynamically accesses two forms of memory during inspection: visual memory via the Perceptive Zoomer (PZ) for fine-grained local analysis, and retrieved memory via the Web Searcher (WS) and Comparative Retriever (CR) for external knowledge acquisition and cross-instance verification. This design allows the model to progressively gather evidence through multi-round perception--action reasoning. To effectively learn such policies under sparse supervision, AgentIAD adopts a two-stage training strategy: tool-aware supervised fine-tuning first initializes structured reasoning and memory-access behaviors, followed by agentic reinforcement learning to refine long-horizon decision policies. Extensive experiments show that, under the same backbone, AgentIAD improves classification accuracy by 5.92% over the previous state-of-the-art method on the MMAD benchmark while providing more reliable and interpretable anomaly analysis.", "date": "2025-12-15", "categories": []}
{"id": "2604.14221", "title": "Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling", "abstract": "Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets. Current resources often lack fine-grained anomaly annotations, do not provide explicit intervariable and temporal dependencies, and offer little insight into the underlying generative mechanisms. These shortcomings hinder the development and rigorous comparison of detection models, especially those targeting interpretable and variable-specific outputs. To address this gap, we introduce Fun-TSG, a fully customizable time series generator designed to support high-quality evaluation of anomaly detection systems. Our tool enables both fully automated generation, based on randomly sampled dependency structures and anomaly types, and manual generation through user-defined equations and anomaly configurations. In both cases, it provides full transparency over the data generation process, including access to ground-truth anomaly labels at the variable and timestamp levels. Fun-TSG supports the creation of diverse, interpretable, and reproducible benchmarking scenarios, enabling fine-grained performance analysis for both classical and modern anomaly detection models.", "date": "2026-04-14", "categories": []}
{"id": "2604.14233", "title": "Anomaly Detection in IEC-61850 GOOSE Networks: Evaluating Unsupervised and Temporal Learning for Real-Time Intrusion Detection", "abstract": "The IEC-61850 GOOSE protocol underpins time-critical communication in modern digital substations but lacks native security mechanisms, leaving it vulnerable to replay, masquerade, and data injection attacks. Intrusion detection in this setting is challenging due to strict latency constraints (sub-4ms) and limited availability of labeled attack data. This paper evaluates whether unsupervised temporal modeling can provide effective and deployable anomaly detection for GOOSE networks. Five models are compared on the ERENO IEC-61850 dataset: a supervised Random Forest baseline, a feedforward Autoencoder, and three recurrent sequence autoencoders (RNN, LSTM, and GRU). The supervised Random Forest achieves the highest detection performance (F1=0.9516) but fails to meet real-time constraints at 21.8ms per prediction. All four unsupervised models satisfy the 4ms requirement, with the GRU achieving the best accuracy to latency tradeoff among them (F1=0.8737 at 1.118ms). A cross-environment evaluation on an independent dataset shows that all models degrade under distribution shift. However, recurrent models retain substantially higher relative performance than the supervised baseline, suggesting that temporal sequence modeling generalizes better than fitting labeled attack distributions. Anomaly thresholds for the unsupervised models are selected on a held out validation partition to avoid test set leakage. These results support unsupervised temporal models as a practical choice for real-time GOOSE intrusion detection, particularly in environments where labeled training data may be unavailable or where large-scale deployment across diverse substations is required.", "date": "2026-04-14", "categories": []}
{"id": "2604.14507", "title": "H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection", "abstract": "As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a frequently-faced issue. To solve it, the few-shot anomaly detection (FSAD) scheme is attracting increasing attention. In recent years, beyond traditional visual paradigm, Vision-Language Model (VLM) has been extensively explored to boost this field. However, in currently-existing VLM-based FSAD schemes, almost all perform anomaly inference only by pairwise feature matching, ignoring structural dependencies and global consistency. To further redound to FSAD via VLM, we propose a Heterogeneous Hypergraph Vision-Language Reasoning (H2VLR) framework. It reformulates the FSAD as a high-order inference problem of visual-semantic relations, by jointly modeling visual regions and semantic concepts in a unified hypergraph. Experimental comparisons verify the effectiveness and advantages of H2VLR. It could often achieve state-of-the-art (SOTA) performance on representative industrial and medical benchmarks. Our code will be released upon acceptance.", "date": "2026-04-16", "categories": []}
{"id": "2604.06448", "title": "From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures", "abstract": "Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as video-on-demand (VOD) events such as Rings of Power. While these stress tests validate system capacity, they can sometimes miss service behaviors unique to real event traffic. We present a graph-based anomaly detection system that identifies under-represented services using unsupervised node-level graph embeddings. Built on a GCN-GAE, our approach learns structural representations from directed, weighted service graphs at minute-level resolution and flags anomalies based on cosine similarity between load test and event embeddings. The system identifies incident-related services that are documented and demonstrates early detection capability. We also introduce a preliminary synthetic anomaly injection framework for controlled evaluation that show promising precision (96%) and low false positive rate (0.08%), though recall (58%) remains limited under conservative propagation assumptions. This framework demonstrates practical utility within Prime Video while also surfacing methodological lessons and directions, providing a foundation for broader application across microservice ecosystems.", "date": "2026-04-07", "categories": []}
{"id": "2604.13252", "title": "Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference", "abstract": "Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on normal data to learn a single reference model of normality. This implicitly assumes that normal behavior can be captured by a single, unconditional reference distribution. In practice, however, anomalies are often context-dependent: A specific observation may be normal under one operating condition, yet anomalous under another. As machine learning systems are deployed in dynamic and heterogeneous environments, these fixed-context assumptions introduce structural ambiguity, i.e., the inability to distinguish contextual variation from genuine abnormality under marginal modeling, leading to unstable performance and unreliable anomaly assessments. While modern sensing systems frequently collect multimodal data capturing complementary aspects of both system behavior and operating conditions, existing methods treat all data streams equally, without distinguishing contextual information from anomaly-relevant signals. As a result, abnormality is often evaluated without explicitly conditioning on operating conditions. We argue that multimodal anomaly detection should be reframed as a cross-modal contextual inference problem, in which modalities play asymmetric roles, separating context from observation, to define abnormality conditionally rather than relative to a single global reference. This perspective has implications for model design, evaluation protocols, and benchmark construction, and outline open research challenges toward robust, context-aware multimodal anomaly detection.", "date": "2026-04-14", "categories": []}
{"id": "2604.13928", "title": "Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study", "abstract": "Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability.\n  We evaluate which model classes are capable of capturing this complexity, starting with a classical Isolation Forest baseline and extending to multiple autoencoder architectures. Experimental results show that Isolation Forest is insufficient for modeling the non-periodic, multi-scale dynamics present in the data, whereas autoencoders consistently perform better. Among them, temporal convolutional autoencoders achieve the most robust performance, while recurrent and variational variants require more careful tuning.", "date": "2026-04-15", "categories": []}
{"id": "2309.13904", "title": "Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization", "abstract": "Unsupervised anomaly localization aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or reconstruction for the target sample with pre-trained deep neural networks. However, they still struggle to address challenging anomalies because the deep embeddings stored in the memory bank can be less powerful and informative. Specifically, prior methods often overly rely on the finite resources stored in the memory bank, which leads to low robustness to unseen targets. In this paper, we propose a novel subspace-guided feature reconstruction framework to pursue adaptive feature approximation for anomaly localization. It first learns to construct low-dimensional subspaces from the given nominal samples, and then learns to reconstruct the given deep target embedding by linearly combining the subspace basis vectors using the self-expressive model. Our core is that, despite the limited resources in the memory bank, the out-of-bank features can be alternatively ``mimicked'' to adaptively model the target. Moreover, we propose a sampling method that leverages the sparsity of subspaces and allows the feature reconstruction to depend only on a small resource subset, contributing to less memory overhead. Extensive experiments on three benchmark datasets demonstrate that our approach generally achieves state-of-the-art anomaly localization performance.", "date": "2023-09-25", "categories": []}
{"id": "2508.05461", "title": "Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection", "abstract": "Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their strict architectural invertibility needs often constrain scalability, particularly in large-scale data regimes. Although time-parameterized Flow Matching (FM) serves as a scalable alternative, it remains computationally challenging in IAD due to the prohibitive costs of Jacobian-trace estimation. This paper proposes time-reversed Flow Matching (rFM), which shifts the objective from exact likelihood computation to evaluating target-domain regularity through density proxy estimation. We uncover two fundamental theoretical bottlenecks in this paradigm: first, the reversed vector field exhibits a non-Lipschitz singularity at the initial temporal boundary, precipitating explosive estimation errors. Second, the concentration of measure in high-dimensional Gaussian manifolds induces structured irregularities, giving rise to a Centripetal Potential Field (CPF) that steers trajectories away from Optimal Transport (OT) paths. We identify these observations as the inherent dualities between FM and rFM. To address these issues, we introduce local Worst Transport Flow matching (WT-Flow), which amplifies the observed CPF of rFM to mitigate the initial singularity while circumventing the need for exact distribution transformations via density proxy. Experiments on five datasets demonstrate that WT-Flow achieves state-of-the-art performance among single-scale flow-based methods, and competitive performance against leading multi-scale approaches. Furthermore, the proposed framework enables superior one-step inference, achieving a per-image flow latency of only 6.7 ms. Our code is available on https://github.com/lil-wayne-0319/fmad.", "date": "2025-08-07", "categories": []}
{"id": "2604.09166", "title": "Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection", "abstract": "Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rarely available from industrial operations. In a recent work, we introduced a large, fully annotated experimental dataset for batch distillation under normal and anomalous operating conditions. In the present study, we augment this dataset with a corresponding simulation dataset, creating a novel hybrid dataset. The simulation data is generated in an automated workflow with a novel Python-based process simulator that employs a tailored index-reduction strategy for the underlying differential-algebraic equations. Leveraging the rich metadata and structured anomaly annotations of the experimental database, experimental records are automatically translated into simulation scenarios. After calibration to a single reference experiment, the dynamics of the other experiments are well predicted. This enabled the fully automated, consistent generation of time-series data for a large number of experimental runs, covering both normal operation and a wide range of actuator- and control-related anomalies. The resulting hybrid dataset is released openly. From a process simulation perspective, this work demonstrates the automated, consistent simulation of large-scale experimental campaigns, using batch distillation as an example. From a data-driven AD perspective, the hybrid dataset provides a unique basis for simulation-to-experiment style transfer, the generation of pseudo-experimental data, and future research on deep AD methods in chemical process monitoring.", "date": "2026-04-10", "categories": []}
{"id": "2604.11390", "title": "Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection", "abstract": "While Hyperspectral Anomaly Detection (HAD) excels at identifying sparse targets in complex scenes, existing models remain trapped in a scalar \"reconstruction-as-endpoint\" paradigm. This reliance on ambiguous scalar residuals consistently triggers sub-pixel anomaly vanishing during spatial downsampling, alongside severe confirmation bias when unpurified anomalies corrupt training weights. In this paper, we propose Reconstruction-to-Vector Diffusion (R2VD), which fundamentally redefines reconstruction as a manifold purification origin to establish a novel residual-guided generative dynamics paradigm. Our framework introduces a four-stage pipeline: (1) a Physical Prior Extraction (PPE) stage that mitigates early confirmation bias via dual-stream statistical guidance; (2) a Guided Manifold Purification (GMP) stage utilizing an OmniContext Autoencoder (OCA) to extract purified residual maps while preserving fragile sub-pixel topologies; (3) a Residual Score Modeling (RSM) stage where a Diffusion Transformer (DiT), guarded by a Physical Spectral Firewall (PSF), effectively isolates cross-spectral leakage; and (4) a Vector Dynamics Inference (VDI) stage that robustly decouples targets from backgrounds by evaluating high-dimensional vector interference patterns instead of conventional scalar errors. Comprehensive evaluations on eight datasets confirm that R2VD establishes a new state-of-the-art, delivering exceptional target detectability and background suppression. The code is available at https://github.com/Bondojijun/R2VD.", "date": "2026-04-13", "categories": []}
{"id": "2604.12218", "title": "LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics", "abstract": "System log anomaly detection is critical for maintaining the reliability of large-scale software systems, yet traditional methods struggle with the heterogeneous and evolving nature of modern log data. Recent advances in Large Language Models (LLMs) offer promising new approaches to log understanding, but a systematic comparison of LLM-based methods against established techniques remains lacking. In this paper, we present a comprehensive benchmark study evaluating both LLM-based and traditional approaches for log anomaly detection across four widely-used public datasets: HDFS, BGL, Thunderbird, and Spirit. We evaluate three categories of methods: (1) classical log parsers (Drain, Spell, AEL) combined with machine learning classifiers, (2) fine-tuned transformer models (BERT, RoBERTa), and (3) prompt-based LLM approaches (GPT-3.5, GPT-4, LLaMA-3) in zero-shot and few-shot settings. Our experiments reveal that while fine-tuned transformers achieve the highest F1-scores (0.96-0.99), prompt-based LLMs demonstrate remarkablezero-shot capabilities (F1: 0.82-0.91) without requiring any labeled training data -- a significant advantage for real-world deployment where labeled anomalies are scarce. We further analyze the cost-accuracy trade-offs, latency characteristics, and failure modes of each approach. Our findings provide actionable guidelines for practitioners choosing log anomaly detection methods based on their specific constraints regarding accuracy, latency, cost, and label availability. All code and experimental configurations are publicly available to facilitate reproducibility.", "date": "2026-04-14", "categories": []}
{"id": "2604.12440", "title": "IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation", "abstract": "Real-world industrial inspection requires not only localizing defects, but also explaining them in natural language and generating controlled defect edits. However, existing approaches fail to jointly support all three capabilities within a unified framework and evaluation protocol. We propose IAD-Unify, a dual-encoder unified framework in which a frozen DINOv2-based region expert supplies precise anomaly evidence to a shared Qwen3.5-4B vision-language backbone via lightweight token injection, jointly enabling anomaly segmentation, region-grounded understanding, and mask-guided generation. To enable unified evaluation, we further construct Anomaly-56K, a comprehensive unified multi-task IAD evaluation platform, spanning 59,916 images across 24 categories and 104 defect variants. Controlled ablations yield four findings: (i) region grounding is the decisive mechanism for understanding, removing it degrades location accuracy by >76 pp; (ii) predicted-region performance closely matches oracle, confirming deployment viability; (iii) region-grounded generation achieves the best full-image fidelity and masked-region perceptual quality; and (iv) pre-initialized joint training improves understanding at negligible generation cost (-0.16 dB). IAD-Unify further achieves strong performance on the MMAD benchmark, including categories unseen during training, demonstrating robust cross-category generalization.", "date": "2026-04-14", "categories": []}
{"id": "2604.13024", "title": "CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations", "abstract": "The explosive growth of system logs makes streaming compression essential, yet existing log anomaly detection (LAD) methods incur severe pre-processing overhead by requiring full decompression and parsing. We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams. CLAD bypasses these bottlenecks by exploiting a key insight: normal logs compress into regular byte patterns, while anomalies systematically disrupt them. To extract these multi-scale deviations from opaque bytes, we propose a purpose-built architecture integrating a dilated convolutional byte encoder, a hybrid Transformer--mLSTM, and four-way aggregation pooling. This is coupled with a two-stage training strategy of masked pre-training and focal-contrastive fine-tuning to effectively handle severe class imbalance. Evaluated across five datasets, CLAD achieves a state-of-the-art average F1-score of 0.9909 and outperforms the best baseline by 2.72 percentage points. It delivers superior accuracy while completely eliminating decompression and parsing overheads, offering a robust solution that generalizes to structured streaming compressors.", "date": "2026-04-14", "categories": []}
{"id": "2508.03447", "title": "CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection", "abstract": "Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to capture the continuous and diverse patterns of normal and anomalous states, limiting generalization to unseen categories; (ii) fixed textual labels provide overly sparse category information, making the model prone to overfitting to a specific semantic subspace. To address these issues, we propose Conditional Prompt Synthesis (CoPS), a novel framework that synthesizes dynamic prompts conditioned on visual features to enhance ZSAD performance. Specifically, we extract representative normal and anomaly prototypes from fine-grained patch features and explicitly inject them into prompts, enabling adaptive state modeling. Given the sparsity of class labels, we leverage a variational autoencoder to model semantic image features and implicitly fuse varied class tokens into prompts. Additionally, integrated with our spatially-aware alignment mechanism, extensive experiments demonstrate that CoPS surpasses state-of-the-art methods by 1.4% in classification AUROC and 1.9% in segmentation AUROC across 13 industrial and medical datasets. The code is available at https://github.com/cqylunlun/CoPS.", "date": "2025-08-05", "categories": []}
{"id": "2509.25612", "title": "Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN", "abstract": "Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN, a novel framework that integrates window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN) to address this challenge. Its self-attention encoder-decoder architecture captures complex spatio-temporal dependencies across the grid, while a joint discriminator enforces cycle consistency to align the learned latent space with the true data distribution. Anomalies are flagged in real-time using an adaptive score that combines reconstruction error, latent space drift, and discriminator confidence. Evaluated on a realistic hardware-in-the-loop PMU benchmark, T-BiGAN achieves an ROC-AUC of 0.95 and an average precision of 0.996, significantly outperforming leading supervised and unsupervised methods. It shows particular strength in detecting subtle frequency and voltage deviations, demonstrating its practical value for live, wide-area monitoring without relying on manually labeled fault data.", "date": "2025-09-30", "categories": []}
{"id": "2510.07927", "title": "ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection", "abstract": "Anomaly detection plays a pivotal role in manufacturing quality control, yet its application is constrained by limited abnormal samples and high manual annotation costs. While anomaly synthesis offers a promising solution, existing studies predominantly treat anomaly synthesis as an auxiliary component within anomaly detection frameworks, lacking systematic evaluation of anomaly synthesis algorithms. Current research also overlook crucial factors specific to anomaly synthesis, such as decoupling its impact from detection, quantitative analysis of synthetic data and adaptability across different scenarios. To address these limitations, we propose ASBench, the first comprehensive benchmarking framework dedicated to evaluating anomaly synthesis methods. Our framework introduces four critical evaluation dimensions: (i) the generalization performance across different datasets and pipelines (ii) the ratio of synthetic to real data (iii) the correlation between intrinsic metrics of synthesis images and anomaly detection performance metrics , and (iv) strategies for hybrid anomaly synthesis methods. Through extensive experiments, ASBench not only reveals limitations in current anomaly synthesis methods but also provides actionable insights for future research directions in anomaly synthesis", "date": "2025-10-09", "categories": []}
{"id": "2511.15083", "title": "Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection", "abstract": "Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recently, Mamba-based state-space models have shown remarkable efficiency in long-sequence modeling. However, directly applying Mamba to anomaly detection tasks still faces challenges in capturing complex temporal patterns and nonlinear dynamics. In this paper, we propose Fourier-KAN-Mamba, a novel hybrid architecture that integrates Fourier layer, Kolmogorov-Arnold Networks (KAN), and Mamba selective state-space model. The Fourier layer extracts multi-scale frequency features, KAN enhances nonlinear representation capability, and a temporal gating control mechanism further improves the model's ability to distinguish normal and anomalous patterns. Extensive experiments on MSL, SMAP, and SWaT datasets demonstrate that our method significantly outperforms existing state-of-the-art approaches.\n  Keywords: time-series anomaly detection, state-space model, Mamba, Fourier transform, Kolmogorov-Arnold Network", "date": "2025-11-19", "categories": []}
{"id": "2604.09630", "title": "Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange", "abstract": "This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It aims to (1) identify the organisational and digital capabilities required for successful implementation and (2) evaluate the performance and interpretability of lightweight anomaly detection approaches using contextual audit data. A semi-systematic scoping synthesis is conducted to derive a four-pillar readiness framework covering governance, infrastructure/interoperability, workforce, and AI integration, operationalised as a 10-item checklist with measurable indicators. This is complemented by a simulation of cross-provider audit logs incorporating contextual features such as provider mismatch, time of access, days since discharge, session duration, and access frequency. A rule-based approach is benchmarked against Isolation Forest, with SHAP used to explain model behaviour. Results show that rule-based methods achieve high recall but generate higher alert volumes, while Isolation Forest reduces alert burden at the cost of lower sensitivity. SHAP analysis highlights provider mismatch and off-hours access as dominant anomaly drivers. The study proposes a staged deployment strategy combining rules for coverage and machine learning for prioritisation, supported by explainability and continuous monitoring. The findings contribute a practical readiness framework and empirical insights to guide the implementation of AI-based anomaly detection in multi-provider healthcare environments.", "date": "2026-03-19", "categories": []}
{"id": "2604.10971", "title": "MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models", "abstract": "In the progress of industrial anomaly detection, general anomaly detection (GAD) is an emerging trend and also the ultimate goal. Unlike the conventional single- and multi-class AD, general AD aims to train a general AD model that can directly detect anomalies in diverse novel classes without any retraining or fine-tuning on the target data. Recently, Multimodal Large Language Models (MLLMs) have shown great promise in achieving general anomaly detection due to their revolutionary visual understanding and language reasoning capabilities. However, MLLM's general AD ability remains underexplored due to: (1) MLLMs are pretrained on amounts of data sourced from the Web, these data still have significant gaps with the data in AD scenarios. Moreover, the image-text pairs during pretraining are also not specifically for AD tasks. (2) The current mainstream AD datasets are image-based and not yet suitable for post-training MLLMs. To facilitate MLLM-based general AD research, we present MMR-AD, which is a comprehensive benchmark for both training and evaluating MLLM-based AD models. With MMR-AD, we reveal that the AD performance of current SOTA generalist MLLMs still falls far behind the industrial requirements. Based on MMR-AD, we also propose a baseline model, Anomaly-R1, which is a reasoning-based AD model that learns from the CoT data in MMR-AD and is further enhanced by reinforcement learning. Extensive experiments show that our Anomaly-R1 achieves remarkable improvements over generalist MLLMs in both anomaly detection and localization.", "date": "2026-04-13", "categories": []}
{"id": "2604.11082", "title": "RESP: Reference-guided Sequential Prompting for Visual Glitch Detection in Video Games", "abstract": "Visual glitches in video games degrade player experience and perceived quality, yet manual quality assurance cannot scale to the growing test surface of modern game development. Prior automation efforts, particularly those using vision-language models (VLMs), largely operate on single frames or rely on limited video-level baselines that struggle under realistic scene variation, making robust video-level glitch detection challenging. We present RESP, a practical multi-frame framework for gameplay glitch detection with VLMs. Our key idea is reference-guided prompting: for each test frame, we select a reference frame from earlier in the same video, establishing a visual baseline and reframing detection as within-video comparison rather than isolated classification. RESP sequentially prompts the VLM with reference/test pairs and aggregates noisy frame predictions into a stable video-level decision without fine-tuning the VLM. To enable controlled analysis of reference effects, we introduce RefGlitch, a synthetic dataset of manually labeled reference/test frame pairs with balanced coverage across five glitch types. Experiments across five VLMs and three datasets (one synthetic, two real-world) show that reference guidance consistently strengthens frame-level detection and that the improved frame-level evidence reliably transfers to stronger video-level triage under realistic QA conditions. Code and data are available at: \\href{https://github.com/PipiZong/RESP_code.git}{this https URL}.", "date": "2026-04-13", "categories": []}
{"id": "2510.18075", "title": "Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods", "abstract": "Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental database, covering fault-free experiments and experiments in which anomalies were intentionally induced, for training advanced ML-based AD methods. In total, 119 experiments were conducted across a wide range of operating conditions and mixtures. Most experiments containing anomalies were paired with a corresponding fault-free one. The database that we provide here includes time-series data from numerous sensors and actuators, along with estimates of measurement uncertainty. In addition, unconventional data sources -- such as concentration profiles obtained via online benchtop NMR spectroscopy and video and audio recordings -- are provided. Extensive metadata and expert annotations of all experiments are included. The anomaly annotations are based on an ontology developed in this work. The data are organized in a structured database and made freely available via doi.org/10.5281/zenodo.17395543. This new database paves the way for the development of advanced ML-based AD methods. As it includes information on the causes of anomalies, it further enables the development of interpretable and explainable ML approaches, as well as methods for anomaly mitigation.", "date": "2025-10-20", "categories": []}
{"id": "2604.08582", "title": "Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation", "abstract": "Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. Mainstream reconstruction-based anomaly detection methods suffer from two key limitations: first, overfitting to spurious correlations induced by an overemphasis on cross-variable modeling; second, the generation of misleading anomaly scores by simply summing up multivariable reconstruction errors, which makes it difficult to distinguish between hard-to-reconstruct samples and genuine anomalies. To address these issues, we propose DBR-AF, a novel framework that integrates a dual-branch reconstruction (DBR) encoder and an autoregressive flow (AF) module. The DBR encoder decouples cross-variable correlation learning and intra-variable statistical property modeling to mitigate spurious correlations, while the AF module employs multiple stacked reversible transformations to model the complex multivariate residual distribution and further leverages density estimation to accurately identify normal samples with large reconstruction errors. Extensive experiments on seven benchmark datasets demonstrate that DBR-AF achieves state-of-the-art performance, with ablation studies validating the indispensability of its core components.", "date": "2026-03-29", "categories": []}
{"id": "2604.09023", "title": "CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection", "abstract": "Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hindered by the absence of a unified benchmark for multi-task evaluation. To fill in this gap, We present the CAD Dataset, a large-scale and comprehensive benchmark designed for car-related multi-task visual anomaly detection. The dataset contains over 100\n  images crossing 7 vehicle domains and 3 tasks, providing models a comprehensive view for car-related anomaly detection. It is the first car-related anomaly dataset specialized for multi-task learning(MTL), while combining synthesis data augmentation for few-shot anomaly images. We implement a multi-task baseline and conduct extensive empirical studies. Results show MTL promotes task interaction and knowledge transfer, while also exposing challenging conflicts between tasks. The CAD dataset serves as a standardized platform to drive future advances in car-related multi-task visual anomaly detection.", "date": "2026-04-10", "categories": []}
{"id": "2604.09326", "title": "Multimodal Anomaly Detection for Human-Robot Interaction", "abstract": "Ensuring safety and reliability in human-robot interaction (HRI) requires the timely detection of unexpected events that could lead to system failures or unsafe behaviours. Anomaly detection thus plays a critical role in enabling robots to recognize and respond to deviations from normal operation during collaborative tasks. While reconstruction models have been actively explored in HRI, approaches that operate directly on feature vectors remain largely unexplored. In this work, we propose MADRI, a framework that first transforms video streams into semantically meaningful feature vectors before performing reconstruction-based anomaly detection. Additionally, we augment these visual feature vectors with the robot's internal sensors' readings and a Scene Graph, enabling the model to capture both external anomalies in the visual environment and internal failures within the robot itself. To evaluate our approach, we collected a custom dataset consisting of a simple pick-and-place robotic task under normal and anomalous conditions. Experimental results demonstrate that reconstruction on vision-based feature vectors alone is effective for detecting anomalies, while incorporating other modalities further improves detection performance, highlighting the benefits of multimodal feature reconstruction for robust anomaly detection in human-robot collaboration.", "date": "2026-04-10", "categories": []}
{"id": "2604.09327", "title": "From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection", "abstract": "Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations. However, traditional frame-level evaluations treat video as a collection of isolated frames, fundamentally misaligned with how anomalies manifest and are acted upon in the real world. In operational surveillance systems, what matters is not the flagging of individual frames, but the reliable detection, localization, and reporting of a coherent anomalous event, a contiguous temporal episode with an identifiable onset and duration. Frame-level metrics are blind to this distinction, and as a result, they systematically overestimate model performance for any deployment that requires actionable, event-level alerts. In this work, we propose a shift toward an event-centric perspective in VAD. We first audit widely used VAD benchmarks, including SHT[19], CHAD[6], NWPUC[4], and HuVAD[25], to characterize their event structure. We then introduce two strategies for temporal event localization: a score-refinement pipeline with hierarchical Gaussian smoothing and adaptive binarization, and an end-to-end Dual-Branch Model that directly generates event-level detections. Finally, we establish the first event-based evaluation standard for VAD by adapting Temporal Action Localization metrics, including tIoU-based event matching and multi-threshold F1 evaluation. Our results quantify a substantial performance gap: while all SoTA models achieve frame-level AUC-ROC exceeding 52% on the NWPUC[4], their event-level localization precision falls below 10% even at a minimal tIoU=0.2, with an average event-level F1 of only 0.11 across all thresholds. The code base for this work is available at https://github.com/TeCSAR-UNCC/EventCentric-VAD.", "date": "2026-04-10", "categories": []}
{"id": "2601.20524", "title": "AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors", "abstract": "Zero-shot anomaly detection aims to detect and localise abnormal regions in the image without access to any in-domain training images. While recent approaches leverage vision-language models (VLMs), such as CLIP, to transfer high-level concept knowledge, methods based on purely vision foundation models (VFMs), like DINOv2, have lagged behind in performance. We argue that this gap stems from two practical issues: (i) limited diversity in existing auxiliary anomaly detection datasets and (ii) overly shallow VFM adaptation strategies. To address both challenges, we propose AnomalyVFM, a general and effective framework that turns any pretrained VFM into a strong zero-shot anomaly detector. Our approach combines a robust three-stage synthetic dataset generation scheme with a parameter-efficient adaptation mechanism, utilising low-rank feature adapters and a confidence-weighted pixel loss. Together, these components enable modern VFMs to substantially outperform current state-of-the-art methods. More specifically, with RADIO as a backbone, AnomalyVFM achieves an average image-level AUROC of 94.1% across 9 diverse datasets, surpassing previous methods by significant 3.3 percentage points. Project Page: https://maticfuc.github.io/anomaly_vfm/", "date": "2026-01-28", "categories": []}
{"id": "2604.07772", "title": "ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions", "abstract": "Open-world video anomaly detection (OWVAD) aims to detect and explain abnormal events under different anomaly definitions, which is important for applications such as intelligent surveillance and live-streaming content moderation. Recent MLLM-based methods have shown promising open-world generalization, but still suffer from three major limitations: inefficiency for practical deployment, lack of streaming processing adaptation, and limited support for dynamic anomaly definitions in both modeling and evaluation. To address these issues, this paper proposes ESOM, an efficient streaming OWVAD model that operates in a training-free manner. ESOM includes a Definition Normalization module to structure user prompts for reducing hallucination, an Inter-frame-matched Intra-frame Token Merging module to compress redundant visual tokens, a Hybrid Streaming Memory module for efficient causal inference, and a Probabilistic Scoring module that converts interval-level textual outputs into frame-level anomaly scores. In addition, this paper introduces OpenDef-Bench, a new benchmark with clean surveillance videos and diverse natural anomaly definitions for evaluating performance under varying conditions. Extensive experiments show that ESOM achieves real-time efficiency on a single GPU and state-of-the-art performance in anomaly temporal localization, classification, and description generation. The code and benchmark will be released at https://github.com/Kamino666/ESOM_OpenDef-Bench.", "date": "2026-04-09", "categories": []}
{"id": "2604.07900", "title": "AnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning", "abstract": "Industrial anomaly generation is a crucial method for alleviating the data scarcity problem in anomaly detection tasks. Most existing anomaly synthesis methods rely on single-step generation mechanisms, lacking complex reasoning and iterative optimization capabilities, making it difficult to generate anomaly samples with high semantic realism. We propose AnomalyAgent, an anomaly synthesis agent with self-reflection, knowledge retrieval, and iterative refinement capabilities, aiming to generate realistic and diverse anomalies. Specifically, AnomalyAgent is equipped with five tools: Prompt Generation (PG), Image Generation (IG), Quality Evaluation (QE), Knowledge Retrieval (KR), and Mask Generation (MG), enabling closed-loop optimization. To improve decision-making and self-reflection, we construct structured trajectories from real anomaly images and design a two-stage training framework: supervised fine-tuning followed by reinforcement learning. This process is driven by a three-part reward mechanism: (1) task rewards to supervise the quality and location rationality of generated anomalies; (2) reflection rewards to train the model's ability to improve anomaly synthesis prompt; (3) behavioral rewards to ensure adherence to the trajectory. On the MVTec-AD dataset, AnomalyAgent achieves IS/IC-L of 2.10/0.33 for anomaly generation, 57.0% classification accuracy using ResNet34, and 99.3%/74.2% AP at the image/pixel level using a simple UNet, surpassing all zero-shot SOTA methods. The code and data will be made publicly available.", "date": "2026-04-09", "categories": []}
{"id": "2604.08301", "title": "GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis", "abstract": "The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently, anomaly synthesis techniques have been developed to enlarge training sets and enhance downstream inspection. However, existing methods either suffer from poor integration caused by inpainting or fail to provide accurate masks. To address these limitations, we propose GroundingAnomaly, a novel few-shot anomaly image generation framework. Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps to enable precise spatial control over the synthesized anomalies. Furthermore, a Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net via gated attention layers. This carefully preserves pretrained priors while ensuring stable few-shot adaptation. Extensive evaluations on the MVTec AD and VisA datasets demonstrate that GroundingAnomaly generates high-quality anomalies and achieves state-of-the-art performance across multiple downstream tasks, including anomaly detection, segmentation, and instance-level detection.", "date": "2026-04-09", "categories": []}
{"id": "2604.08424", "title": "On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities", "abstract": "The increasing autonomy of spacecraft demands fault-detection systems that are both reliable and explainable. This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Control Subsystem by introducing a framework that enhances interpretability in neural anomaly detectors. We propose a method to derive low-dimensional, semantically annotated encodings from intermediate neural activations, called peepholes. Applied to a convolutional autoencoder, the framework produces interpretable indicators that enable the identification and localization of anomalies in reaction-wheel telemetry. Peepholes analysis further reveals bias detection and supports fault localization. The proposed framework enables the semantic characterization of detected anomalies while requiring only a marginal increase in computational resources, thus supporting its feasibility for on-board deployment.", "date": "2026-04-09", "categories": []}
{"id": "2409.09298", "title": "Matrix Profile for Anomaly Detection on Multidimensional Time Series", "abstract": "The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in real-world applications. For instance, in a manufacturing factory, multiple sensors installed across the site collect time-varying data for analysis. The Matrix Profile, named for its role in profiling the matrix storing pairwise distance between subsequences of univariate time series, becomes complex in multidimensional scenarios. If the input univariate time series has n subsequences, the pairwise distance matrix is a n x n matrix. In a multidimensional time series with d dimensions, the pairwise distance information must be stored in a n x n x d tensor. In this paper, we first analyze different strategies for condensing this tensor into a profile vector. We then investigate the potential of extending the MP to efficiently find k-nearest neighbors for anomaly detection. Finally, we benchmark the multidimensional MP against 19 baseline methods on 119 multidimensional TSAD datasets. The experiments covers three learning setups: unsupervised, supervised, and semi-supervised. MP is the only method that consistently delivers high performance across all setups.\n  To ensure complete transparency and facilitate future research, our full Matrix Profile-based implementation, which includes newly added evaluations against the TSB-AD benchmark, is publicly available at: https://github.com/mcyeh/mmpad_tsb", "date": "2024-09-14", "categories": []}
{"id": "2504.20906", "title": "A Giant-Step Baby-Step Classifier For Scalable and Real-Time Anomaly Detection In Industrial Control Systems and Water Treatment Systems", "abstract": "The continuous monitoring of the interactions between cyber-physical components of any industrial control system (ICS) is required to secure automation of the system controls, and to guarantee plant processes are fail-safe and remain in an acceptably safe state. Safety is achieved by managing actuation (where electric signals are used to trigger physical movement), dependent on corresponding sensor readings; used as ground truth in decision making. Timely detection of anomalies (attacks, faults and unascertained states) in ICSs is crucial for the safe running of a plant, the safety of its personnel, and for the safe provision of any services provided. We propose an anomaly detection method that involves accurate linearization of the non-linear forms arising from sensor-actuator(s) relationships, primarily because solving linear models is easier and well understood. We accomplish this by using a well-known water treatment testbed as a use case. Our experiments show millisecond time response to detect anomalies, all of which are explainable and traceable; this simultaneous coupling of detection speed and explainability has not been achieved by other state of the art Artificial Intelligence (AI)/ Machine Learning (ML) models with eXplainable AI (XAI) used for the same purpose. Our methods explainability enables us to pin-point the sensor(s) and the actuation state(s) for which the anomaly was detected. The proposed algorithm showed an accuracy of 97.72% by flagging deviations within safe operation limits as non-anomalous; indicative that slower detectors with highest detection resolution is unnecessary, for systems whose safety boundaries provide leeway within safety limits.", "date": "2025-04-29", "categories": []}
{"id": "2604.06435", "title": "Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions", "abstract": "Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively studied, two key challenges remain largely unaddressed in conjunction: edge deployment, where computational resources are severely constrained, and continual learning, where models must adapt to evolving data distributions without forgetting previously acquired knowledge. Our benchmark provides guidance for the selection of the optimal backbone and VAD method under joint efficiency and adaptability constraints, characterizing the trade-offs between memory footprint, inference cost, and detection performance. Studying these challenges in isolation is insufficient, as methods designed for one setting make assumptions that break down when the other constraint is simultaneously imposed. In this work, we propose the first comprehensive benchmark for VAD on the edge in the continual learning scenario, evaluating seven VAD models across three lightweight backbone architectures. Furthermore, we propose Tiny-Dinomaly, a lightweight adaptation of the Dinomaly model built on the DINO foundation model that achieves 13x smaller memory footprint and 20x lower computational cost while improving Pixel F1 by 5 percentage points. Finally, we introduce targeted modifications to PatchCore and PaDiM to improve their efficiency in the continual learning setting.", "date": "2026-04-07", "categories": []}
{"id": "2604.06541", "title": "Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach", "abstract": "We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced by a branching cascade, so their internal structure is naturally organised across angular and momentum scales. This motivates an autoencoder that compresses information hierarchically and can reorganise short-range correlations before coarse-graining. Guided by this picture, we formulate a MERA-inspired autoencoder acting directly on ordered jet constituents. To the best of our knowledge, a MERA-inspired autoencoder has not previously been proposed, and this architecture has not been explored in collider anomaly detection.\n  We compare this architecture to a dense autoencoder, the corresponding tree-tensor-network limit, and standard classical baselines within a common background-only reconstruction framework. The paper is organised around two main questions: whether locality-aware hierarchical compression is genuinely supported by the data, and whether the disentangling layers of MERA contribute beyond a simpler tree hierarchy. To address these questions, we combine benchmark comparisons with a training-free local-compressibility diagnostic and a direct identity-disentangler ablation. The resulting picture is that the locality-preserving multiscale structure is well matched to jet data, and that the MERA disentanglers become beneficial precisely when the compression bottleneck is strongest. Overall, the study supports locality-aware hierarchical compression as a useful inductive bias for jet anomaly detection.", "date": "2026-04-08", "categories": []}
{"id": "2604.07097", "title": "Novel Anomaly Detection Scenarios and Evaluation Metrics to Address the Ambiguity in the Definition of Normal Samples", "abstract": "In conventional anomaly detection, training data consist of only normal samples. However, in real-world scenarios, the definition of a normal sample is often ambiguous. For example, there are cases where a sample has small scratches or stains but is still acceptable for practical usage. On the other hand, higher precision is required when manufacturing equipment is upgraded. In such cases, normal samples may include small scratches, tiny dust particles, or a foreign object that we would prefer to classify as an anomaly. Such cases frequently occur in industrial settings, yet they have not been discussed until now. Thus, we propose novel scenarios and an evaluation metric to accommodate specification changes in real-world applications. Furthermore, to address the ambiguity of normal samples, we propose the RePaste, which enhances learning by re-pasting regions with high anomaly scores from the previous step into the input for the next step. On our scenarios using the MVTec AD benchmark, RePaste achieved the state-of-the-art performance with respect to the proposed evaluation metric, while maintaining high AUROC and PRO scores. Code: https://github.com/ReijiSoftmaxSaito/Scenario", "date": "2026-04-08", "categories": []}
{"id": "2602.15089", "title": "Triplet Feature Fusion for Equipment Anomaly Prediction : An Open-Source Methodology Using Small Foundation Models", "abstract": "Predicting equipment anomalies before they escalate into failures is a critical challenge in industrial facility management. Existing approaches rely either on hand-crafted threshold rules, which lack generalizability, or on large neural models that are impractical for on-site, air-gapped deployments. We present an industrial methodology that resolves this tension by combining open-source small foundation models into a unified 1,116-dimensional Triplet Feature Fusion pipeline. This pipeline integrates: (1) statistical features (x in $R^{28}$) derived from 90-day sensor histories, (2) time-series embeddings (y in $R^{64}$) from a LoRA-adapted IBM Granite TinyTimeMixer (TTM, 133K parameters), and (3) multilingual text embeddings (z in $R^{1024}$) extracted from Japanese equipment master records via multilingual-e5-large. The concatenated triplet h = [x; y; z] is processed by a LightGBM classifier (< 3 MB) trained to predict anomalies at 30-, 60-, and 90-day horizons. All components use permissive open-source licenses (Apache 2.0 / MIT). The inference-time pipeline runs entirely on CPU in under 2 ms, enabling edge deployment on co-located hardware without cloud dependency. On a dataset of 64 HVAC units comprising 67,045 samples, the triplet model achieves Precision = 0.992, F1 = 0.958, and ROC-AUC = 0.998 at the 30-day horizon. Crucially, it reduces the False Positive Rate from 0.6 percent (baseline) to 0.1 percent - an 83 percent reduction attributable to equipment-type conditioning via text embedding z. Cluster analysis reveals that the embeddings align time-series signatures with distinct fault archetypes, explaining how compact multilingual representations improve discrimination without explicit categorical encoding.", "date": "2026-02-16", "categories": []}
{"id": "2603.18093", "title": "One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control", "abstract": "Industrial anomaly detection (AD) is characterized by an abundance of normal images but a scarcity of anomalous ones. Although numerous few-shot anomaly synthesis methods have been proposed to augment anomalous data for downstream AD tasks, most existing approaches require time-consuming training and struggle to learn distributions that are faithful to real anomalies, thereby restricting the efficacy of AD models trained on such data. To address these limitations, we propose a training-free few-shot anomaly generation method, namely O2MAG, which leverages the self-attention in One reference anomalous image to synthesize More realistic anomalies, supporting effective downstream anomaly detection. Specifically, O2MAG manipulates three parallel diffusion processes via self-attention grafting and incorporates the anomaly mask to mitigate foreground-background query confusion, synthesizing text-guided anomalies that closely adhere to real anomalous distributions. To bridge the semantic gap between the encoded anomaly text prompts and the true anomaly semantics, Anomaly-Guided Optimization is further introduced to align the synthesis process with the target anomalous distribution, steering the generation toward realistic and text-consistent anomalies. Moreover, to mitigate faint anomaly synthesis inside anomaly masks, Dual-Attention Enhancement is adopted during generation to reinforce both self- and cross-attention on masked regions. Extensive experiments validate the effectiveness of O2MAG, demonstrating its superior performance over prior state-of-the-art methods on downstream AD tasks.", "date": "2026-03-18", "categories": []}
{"id": "2604.04993", "title": "The Hiremath Early Detection (HED) Score: A Measure-Theoretic Evaluation Standard for Temporal Intelligence", "abstract": "We introduce the Hiremath Early Detection (HED) Score, a principled, measure-theoretic evaluation criterion for quantifying the time-value of information in systems operating over non-stationary stochastic processes subject to abrupt regime transitions. Existing evaluation paradigms, chiefly the ROC/AUC framework and its downstream variants, are temporally agnostic: they assign identical credit to a detection at t + 1 and a detection at t + tau for arbitrarily large tau. This indifference to latency is a fundamental inadequacy in time-critical domains including cyber-physical security, algorithmic surveillance, and epidemiological monitoring.\n  The HED Score resolves this by integrating a baseline-neutral, exponentially decaying kernel over the posterior probability stream of a target regime, beginning precisely at the onset of the regime shift. The resulting scalar simultaneously encodes detection acuity, temporal lead, and pre-transition calibration quality. We prove that the HED Score satisfies three axiomatic requirements: (A1) Temporal Monotonicity, (A2) Invariance to Pre-Attack Bias, and (A3) Sensitivity Decomposability. We further demonstrate that the HED Score admits a natural parametric family indexed by the Hiremath Decay Constant (lambda_H), whose domain-specific calibration constitutes the Hiremath Standard Table.\n  As an empirical vehicle, we present PARD-SSM (Probabilistic Anomaly and Regime Detection via Switching State-Space Models), which couples fractional Stochastic Differential Equations (fSDEs) with a Switching Linear Dynamical System (S-LDS) inference backend. On the NSL-KDD benchmark, PARD-SSM achieves a HED Score of 0.0643, representing a 388.8 percent improvement over a Random Forest baseline (0.0132), with statistical significance confirmed via block-bootstrap resampling (p < 0.001). We propose the HED Score as the successor evaluation standard to ROC/AUC.", "date": "2026-04-05", "categories": []}
{"id": "2604.05335", "title": "Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model", "abstract": "Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among different individual machines which are nominally the same and are executing the same processes. To address the problem of detecting anomalies in a machine using sensory data gathered from different individual machines executing the same procedure, this paper proposes a cross-machine time-series anomaly detection framework that integrates a domain-invariant feature extractor with an unsupervised anomaly detection module. Leveraging the pre-trained foundation model MOMENT, the extractor employs Random Forest Classifiers to disentangle embeddings into machine-related and condition-related features, with the latter serving as representations which are invariant to differences between individual machines. These refined features enable the downstream anomaly detectors to generalize effectively to unseen target machines. Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines, confirming its effectiveness in enhancing cross-machine generalization.", "date": "2026-04-07", "categories": []}
{"id": "2604.05632", "title": "SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection", "abstract": "Multi-view anomaly detection aims to identify surface defects on complex objects using observations captured from multiple viewpoints. However, existing unsupervised methods often suffer from feature inconsistency arising from viewpoint variations and modality discrepancies. To address these challenges, we propose a Semantic and Geometric Alignment Network (SGANet), a unified framework for multimodal multi-view anomaly detection that effectively combines semantic and geometric alignment to learn physically coherent feature representations across viewpoints and modalities. SGANet consists of three key components. The Selective Cross-view Feature Refinement Module (SCFRM) selectively aggregates informative patch features from adjacent views to enhance cross-view feature interaction. The Semantic-Structural Patch Alignment (SSPA) enforces semantic alignment across modalities while maintaining structural consistency under viewpoint transformations. The Multi-View Geometric Alignment (MVGA) further aligns geometrically corresponding patches across viewpoints. By jointly modeling feature interaction, semantic and structural consistency, and global geometric correspondence, SGANet effectively enhances anomaly detection performance in multimodal multi-view settings. Extensive experiments on the SiM3D and Eyecandies datasets demonstrate that SGANet achieves state-of-the-art performance in both anomaly detection and localization, validating its effectiveness in realistic industrial scenarios.", "date": "2026-04-07", "categories": []}
{"id": "2405.02068", "title": "Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection", "abstract": "With the wide application of knowledge distillation between an ImageNet pre-trained teacher model and a learnable student model, unsupervised anomaly detection has witnessed a significant achievement in the past few years. The success of this framework mainly relies on how to keep the feature discrepancy between the teacher and student model, in which it has two underlying sub-assumptions: (1) The teacher model can represent two separable distributions for the normal and abnormal patterns, while (2) the student model can only reconstruct the normal distribution. However, it still remains a challenging issue to maintain these ideal assumptions in practice. In this paper, we propose a simple yet effective two-stage industrial anomaly detection framework, termed AAND, which sequentially performs Anomaly Amplification and Normality Distillation to enhance the two assumptions. In the first anomaly amplification stage, we propose a novel Residual Anomaly Amplification (RAA) module to advance the pre-trained teacher encoder with synthetic anomalies. It generates adaptive residuals to amplify anomalies while maintaining the feature integrity of pre-trained model. It mainly comprises a Matching-guided Residual Gate and an Attribute-scaling Residual Generator, which can determine the residuals' proportion and characteristic, respectively. In the second normality distillation stage, we further employ a reverse distillation paradigm to train a student decoder, in which a novel Hard Knowledge Distillation (HKD) loss is built to better facilitate the reconstruction of normal patterns. Comprehensive experiments on the MvTecAD, VisA, and MvTec3D-RGB datasets show that our method achieves state-of-the-art performance.", "date": "2024-05-03", "categories": []}
{"id": "2604.03972", "title": "Hierarchical Point-Patch Fusion with Adaptive Patch Codebook for 3D Shape Anomaly Detection", "abstract": "3D shape anomaly detection is a crucial task for industrial inspection and geometric analysis. Existing deep learning approaches typically learn representations of normal shapes and identify anomalies via out-of-distribution feature detection or decoder-based reconstruction. They often fail to generalize across diverse anomaly types and scales, such as global geometric errors (e.g., planar shifts, angle misalignments), and are sensitive to noisy or incomplete local points during training. To address these limitations, we propose a hierarchical point-patch anomaly scoring network that jointly models regional part features and local point features for robust anomaly reasoning. An adaptive patchification module integrates self-supervised decomposition to capture complex structural deviations. Beyond evaluations on public benchmarks (Anomaly-ShapeNet and Real3D-AD), we release an industrial test set with real CAD models exhibiting planar, angular, and structural defects. Experiments on public and industrial datasets show superior AUC-ROC and AUC-PR performance, including over 40% point-level improvement on the new industrial anomaly type and average object-level gains of 7% on Real3D-AD and 4% on Anomaly-ShapeNet, demonstrating strong robustness and generalization.", "date": "2026-04-05", "categories": []}
{"id": "2604.04051", "title": "Extended Hybrid Timed Petri Nets with Semi-Supervised Anomaly Detection for Switched Systems, Modelling and Fault Detection", "abstract": "Hybrid physical systems combine continuous and discrete dynamics, which can be simultaneously affected by faults. Conventional fault detection methods often treat these dynamics separately, limiting their ability to capture interacting fault patterns. This paper proposes a unified fault detection framework for hybrid dynamical systems by integrating an Extended Timed Continuous Petri Net (ETCPN) model with semi-supervised anomaly detection. The proposed ETCPN extends existing Petri net formalisms by introducing marking-dependent flow functions, enabling intrinsic coupling between discrete and continuous dynamics. Based on this structure, a mode-dependent hybrid observer is designed, whose stability under arbitrary switching is ensured via Linear Matrix Inequalities (LMIs), solved offline to determine observer gains. The observer generates residuals that reflect discrepancies between the estimated and measured outputs. These residuals are processed using semi-supervised methods, including One-Class SVM (OC-SVM), Support Vector Data Description (SVDD), and Elliptic Envelope (EE), trained exclusively on normal data to avoid reliance on labeled faults. The framework is validated through simulations involving discrete faults, continuous faults, and hybrid faults. Results demonstrate high detection accuracy, fast convergence, and robust performance, with OC-SVM and SVDD providing the best trade-off between detection rate and false alarms. The framework is computationally efficient for real-time deployment, as the main complexity is confined to the offline LMI design phase.", "date": "2026-04-05", "categories": []}
{"id": "2604.04632", "title": "InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation", "abstract": "While recent anomaly detection (AD) methods have made substantial progress in recognizing abnormal patterns within specific domains, most of them are specialist models that are trained on large training samples from a specific target dataset, struggling to generalize to unseen datasets. To address this limitation, the paradigm of Generalist Anomaly Detection (GAD) has emerged in recent years, aiming to learn a single generalist model to detect anomalies across diverse domains without retraining. To this end, this work introduces InCTRLv2, a novel few-shot Generalist Anomaly Detection and Segmentation (GADS) framework that significantly extends our previously proposed GAD model, InCTRL. Building on the idea of learning in-context residuals with few-shot normal examples to detect anomalies as in InCTRL, InCTRLv2 introduces two new, complementary perspectives of anomaly perception under a dual-branch framework. This is accomplished by two novel modules upon InCTRL: i) Discriminative Anomaly Score Learning (DASL) with both normal and abnormal data in the main branch, which learns a semantic-guided abnormality and normality space that supports the classification of query samples from both the abnormality and normality perspectives; and ii) One-class Anomaly Score Learning (OASL) using only the normal data, which learns generalized normality patterns in a semantic space via an auxiliary branch, focusing on detecting anomalies through the lens of normality solely. Both branches are guided by rich visual-text semantic priors encoded by large-scale vision-language models. Together, they offer a dual semantic perspective for AD: one emphasizes normal-abnormal discriminations, while the other emphasizes normality-deviated semantics. Extensive experiments on ten AD datasets demonstrate that InCTRLv2 achieves SotA performance in both anomaly detection and segmentation tasks across various settings.", "date": "2026-04-06", "categories": []}
{"id": "2604.04658", "title": "Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection", "abstract": "Industrial 3D anomaly detection performance is fundamentally constrained by the scarcity and long-tailed distribution of abnormal samples. To address this challenge, we propose Synthesis4AD, an end-to-end paradigm that leverages large-scale, high-fidelity synthetic anomalies to learn more discriminative representations for 3D anomaly detection. At the core of Synthesis4AD is 3D-DefectStudio, a software platform built upon the controllable synthesis engine MPAS, which injects geometrically realistic defects guided by higher-dimensional support primitives while simultaneously generating accurate point-wise anomaly masks. Furthermore, Synthesis4AD incorporates a multimodal large language model (MLLM) to interpret product design information and automatically translate it into executable anomaly synthesis instructions, enabling scalable and knowledge-driven anomalous data generation. To improve the robustness and generalization of the downstream detector on unstructured point clouds, Synthesis4AD further introduces a training pipeline based on spatial-distribution normalization and geometry-faithful data augmentations, which alleviates the sensitivity of Point Transformer architectures to absolute coordinates and improves feature learning under realistic data variations. Extensive experiments demonstrate state-of-the-art performance on Real3D-AD, MulSen-AD, and a real-world industrial parts dataset. The proposed synthesis method MPAS and the interactive system 3D-DefectStudio will be publicly released at https://github.com/hustCYQ/Synthesis4AD.", "date": "2026-04-06", "categories": []}
{"id": "2603.23766", "title": "Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detection", "abstract": "Unsupervised medical anomaly detection is severely limited by the scarcity of normal training samples. Existing methods typically train dedicated models for each dataset or disease, requiring hundreds of normal images per task and lacking cross-modality generalization.\n  We propose Semantic Iterative Reconstruction (SIR), a framework that enables a single universal model to detect anomalies across diverse medical domains using extremely few normal samples. SIR leverages a pretrained teacher encoder to extract multi-scale deep features and employs a compact up-then-down decoder with multi-loop iterative refinement to enforce robust normality priors in deep feature space. The framework adopts a one-shot universal design: a single model is trained by mixing exactly one normal sample from each of nine heterogeneous datasets, enabling effective anomaly detection on all corresponding test sets without task-specific retraining.\n  Extensive experiments on nine medical benchmarks demonstrate that SIR achieves state-of-the-art under all four settings -- one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized -- consistently outperforming previous methods. SIR offers an efficient and scalable solution for multi-domain clinical anomaly detection.", "date": "2026-03-24", "categories": []}
{"id": "2604.02549", "title": "Financial Anomaly Detection for the Canadian Market", "abstract": "In this work we evaluate the performance of three classes of methods for detecting financial anomalies: topological data analysis (TDA), principal component analyis (PCA), and Neural Network-based approaches. We apply these methods to the TSX-60 data to identify major financial stress events in the Canadian stock market. We show how neural network-based methods (such as GlocalKD and One-Shot GIN(E)) and TDA methods achieve the strongest performance. The effectiveness of TDA in detecting financial anomalies suggests that global topological properties are meaningful in distinguishing financial stress events.", "date": "2026-04-02", "categories": []}
{"id": "2604.02905", "title": "UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting", "abstract": "Although industrial inspection systems should be capable of recognizing unprecedented defects, most existing approaches operate under a closed-set assumption, which prevents them from detecting novel anomalies. While visual prompting offers a scalable alternative for industrial inspection, existing methods often suffer from prompt embedding collapse due to high intra-class variance and subtle inter-class differences. To resolve this, we propose UniSpector, which shifts the focus from naive prompt-to-region matching to the principled design of a semantically structured and transferable prompt topology. UniSpector employs the Spatial-Spectral Prompt Encoder to extract orientation-invariant, fine-grained representations; these serve as a solid basis for the Contrastive Prompt Encoder to explicitly regularize the prompt space into a semantically organized angular manifold. Additionally, Prompt-guided Query Selection generates adaptive object queries aligned with the prompt. We introduce Inspect Anything, the first benchmark for visual-prompt-based open-set defect localization, where UniSpector significantly outperforms baselines by at least 19.7% and 15.8% in AP50b and AP50m, respectively. These results show that our method enable a scalable, retraining-free inspection paradigm for continuously evolving industrial environments, while offering critical insights into the design of generic visual prompting.", "date": "2026-04-03", "categories": []}
{"id": "2604.03040", "title": "QVAD: A Question-Centric Agentic Framework for Efficient and Training-Free Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) is a fundamental challenge in computer vision, particularly due to the open-set nature of anomalies. While recent training-free approaches utilizing Vision-Language Models (VLMs) have shown promise, they typically rely on massive, resource-intensive foundation models to compensate for the ambiguity of static prompts. We argue that the bottleneck in VAD is not necessarily model capacity, but rather the static nature of inquiry. We propose QVAD, a question-centric agentic framework that treats VLM-LLM interaction as a dynamic dialogue. By iteratively refining queries based on visual context, our LLM agent guides smaller VLMs to produce high-fidelity captions and precise semantic reasoning without parameter updates. This ``prompt-updating\" mechanism effectively unlocks the latent capabilities of lightweight models, enabling state-of-the-art performance on UCF-Crime, XD-Violence, and UBNormal using a fraction of the parameters required by competing methods. We further demonstrate exceptional generalizability on the single-scene ComplexVAD dataset. Crucially, QVAD achieves high inference speeds with minimal memory footprints, making advanced VAD capabilities deployable on resource-constrained edge devices.", "date": "2026-04-03", "categories": []}
{"id": "2405.01158", "title": "Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI", "abstract": "Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.", "date": "2024-05-02", "categories": []}
{"id": "2510.00463", "title": "On the Adversarial Robustness of Learning-based Conformal Novelty Detection", "abstract": "This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false discovery rate (FDR) control: one is AdaDetect (by Marandon et al., 2024) that is based on the positive-unlabeled classifier, and the other is a one-class classifier-based approach (by Bates et al., 2023). While they provide rigorous statistical guarantees under benign conditions, their behavior under adversarial perturbations remains underexplored. We first formulate an oracle attack setup, under the AdaDetect formulation, that quantifies the worst-case degradation of FDR, deriving an upper bound that characterizes the statistical cost of attacks. This idealized formulation directly motivates a practical and effective attack scheme that only requires query access to the output labels of both frameworks. Coupling these formulations with two popular and complementary black-box adversarial algorithms, we systematically evaluate the vulnerability of both frameworks on synthetic and real-world datasets. Our results show that adversarial perturbations can significantly increase the FDR while maintaining high detection power, exposing fundamental limitations of current error-controlled novelty detection methods and motivating the development of more robust alternatives.", "date": "2025-10-01", "categories": []}
{"id": "2511.16145", "title": "Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection", "abstract": "Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity. Consequently, current research predominantly focuses on Unsupervised Time-series Anomaly Detection (UTAD), relying on increasingly complex architectures to model normal data distributions. However, this algorithm-centric trend often overlooks the significant performance gains achievable from limited anomaly labels available in practical scenarios. This paper challenges the premise that algorithmic complexity is the optimal path for TSAD. Instead of proposing another intricate unsupervised model, we present a comprehensive benchmark and empirical study to rigorously compare supervised and unsupervised paradigms. To isolate the value of labels, we introduce \\stand, a deliberately minimalist supervised baseline. Extensive experiments on five public datasets demonstrate that: (1) Labels matter more than models: under a limited labeling budget, simple supervised models significantly outperform complex state-of-the-art unsupervised methods; (2) Supervision yields higher returns: the performance gain from minimal supervision far exceeds the incremental gains from architectural innovations; and (3) Practicality: \\stand~exhibits superior prediction consistency and anomaly localization compared to unsupervised counterparts. These findings advocate for a paradigm shift in TSAD research, urging the community to prioritize data-centric label utilization over purely algorithmic complexity. The code and benchmark are publicly available at https://github.com/EmorZz1G/STAND.", "date": "2025-11-20", "categories": []}
{"id": "2512.05069", "title": "Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection", "abstract": "Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this task. We construct a unified experimental framework that iterates over key quantum design choices, including quantum-layer placement, measurement approach, variational and non-variational formulations, and latent-space regularization. Experiments across three benchmark NIDS datasets show that HQC autoencoders can match or exceed classical performance in their best configurations, although they exhibit higher sensitivity to architectural decisions. Under zero-day evaluation, well-configured HQC models provide stronger and more stable generalization than classical and supervised baselines. Simulated gate-noise experiments reveal early performance degradation, indicating the need for noise-aware HQC designs. These results provide the first data-driven characterization of HQC autoencoder behavior for network intrusion detection and outline key factors that govern their practical viability. All experiment code and configurations are available at https://github.com/arasyi/hqcae-network-intrusion-detection.", "date": "2025-12-04", "categories": []}
{"id": "2603.25467", "title": "GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Grids", "abstract": "Vision-Language Models (VLMs) are powerful open-set reasoners, yet their direct use as anomaly detectors in video surveillance is fragile: without calibrated anomaly priors, they alternate between missed detections and hallucinated false alarms. We argue the problem is not the VLM itself but how it is used. VLMs should function as anomaly proposers, generating open-set candidate descriptions that are then grounded and tracked by purpose-built spatial and temporal modules. We instantiate this propose-ground-propagate principle in GridVAD, a training-free pipeline that produces pixel-level anomaly masks without any domain-specific training. A VLM reasons over stratified grid representations of video clips to generate natural-language anomaly proposals. Self-Consistency Consolidation (SCC) filters hallucinations by retaining only proposals that recur across multiple independent samplings. Grounding DINO anchors each surviving proposal to a bounding box, and SAM2 propagates it as a dense mask through the anomaly interval. The per-clip VLM budget is fixed at M+1 calls regardless of video length, where M can be set according to the proposals needed. On UCSD Ped2, GridVAD achieves the highest Pixel-AUROC (77.59) among all compared methods, surpassing even the partially fine-tuned TAO (75.11) and outperforms other zero-shot approaches on object-level RBDC by over 5x. Ablations reveal that SCC provides a controllable precision-recall tradeoff: filtering improves all pixel level metrics at a modest cost in object-level recall. Efficiency experiments show GridVAD is 2.7x more call-efficient than uniform per-frame VLM querying while additionally producing dense segmentation masks.Code and qualitative video results are available at https://gridvad.github.io.", "date": "2026-03-26", "categories": []}
{"id": "2604.01845", "title": "CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift", "abstract": "Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in real-world deployments, distribution shifts are ubiquitous and cause severe performance degradation in pre-trained anomaly detector. Test-time adaptation (TTA) updates a pre-trained model on-the-fly using only unlabeled test data, making it promising for addressing this challenge. In this study, we propose CANDI (Curated test-time adaptation for multivariate time-series ANomaly detection under DIstribution shift), a novel TTA framework that selectively identifies and adapts to potential false positives while preserving pre-trained knowledge. CANDI introduces a False Positive Mining (FPM) strategy to curate adaptation samples based on anomaly scores and latent similarity, and incorporates a plug-and-play Spatiotemporally-Aware Normality Adaptation (SANA) module for structurally informed model updates. Extensive experiments demonstrate that CANDI significantly improves the performance of MTSAD under distribution shift, improving AUROC up to 14% while using fewer adaptation samples.", "date": "2026-04-02", "categories": []}
{"id": "2604.02328", "title": "Modulate-and-Map: Crossmodal Feature Mapping with Cross-View Modulation for 3D Anomaly Detection", "abstract": "We present ModMap, a natively multiview and multimodal framework for 3D anomaly detection and segmentation. Unlike existing methods that process views independently, our method draws inspiration from the crossmodal feature mapping paradigm to learn to map features across both modalities and views, while explicitly modelling view-dependent relationships through feature-wise modulation. We introduce a cross-view training strategy that leverages all possible view combinations, enabling effective anomaly scoring through multiview ensembling and aggregation. To process high-resolution 3D data, we train and publicly release a foundational depth encoder tailored to industrial datasets. Experiments on SiM3D, a recent benchmark that introduces the first multiview and multimodal setup for 3D anomaly detection and segmentation, demonstrate that ModMap attains state-of-the-art performance by surpassing previous methods by wide margins.", "date": "2026-04-02", "categories": []}
{"id": "2409.20146", "title": "VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection", "abstract": "Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects by establishing feature mapping between textual prompts and inspection images, demonstrating excellent research value in flexible industrial manufacturing. However, existing ZSAD methods are limited by closed-world settings, struggling to unseen defects with predefined prompts. Recently, adapting Multimodal Large Language Models (MLLMs) for Industrial Anomaly Detection (IAD) presents a viable solution. Unlike fixed-prompt methods, MLLMs exhibit a generative paradigm with open-ended text interpretation, enabling more adaptive anomaly analysis. However, this adaption faces inherent challenges as anomalies often manifest in fine-grained regions and exhibit minimal visual discrepancies from normal samples. To address these challenges, we propose a novel framework VMAD (Visual-enhanced MLLM Anomaly Detection) that enhances MLLM with visual-based IAD knowledge and fine-grained perception, simultaneously providing precise detection and comprehensive analysis of anomalies. Specifically, we design a Defect-Sensitive Structure Learning scheme that transfers patch-similarities cues from visual branch to our MLLM for improved anomaly discrimination. Besides, we introduce a novel visual projector, Locality-enhanced Token Compression, which mines multi-level features in local contexts to enhance fine-grained detection. Furthermore, we introduce the Real Industrial Anomaly Detection (RIAD), a comprehensive IAD dataset with detailed anomaly descriptions and analyses, offering a valuable resource for MLLM-based IAD development. Extensive experiments on zero-shot benchmarks, including MVTec-AD, Visa, WFDD, and RIAD datasets, demonstrate our superior performance over state-of-the-art methods. The code and dataset will be available soon.", "date": "2024-09-30", "categories": []}
{"id": "2603.26461", "title": "Neuro-Symbolic Process Anomaly Detection", "abstract": "Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets demonstrate that our approach improves F1 scores even when as few as 10 conformant traces exist, and that the choice of Declare constraint and by extension human domain knowledge significantly influences performance gains.", "date": "2026-03-27", "categories": []}
{"id": "2604.00020", "title": "Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation", "abstract": "In many real-world applications, such as customer feedback monitoring, brand reputation management, and product health tracking, understanding the temporal dynamics of user sentiment is crucial for early detection of anomalous events such as malicious review campaigns or sudden declines in user satisfaction. Traditional sentiment analysis methods focus on individual text classification, which is insufficient to capture collective behavioral shifts over time due to inherent noise and class imbalance in short user comments. In this work, we propose a temporal sentiment aggregation framework that leverages pretrained transformer-based language models to extract per-comment sentiment signals and aggregates them into time-window-level scores. Significant downward shifts in these aggregated scores are interpreted as potential anomalies in user feedback patterns. We adopt RoBERTa as our core semantic feature extractor and demonstrate, through empirical evaluation on real social media data, that the aggregated sentiment scores reveal meaningful trends and support effective anomaly detection. Experiments on real-world social media data demonstrate that our method successfully identifies statistically significant sentiment drops that correspond to coherent complaint patterns, providing an effective and interpretable solution for feedback anomaly monitoring.", "date": "2026-03-11", "categories": []}
{"id": "2604.00360", "title": "VADMamba++: Efficient Video Anomaly Detection via Hybrid Modeling in Grayscale Space", "abstract": "VADMamba pioneered the introduction of Mamba to Video Anomaly Detection (VAD), achieving high accuracy and fast inference through hybrid proxy tasks. Nevertheless, its heavy reliance on optical flow as auxiliary input and inter-task fusion scoring constrains its applicability to a single proxy task. In this paper, we introduce VADMamba++, an efficient VAD method based on the Gray-to-RGB paradigm that enforces a Single-Channel to Three-Channel reconstruction mapping, designed for a single proxy task and operating without auxiliary inputs. This paradigm compels inferring color appearances from grayscale structures, allowing anomalies to be more effectively revealed through dual inconsistencies between structure and chromatic cues. Specifically, VADMamba++ reconstructs grayscale frames into the RGB space to simultaneously discriminate structural geometry and chromatic fidelity, thereby enhancing sensitivity to explicit visual anomalies. We further design a hybrid modeling backbone that integrates Mamba, CNN, and Transformer modules to capture diverse normal patterns while suppressing the appearance of anomalies. Furthermore, an intra-task fusion scoring strategy integrates explicit future-frame prediction errors with implicit quantized feature errors, further improving accuracy under a single task setting. Extensive experiments on three benchmark datasets demonstrate that VADMamba++ outperforms state-of-the-art methods while meeting performance and efficiency, especially under a strict single-task setting with only frame-level inputs.", "date": "2026-04-01", "categories": []}
{"id": "2604.00382", "title": "mmAnomaly: Leveraging Visual Context for Robust Anomaly Detection in the Non-Visual World with mmWave Radar", "abstract": "mmWave radar enables human sensing in non-visual scenarios-e.g., through clothing or certain types of walls-where traditional cameras fail due to occlusion or privacy limitations. However, robust anomaly detection with mmWave remains challenging, as signal reflections are influenced by material properties, clutter, and multipath interference, producing complex, non-Gaussian distortions. Existing methods lack contextual awareness and misclassify benign signal variations as anomalies. We present mmAnomaly, a multi-modal anomaly detection framework that combines mmWave radar with RGBD input to incorporate visual context. Our system extracts semantic cues-such as scene geometry and material properties-using a fast ResNet-based classifier, and uses a conditional latent diffusion model to synthesize the expected mmWave spectrum for the given visual context. A dual-input comparison module then identifies spatial deviations between real and generated spectra to localize anomalies. We evaluate mmAnomaly on two multi-modal datasets across three applications: concealed weapon localization, through-wall intruder localization, and through-wall fall localization. The system achieves up to 94% F1 score and sub-meter localization error, demonstrating robust generalization across clothing, occlusions, and cluttered environments. These results establish mmAnomaly as an accurate and interpretable framework for context-aware anomaly detection in mmWave sensing.", "date": "2026-04-01", "categories": []}
{"id": "2604.00854", "title": "Perturb-and-Restore: Simulation-driven Structural Augmentation Framework for Imbalance Chromosomal Anomaly Detection", "abstract": "Detecting structural chromosomal abnormalities is crucial for accurate diagnosis and management of genetic disorders. However, collecting sufficient structural abnormality data is extremely challenging and costly in clinical practice, and not all abnormal types can be readily collected. As a result, deep learning approaches face significant performance degradation due to the severe imbalance and scarcity of abnormal chromosome data. To address this challenge, we propose a Perturb-and-Restore (P&R), a simulation-driven structural augmentation framework that effectively alleviates data imbalance in chromosome anomaly detection. The P&R framework comprises two key components: (1) Structure Perturbation and Restoration Simulation, which generates synthetic abnormal chromosomes by perturbing chromosomal banding patterns of normal chromosomes followed by a restoration diffusion network that reconstructs continuous chromosome content and edges, thus eliminating reliance on rare abnormal samples; and (2) Energy-guided Adaptive Sampling, an energy score-based online selection strategy that dynamically prioritizes high-quality synthetic samples by referencing the energy distribution of real samples. To evaluate our method, we construct a comprehensive structural anomaly dataset consisting of over 260,000 chromosome images, including 4,242 abnormal samples spanning 24 categories. Experimental results demonstrate that the P&R framework achieves state-of-the-art (SOTA) performance, surpassing existing methods with an average improvement of 8.92% in sensitivity, 8.89% in precision, and 13.79% in F1-score across all categories.", "date": "2026-04-01", "categories": []}
{"id": "2604.01171", "title": "Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects", "abstract": "Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible to collect a limited number of anomalous samples. Therefore, we study open-set supervised 3D anomaly detection, where the model is trained with only normal samples and a small number of known anomalous samples, aiming to identify unknown anomalies at test time. We present Open-Industry, a high-quality industrial dataset containing 15 categories, each with five real anomaly types collected from production lines. We first adapt general open-set anomaly detection methods to accommodate 3D point cloud inputs better. Building upon this, we propose Open3D-AD, a point-cloud-oriented approach that leverages normal samples, simulated anomalies, and partially observed real anomalies to model the probability density distributions of normal and anomalous data. Then, we introduce a simple Correspondence Distributions Subsampling to reduce the overlap between normal and non-normal distributions, enabling stronger dual distributions modeling. Based on these contributions, we establish a comprehensive benchmark and evaluate the proposed method extensively on Open-Industry as well as established datasets including Real3D-AD and Anomaly-ShapeNet. Benchmark results and ablation studies demonstrate the effectiveness of Open3D-AD and further reveal the potential of open-set supervised 3D anomaly detection.", "date": "2026-04-01", "categories": []}
{"id": "2511.00524", "title": "Text-guided Fine-Grained Video Anomaly Understanding", "abstract": "Subtle abnormal events in videos often manifest as weak spatio-temporal cues that are easily overlooked by conventional anomaly detection systems. Existing video anomaly detection approaches typically provide coarse binary anomaly decisions without interpretable evidence, while large vision-language models (LVLMs) can produce textual judgments but lack precise localization of subtle visual signals. To address this gap, we propose Text-guided Fine-Grained Video Anomaly Understanding T-VAU, a framework that grounds subtle anomaly evidence into multimodal reasoning. Specifically, we introduce an Anomaly Heatmap Decoder (AHD) that performs visual-textual feature alignment to extract pixel-level spatio-temporal anomaly heatmaps from intermediate visual representations. We further design a Region-aware Anomaly Encoder (RAE) that converts these heatmaps into structured prompt embeddings, enabling the LVLM to perform anomaly detection, localization, and semantic explanation in a unified reasoning pipeline. To support fine-grained supervision, we construct a target-level fine-grained video-text anomaly dataset derived from ShanghaiTech and UBnormal with detailed annotations of object appearance, localization, and motion trajectories. Extensive experiments demonstrate that T-VAU significantly improves anomaly localization and textual reasoning performance on both benchmarks, achieving strong results in BLEU-4 metrics and Yes/No decision accuracy while providing interpretable pixel-level spatio-temporal evidence for anomaly understanding. The code will be available at https://github.com/momiji-bit/T-VAU.", "date": "2025-11-01", "categories": []}
{"id": "2603.29375", "title": "Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices", "abstract": "Spacecraft anomaly detection is critical for mission safety, yet deploying sophisticated models on-board presents significant challenges due to hardware constraints. This paper investigates three approaches for spacecraft telemetry anomaly detection -- forecasting & threshold, direct classification, and image classification -- and optimizes them for edge deployment using multi-objective neural architecture optimization on the European Space Agency Anomaly Dataset. Our baseline experiments demonstrate that forecasting & threshold achieves superior detection performance (92.7% Corrected Event-wise F0.5-score (CEF0.5)) [1] compared to alternatives. Through Pareto-optimal architecture optimization, we dramatically reduced computational requirements while maintaining capabilities -- the optimized forecasting & threshold model preserved 88.8% CEF0.5 while reducing RAM usage by 97.1% to just 59 KB and operations by 99.4%. Analysis of deployment viability shows our optimized models require just 0.36-6.25% of CubeSat RAM, making on-board anomaly detection practical even on highly constrained hardware. This research demonstrates that sophisticated anomaly detection capabilities can be successfully deployed within spacecraft edge computing constraints, providing near-instantaneous detection without exceeding hardware limitations or compromising mission safety.", "date": "2026-03-31", "categories": []}
{"id": "2408.13516", "title": "Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection", "abstract": "Few-shot multi-class anomaly detection is crucial in real industrial settings, where only a few normal samples are available while numerous object types must be inspected. This setting is challenging as defect patterns vary widely across categories while normal samples remain scarce. Existing vision-language model-based approaches typically depend on class-specific anomaly descriptions or auxiliary modules, limiting both scalability and computational efficiency. In this work, we propose AnoPLe, a lightweight multimodal prompt learning framework that removes reliance on anomaly-type textual descriptions and avoids any external modules. AnoPLe employs bidirectional interactions between textual and visual prompts, allowing class semantics and instance-level cues to refine one another and form class-conditioned representations that capture shared normal patterns across categories. To enhance localization, we design a scale-aware prefix trained on both global and local views, enabling the prompts to capture both global context and fine-grained details. In addition, alignment loss propagates local anomaly evidence to global features, strengthening the consistency between pixel- and image-level predictions. Despite its simplicity, AnoPLe achieves strong performance on MVTec-AD, VisA, and Real-IAD under the few-shot multi-class setting, surpassing prior approaches while remaining efficient and free from expert-crafted anomaly descriptions. Moreover, AnoPLe generalizes well to unseen anomalies and extends effectively to the medical domain.", "date": "2024-08-24", "categories": []}
{"id": "2501.05675", "title": "Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection", "abstract": "In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at extracting normal data patterns and detecting value fluctuations from training data of target applications. Inspired by the human nervous system, where the brain stores expert knowledge and the peripheral nervous system and spinal cord handle specific tasks like withdrawal and knee-jerk reflexes, we propose CoLLaTe, a framework designed to facilitate collaboration between LLMs and task-specific models, leveraging the strengths of both models for anomaly detection.\n  In particular, we first formulate the collaboration process and identify two key challenges in the collaboration:\n  (1) the misalignment between the expression domains of the LLMs and task-specific small models, and (2) error accumulation arising from the predictions of both models.\n  To address these challenges, we then introduce two key components in CoLLaTe: a model alignment module and a collaborative loss function. Through theoretical analysis and experimental validation, we demonstrate that these components effectively mitigate the identified challenges and achieve better performance than both LLM-based and task-specific models.", "date": "2025-01-10", "categories": []}
{"id": "2510.03904", "title": "LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis", "abstract": "Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance in real-world scenarios. While Large Language Models (LLMs) show remarkable reasoning capabilities, their direct application to tabular AD is impeded by fundamental challenges, including difficulties in processing heterogeneous data and significant privacy risks. To address these limitations, we propose LLM-DAS, a novel framework that repositions the LLM from a ``data processor'' to an ``algorithmist''. Instead of being exposed to raw data, our framework leverages the LLM's ability to reason about algorithms. It analyzes a high-level description of a given detector to understand its intrinsic weaknesses and then generates detector-specific, data-agnostic Python code to synthesize ``hard-to-detect'' anomalies that exploit these vulnerabilities. This generated synthesis program, which is reusable across diverse datasets, is then instantiated to augment training data, systematically enhancing the detector's robustness by transforming the problem into a more discriminative two-class classification task. Extensive experiments on 36 TAD benchmarks show that LLM-DAS consistently boosts the performance of mainstream detectors. By bridging LLM reasoning with classic AD algorithms via programmatic synthesis, LLM-DAS offers a scalable, effective, and privacy-preserving approach to patching the logical blind spots of existing detectors.", "date": "2025-10-04", "categories": []}
{"id": "2510.18342", "title": "ShortcutBreaker: Low-Rank Noisy Bottleneck and Frequency Filtering Block for Multi-Class Unsupervised Anomaly Detection", "abstract": "Multi-class unsupervised anomaly detection (MUAD) has garnered growing research interest, as it seeks to develop a unified model for anomaly detection across multiple classes, i.e., eliminating the need to train separate models for distinct objects and thereby saving substantial computational resources. Under the MUAD setting, while advanced Transformer-based architectures have brought significant performance improvements, identity shortcuts persist: they directly copy inputs to outputs, narrowing the gap in reconstruction errors between normal and abnormal cases, and thereby making the two harder to distinguish. Therefore, we propose ShortcutBreaker, a novel unified feature-reconstruction framework for MUAD tasks, featuring two key innovations to address the issue of shortcuts. First, drawing on matrix rank inequality, we design a low-rank noisy bottleneck (LRNB) to project highdimensional features into a low-rank latent space, and theoretically demonstrate its capacity to prevent trivial identity reproduction. Second, leveraging ViTs global modeling capability instead of merely focusing on local features, we incorporate a global perturbation attention to prevent information shortcuts in the decoders. Extensive experiments are performed on four widely used anomaly detection benchmarks, including three industrial datasets (MVTec-AD, ViSA, and Real-IAD) and one medical dataset (Universal Medical). The proposed method achieves a remarkable image-level AUROC of 99.8%, 98.9%, 90.6%, and 87.8% on these four datasets, respectively, consistently outperforming previous MUAD methods across different scenarios Our code will be released..", "date": "2025-10-21", "categories": []}
{"id": "2603.01305", "title": "AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models", "abstract": "Large multimodal models (LMMs) exhibit strong task generalization capabilities, offering new opportunities for zero-shot visual anomaly segmentation (ZSAS). However, existing LMM-based segmentation approaches still face fundamental limitations: anomaly concepts are inherently abstract and context-dependent, lacking stable visual prototypes, and the weak alignment between high-level semantic embeddings and pixel-level spatial features hinders precise anomaly localization. To address these challenges, we present AG-VAS (Anchor-Guided Visual Anomaly Segmentation), a new framework that expands the LMM vocabulary with three learnable semantic anchor tokens-[SEG], [NOR], and [ANO], establishing a unified anchor-guided segmentation paradigm. Specifically, [SEG] serves as an absolute semantic anchor that translates abstract anomaly semantics into explicit, spatially grounded visual entities (e.g., holes or scratches), while [NOR] and [ANO] act as relative anchors that model the contextual contrast between normal and abnormal patterns across categories. To further enhance cross-modal alignment, we introduce a Semantic-Pixel Alignment Module (SPAM) that aligns language-level semantic embeddings with high-resolution visual features, along with an Anchor-Guided Mask Decoder (AGMD) that performs anchor-conditioned mask prediction for precise anomaly localization. In addition, we curate Anomaly-Instruct20K, a large-scale instruction dataset that organizes anomaly knowledge into structured descriptions of appearance, shape, and spatial attributes, facilitating effective learning and integration of the proposed semantic anchors. Extensive experiments on six industrial and medical benchmarks demonstrate that AG-VAS achieves consistent state-of-the-art performance in the zero-shot setting.", "date": "2026-03-01", "categories": []}
{"id": "2603.26745", "title": "Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection", "abstract": "As embodied perception systems increasingly bridge digital and physical realms in interactive multimedia applications, the need for privacy-preserving approaches to understand human activities in physical environments has become paramount. Video anomaly detection is a critical task in such embodied multimedia systems for intelligent surveillance and forensic analysis. Skeleton-based approaches have emerged as a privacy-preserving alternative that processes physical world information through abstract human pose representations while discarding sensitive visual attributes such as identity and facial features. However, existing skeleton-based methods predominantly model continuous motion trajectories in a monolithic manner, failing to capture the hierarchical nature of human activities composed of discrete semantic primitives and fine-grained kinematic details, which leads to reduced discriminability when anomalies manifest at different abstraction levels. In this regard, we propose Motion Semantics Guided Normalizing Flow (MSG-Flow) that decomposes skeleton-based VAD into hierarchical motion semantics modeling. It employs vector quantized variational auto-encoder to discretize continuous motion into interpretable primitives, an autoregressive Transformer to model semantic-level temporal dependencies, and a conditional normalizing flow to capture detail-level pose variations. Extensive experiments on benchmarks (HR-ShanghaiTech & HR-UBnormal) demonstrate that MSG-Flow achieves state-of-the-art performance with 88.1% and 75.8% AUC respectively.", "date": "2026-03-23", "categories": []}
{"id": "2603.27179", "title": "Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision", "abstract": "Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning and perceptual abilities for anomaly detection. However, most approaches remain confined to image-level anomaly detection and textual reasoning, while pixel-level localization still relies on external vision modules and dense annotations. In this work, we activate the intrinsic reasoning potential of MLLMs to perform anomaly detection, pixel-level localization, and interpretable reasoning solely from image-level supervision, without any auxiliary components or pixel-wise labels. Specifically, we propose Reasoning-Driven Anomaly Localization (ReAL), which extracts anomaly-related tokens from the autoregressive reasoning process and aggregates their attention responses to produce pixel-level anomaly maps. We further introduce a Consistency-Guided Reasoning Optimization (CGRO) module that leverages reinforcement learning to align reasoning tokens with visual attentions, resulting in more coherent reasoning and accurate anomaly localization. Extensive experiments on four public benchmarks demonstrate that our method significantly improves anomaly detection, localization, and interpretability. Remarkably, despite relying solely on image-level supervision, our approach achieves performance competitive with MLLM-based methods trained under dense pixel-level supervision. Code is available at https://github.com/YizhouJin313/ReADL.", "date": "2026-03-28", "categories": []}
{"id": "2603.27412", "title": "The Geometry of Harmful Intent: Training-Free Anomaly Detection via Angular Deviation in LLM Residual Streams", "abstract": "We present LatentBiopsy, a training-free method for detecting harmful prompts by analysing the geometry of residual-stream activations in large language models. Given 200 safe normative prompts, LatentBiopsy computes the leading principal component of their activations at a target layer and characterises new prompts by their radial deviation angle $\\theta$ from this reference direction. The anomaly score is the negative log-likelihood of $\\theta$ under a Gaussian fit to the normative distribution, flagging deviations symmetrically regardless of orientation. No harmful examples are required for training.\n  We evaluate two complete model triplets from the Qwen3.5-0.8B and Qwen2.5-0.5B families: base, instruction-tuned, and \\emph{abliterated} (refusal direction surgically removed via orthogonalisation). Across all six variants, LatentBiopsy achieves AUROC $\\geq$0.937 for harmful-vs-normative detection and AUROC = 1.000 for discriminating harmful from benign-aggressive prompts (XSTest), with sub-millisecond per-query overhead.\n  Three empirical findings emerge. First, geometry survives refusal ablation: both abliterated variants achieve AUROC at most 0.015 below their instruction-tuned counterparts, establishing a geometric dissociation between harmful-intent representation and the downstream generative refusal mechanism. Second, harmful prompts exhibit a near-degenerate angular distribution ($\\sigma_\\theta \\approx 0.03$ rad), an order of magnitude tighter than the normative distribution ($\\sigma_\\theta \\approx 0.27$ rad), preserved across all alignment stages including abliteration. Third, the two families exhibit opposite ring orientations at the same depth: harmful prompts occupy the outer ring in Qwen3.5-0.8B but the inner ring in Qwen2.5-0.5B, directly motivating the direction-agnostic scoring rule.", "date": "2026-03-28", "categories": []}
{"id": "2603.28300", "title": "NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information", "abstract": "Graph anomaly detection (GAD) aims to identify irregular nodes or structures in attributed graphs. Neighbor information, which reflects both structural connectivity and attribute consistency with surrounding nodes, is essential for distinguishing anomalies from normal patterns. Although recent graph neural network (GNN)-based methods incorporate such information through message passing, they often fail to explicitly model its effect or interaction with attributes, limiting detection performance. This work introduces NeiGAD, a novel plug-and-play module that captures neighbor information through spectral graph analysis. Theoretical insights demonstrate that eigenvectors of the adjacency matrix encode local neighbor interactions and progressively amplify anomaly signals. Based on this, NeiGAD selects a compact set of eigenvectors to construct efficient and discriminative representations. Experiments on eight real-world datasets show that NeiGAD consistently improves detection accuracy and outperforms state-of-the-art GAD methods. These results demonstrate the importance of explicit neighbor modeling and the effectiveness of spectral analysis in anomaly detection. Code is available at: https://github.com/huafeihuang/NeiGAD.", "date": "2026-03-30", "categories": []}
{"id": "2508.07819", "title": "ACD-CLIP: Decoupling Representation and Dynamic Fusion for Zero-Shot Anomaly Detection", "abstract": "Pre-trained Vision-Language Models (VLMs) struggle with Zero-Shot Anomaly Detection (ZSAD) due to a critical adaptation gap: they lack the local inductive biases required for dense prediction and employ inflexible feature fusion paradigms. We address these limitations through an Architectural Co-Design framework that jointly refines feature representation and cross-modal fusion. Our method proposes a parameter-efficient Convolutional Low-Rank Adaptation (Conv-LoRA) adapter to inject local inductive biases for fine-grained representation, and introduces a Dynamic Fusion Gateway (DFG) that leverages visual context to adaptively modulate text prompts, enabling a powerful bidirectional fusion. Extensive experiments on diverse industrial and medical benchmarks demonstrate superior accuracy and robustness, validating that this synergistic co-design is critical for robustly adapting foundation models to dense perception tasks. The source code is available at https://github.com/cockmake/ACD-CLIP.", "date": "2025-08-11", "categories": []}
{"id": "2603.25538", "title": "Missing-Aware Multimodal Fusion for Unified Microservice Incident Management", "abstract": "Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume perfect data completeness. In practice, network fluctuations and agent failures frequently cause missing modalities. Existing approaches relying on static placeholders introduce imputation noise that masks anomalies and degrades performance. To address this, we propose ARMOR, a robust self-supervised framework designed for missing modality scenarios. ARMOR features: (i) a modality-specific asymmetric encoder that isolates distribution disparities among metrics, logs, and traces; and (ii) a missing-aware gated fusion mechanism utilizing learnable placeholders and dynamic bias compensation to prevent cross-modal interference from incomplete inputs. By employing self-supervised auto-regression with mask-guided reconstruction, ARMOR jointly optimizes anomaly detection (AD), failure triage (FT), and root cause localization (RCL). AD and RCL require no fault labels, while FT relies solely on failure-type annotations for the downstream classifier. Extensive experiments demonstrate that ARMOR achieves state-of-the-art performance under complete data conditions and maintains robust diagnostic accuracy even with severe modality loss.", "date": "2026-03-26", "categories": []}
{"id": "2603.25955", "title": "EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset", "abstract": "The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset comprising high-resolution sensor telemetry collected from a fleet of 25 commercial vehicles over a six-month period. Unlike synthetic datasets, EngineAD features authentic operational data labeled with expert annotations, distinguishing normal states from subtle indicators of incipient engine faults. We preprocess the data into $300$-timestep segments of $8$ principal components and establish an initial benchmark using nine diverse one-class anomaly detection models. Our experiments reveal significant performance variability across the vehicle fleet, underscoring the challenge of cross-vehicle generalization. Furthermore, our findings corroborate recent literature, showing that simple classical methods (e.g., K-Means and One-Class SVM) are often highly competitive with, or superior to, deep learning approaches in this segment-based evaluation. By publicly releasing EngineAD, we aim to provide a realistic, challenging resource for developing robust and field-deployable anomaly detection and anomaly prediction solutions for the automotive industry.", "date": "2026-03-26", "categories": []}
{"id": "2603.26135", "title": "TinyML for Acoustic Anomaly Detection in IoT Sensor Networks", "abstract": "Tiny Machine Learning enables real-time, energy-efficient data processing directly on microcontrollers, making it ideal for Internet of Things sensor networks. This paper presents a compact TinyML pipeline for detecting anomalies in environmental sound within IoT sensor networks. Acoustic monitoring in IoT systems can enhance safety and context awareness, yet cloud-based processing introduces challenges related to latency, power usage, and privacy. Our pipeline addresses these issues by extracting Mel Frequency Cepstral Coefficients from sound signals and training a lightweight neural network classifier optimized for deployment on edge devices. The model was trained and evaluated using the UrbanSound8K dataset, achieving a test accuracy of 91% and balanced F1-scores of 0.91 across both normal and anomalous sound classes. These results demonstrate the feasibility and reliability of embedded acoustic anomaly detection for scalable and responsive IoT deployments.", "date": "2026-03-27", "categories": []}
{"id": "2603.26354", "title": "Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) systems are increasingly deployed in safety critical environments and require a large amount of data for accurate detection. However, such data may contain personally identifiable information (PII), including facial cues and sensitive demographic attributes, creating compliance challenges under the EU General Data Protection Regulation (GDPR). In particular, GDPR requires that personal data be limited to what is strictly necessary for a specified processing purpose. To address this, we introduce Only What's Necessary, a privacy-by-design framework for VAD that explicitly controls the amount and type of visual information exposed to the detection pipeline. The framework combines breadth based and depth based data minimization mechanisms to suppress PII while preserving cues relevant to anomaly detection. We evaluate a range of minimization configurations by feeding the minimized videos to both a VAD model and a privacy inference model. We employ two ranking based methods, along with Pareto analysis, to characterize the resulting trade off between privacy and utility. From the non-dominated frontier, we identify sweet spot operating points that minimize personal data exposure with limited degradation in detection performance. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed framework.", "date": "2026-03-27", "categories": []}
{"id": "2603.26604", "title": "Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders", "abstract": "Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the potential for unprecedented computational efficiency in future quantum processors. Near-term utilization of these benefits can be achieved by developing quantum-inspired algorithms for deployment in classical hardware to enable applications at the \"edge\" of current scientific experiments. This work demonstrates the use of tensor networks for real-time anomaly detection in collider detectors. A spaced matrix product operator (SMPO) is developed that provides sensitivity to a variety beyond the Standard Model benchmarks, and can be implemented in field programmable gate array hardware with resources and latency consistent with trigger deployment. The cascaded SMPO architecture is introduced as an SMPO variation that affords greater flexibility and efficiency in ways that are key to edge applications in resource-constrained environments. These results reveal the benefit and near-term feasibility of deploying quantum-inspired ML in high energy colliders.", "date": "2026-03-27", "categories": []}
{"id": "2511.05878", "title": "FusionLog: Cross-System Log-based Anomaly Detection via Fusion of General and Proprietary Knowledge", "abstract": "Log-based anomaly detection is critical for ensuring the stability and reliability of web systems. One of the key problems in this task is the lack of sufficient labeled logs, which limits the rapid deployment in new systems. Existing works usually leverage large-scale labeled logs from a mature web system and a small amount of labeled logs from a new system, using transfer learning to extract and generalize general knowledge across both domains. However, these methods focus solely on the transfer of general knowledge and neglect the disparity and potential mismatch between such knowledge and the proprietary knowledge of target system, thus constraining performance. To address this limitation, we propose FusionLog, a novel zero-label cross-system log-based anomaly detection method that effectively achieves the fusion of general and proprietary knowledge, enabling cross-system generalization without any labeled target logs. Specifically, we first design a training-free router based on semantic similarity that dynamically partitions unlabeled target logs into 'general logs' and 'proprietary logs.' For general logs, FusionLog employs a small model based on system-agnostic representation meta-learning for direct training and inference, inheriting the general anomaly patterns shared between the source and target systems. For proprietary logs, we iteratively generate pseudo-labels and fine-tune the small model using multi-round collaborative knowledge distillation and fusion based on large language model (LLM) and small model (SM) to enhance its capability to recognize anomaly patterns specific to the target system. Experimental results on three public log datasets from different systems show that FusionLog achieves over 90% F1-score under a fully zero-label setting, significantly outperforming state-of-the-art cross-system log-based anomaly detection methods.", "date": "2025-11-08", "categories": []}
{"id": "2601.19102", "title": "OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection", "abstract": "Graph data is informative to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Correspondingly, graph anomaly detection (GAD) plays a critical role in identifying anomalies across various domains, including finance, cybersecurity, manufacturing, etc. Facing the large-volume and multi-domain graph data, nascent efforts attempt to develop foundational generalist models capable of detecting anomalies in unseen graphs without retraining. To the best of our knowledge, the different feature semantics and dimensions of cross-domain graph data heavily hinder the development of the graph foundation model, leaving further in-depth continual learning and inference capabilities a quite open problem. Hence, we propose OWLEYE, a novel zero-shot GAD framework that learns transferable patterns of normal behavior from multiple graphs, with a threefold contribution. First, OWLEYE proposes a cross-domain feature alignment module to harmonize feature distributions, which preserves domain-specific semantics during alignment. Second, with aligned features, to enable continuous learning capabilities, OWLEYE designs the multi-domain multi-pattern dictionary learning to encode shared structural and attribute-based patterns. Third, for achieving the in-context learning ability, OWLEYE develops a truncated attention-based reconstruction module to robustly detect anomalies without requiring labeled data for unseen graph-structured data. Extensive experiments on real-world datasets demonstrate that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines, establishing a strong foundation for scalable and label-efficient anomaly detection.", "date": "2026-01-27", "categories": []}
{"id": "2603.24648", "title": "Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation", "abstract": "Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth, energy-intensive, and often unable to support direct sensor-to-surface communication. Standard flat federated learning therefore faces two coupled limitations in underwater deployments: expensive long-range transmissions and reduced participation when only a subset of sensors can reach the gateway. This paper proposes an energy-efficient hierarchical federated learning framework for underwater anomaly detection based on three components: feasibility-aware sensor-to-fog association, compressed model-update transmission, and selective cooperative aggregation among fog nodes. The proposed three-tier architecture localises most communication within short-range clusters while activating fog-to-fog exchange only when smaller clusters can benefit from nearby larger neighbours. A physics-grounded underwater acoustic model is used to evaluate detection quality, communication energy, and network participation jointly. In large synthetic deployments, only about 48% of sensors can directly reach the gateway in the 200-sensor case, whereas hierarchical learning preserves full participation through feasible fog paths. Selective cooperation matches the detection accuracy of always-on inter-fog exchange while reducing its energy by 31-33%, and compressed uploads reduce total energy by 71-95% in matched sensitivity tests. Experiments on three real benchmarks further show that low-overhead hierarchical methods remain competitive in detection quality, while flat federated learning defines the minimum-energy operating point. These results provide practical design guidance for underwater deployments operating under severe acoustic communication constraints.", "date": "2026-03-25", "categories": []}
{"id": "2603.24991", "title": "Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets", "abstract": "Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands of video anomaly detection (VAD). However, the absence of dedicated event-stream anomaly detection datasets and effective modeling strategies has significantly hindered progress in this field. In this work, we take the first major step toward establishing event-based VAD as a unified research direction. We first construct multiple event-stream based benchmarks for video anomaly detection, featuring synchronized event and RGB recordings. Leveraging the unique properties of events, we then propose an EVent-centric spatiotemporal Video Anomaly Detection framework, namely EWAD, with three key innovations: an event density aware dynamic sampling strategy to select temporally informative segments; a density-modulated temporal modeling approach that captures contextual relations from sparse event streams; and an RGB-to-event knowledge distillation mechanism to enhance event-based representations under weak supervision. Extensive experiments on three benchmarks demonstrate that our EWAD achieves significant improvements over existing approaches, highlighting the potential and effectiveness of event-driven modeling for video anomaly detection. The benchmark datasets will be made publicly available.", "date": "2026-03-26", "categories": []}
{"id": "2603.25159", "title": "A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection", "abstract": "3D anomaly detection targets the detection and localization of defects in 3D point clouds trained solely on normal data. While a unified model improves scalability by learning across multiple categories, it often suffers from Inter-Category Entanglement (ICE)-where latent features from different categories overlap, causing the model to adopt incorrect semantic priors during reconstruction and ultimately yielding unreliable anomaly scores. To address this issue, we propose the Semantically Disentangled Unified Model for 3D Anomaly Detection, which reconstructs features conditioned on disentangled semantic representations. Our framework consists of three key components: (i) Coarse-to-Fine Global Tokenization for forming instance-level semantic identity, (ii) Category-Conditioned Contrastive Learning for disentangling category semantics, and (iii) a Geometry-Guided Decoder for semantically consistent reconstruction. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that our method achieves state-of-the-art for both unified and category-specific models, improving object-level AUROC by 2.8% and 9.1%, respectively, while enhancing the reliability of unified 3D anomaly detection.", "date": "2026-03-26", "categories": []}
{"id": "2603.25255", "title": "Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection", "abstract": "Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidence such as abnormal speeds and short-duration events, but their quadratic cost makes multi-month analysis intractable; consequently, no existing approach detects anomalies over multi-month dense GPS trajectories. The field instead relies on scalable sparse stay-point methods that discard this evidence, forcing separate architectures for each regime and preventing knowledge transfer. We argue this bottleneck is unnecessary: human trajectories, dense or sparse, share a natural two-dimensional cyclic structure along within-day and across-day axes. We therefore propose TITAnD (Trajectory Image Transformer for Anomaly Detection), which reformulates trajectory anomaly detection as a vision problem by representing trajectories as a Hyperspectral Trajectory Image (HTI): a day x time-of-day grid whose channels encode spatial, semantic, temporal, and kinematic information from either modality, unifying both under a single representation. Under this formulation, agent-level detection reduces to image classification and temporal localization to semantic segmentation. To model this representation, we introduce the Cyclic Factorized Transformer (CFT), which factorizes attention along the two temporal axes, encoding the cyclic inductive bias of human routines, while reducing attention cost by orders of magnitude and enabling dense multi-month anomaly detection for the first time. Empirically, TITAnD achieves the best AUC-PR across sparse and dense benchmarks, surpassing vision models like UNet while being 11-75x faster than the Transformer with comparable memory, demonstrating that vision reformulation and structure-aware modeling are jointly essential. Code will be made public soon.", "date": "2026-03-26", "categories": []}
{"id": "2603.22757", "title": "Multimodal Industrial Anomaly Detection via Geometric Prior", "abstract": "The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to detect in 2D-based methods. However, current multimodal industrial anomaly detection lacks the effective use of crucial geometric information like surface normal vectors and 3D shape topology, resulting in low detection accuracy. In this paper, we propose a novel Geometric Prior-based Anomaly Detection network (GPAD). Firstly, we propose a point cloud expert model to perform fine-grained geometric feature extraction, employing differential normal vector computation to enhance the geometric details of the extracted features and generate geometric prior. Secondly, we propose a two-stage fusion strategy to efficiently leverage the complementarity of multimodal data as well as the geometric prior inherent in 3D points. We further propose attention fusion and anomaly regions segmentation based on geometric prior, which enhance the model's ability to perceive geometric defects. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the State-of-the-art (SOTA) methods in detection accuracy on both MVTec-3D AD and Eyecandies datasets.", "date": "2026-03-24", "categories": []}
{"id": "2603.22840", "title": "URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection", "abstract": "Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However, these methods may suffer from over-generalization, enabling them to reconstruct anomalies well, which leads to poor detection performance. To address this issue, instead of focusing solely on normality reconstruction, we propose an innovative Uncertainty-Integrated Anomaly Perception and Restoration Attention Network (URA-Net), which explicitly restores abnormal patterns to their corresponding normality. First, unlike traditional image reconstruction methods, we utilize a pre-trained convolutional neural network to extract multi-level semantic features as the reconstruction target. To assist the URA-Net learning to restore anomalies, we introduce a novel feature-level artificial anomaly synthesis module to generate anomalous samples for training. Subsequently, a novel uncertainty-integrated anomaly perception module based on Bayesian neural networks is introduced to learn the distributions of anomalous and normal features. This facilitates the estimation of anomalous regions and ambiguous boundaries, laying the foundation for subsequent anomaly restoration. Then, we propose a novel restoration attention mechanism that leverages global normal semantic information to restore detected anomalous regions, thereby obtaining defect-free restored features. Finally, we employ residual maps between input features and restored features for anomaly detection and localization. The comprehensive experimental results on two industrial datasets, MVTec AD and BTAD, along with a medical image dataset, OCT-2017, unequivocally demonstrate the effectiveness and superiority of the proposed method.", "date": "2026-03-24", "categories": []}
{"id": "2603.22861", "title": "A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection", "abstract": "Unsupervised anomaly detection is vital in industrial fields, with reconstruction-based methods favored for their simplicity and effectiveness. However, reconstruction methods often encounter an identical shortcut issue, where both normal and anomalous regions can be well reconstructed and fail to identify outliers. The severity of this problem increases with the complexity of the normal data distribution. Consequently, existing methods may exhibit excellent detection performance in a specific scenario, but their performance sharply declines when transferred to another scenario. This paper focuses on establishing a universal model applicable to anomaly detection tasks across different settings, termed as universal anomaly detection. In this work, we introduce a novel, straightforward yet efficient framework for universal anomaly detection: \\uline{F}eature \\uline{S}huffling and \\uline{R}estoration (FSR), which can alleviate the identical shortcut issue across different settings. First and foremost, FSR employs multi-scale features with rich semantic information as reconstruction targets, rather than raw image pixels. Subsequently, these multi-scale features are partitioned into non-overlapping feature blocks, which are randomly shuffled and then restored to their original state using a restoration network. This simple paradigm encourages the model to focus more on global contextual information. Additionally, we introduce a novel concept, the shuffling rate, to regulate the complexity of the FSR task, thereby alleviating the identical shortcut across different settings. Furthermore, we provide theoretical explanations for the effectiveness of FSR framework from two perspectives: network structure and mutual information. Extensive experimental results validate the superiority and efficiency of the FSR framework across different settings.Code is available at https://github.com/luow23/FSR.", "date": "2026-03-24", "categories": []}
{"id": "2603.22874", "title": "Template-Based Feature Aggregation Network for Industrial Anomaly Detection", "abstract": "Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the \\textbf{T}emplate-based \\textbf{F}eature \\textbf{A}ggregation \\textbf{Net}work (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Additionally, a random masking strategy for input features is employed to enhance the overall inspection performance of the model. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry. Code is available at https://github.com/luow23/TFA-Net.", "date": "2026-03-24", "categories": []}
{"id": "2603.23122", "title": "PiCo: Active Manifold Canonicalization for Robust Robotic Visual Anomaly Detection", "abstract": "Industrial deployment of robotic visual anomaly detection (VAD) is fundamentally constrained by passive perception under diverse 6-DoF pose configurations and unstable operating conditions such as illumination changes and shadows, where intrinsic semantic anomalies and physical disturbances coexist and interact. To overcome these limitations, a paradigm shift from passive feature learning to Active Canonicalization is proposed. PiCo (Pose-in-Condition Canonicalization) is introduced as a unified framework that actively projects observations onto a condition-invariant canonical manifold. PiCo operates through a cascaded mechanism. The first stage, Active Physical Canonicalization, enables a robotic agent to reorient objects in order to reduce geometric uncertainty at its source. The second stage, Neural Latent Canonicalization, adopts a three-stage denoising hierarchy consisting of photometric processing at the input level, latent refinement at the feature level, and contextual reasoning at the semantic level, progressively eliminating nuisance factors across representational scales. Extensive evaluations on the large-scale M2AD benchmark demonstrate the superiority of this paradigm. PiCo achieves a state-of-the-art 93.7% O-AUROC, representing a 3.7% improvement over prior methods in static settings, and attains 98.5% accuracy in active closed-loop scenarios. These results demonstrate that active manifold canonicalization is critical for robust embodied perception.", "date": "2026-03-24", "categories": []}
{"id": "2603.23205", "title": "Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes", "abstract": "Standard conformal anomaly detection provides marginal finite-sample guarantees under the assumption of exchangeability . However, real-world data often exhibit distribution shifts, necessitating a weighted conformal approach to adapt to local non-stationarity. We show that this adaptation induces a critical trade-off between the minimum attainable p-value and its stability. As importance weights localize to relevant calibration instances, the effective sample size decreases. This can render standard conformal p-values overly conservative for effective error control, while the smoothing technique used to mitigate this issue introduces conditional variance, potentially masking anomalies. We propose a continuous inference relaxation that resolves this dilemma by decoupling local adaptation from tail resolution via continuous weighted kernel density estimation. While relaxing finite-sample exactness to asymptotic validity, our method eliminates Monte Carlo variability and recovers the statistical power lost to discretization. Empirical evaluations confirm that our approach not only restores detection capabilities where discrete baselines yield zero discoveries, but outperforms standard methods in statistical power while maintaining valid marginal error control in practice.", "date": "2026-03-24", "categories": []}
{"id": "2504.21247", "title": "Subject Information Extraction for Novelty Detection with Domain Shifts", "abstract": "Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industrial quality control. Most existing UND methods assume that the training data and testing normal data originate from the same domain and only consider the distribution variation between training data and testing data. However, in real scenarios, it is common for normal testing and training data to originate from different domains, a challenge known as domain shift. The discrepancies between training and testing data often lead to incorrect classification of normal data as novel by existing methods. A typical situation is that testing normal data and training data describe the same subject, yet they differ in the background conditions. To address this problem, we introduce a novel method that separates subject information from background variation encapsulating the domain information to enhance detection performance under domain shifts. The proposed method minimizes the mutual information between the representations of the subject and background while modelling the background variation using a deep Gaussian mixture model, where the novelty detection is conducted on the subject representations solely and hence is not affected by the variation of domains. Extensive experiments demonstrate that our model generalizes effectively to unseen domains and significantly outperforms baseline methods, especially under substantial domain shifts between training and testing data.", "date": "2025-04-30", "categories": []}
{"id": "2602.13091", "title": "BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection", "abstract": "Detecting anomalies in images and video is an essential task for multiple real-world problems, including industrial inspection, computer-assisted diagnosis, and environmental monitoring. Anomaly detection is typically formulated as a one-class classification problem, where the training data consists solely of nominal values, leaving methods built on this assumption susceptible to training label noise. We present Bootstrap Aggregation Anomaly Filtering (BAAF), a method that transforms an arbitrary one-class classifier-based anomaly detector into a fully unsupervised method. This is achieved by leveraging the unique intrinsic properties of anomaly detection: anomalies are uncommon in the sampled data and generally heterogeneous. These properties enable us to design a modified Bootstrap Aggregation method that uses multiple independently trained instances of supervised one-class classifiers to filter the training dataset for anomalies. This transformation requires no modifications to the underlying anomaly detector; only the algorithmically selected data bags used for training change. We demonstrate empirically that our method can transform a wide variety of one-class classifier-based image anomaly detectors into unsupervised ones. Consequently, we present the first unsupervised logical anomaly detectors for images. We also demonstrate that our method achieves state-of-the-art performance in fully unsupervised anomaly detection on the MVTec AD, ViSA, and MVTec Loco AD datasets. As improvements to one-class classifiers are made, our method directly transfers those improvements to the unsupervised domain, linking the domains.", "date": "2026-02-13", "categories": []}
{"id": "2602.19248", "title": "No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection", "abstract": "The collection and detection of video anomaly data has long been a challenging problem due to its rare occurrence and spatio-temporal scarcity. Existing video anomaly detection (VAD) methods under perform in open-world scenarios. Key contributing factors include limited dataset diversity, and inadequate understanding of context-dependent anomalous semantics. To address these issues, i) we propose LAVIDA, an end-to-end zero-shot video anomaly detection framework. ii) LAVIDA employs an Anomaly Exposure Sampler that transforms segmented objects into pseudo-anomalies to enhance model adaptability to unseen anomaly categories. It further integrates a Multimodal Large Language Model (MLLM) to bolster semantic comprehension capabilities. Additionally, iii) we design a token compression approach based on reverse attention to handle the spatio-temporal scarcity of anomalous patterns and decrease computational cost. The training process is conducted solely on pseudo anomalies without any VAD data. Evaluations across four benchmark VAD datasets demonstrate that LAVIDA achieves SOTA performance in both frame-level and pixel-level anomaly detection under the zero-shot setting. Our code is available in https://github.com/VitaminCreed/LAVIDA.", "date": "2026-02-22", "categories": []}
{"id": "2603.16451", "title": "TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection", "abstract": "Anomaly detection plays a key role in industrial quality control, where defects must be identified despite the scarcity of labeled faulty samples. Recent self-supervised approaches, such as GLASS, learn normal visual patterns using only defect-free data and have shown strong performance on industrial benchmarks. However, their computational requirements limit the deployment on resource-constrained edge platforms, and even more so within in-sensor processing architectures. This work introduces TinyGLASS, a lightweight adaptation of the GLASS framework designed for real-time edge and in-sensor anomaly detection. The proposed architecture replaces the original WideResNet-50 backbone with a compact ResNet-18 and introduces deployment-based modifications that enable static graph tracing and INT8 quantization. We evaluated the proposed approach on the Sony IMX500 intelligent vision sensor, exploiting the in-sensor processor using the Sony Model Compression Toolkit. In addition to evaluating performance on the MVTec-AD benchmark, we investigate robustness to contaminated training data and introduce a custom industrial dataset, named MMS Dataset, for cross-device evaluation. Experimental results show that TinyGLASS achieves 8.6x parameter compression while maintaining competitive detection performance, reaching 94.2% image-level AUROC on MVTec-AD and operating at 20 FPS within the 8 MB memory constraints of the IMX500 platform. System profiling showcases low power consumption (4.0 mJ per inference), real-time end-to-end latency (20 FPS), and high energy efficiency (470 GMAC/J). Furthermore, the model demonstrates stable performance under moderate levels of training data contamination.", "date": "2026-03-17", "categories": []}
{"id": "2603.20288", "title": "Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices", "abstract": "Visual Anomaly Detection (VAD) is essential for industrial quality control, enabling automatic defect detection in manufacturing. In real production lines, VAD systems must satisfy strict real-time and privacy requirements, necessitating a shift from cloud-based processing to local edge deployment. However, processing data locally on edge devices introduces new challenges because edge hardware has limited memory and computational resources. To overcome these limitations, we propose two efficient VAD methods designed for edge deployment: PatchCore-Lite and Padim-Lite, based on the popular PatchCore and PaDiM models. PatchCore-Lite runs first a coarse search on a product-quantized memory bank, then an exact search on a decoded subset. Padim-Lite is sped up using diagonal covariance, turning Mahalanobis distance into efficient element-wise computation. We evaluate our methods on the MVTec AD and VisA benchmarks and show their suitability for edge environments. PatchCore-Lite achieves a remarkable 79% reduction in total memory footprint, while PaDiM-Lite achieves substantial efficiency gains with a 77% reduction in total memory and a 31% decrease in inference time. These results show that VAD can be effectively deployed on edge devices, enabling real-time, private, and cost-efficient industrial inspection.", "date": "2026-03-18", "categories": []}
{"id": "2603.20335", "title": "Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX", "abstract": "The Interest Public Group ARRONAX's C70XP cyclotron, used for radioisotope production for medical and research applications, relies on complex and costly systems that are prone to failures, leading to operational disruptions. In this context, this study aims to develop a machine learning-based method for early anomaly detection, from sensor measurements over a temporal window, to enhance system performance. One of the most widely recognized methods for anomaly detection is Isolation Forest (IF), known for its effectiveness and scalability. However, its reliance on axis-parallel splits limits its ability to detect subtle anomalies, especially those occurring near the mean of normal data. This study proposes a hybrid approach that combines a fully connected Autoencoder (AE) with IF to enhance the detection of subtle anomalies. In particular, the Mean Cubic Error (MCE) of the sensor data reconstructed by the AE is used as input to the IF model. Validated on proton beam intensity time series data, the proposed method demonstrates a clear improvement in detection performance, as confirmed by the experimental results.", "date": "2026-03-20", "categories": []}
{"id": "2603.20636", "title": "A Modular LLM Framework for Explainable Price Outlier Detection", "abstract": "Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework that treats outlier price flagging as a reasoning task grounded in related product detection and comparison. The system processes the prices of target products in three stages: (i) relevance classification selects price-relevant similar products using product descriptions and attributes; (ii) relative utility assessment evaluates the target product against each similar product along price influencing dimensions (e.g., brand, size, features); (iii) reasoning-based decision aggregates these justifications into an explainable price outlier judgment. The framework attains over 75% agreement with human auditors on a test dataset, and outperforms zero-shot and retrieval based LLM techniques. Ablation studies show the sensitivity of the method to key hyper-parameters and testify on its flexibility to be applied to cases with different accuracy requirement and auditor agreements.", "date": "2026-03-21", "categories": []}
{"id": "2603.20993", "title": "Long-Term Outlier Prediction Through Outlier Score Modeling", "abstract": "This study addresses an important gap in time series outlier detection by proposing a novel problem setting: long-term outlier prediction. Conventional methods primarily focus on immediate detection by identifying deviations from normal patterns. As a result, their applicability is limited when forecasting outlier events far into the future. To overcome this limitation, we propose a simple and unsupervised two-layer method that is independent of specific models. The first layer performs standard outlier detection, and the second layer predicts future outlier scores based on the temporal structure of previously observed outliers. This framework enables not only pointwise detection but also long-term forecasting of outlier likelihoods. Experiments on synthetic datasets show that the proposed method performs well in both detection and prediction tasks. These findings suggest that the method can serve as a strong baseline for future work in outlier detection and forecasting.", "date": "2026-03-22", "categories": []}
{"id": "2603.21160", "title": "Beyond a Single Signal: SPECTREG2, A Unified MultiExpert Anomaly Detector for Unknown Unknowns", "abstract": "Epistemic intelligence requires machine learning systems to recognise the limits of their own knowledge and act safely under uncertainty, especially when faced with unknown unknowns. Existing uncertainty quantification methods rely on a single signal such as confidence or density and fail to detect diverse structural anomalies. We introduce SPECTRE-G2, a multi-signal anomaly detector that combines eight complementary signals from a dual-backbone neural network. The architecture includes a spectral normalised Gaussianization encoder, a plain MLP preserving feature geometry, and an ensemble of five models. These produce density, geometry, uncertainty, discriminative, and causal signals. Each signal is normalised using validation statistics and calibrated with synthetic out-of-distribution data. An adaptive top-k fusion selects the most informative signals and averages their scores. Experiments on synthetic, Adult, CIFAR-10, and Gridworld datasets show strong performance across diverse anomaly types, outperforming multiple baselines on AUROC, AUPR, and FPR95. The model is stable across seeds and particularly effective for detecting new variables and confounders. SPECTRE-G2 provides a practical approach for detecting unknown unknowns in open-world settings.", "date": "2026-03-22", "categories": []}
{"id": "2603.21562", "title": "Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection", "abstract": "Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Unsupervised Anomaly Detection (UAD). However, existing UCAD approaches that rely solely on visual information are insufficient to capture the manifold of normality in complex scenes, thereby impeding further gains in anomaly detection accuracy. To overcome this limitation, we propose an unsupervised continual anomaly detection framework grounded in multimodal prompting. Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains prototypical normal patterns from both visual and textual domains across consecutive tasks, yielding a richer representation of normality. Furthermore, we devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness. Benchmark experiments on MVTec AD and VisA datasets show that our approach achieves state-of-the-art (SOTA) performance on image-level AUROC and pixel-level AUPR metrics.", "date": "2026-03-23", "categories": []}
{"id": "2603.21596", "title": "In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis", "abstract": "The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.", "date": "2026-03-23", "categories": []}
{"id": "2603.21612", "title": "Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction", "abstract": "Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooking the importance of complementary information from other modalities. In this paper, we propose a novel multimodal time series anomaly detection model (MindTS) that focuses on addressing two key challenges: (1) how to achieve semantically consistent alignment across heterogeneous multimodal data, and (2) how to filter out redundant modality information to enhance cross-modal interaction effectively. To address the first challenge, we propose Fine-grained Time-text Semantic Alignment. It integrates exogenous and endogenous text information through cross-view text fusion and a multimodal alignment mechanism, achieving semantically consistent alignment between time and text modalities. For the second challenge, we introduce Content Condenser Reconstruction, which filters redundant information within the aligned text modality and performs cross-modal reconstruction to enable interaction. Extensive experiments on six real-world multimodal datasets demonstrate that the proposed MindTS achieves competitive or superior results compared to existing methods. The code is available at: https://github.com/decisionintelligence/MindTS.", "date": "2026-03-23", "categories": []}
{"id": "2603.19497", "title": "ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes", "abstract": "Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically train dataset-specific models under the assumption of a single supervision regime, which limits their ability to leverage shared structures across anomaly detection tasks and to adapt to different supervision levels. We propose ICLAD, an in-context learning foundation model for tabular anomaly detection that generalizes across both datasets and supervision regimes. ICLAD is trained via meta-learning on synthetic tabular anomaly detection tasks, and at inference time, the model assigns anomaly scores by conditioning on the training set without updating model weights. Comprehensive experiments on 57 tabular datasets from ADBench show that our method achieves state-of-the-art performance across three supervision regimes, establishing a unified framework for tabular anomaly detection.", "date": "2026-03-19", "categories": []}
{"id": "2603.19608", "title": "FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement", "abstract": "Fine-grained anomaly detection is crucial in industrial and medical applications, but labeled anomalies are often scarce, making zero-shot detection challenging. While vision-language models like CLIP offer promising solutions, they struggle with foreground-background feature entanglement and coarse textual semantics. We propose FB-CLIP, a framework that enhances anomaly localization via multi-strategy textual representations and foreground-background separation. In the textual modality, it combines End-of-Text features, global-pooled representations, and attention-weighted token features for richer semantic cues. In the visual modality, multi-view soft separation along identity, semantic, and spatial dimensions, together with background suppression, reduces interference and improves discriminability. Semantic Consistency Regularization (SCR) aligns image features with normal and abnormal textual prototypes, suppressing uncertain matches and enlarging semantic gaps. Experiments show that FB-CLIP effectively distinguishes anomalies from complex backgrounds, achieving accurate fine-grained anomaly detection and localization under zero-shot settings.", "date": "2026-03-20", "categories": []}
{"id": "2603.19695", "title": "Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis", "abstract": "Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities in diagnostic performance. These limitations contribute to delayed recognition and uneven quality of care, creating an urgent need for a generalizable framework that enhances sensitivity while ensuring equity across diverse populations. In this study, we developed an AI-assisted two-stage ECG framework integrating self-supervised anomaly detection with demographic-aware representation learning. The first stage performs self-supervised anomaly detection pretraining by reconstructing masked global and local ECG signals, modeling signal trends, and predicting patient attributes to learn robust ECG representations without diagnostic labels. The pretrained model is then fine-tuned for multi-label ECG classification using asymmetric loss to better handle long-tail cardiac abnormalities, and additionally produces anomaly score maps for localization, with CPU-based optimization enabling practical deployment. Evaluated on a longitudinal cohort of over one million clinical ECGs, our method achieves an AUROC of 94.7% for rare anomalies and reduces the common-rare performance gap by 73%, while maintaining consistent diagnostic accuracy across age and sex groups. In conclusion, the proposed equity-aware AI framework demonstrates strong clinical utility, interpretable anomaly localization, and scalable performance across multiple cohorts, highlighting its potential to mitigate diagnostic disparities and advance equitable anomaly detection in biomedical signals and digital health. Source code is available at https://github.com/MediaBrain-SJTU/Rare-ECG.", "date": "2026-03-20", "categories": []}
{"id": "2603.19780", "title": "Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection", "abstract": "Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoothed responses. To address this limitation, we propose DeSC, a novel Decoupled Sensitivity-Consistency framework that trains two specialized streams using distinct optimization strategies. The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise. Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions. Extensive experiments demonstrate that DeSC establishes new state-of-the-art performance by achieving 89.37% AUC on UCF-Crime (+1.29%) and 87.18% AP on XD-Violence (+2.22%). Code is available at https://github.com/imzht/DeSC.", "date": "2026-03-20", "categories": []}
{"id": "2311.04095", "title": "Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset", "abstract": "We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task may fail to capture the geometric structures of anomalies due to uncertainty in lighting conditions or shooting angles. PD-REAL consists entirely of Play-Doh models for 15 object categories and focuses on the analysis of potential benefits from 3D information in a controlled environment. Specifically, objects are first created with six types of anomalies, such as \\textit{dent}, \\textit{crack}, or \\textit{perforation}, and then photographed under different lighting conditions to mimic real-world inspection scenarios. To demonstrate the usefulness of 3D information, we use a commercially available RealSense camera to capture RGB and depth images. Compared to the existing 3D dataset for AD tasks, the data acquisition of PD-REAL is significantly cheaper, easily scalable, and easier to control variables. Furthermore, we introduce a multi-scale teacher--student framework with hierarchical distillation for multimodal anomaly detection. This architecture overcomes the inherent limitation of single-scale distillation approaches, which often struggle to reconcile global context with local features. Leveraging multi-level guidance from the teacher network, the student network can effectively capture richer features for anomaly detection. Extensive evaluations with our method and state-of-the-art AD algorithms on our dataset qualitatively and quantitatively demonstrate the higher detection accuracy of our method. Our dataset can be downloaded from https://github.com/Andy-cs008/PD-REAL", "date": "2023-11-07", "categories": []}
{"id": "2506.02535", "title": "Video Anomaly Detection with Semantics-Aware Information Bottleneck", "abstract": "Semi-supervised video anomaly detection methods face two critical challenges: (1) Strong generalization blurs the boundary between normal and abnormal patterns. Although existing approaches attempt to alleviate this issue using memory modules, their rigid prototype-matching process limits adaptability to diverse scenarios; (2) Relying solely on low-level appearance and motion cues makes it difficult to perceive high-level semantic anomalies in complex scenes. To address these limitations, we propose SIB-VAD, a novel framework based on adaptive information bottleneck filtering and semantic-aware enhancement. We propose the Sparse Feature Filtering Module (SFFM) to replace traditional memory modules. It compresses normal features directly into a low-dimensional manifold based on the information bottleneck principle and uses an adaptive routing mechanism to dynamically select the most suitable normal bottleneck subspace. Trained only on normal data, SFFMs only learn normal low-dimensional manifolds, while abnormal features deviate and are effectively filtered. Unlike memory modules, SFFM directly removes abnormal information and adaptively handles scene variations. To improve semantic awareness, we further design a multimodal prediction framework that jointly models appearance, motion, and semantics. Through multimodal consistency constraints and joint error computation, it achieves more robust VAD performance. Experimental results validate the effectiveness of our feature filtering paradigm based on semantics-aware information bottleneck. Project page at https://qzfm.github.io/sib_vad_project_page/", "date": "2025-06-03", "categories": []}
{"id": "2509.19985", "title": "Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detection", "abstract": "Anomalies in multivariate time series often arise from temporal context and cross-channel coordination rather than isolated outliers. We present Pi-Transformer (Prior-Informed Transformer), a transformer with two attention pathways: data-driven series attention and a smoothly evolving prior attention that encodes temporal invariants such as scale-related self-similarity and phase synchrony. The prior provides an amplitude-insensitive temporal reference that calibrates reconstruction error. During training, we pair a reconstruction objective with a divergence term that encourages agreement between the two attentions while keeping them meaningfully distinct. The prior is regularised to evolve smoothly and is lightly distilled towards dataset-level statistics. At inference, the model combines an alignment-weighted reconstruction signal (Energy) with a mismatch signal that highlights timing and phase disruptions, and fuses them into a single score for detection. Across five benchmarks (SMD, MSL, SMAP, SWaT, and PSM), Pi-Transformer achieves state-of-the-art or highly competitive F1, with particular strength on timing and phase-breaking anomalies. Case analyses show complementary behaviour of the two streams and interpretable detections around regime changes. Embedding prior attention into transformer scoring yields a calibrated and robust approach to anomaly detection in complex multivariate systems.", "date": "2025-09-24", "categories": []}
{"id": "2603.18032", "title": "Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams", "abstract": "Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory.", "date": "2026-03-09", "categories": []}
{"id": "2603.18111", "title": "BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection", "abstract": "Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing strategies based on random perturbations or pseudo-anomaly injection often struggle to simultaneously preserve temporal semantic consistency and provide effective decision-boundary supervision. Most existing methods rely on prior anomaly injection, while overlooking the potential of generating hard negatives near the data manifold boundary directly from normal samples themselves. To address this issue, we propose a reconstruction-driven boundary negative generation framework that automatically constructs hard negatives through the reconstruction process of normal samples. Specifically, the method first employs a reconstruction network to capture normal temporal patterns, and then introduces a reinforcement learning strategy to adaptively adjust the optimization update magnitude according to the current reconstruction state. In this way, boundary-shifted samples close to the normal data manifold can be induced along the reconstruction trajectory and further used for subsequent contrastive representation learning. Unlike existing methods that depend on explicit anomaly injection, the proposed framework does not require predefined anomaly patterns, but instead mines more challenging boundary negatives from the model's own learning dynamics. Experimental results show that the proposed method effectively improves anomaly representation learning and achieves competitive detection performance on the current dataset.", "date": "2026-03-18", "categories": []}
{"id": "2603.18954", "title": "Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring", "abstract": "Reliable anomaly detection in distributed power plant monitoring systems is essential for ensuring operational continuity and reducing maintenance costs, particularly in regions where telecom operators heavily rely on diesel generators. However, this task is challenged by extreme class imbalance, lack of interpretability, and potential fairness issues across regional clusters. In this work, we propose a supervised ML framework that integrates ensemble methods (LightGBM, XGBoost, Random Forest, CatBoost, GBDT, AdaBoost) and baseline models (Support Vector Machine, K-Nearrest Neighbors, Multilayer Perceptrons, and Logistic Regression) with advanced resampling techniques (SMOTE with Tomek Links and ENN) to address imbalance in a dataset of diesel generator operations in Cameroon. Interpretability is achieved through SHAP (SHapley Additive exPlanations), while fairness is quantified using the Disparate Impact Ratio (DIR) across operational clusters. We further evaluate model generalization using Maximum Mean Discrepancy (MMD) to capture domain shifts between regions. Experimental results show that ensemble models consistently outperform baselines, with LightGBM achieving an F1-score of 0.99 and minimal bias across clusters (DIR $\\approx 0.95$). SHAP analysis highlights fuel consumption rate and runtime per day as dominant predictors, providing actionable insights for operators. Our findings demonstrate that it is possible to balance performance, interpretability, and fairness in anomaly detection, paving the way for more equitable and explainable AI systems in industrial power management. {\\color{black} Finally, beyond offline evaluation, we also discuss how the trained models can be deployed in practice for real-time monitoring. We show how containerized services can process in real-time, deliver low-latency predictions, and provide interpretable outputs for operators.", "date": "2026-03-19", "categories": []}
{"id": "2603.18985", "title": "Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models", "abstract": "Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial performance improvements over classical statistical methods. However, these gains are frequently evaluated under heterogeneous thresholding strategies and evaluation protocols, making fair comparisons difficult. This work revisits OmniAnomaly, a widely used stochastic recurrent model for MTSAD, and systematically compares it with a simple linear baseline based on Principal Component Analysis (PCA) on the Server Machine Dataset (SMD). Both methods are evaluated under identical thresholding and evaluation procedures, with experiments repeated across 100 runs for each of the 28 machines in the dataset. Performance is evaluated using Precision, Recall and F1-score at point-level, with and without point-adjustment, and under different aggregation strategies across machines and runs, with the corresponding standard deviations also reported. The results show large variability across machines and show that PCA can achieve performance comparable to OmniAnomaly, and even outperform it when point-adjustment is not applied. These findings question the added value of more complex architectures under current benchmarking practices and highlight the critical role of evaluation methodology in MTSAD research.", "date": "2026-03-19", "categories": []}
{"id": "2603.06313", "title": "WMoE-CLIP: Wavelet-Enhanced Mixture-of-Experts Prompt Learning for Zero-Shot Anomaly Detection", "abstract": "Vision-language models have recently shown strong generalization in zero-shot anomaly detection (ZSAD), enabling the detection of unseen anomalies without task-specific supervision. However, existing approaches typically rely on fixed textual prompts, which struggle to capture complex semantics, and focus solely on spatial-domain features, limiting their ability to detect subtle anomalies. To address these challenges, we propose a wavelet-enhanced mixture-of-experts prompt learning method for ZSAD. Specifically, a variational autoencoder is employed to model global semantic representations and integrate them into prompts to enhance adaptability to diverse anomaly patterns. Wavelet decomposition extracts multi-frequency image features that dynamically refine textual embeddings through cross-modal interactions. Furthermore, a semantic-aware mixture-of-experts module is introduced to aggregate contextual information. Extensive experiments on 14 industrial and medical datasets demonstrate the effectiveness of the proposed method.", "date": "2026-03-06", "categories": []}
{"id": "2603.17325", "title": "MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation", "abstract": "Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis by identifying abnormal regions in medical images and localizing pathological regions. Recent CLIP-based studies are promising for anomaly detection in zero-/few-shot settings, and typically rely on global representations and weak supervision, often producing coarse localization and limited segmentation quality. In this work, we study supervised adaptation of CLIP for MAD under a realistic clinical setting where a limited yet meaningful amount of labeled abnormal data is available. Our model MedSAD-CLIP leverages fine-grained text-visual cues via the Token-Patch Cross-Attention(TPCA) to improve lesion localization while preserving the generalization capability of CLIP representations. Lightweight image adapters and learnable prompt tokens efficiently adapt the pretrained CLIP encoder to the medical domain while preserving its rich semantic alignment. Furthermore, a Margin-based image-text Contrastive Loss is designed to enhance global feature discrimination between normal and abnormal representations. Extensive experiments on four diverse benchmarks-Brain, Retina, Lung, and Breast datasets-demonstrate the effectiveness of our approach, achieving superior performance in both pixel-level segmentation and image-level classification over state-of-the-art methods. Our results highlight the potential of supervised CLIP adaptation as a unified and scalable paradigm for medical anomaly understanding. Code will be made available at https://github.com/thuy4tbn99/MedSAD-CLIP", "date": "2026-03-18", "categories": []}
{"id": "2603.17530", "title": "AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection", "abstract": "Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and continual learning demands of real-world environments. While Teacher-Student (TS) architectures are efficient, they remain unexplored for the Continual Setting. To bridge this gap, we propose AdapTS, a unified TS framework designed for multi-class and continual settings, optimized for edge deployment. AdapTS eliminates the need for two different architectures by utilizing a single shared frozen backbone and injecting lightweight trainable adapters into the student pathway. Training is enhanced via a segmentation-guided objective and synthetic Perlin noise, while a prototype-based task identification mechanism dynamically selects adapters at inference with 99\\% accuracy.\n  Experiments on MVTec AD and VisA demonstrate that AdapTS matches the performance of existing TS methods across multi-class and continual learning scenarios, while drastically reducing memory overhead. Our lightest variant, AdapTS-S, requires only 8 MB of additional memory, 13x less than STFPM (95 MB), 48x less than RD4AD (360 MB), and 149x less than DeSTSeg (1120 MB), making it a highly scalable solution for edge deployment in complex industrial environments.", "date": "2026-03-18", "categories": []}
{"id": "2603.17570", "title": "FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models", "abstract": "Tabular foundation models, specifically Prior-Data Fitted Networks (PFNs), have revolutionized outlier detection (OD) by enabling unsupervised zero-shot adaptation to new datasets without training. However, despite their predictive power, these models typically function as opaque black boxes, outputting scalar outlier scores that lack the operational context required for safety-critical decision-making. Existing post-hoc explanation methods are often computationally prohibitive for real-time deployment or fail to capture the epistemic uncertainty inherent in zero-shot inference. In this work, we introduce FoMo-X, a modular framework that equips OD foundation models with intrinsic, lightweight diagnostic capabilities. We leverage the insight that the frozen embeddings of a pretrained PFN backbone already encode rich, context-conditioned relational information. FoMo-X attaches auxiliary diagnostic heads to these embeddings, trained offline using the same generative simulator prior as the backbone. This allows us to distill computationally expensive properties, such as Monte Carlo dropout based epistemic uncertainty, into a deterministic, single-pass inference. We instantiate FoMo-X with two novel heads: a Severity Head that discretizes deviations into interpretable risk tiers, and an Uncertainty Head that provides calibrated confidence measures. Extensive evaluation on synthetic and real-world benchmarks (ADBench) demonstrates that FoMo-X recovers ground-truth diagnostic signals with high fidelity and negligible inference overhead. By bridging the gap between foundation model performance and operational explainability, FoMo-X offers a scalable path toward trustworthy, zero-shot outlier detection.", "date": "2026-03-18", "categories": []}
{"id": "2603.17575", "title": "Unsupervised Symbolic Anomaly Detection", "abstract": "We propose SYRAN, an unsupervised anomaly detection method based on symbolic regression. Instead of encoding normal patterns in an opaque, high-dimensional model, our method learns an ensemble of human-readable equations that describe symbolic invariants: functions that are approximately constant on normal data. Deviations from these invariants yield anomaly scores, so that the detection logic is interpretable by construction, rather than via post-hoc explanation. Experimental results demonstrate that SYRAN is highly interpretable, providing equations that correspond to known scientific or medical relationships, and maintains strong anomaly detection performance comparable to that of state-of-the-art methods.", "date": "2026-03-18", "categories": []}
{"id": "2603.17795", "title": "RangeAD: Fast On-Model Anomaly Detection", "abstract": "In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts. Typically, this is implemented by running a separate AD model alongside the primary model. However, this separation ignores the fact that the primary model already encodes substantial information about the target distribution. In this paper, we introduce On-Model AD, a setting for anomaly detection that explicitly leverages access to a related machine learning model. Within this setting, we propose RangeAD, an algorithm that utilizes neuron-wise output ranges derived from the primary model. RangeAD achieves superior performance even on high-dimensional tasks while incurring substantially lower inference costs. Our results demonstrate the potential of the On-Model AD setting as a practical framework for efficient anomaly detection.", "date": "2026-03-18", "categories": []}
{"id": "2603.16645", "title": "BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection", "abstract": "We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generated from images. Our work follows a multimodal approach, embedding object and relationship tokens from scene graphs with a language model to leverage semantic knowledge from the real world. A normalizing flow model is used to learn bijective transformations that map object-relation-object triplets from scene graphs to a simple base distribution (typically Gaussian), allowing anomaly detection through likelihood estimation. We evaluate our approach on the SARD dataset containing office and dining room scenes. Our method achieves around 10% better AUROC results compared to the current state-of-the-art model, while simultaneously being five times faster. Through ablation studies, we demonstrate superior robustness and universality, particularly regarding the use of synonyms, with our model maintaining stable performance while the baseline shows 17.5% deviation. This work demonstrates the strong potential of learning-based methods for relationship anomaly detection in scene graphs. Our code is available at https://github.com/mschween/BUSSARD .", "date": "2026-03-17", "categories": []}
{"id": "2603.16697", "title": "Cost Trade-offs in Matrix Inversion Updates for Streaming Outlier Detection", "abstract": "Outlier detection identifies data points that deviate significantly from expected patterns, revealing anomalies that may require special attention. Incorporating online learning further improves accuracy by continuously updating the model to reflect the most recent data. When employing the Christoffel function as an outlier score, online learning requires updating the inverse of a matrix following a rank-k update, given the initial inverse. Surprisingly, there is no consensus on the optimal method for this task. This technical note aims to compare three different updating methods: Direct Inversion (DI), Iterative Sherman-Morrison (ISM), and Woodbury Matrix Identity (WMI), to identify the most suitable approach for different scenarios. We first derive the theoretical computational costs of each method and then validate these findings through comprehensive Python simulations run on a CPU. These results allow us to propose a simple, quantitative, and easy-to-remember rule that can be stated qualitatively as follows: ISM is optimal for rank-1 updates, WMI excels for small updates relative to matrix size, and DI is preferable otherwise. This technical note produces a general result for any problem involving a matrix inversion update. In particular, it contributes to the ongoing development of efficient online outlier detection techniques.", "date": "2026-03-17", "categories": []}
{"id": "2410.21322", "title": "Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection", "abstract": "Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. These two samples exhibit analogous loss behavior that conventional loss-based methodologies struggle to differentiate. To tackle this problem, we propose a novel approach that supplements traditional loss behavior with `parameter behavior', enabling a more granular characterization of anomalous patterns. Parameter behavior is formalized by measuring the parametric response to minute perturbations in input samples. Leveraging the complementary nature of parameter and loss behaviors, we further propose a dual Parameter-Loss Data Augmentation method (termed PLDA), implemented within the reinforcement learning paradigm. During the training phase of anomaly detection, PLDA dynamically augments the training data through an iterative process that simultaneously mitigates anomaly contaminations while amplifying informative hard normal samples. PLDA demonstrates remarkable versatility, which can serve as an additional component that seamlessly integrated with existing anomaly detectors to enhance their detection performance. Extensive experiments on ten datasets show that PLDA significantly improves the performance of four distinct detectors by up to 8\\%, outperforming three state-of-the-art data augmentation methods.", "date": "2024-10-26", "categories": []}
{"id": "2507.06624", "title": "UniOD: A Universal Model for Outlier Detection across Diverse Domains", "abstract": "Outlier detection (OD), distinguishing inliers and outliers in completely unlabeled datasets, plays a vital role in science and engineering. Although there have been many insightful OD methods, most of them require troublesome hyperparameter tuning (a challenge in unsupervised learning) and costly model training for every task or dataset. In this work, we propose UniOD, a universal OD framework that leverages labeled datasets to train a single model capable of detecting outliers of datasets with different feature dimensions and heterogeneous feature spaces from diverse domains. Specifically, UniOD extracts uniform and comparable features across different datasets by constructing and factorizing multi-scale point-wise similarity matrices. It then employs graph neural networks to capture comprehensive within-dataset and between-dataset information simultaneously, and formulates outlier detection tasks as node classification tasks. As a result, once the training is complete, UniOD can identify outliers in datasets from diverse domains without any further model/hyperparameter selection and parameter optimization, which greatly improves convenience and accuracy in real applications. More importantly, we provide theoretical guarantees for the effectiveness of UniOD, consistent with our numerical results. We evaluate UniOD on 30 benchmark OD datasets against 17 baselines, demonstrating its effectiveness and superiority. Our code is available at https://github.com/fudazhiaka/UniOD.", "date": "2025-07-09", "categories": []}
{"id": "2509.25934", "title": "UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression", "abstract": "Existing anomaly detection (AD) methods often treat the modality and class as independent factors. Although this paradigm has enriched the development of AD research branches and produced many specialized models, it has also led to fragmented solutions and excessive memory overhead. Moreover, reconstruction-based multi-class approaches typically rely on shared decoding paths, which struggle to handle large variations across domains, resulting in distorted normality boundaries, domain interference, and high false alarm rates. To address these limitations, we propose UniMMAD, a unified framework for multi-modal and multi-class anomaly detection. At the core of UniMMAD is a Mixture-of-Experts (MoE)-driven feature decompression mechanism, which enables adaptive and disentangled reconstruction tailored to specific domains. This process is guided by a ``general to specific'' paradigm. In the encoding stage, multi-modal inputs of varying combinations are compressed into compact, general-purpose features. The encoder incorporates a feature compression module to suppress latent anomalies, encourage cross-modal interaction, and avoid shortcut learning. In the decoding stage, the general features are decompressed into modality-specific and class-specific forms via a sparsely-gated cross MoE, which dynamically selects expert pathways based on input modality and class. To further improve efficiency, we design a grouped dynamic filtering mechanism and a MoE-in-MoE structure, reducing parameter usage by 75\\% while maintaining sparse activation and fast inference. UniMMAD achieves state-of-the-art performance on 9 anomaly detection datasets, spanning 3 fields, 12 modalities, and 66 classes.\n  The source code will be available at https://github.com/yuanzhao-CVLAB/UniMMAD.", "date": "2025-09-30", "categories": []}
{"id": "2511.20088", "title": "Explainable Visual Anomaly Detection via Concept Bottleneck Models", "abstract": "In recent years, Visual Anomaly Detection (VAD) has gained significant attention due to its ability to identify defects using only normal images during training. Many VAD models work without supervision but are still able to provide visual explanations by highlighting the anomalous regions within an image. However, although these visual explanations can be helpful, they lack a direct and semantically meaningful interpretation for users. To address this limitation, we propose extending Concept Bottleneck Models (CBMs) to the VAD setting. By learning meaningful concepts, the network can provide human-interpretable descriptions of anomalies, offering a novel and more insightful way to explain them. Our main contributions are threefold: (i) we introduce a concept-based framework for anomaly explanation by extending CBMs to the VAD setting for the first time; (ii) we evaluate multiple supervision regimes, ranging from fully-supervised to synthetic-only anomaly settings, analyzing the trade-off between performance and labeling effort; (iii) we propose a dual-branch architecture that combines a CBM branch for concept-level explanations with a visual branch for pixel-level anomaly localization, bridging semantic and spatial interpretability. When evaluated across three well-established VAD benchmarks, our approach, Concept-Aware Visual Anomaly Detection (CONVAD), achieves performance comparable to classic VAD methods, while providing richer, concept-driven explanations that enhance interpretability and trust in VAD systems.", "date": "2025-11-25", "categories": []}
{"id": "2511.22436", "title": "ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection", "abstract": "Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-language models offer strong generalization, modeling multiple distinct category manifolds concurrently without actual anomalous data causes feature space collapse and cross-class interference. Consequently, existing methods often fail to balance scalability and precision, leading to either isolated single-class retraining or excessively loose decision margins. To address this limitation, we present a one-for-all learning framework called ABounD that unites semantic concept anchoring with geometric boundary optimization. This method employs two lightweight mechanisms to resolve multi-class ambiguity. First, the Dynamic Concept Fusion module generates class-adaptive semantic anchors via query-aware hierarchical calibration, disentangling overlapping category concepts. Second, using these anchors, the Adversarial Boundary Forging module constructs a tight, class-tailored decision margin by synthesizing adversarial boundary-level fence features to prevent cross-class boundary blurring. Optimized in a single stage, ABounD removes the requirement for isolated per-category retraining in few-shot settings. Experiments on seven industrial benchmarks show that the proposed method achieves state-of-the-art detection and localization performance for multi-class few-shot anomaly detection while maintaining low computational costs during training and inference.", "date": "2025-11-27", "categories": []}
{"id": "2603.13237", "title": "A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems", "abstract": "High-frequency banking environments face a critical trade-off between low-latency fraud detection and the regulatory explainability demanded by GDPR. Traditional rule-based and discriminative models struggle with \"zero-day\" attacks due to extreme class imbalance and the lack of historical precedents. This paper proposes a Dual-Path Generative Framework that decouples real-time anomaly detection from offline adversarial training. The architecture employs a Variational Autoencoder (VAE) to establish a legitimate transaction manifold based on reconstruction error, ensuring <50ms inference latency. In parallel, an asynchronous Wasserstein GAN with Gradient Penalty (WGAN-GP) synthesizes high-entropy fraudulent scenarios to stress-test the detection boundaries. Crucially, to address the non-differentiability of discrete banking data (e.g., Merchant Category Codes), we integrate a Gumbel-Softmax estimator. Furthermore, we introduce a trigger-based explainability mechanism where SHAP (Shapley Additive Explanations) is activated only for high-uncertainty transactions, reconciling the computational cost of XAI with real-time throughput requirements.", "date": "2026-02-17", "categories": []}
{"id": "2603.13290", "title": "TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems", "abstract": "Decentralized financial platforms rely heavily on Web of Trust reputation systems to mitigate counterparty risk in the absence of centralized identity verification. However, these pseudonymous networks are inherently vulnerable to adversarial behaviors, such as Sybil attacks and camouflaged fraud, where malicious actors cultivate artificial reputations before executing exit scams. Traditional anomaly detection in this domain faces two critical limitations. First, reliance on naive statistical heuristics (e.g., flagging the lowest 5% of rated users) fails to distinguish between victims of bad-mouthing attacks and actual fraudsters. Second, standard Graph Neural Networks (GNNs) operate on the assumption of homophily and cannot effectively process the semantic inversion inherent in signed (trust vs. distrust) and directed (status) edges. We propose TAS-GNN (Topology-Aware Signed Graph Neural Network), a novel framework designed for feature-sparse signed networks like Bitcoin-Alpha. TAS-GNN integrates recursive Web-of-Trust labeling and a dual-channel message-passing architecture that separately models trust and distrust signals, fused through a Status-Aware Attention mechanism. Experiments demonstrate that TAS-GNN achieves state-of-the-art performance, significantly outperforming existing signed GNN baselines.", "date": "2026-02-28", "categories": []}
{"id": "2603.13306", "title": "Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision", "abstract": "CCTV safety monitoring demands anomaly detectors combine reliable clip-level accuracy with predictable per-clip latency despite weak supervision. This work investigates compact vision-language models (VLMs) as practical detectors for this regime. A unified evaluation protocol standardizes preprocessing, prompting, dataset splits, metrics, and runtime settings to compare parameter-efficiently adapted compact VLMs against training-free VLM pipelines and weakly supervised baselines. Evaluation spans accuracy, precision, recall, F1, ROC-AUC, and average per-clip latency to jointly quantify detection quality and efficiency. With parameter-efficient adaptation, compact VLMs achieve performance on par with, and in several cases exceeding, established approaches while retaining competitive per-clip latency. Adaptation further reduces prompt sensitivity, producing more consistent behavior across prompt regimes under the shared protocol. These results show that parameter-efficient fine-tuning enables compact VLMs to serve as dependable clip-level anomaly detectors, yielding a favorable accuracy-efficiency trade-off within a transparent and consistent experimental setup.", "date": "2026-03-03", "categories": []}
{"id": "2603.13374", "title": "Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection", "abstract": "Training-free video anomaly detection (VAD) has recently emerged as a scalable alternative to supervised approaches, yet existing methods largely rely on static prompting and geometry-agnostic feature fusion. As a result, anomaly inference is often reduced to shallow similarity matching over Euclidean embeddings, leading to unstable predictions and limited interpretability, especially in complex or hierarchically structured scenes. We introduce MM-VAD, a geometry-aware semantic reasoning framework for training free VAD that reframes anomaly detection as adaptive test-time inference rather than fixed feature comparison. Our approach projects caption-derived scene representations into hyperbolic space to better preserve hierarchical structure and performs anomaly assessment through an adaptive question answering process over a frozen large language model. A lightweight, learnable prompt is optimised at test time using an unsupervised confidence-sparsity objective, enabling context-specific calibration without updating any backbone parameters. To further ground semantic predictions in visual evidence, we incorporate a covariance-aware Mahalanobis refinement that stabilises cross-modal alignment. Across four benchmarks, MM-VAD consistently improves over prior training-free methods, achieving 90.03% AUC on XD-Violence and 83.24%, 96.95%, and 98.81% on UCF-Crime, ShanghaiTech, and UCSD Ped2, respectively. Our results demonstrate that geometry-aware representation and adaptive semantic calibration provide a principled and effective alternative to static Euclidean matching in training-free VAD.", "date": "2026-03-10", "categories": []}
{"id": "2603.13507", "title": "MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection", "abstract": "Industrial visual anomaly detection (VAD) methods are typically trained on normal samples only, yet performance improves substantially when even limited anomalous data is available. Existing anomaly generation approaches either require real anomalous examples, demand expensive hardware, or produce synthetic defects that lack realism. We present MIRAGE (Model-agnostic Industrial Realistic Anomaly Generation and Evaluation), a fully automated pipeline for realistic anomalous image generation and pixel-level mask creation that requires no training and no anomalous images. Our pipeline accesses any generative model as a black box via API calls, uses a VLM for automatic defect prompt generation, and includes a CLIP-based quality filter to retain only well-aligned generated images. For mask generation at scale, we introduce a lightweight, training-free dual-branch semantic change detection module combining text-conditioned Grounding DINO features with fine-grained YOLOv26-Seg structural features. We benchmark four generation methods using Gemini 2.5 Flash Image (Nano Banana) as the generative backbone, evaluating performance on MVTec AD and VisA across two distinct tasks: (i) downstream anomaly segmentation and (ii) visual quality of the generated images, assessed via standard metrics (IS, IC-LPIPS) and a human perceptual study involving 31 participants and 1,550 pairwise votes. The results demonstrate that MIRAGE offers a scalable, accessible foundation for anomaly-aware industrial inspection that requires no real defect data. As a final contribution, we publicly release a large-scale dataset comprising 500 image-mask pairs per category for every MVTec AD and VisA class, over 13,000 pairs in total, alongside all generation prompts and pipeline code.", "date": "2026-03-13", "categories": []}
{"id": "2603.13617", "title": "Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE", "abstract": "Fraud-related financial losses continue to rise, while regulatory, privacy, and data-sovereignty constraints increasingly limit the feasibility of centralized fraud detection systems. Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative model training across institutions without sharing raw transaction data. Yet, its practical effectiveness under realistic, non-IID financial data distributions remains insufficiently validated. In this work, we present a multi-institution, industry-oriented proof-of-concept study evaluating federated anomaly detection for payment transactions using the NVIDIA FLARE framework. We simulate a realistic federation of heterogeneous financial institutions, each observing distinct fraud typologies and operating under strict data isolation. Using a deep neural network trained via federated averaging (FedAvg), we demonstrate that federated models achieve a mean F1-score of 0.903 - substantially outperforming locally trained models (0.643) and closely approaching centralized training performance (0.925), while preserving full data sovereignty. We further analyze convergence behavior, showing that strong performance is achieved within 10 federated communication rounds, highlighting the operational viability of FL in latency- and cost-sensitive financial environments. To support deployment in regulated settings, we evaluate model interpretability using Shapley-based feature attribution and confirm that federated models rely on semantically coherent, domain-relevant decision signals. Finally, we incorporate sample-level differential privacy via DP-SGD and demonstrate favorable privacy-utility trade-offs...", "date": "2026-03-13", "categories": []}
{"id": "2603.13749", "title": "Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection", "abstract": "Detecting subtle deviations in noisy acoustic environments is central to anomalous sound detection (ASD). A common training-free ASD pipeline temporally pools frame-level representations into a band-preserving feature vector and scores anomalies using a single nearest-neighbor match. However, this global matching can inflate normal-score variance through two effects. First, when normal sounds exhibit band-wise variability, a single global neighbor forces all bands to share the same reference, increasing band-level mismatch. Second, cosine-based matching is energy-coupled, allowing a few high-energy bands to dominate score computation under normal energy fluctuations and further increase variance. We propose BEAM, which stores temporally pooled sub-band vectors in a memory bank, retrieves neighbors per sub-band, and uniformly aggregates scores to reduce normal-score variability and improve discriminability. We further introduce a parameter-free adaptive fusion to better handle diverse temporal dynamics in sub-band responses. Experiments on multiple DCASE Task 2 benchmarks show strong performance without task-specific training, robustness to noise and domain shifts, and complementary gains when combined with encoder fine-tuning.", "date": "2026-03-14", "categories": []}
{"id": "2603.13895", "title": "MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection", "abstract": "Stacked AutoEncoders (SAE) have been widely adopted in edge anomaly detection scenarios. However, the resource-intensive nature of SAE can pose significant challenges for edge devices, which are typically resource-constrained and must adapt rapidly to dynamic and changing conditions. Optimizing SAE to meet the heterogeneous demands of real-world deployment scenarios, including high performance under constrained storage, low power consumption, fast inference, and efficient model updates, remains a substantial challenge. To address this, we propose an integrated optimization framework that jointly considers these critical factors to achieve balanced and adaptive system-level optimization. Specifically, we formulate SAE optimization for edge anomaly detection as a multi-objective optimization problem and propose MO-SAE (Multi-Objective Stacked AutoEncoders). The multiple objectives are addressed by integrating model clipping, multi-branch exit design, and a matrix approximation technique. In addition, a multi-objective heuristic algorithm is employed to effectively balance the competing objectives in SAE optimization. Our results demonstrate that the proposed MO-SAE delivers substantial improvements over the original approach. On the x86 architecture, it reduces storage space and power consumption by at least 50%, improves runtime efficiency by no less than 28%, and achieves an 11.8% compression rate, all while maintaining application performance. Furthermore, MO-SAE runs efficiently on edge devices with ARM architecture. Experimental results show a 15% improvement in inference speed, facilitating efficient deployment in cloud-edge collaborative anomaly detection systems.", "date": "2026-03-14", "categories": []}
{"id": "2603.13964", "title": "VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction", "abstract": "Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often distract vision-centric detectors from identifying rule-level violations. However, existing benchmarks rarely provide controlled settings where logical states are fixed while such nuisance factors vary. To address this gap, we introduce VID-AD, a dataset for logical anomaly detection under vision-induced distraction. It comprises 10 manufacturing scenarios and five capture conditions, totaling 50 one-class tasks and 10,395 images. Each scenario is defined by two logical constraints selected from quantity, length, type, placement, and relation, with anomalies including both single-constraint and combined violations. We further propose a language-based anomaly detection framework that relies solely on text descriptions generated from normal images. Using contrastive learning with positive texts and contradiction-based negative texts synthesized from these descriptions, our method learns embeddings that capture logical attributes rather than low-level features. Extensive experiments demonstrate consistent improvements over baselines across the evaluated settings. The dataset is available at: https://github.com/nkthiroto/VID-AD.", "date": "2026-03-14", "categories": []}
{"id": "2603.13993", "title": "VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery", "abstract": "Space missions generate massive volumes of high-resolution orbital and surface imagery that far exceed the capacity for manual inspection. Detecting rare phenomena is scientifically critical, yet traditional supervised learning struggles due to scarce labeled examples and closed-world assumptions that prevent discovery of genuinely novel observations. In this work, we investigate Visual Anomaly Detection (VAD) as a framework for automated discovery in planetary exploration. We present the first empirical evaluation of state-of-the-art feature-based VAD methods on real planetary imagery, encompassing both orbital lunar data and Mars rover surface imagery. To support this evaluation, we introduce two benchmarks: (i) a lunar dataset derived from Lunar Reconnaissance Orbiter Camera Narrow Angle imagery, comprising of fresh and degraded craters as anomalies alongside normal terrain; and (ii) a Mars surface dataset designed to reflect the characteristics of rover-acquired imagery. We evaluate multiple VAD approaches with a focus on computationally efficient, edge-oriented solutions suitable for onboard deployment, applicable to both orbital platforms surveying the lunar surface and surface rovers operating on Mars. Our results demonstrate that feature-based VAD methods can effectively identify rare planetary surface phenomena while remaining feasible for resource-constrained environments. By grounding anomaly detection in planetary science, this work establishes practical benchmarks and highlights the potential of open-world perception systems to support a range of mission-critical applications, including tactical planning, landing site selection, hazard detection, bandwidth-aware data prioritization, and the discovery of unanticipated geological processes.", "date": "2026-03-14", "categories": []}
{"id": "2603.14171", "title": "TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference", "abstract": "Anomaly detection for tabular data has been a long-standing unsupervised learning problem that remains a major challenge for current deep learning models. Recently, in-context learning has emerged as a new paradigm that has shifted efforts from task-specific optimization to large-scale pretraining aimed at creating foundation models that generalize across diverse datasets. Although in-context models, such as TabPFN, perform well in supervised problems, their learned classification-based priors may not readily extend to anomaly detection. In this paper, we study in-context models for anomaly detection and show that the unsupervised extensions to TabPFN exhibit unstable behavior, particularly in noisy or contaminated contexts, in addition to the high computational cost. We address these challenges and introduce TACTIC, an in-context anomaly detection approach based on pretraining with anomaly-centric synthetic priors, which provides fast and data-dependent reasoning about anomalies while avoiding dataset-specific tuning. In contrast to typical score-based approaches, which produce uncalibrated anomaly scores that require post-processing (e.g. threshold selection or ranking heuristics), the proposed model is trained as a discriminative predictor, enabling unambiguous anomaly decisions in a single forward pass. Through experiments on real-world datasets, we examine the performance of TACTIC in clean and noisy contexts with varying anomaly rates and different anomaly types, as well as the impact of prior choices on detection quality. Our experiments clearly show that specialized anomaly-centric in-context models such as TACTIC are highly competitive compared to other task-specific methods.", "date": "2026-03-15", "categories": []}
{"id": "2603.14406", "title": "Graph-Based Deep Learning for Intelligent Detection of Energy Losses, Theft, and Operational Inefficiencies in Oil & Gas Production Networks", "abstract": "Early detection of energy losses, theft, and operational inefficiencies remains a critical challenge in oil and gas production systems due to complex interdependencies among wells and facilities, evolving operating conditions, and limited labeled anomaly data. Traditional machine learning approaches often treat production units independently and struggle under temporal distribution shifts. This study proposes a spatiotemporal graph-based deep learning framework for anomaly detection in oil and gas production networks. The production system is modeled as a hierarchical graph of wells, facilities, and fields, with additional peer connections among wells sharing common infrastructure. Weakly supervised anomaly labels are derived from physically informed heuristics based on production, pressure, and flow behavior. Temporal dynamics are captured through sequence modeling, while relational dependencies are learned using a Temporal Graph Attention Network. Under time-based evaluation, the proposed model achieves an ROC-AUC of about 0.98 and anomaly recall above 0.93, demonstrating improved robustness and practical potential for proactive monitoring in real-world energy operations.", "date": "2026-03-15", "categories": []}
{"id": "2603.14592", "title": "A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions", "abstract": "Financial fraud detection in transaction networks involves modeling sparse anomalies, dynamic patterns, and severe class imbalance in the presence of temporal drift in the data. In real-world transaction systems, a suspicious transaction is rarely isolated: rather, legitimate and suspicious transactions are often connected through accounts, intermediaries or through temporal transaction sequences. Attribute-based or randomly partitioned learning pipelines are therefore insufficient to detect relationally structured fraud. STC-MixHop, a graph-based framework combining spatial multi-resolution propagation with lightweight temporal consistency modeling for anomaly and fraud detection in dynamic transaction networks. It integrates three components: a MixHop-inspired multi-scale neighborhood diffusion encoder a multi-scale neighborhood diffusion MixHop-based encoder for learning structural patterns; a spatial-temporal attention module coupling current and preceding graph snapshots to stabilize representations; and a temporally informed self-supervised pretraining strategy exploiting unlabeled transaction interactions to improve representation quality. We evaluate the framework primarily on the PaySim dataset under strict chronological splits, supplementing the analysis with Porto Seguro and FEMA data to probe cross-domain component behavior. Results show that STC-MixHop is competitive among graph methods and achieves strong screening-oriented recall under highly imbalanced conditions. The experiments also reveal an important boundary condition: when node attributes are highly informative, tabular baselines remain difficult to outperform. Graph structure contributes most clearly where hidden relational dependencies are operationally important. These findings support a stability-focused view of graph learning for financial fraud detection.", "date": "2026-03-15", "categories": []}
{"id": "2603.15300", "title": "GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection", "abstract": "Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) comprises a critical task in modern manufacturing settings, where automated product inspection systems need to identify rare defects using only a handful of normal/defect-free training samples. In this context, the current study introduces a novel reconstruction-based approach termed GATE-AD. In particular, the proposed framework relies on the employment of a masked, representation-aligned Graph Attention Network (GAT) encoding scheme to learn robust appearance patterns of normal samples. By leveraging dense, patch-level, visual feature tokens as graph nodes, the model employs stacked self-attentional layers to adaptively encode complex, irregular, non-Euclidean, local relations. The graph is enhanced with a representation alignment component grounded on a learnable, latent space, where high reconstruction residual areas (i.e., defects) are assessed using a Scaled Cosine Error (SCE) objective function. Extensive comparative evaluation on the MVTec AD, VisA, and MPDD industrial defect detection benchmarks demonstrates that GATE-AD achieves state-of-the-art performance across the $1$- to $8$-shot settings, combining the highest detection accuracy (increase up to $1.8\\%$ in image AUROC in the 8-shot case in MPDD) with the lowest per-image inference latency (at least $25.05\\%$ faster), compared to the best-performing literature methods. In order to facilitate reproducibility and further research, the source code of GATE-AD is available at https://github.com/gthpapadopoulos/GATE-AD.", "date": "2026-03-16", "categories": []}
{"id": "2410.10234", "title": "LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space", "abstract": "Detecting anomalies such as an incorrect combination of objects or deviations in their positions is a challenging problem in unsupervised anomaly detection (AD). Since conventional AD methods mainly focus on local patterns of normal images, they struggle with detecting logical anomalies that appear in the global patterns. To effectively detect these challenging logical anomalies, we introduce Logical Anomaly Detection with Masked Image Modeling (LADMIM), a novel unsupervised AD framework that harnesses the power of masked image modeling and discrete representation learning. Our core insight is that predicting the missing region forces the model to learn the long-range dependencies between patches. Specifically, we formulate AD as a mask completion task, which predicts the distribution of discrete latents in the masked region. As a distribution of discrete latents is invariant to the low-level variance in the pixel space, the model can desirably focus on the logical dependencies in the image, which improves accuracy in the logical AD. We evaluate the AD performance on five benchmarks and show that our approach achieves compatible performance without any pre-trained segmentation models. We also conduct comprehensive experiments to reveal the key factors that influence logical AD performance.", "date": "2024-10-14", "categories": []}
{"id": "2505.13702", "title": "Unsupervised anomaly detection in MeV ultrafast electron diffraction", "abstract": "MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, shot-to-shot instabilities in the electron beam can distort individual patterns, introducing uncertainty. Improving MUED accuracy requires detecting and removing these anomalous patterns from large datasets. In this work, we developed a fully unsupervised methodology for the detection of anomalous diffraction patterns. Using a convolutional autoencoder, we calculate the reconstruction mean squared error of the diffraction patterns. Based on the statistical analysis of this error, we provide the user an estimation of the probability that the pattern is normal, which also allows a posterior visual inspection of the images that are difficult to classify. This method has been trained with only 100 diffraction patterns and tested on 1521 patterns, resulting in a false positive rate between 0.2\\% and 0.4\\%, with a training time of 10 seconds per image and a test time of about 1 second per image. The proposed methodology can also be applied to other diffraction techniques in which large datasets are collected that include faulty images due to instrumental instabilities.", "date": "2025-05-19", "categories": []}
{"id": "2602.17077", "title": "Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection", "abstract": "Weakly supervised video anomaly detection aims to detect anomalies and identify abnormal categories with only video-level labels. We propose CPL-VAD, a dual-branch framework with cross pseudo labeling. The binary anomaly detection branch focuses on snippet-level anomaly localization, while the category classification branch leverages vision-language alignment to recognize abnormal event categories. By exchanging pseudo labels, the two branches transfer complementary strengths, combining temporal precision with semantic discrimination. Experiments on XD-Violence and UCF-Crime demonstrate that CPL-VAD achieves state-of-the-art performance in both anomaly detection and abnormal category classification.", "date": "2026-02-19", "categories": []}
{"id": "2603.12847", "title": "Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers", "abstract": "Most real-world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of outliers. In addition to sporadic scattered outliers caused by factors such as faulty sensor readings, IoT systems often exhibit clustered outliers. These occur when multiple devices or nodes produce similar anomalous measurements, for instance, owing to localized interference, emerging security threats, or regional false alarms, forming micro-clusters. These clustered outliers can be easily mistaken for normal behavior because of their relatively high local density, thereby obscuring the detection of both scattered and contextual anomalies. To address this, we propose a novel outlier detection paradigm that leverages the natural neighboring relationships using graph structures. This facilitates multi-perspective anomaly evaluation by incorporating reference sets at both local and global scales derived from the graph. Our approach enables the effective recognition of scattered outliers without interference from clustered anomalies, whereas the graph structure simultaneously helps reflect and isolate clustered outlier groups. Extensive experiments, including comparative performance analysis, ablation studies, validation on downstream clustering tasks, and evaluation of hyperparameter sensitivity, demonstrate the efficacy of the proposed method. The source code is available at https://github.com/gordonlok/DROD.", "date": "2026-03-13", "categories": []}
{"id": "2603.13068", "title": "GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration", "abstract": "Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization. Existing studies suffer from two key limitations: (1) single region scenarios which limit model generalizability; (2) proprietary datasets, which makes result reproduction unattainable. In this work, we introduce \\textbf{GeoChemAD}, an open-source benchmark dataset compiled from government-led geological surveys, covering multiple regions, sampling sources, and target elements. The dataset comprises eight subsets representing diverse spatial scales and sampling conditions. To establish strong baselines, we reproduce and benchmark a range of unsupervised anomaly detection methods, including statistical models, generative and transformer-based approaches. Furthermore, we propose \\textbf{GeoChemFormer}, a transformer-based framework that leverages self-supervised pretraining to learn target-element-aware geochemical representations for spatial samples. Extensive experiments demonstrate that GeoChemFormer consistently achieves superior and robust performance across all eight subsets, outperforming existing unsupervised methods in both anomaly detection accuracy and generalization capability. The proposed dataset and framework provide a foundation for reproducible research and future development in this direction.", "date": "2026-03-13", "categories": []}
{"id": "2504.05662", "title": "InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models", "abstract": "Despite the remarkable success, recent reconstruction-based anomaly detection (AD) methods via diffusion modeling still involve fine-grained noise-strength tuning and computationally expensive multi-step denoising, leading to a fundamental tension between fidelity and efficiency. In this paper, we propose InvAD, a novel inversion-based anomaly detection approach (\"detection via noising in latent space\") that circumvents explicit reconstruction. Importantly, we contend that the limitations in prior reconstruction-based methods originate from the prevailing \"detection via denoising in RGB space\" paradigm. To address this, we model AD under a reconstruction-free formulation, which directly infers the final latent variable corresponding to the input image via DDIM inversion, and then measures the deviation based on the known prior distribution for anomaly scoring. Specifically, in approximating the original probability flow ODE using the Euler method, we enforce only a few inversion steps to noise the clean image to pursue inference efficiency. As the added noise is adaptively derived with the learned diffusion model, the original features for the clean testing image can still be leveraged to yield high detection accuracy. We perform extensive experiments and detailed analyses across four widely used industrial and medical AD benchmarks under the unsupervised unified setting to demonstrate the effectiveness of our model, achieving state-of-the-art AD performance and approximately 2x inference-time speedup without diffusion distillation.", "date": "2025-04-08", "categories": []}
{"id": "2603.11049", "title": "Comparison of Outlier Detection Algorithms on String Data", "abstract": "Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data outlier detection algorithm could assist with data cleaning or anomaly detection in system log files. In this thesis, we compare two string outlier detection algorithms. Firstly, we introduce a variant of the well-known local outlier factor algorithm, which we tailor to detect outliers on string data using the Levenshtein measure to calculate the density of the dataset. We present a differently weighted Levenshtein measure, which considers hierarchical character classes and can be used to tune the algorithm to a specific string dataset. Secondly, we introduce a new kind of outlier detection algorithm based on the hierarchical left regular expression learner, which infers a regular expression for the expected data. Using various datasets and parameters, we experimentally show that both algorithms can conceptually find outliers in string data. We show that the regular expression-based algorithm is especially good at finding outliers if the expected values have a distinct structure that is sufficiently different from the structure of the outliers. In contrast, the local outlier factor algorithms are best at finding outliers if their edit distance to the expected data is sufficiently distinct from the edit distance between the expected data.", "date": "2026-01-13", "categories": []}
{"id": "2603.11106", "title": "RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation", "abstract": "Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the probability density function. We further present LIBERO-Anomaly-10, a benchmark comprising three categories of robotic anomalies for simulation evaluation. RC-NF achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks. Real-world experiments demonstrate that RC-NF operates as a plug-and-play module for VLA models (e.g., pi0), providing a real-time OOD signal that enables state-level rollback or task-level replanning when necessary, with a response latency under 100 ms. These results demonstrate that RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.", "date": "2026-03-11", "categories": []}
{"id": "2603.11745", "title": "CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data", "abstract": "Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint strategies, which involve detecting errors with one model and imputing them with another. Such approaches can fail to capture the full joint distribution of the data and ignore prediction uncertainty. This work introduces Conditional Imputation and Noisy Data Integrity (CINDI), an unsupervised probabilistic framework designed to restore data integrity in complex time series. Unlike fragmented approaches, CINDI unifies anomaly detection and imputation into a single end-to-end system built on conditional normalizing flows. By modeling the exact conditional likelihood of the data, the framework identifies low-probability segments and iteratively samples statistically consistent replacements. This allows CINDI to efficiently reuse learned information while preserving the underlying physical and statistical properties of the system. We evaluate the framework using real-world grid loss data from a Norwegian power distribution operator, though the methodology is designed to generalize to any multivariate time series domain. The results demonstrate that CINDI yields robust performance compared to competitive baselines, offering a scalable solution for maintaining reliability in noisy environments.", "date": "2026-03-12", "categories": []}
{"id": "2603.11756", "title": "Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows", "abstract": "Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reduced to a statistically grounded compliance test, such that observations are mapped to latent space and evaluated via goodness-of-fit tests against the prescribed latent evolution. This yields a principled decision rule that remains effective even in regions of high observation likelihood. Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance.", "date": "2026-03-12", "categories": []}
{"id": "2511.15339", "title": "STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection", "abstract": "Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods, including sequence variational autoencoders (VAEs), use a single latent process and therefore mix heterogeneous time scales, which can smooth out spikes or inflate variances and weaken anomaly separation.\n  In this paper, we present STREAM-VAE, a variational autoencoder for anomaly detection in automotive telemetry time-series data. Our model uses a dual-path encoder to separate slow drift and fast spike signal dynamics, and a decoder that represents transient deviations separately from the normal operating pattern. STREAM-VAE is designed for deployment, producing stable anomaly scores across operating modes for both in-vehicle monitors and backend fleet analytics.\n  Experiments on an automotive telemetry dataset and the public SMD benchmark show that explicitly separating drift and spike dynamics improves robustness compared to strong forecasting, attention, graph, and VAE baselines.", "date": "2025-11-19", "categories": []}
{"id": "2602.04102", "title": "DMS2F-HAD: A Dual-branch Mamba-based Spatial-Spectral Fusion Network for Hyperspectral Anomaly Detection", "abstract": "Hyperspectral anomaly detection (HAD) aims to identify rare and irregular targets in high-dimensional hyperspectral images (HSIs), which are often noisy and unlabelled data. Existing deep learning methods either fail to capture long-range spectral dependencies (e.g., convolutional neural networks) or suffer from high computational cost (e.g., Transformers). To address these challenges, we propose DMS2F-HAD, a novel dual-branch Mamba-based model. Our architecture utilizes Mamba's linear-time modeling to efficiently learn distinct spatial and spectral features in specialized branches, which are then integrated by a dynamic gated fusion mechanism to enhance anomaly localization. Across fourteen benchmark HSI datasets, our proposed DMS2F-HAD not only achieves a state-of-the-art average AUC of 98.78%, but also demonstrates superior efficiency with an inference speed 4.6 times faster than comparable deep learning methods. The results highlight DMS2FHAD's strong generalization and scalability, positioning it as a strong candidate for practical HAD applications.", "date": "2026-02-04", "categories": []}
{"id": "2503.21622", "title": "The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection", "abstract": "In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models often competing in the range of less than one percentage point. This lack of discriminatory power prevents a meaningful comparison of models and thus hinders progress of the field, especially when considering the inherent stochastic nature of machine learning results. We present MVTec AD 2, a collection of eight anomaly detection scenarios with more than 8000 high-resolution images. It comprises challenging and highly relevant industrial inspection use cases that have not been considered in previous datasets, including transparent and overlapping objects, dark-field and back light illumination, objects with high variance in the normal data, and extremely small defects. We provide comprehensive evaluations of state-of-the-art methods and show that their performance remains below 60% average AU-PRO. Additionally, our dataset provides test scenarios with lighting condition changes to assess the robustness of methods under real-world distribution shifts. We host a publicly accessible evaluation server that holds the pixel-precise ground truth of the test set (https://benchmark.mvtec.com/). All image data is available at https://www.mvtec.com/company/research/datasets/mvtec-ad-2.", "date": "2025-03-27", "categories": []}
{"id": "2603.09349", "title": "TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection", "abstract": "A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of the graph data ecosystem and urgently requires effective identification and processing. With anomalies that span multiple data domains yet exhibit vast differences in features, cross-domain detection models face severe domain shift issues, which limit their generalizability across all domains. This study identifies and quantitatively analyzes a specific feature mismatch pattern exhibited by domain shift in graph anomaly detection, which we define as the \\emph{Anomaly Disassortativity} issue ($\\mathcal{AD}$). Based on the modeling of the issue $\\mathcal{AD}$, we introduce a novel graph foundation model for anomaly detection. It achieves cross-domain generalization in different graphs, requiring only a single training phase to perform effectively across diverse domains. The experimental findings, based on fourteen diverse real-world graphs, confirm a breakthrough in the model's cross-domain adaptation, achieving a pioneering state-of-the-art (SOTA) level in terms of detection accuracy. In summary, the proposed theory of $\\mathcal{AD}$ provides a novel theoretical perspective and a practical route for future research in generalist graph anomaly detection (GGAD). The code is available at https://anonymous.4open.science/r/Anonymization-TA-GGAD/.", "date": "2026-03-10", "categories": []}
{"id": "2603.09490", "title": "Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection", "abstract": "This paper introduces temporal-conditioned normalizing flows (tcNF), a novel framework that addresses anomaly detection in time series data with accurate modeling of temporal dependencies and uncertainty. By conditioning normalizing flows on previous observations, tcNF effectively captures complex temporal dynamics and generates accurate probability distributions of expected behavior. This autoregressive approach enables robust anomaly detection by identifying low-probability events within the learned distribution. We evaluate tcNF on diverse datasets, demonstrating good accuracy and robustness compared to existing methods. A comprehensive analysis of strengths and limitations and open-source code is provided to facilitate reproducibility and future research.", "date": "2026-03-10", "categories": []}
{"id": "2603.09675", "title": "GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation", "abstract": "There is growing interest in applying graph-based methods to Time Series Anomaly Detection (TSAD), particularly Graph Neural Networks (GNNs), as they naturally model dependencies among multivariate signals. GNNs are typically used as backbones in score-based TSAD pipelines, where anomalies are identified through reconstruction or prediction errors followed by thresholding. However, and despite promising results, the field still lacks standardized frameworks for evaluation and suffers from persistent issues with metric design and interpretation. We thus present an open-source framework for TSAD using GNNs, designed to support reproducible experimentation across datasets, graph structures, and evaluation strategies. Built with flexibility and extensibility in mind, the framework facilitates systematic comparisons between TSAD models and enables in-depth analysis of performance and interpretability. Using this tool, we evaluate several GNN-based architectures alongside baseline models across two real-world datasets with contrasting structural characteristics. Our results show that GNNs not only improve detection performance but also offer significant gains in interpretability, an especially valuable feature for practical diagnosis. We also find that attention-based GNNs offer robustness when graph structure is uncertain or inferred. In addition, we reflect on common evaluation practices in TSAD, showing how certain metrics and thresholding strategies can obscure meaningful comparisons. Overall, this work contributes both practical tools and critical insights to advance the development and evaluation of graph-based TSAD systems.", "date": "2026-03-10", "categories": []}
{"id": "2510.14462", "title": "Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review", "abstract": "Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-level annotations. By learning the distribution of healthy anatomy and generating pseudo-healthy reconstructions, these methods aim to localise deviations in a pathology-agnostic manner. Despite rapid methodological development - from autoencoders and variational autoencoders to generative adversarial networks and diffusion-based models - a structured synthesis of their application in structural neuroimaging is lacking.\n  We conducted a PRISMA-ScR-guided scoping review of studies published between January 2018-December 2025 that applied unsupervised deep generative models to anomaly detection in brain MRI (and, less frequently, CT). Thirty-three studies met inclusion criteria. Methods were categorised by architectural family, and reported performance was synthesised across major pathology groups, with segmentation (Dice) and detection metrics (AUROC, AUPRC) disaggregated by evaluation level (voxel, slice, subject). For transparency, we also summarised dataset characteristics, dimensionality (2D vs. 3D), and thresholding strategies.\n  Overall, unsupervised generative approaches demonstrate potential for pathology-agnostic anomaly localisation, particularly in settings where annotated data are scarce. However, methodological heterogeneity, limited external validation, and sensitivity to dataset characteristics remain important challenges. Emerging paradigms - including anatomy-aware modelling, diffusion-based frameworks, and alternative normative evaluation metrics - seek to address these limitations and improve robustness and clinical relevance.", "date": "2025-10-16", "categories": []}
{"id": "2510.17088", "title": "Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing", "abstract": "Financial anomalies arise from heterogeneous mechanisms -- price shocks, liquidity freezes, contagion cascades, and momentum reversals -- yet existing detectors produce uniform scores without revealing which mechanism is failing. This hinders targeted responses: liquidity freezes call for market-making support, whereas price shocks call for circuit breakers. Three key challenges remain: (1) static graphs cannot adapt when correlations shift across regimes; (2) uniform detectors overlook heterogeneous anomaly signatures; and (3) black-box scores provide no actionable guidance on driving mechanisms. We address these challenges with an adaptive graph learning framework that embeds interpretability architecturally rather than post hoc. The framework constructs stress-modulated graphs that adaptively interpolate between known sector and geographic relationships and data-driven correlations as market conditions evolve. Anomalies are decomposed via four mechanism-specific experts -- Price-Shock, Liquidity, Systemic-Contagion, and Momentum-Reversal -- each capturing a distinct anomaly channel documented in the financial economics literature. The resulting routing weights serve as interpretable proxies for mechanism attribution, with their relative values indicating each anomaly's primary driving mechanism. A hierarchical Market Pressure Index aggregates entity-level anomaly scores into graduated market-wide alerts. On 100 U.S. equities (2017-2024), the framework detects all six major stress events with a 3.7-day mean lead time, outperforming baselines by +33 percentage points, with AUC 0.888 and AP 0.626. Case studies on SVB (March 2023) and Japan carry-trade unwind (August 2024) demonstrate that routing weights automatically distinguish localized from systemic crises without labeled supervision.", "date": "2025-10-20", "categories": []}
{"id": "2510.20102", "title": "Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions", "abstract": "We present HCLA, a human-centered multi-agent system for anomaly detection in digital-asset transactions. The system integrates three cognitively aligned roles: Rule Abstraction, Evidence Scoring, and Expert-Style Justification. These roles operate in a conversational workflow that enables non-experts to express analytical intent in natural language, inspect structured risk evidence, and obtain traceable, context-aware reasoning.\n  Implemented with an open-source, web-based interface, HCLA translates user intent into explicit analytical rules, applies classical anomaly detectors to quantify evidential risk, and reconstructs expert-style justifications grounded in observable transactional signals. Experiments on a cryptocurrency anomaly dataset show that, while the underlying detector achieves strong predictive accuracy, HCLA substantially improves interpretability, interaction, and decision transparency.\n  Importantly, HCLA is not designed to explain a black-box model in the conventional XAI sense. Instead, we reconstruct a traceable expert reasoning process that aligns algorithmic evidence with regulatory and investigative judgment. By explicitly separating evidence scoring from expert-style justification, the framework emphasizes accountability beyond explainability and addresses practical requirements for regulatory, audit, and compliance-driven financial forensics.\n  We describe the system architecture, closed-loop interaction design, datasets, evaluation protocol, and limitations. We argue that a human-in-the-loop reasoning reconstruction paradigm is essential for achieving transparency, accountability, and trust in high-stakes financial environments.\n  Keywords: Human-Centered AI; LLM-Agent System; Multi-Agent Architecture; Anomaly Detection; Digital Asset Transactions; Cryptocurrency Forensics; Blockchain Analytics; Human-in-the-Loop; Explainable AI (XAI); Interpretability", "date": "2025-10-23", "categories": []}
{"id": "2512.22179", "title": "Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection", "abstract": "A critical vulnerability of supervised deep learning in high-dimensional tabular domains is \"generalization collapse\": models form precise decision boundaries around known training distributions but fail catastrophically when encountering Out-of-Distribution (OOD) data. To overcome this, we propose Latent Sculpting, a hierarchical, two-stage representation learning architecture designed to enforce explicit structural boundaries prior to density estimation. In the first stage, a Transformer-based tabular encoder is trained using our novel Binary Latent Sculpting loss. This objective explicitly condenses benign network traffic into a dense, low-entropy hypersphere while enforcing a strict geometric minimum-distance margin for anomalous patterns. In the second stage, a Masked Autoregressive Flow (MAF) maps this structurally optimized manifold to calculate exact, probabilistic anomaly thresholds. We evaluate this methodology on the CIC-IDS-2017 benchmark under a rigorous zero-shot protocol, deliberately withholding complex attack classes during training to test true OOD generalization. Averaged across three random initialization seeds to ensure statistical robustness, our framework maintains near-perfect classification on known signatures (F1 = 0.980 +/- 0.000) while achieving an overall zero-shot OOD F1-Score of 0.867 +/- 0.021 and an AUROC of 0.913 +/- 0.010 at an 85th-percentile threshold. Most notably, the model achieves an average recall of 78.7% (peaking at 97.2%) on stealthy \"Infiltration\" attacks and over 94% on low-volume DoS variations - complex distributional shifts where standard supervised and unsupervised baselines historically suffer near-total detection failure. These empirical results demonstrate that explicitly decoupling topological manifold structuring from probabilistic density estimation establishes a highly stable and scalable defense against zero-day cyber threats.", "date": "2025-12-19", "categories": []}
{"id": "2603.06987", "title": "Foundational World Models Accurately Detect Bimanual Manipulator Failures", "abstract": "Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators are no exception; these robots have vast state spaces comprised of high-dimensional images and proprioceptive signals. Explicitly defining failure modes within such state spaces is infeasible. In this work, we overcome these challenges by training a probabilistic, history informed, world model within the compressed latent space of a pretrained vision foundation model (NVIDIA's Cosmos Tokenizer). The model outputs uncertainty estimates alongside its predictions that serve as non-conformity scores within a conformal prediction framework. We use these scores to develop a runtime monitor, correlating periods of high uncertainty with anomalous failures. To test these methods, we use the simulated Push-T environment and the Bimanual Cable Manipulation dataset, the latter of which we introduce in this work. This new dataset features trajectories with multiple synchronized camera views, proprioceptive signals, and annotated failures from a challenging data center maintenance task. We benchmark our methods against baselines from the anomaly detection and out-of-distribution detection literature, and show that our approach considerably outperforms statistical techniques. Furthermore, we show that our approach requires approximately one twentieth of the trainable parameters as the next-best learning-based approach, yet outperforms it by 3.8% in terms of failure detection rate, paving the way toward safely deploying manipulator robots in real-world environments where reliability is non-negotiable.", "date": "2026-03-07", "categories": []}
{"id": "2603.07073", "title": "Interpretable Maximum Margin Deep Anomaly Detection", "abstract": "Anomaly detection is a crucial machine-learning task with wide-ranging applications. Deep Support Vector Data Description (Deep SVDD) is a prominent deep one-class method, but it is vulnerable to hypersphere collapse, often relies on heuristic choices for hypersphere parameters, and provides limited interpretability. To address these issues, we propose Interpretable Maximum Margin Deep Anomaly Detection (IMD-AD), which leverages a small set of labeled anomalies and a maximum margin objective to stabilize training and improve discrimination. It is inherently resilient to hypersphere collapse. Furthermore, we prove an equivalence between hypersphere parameters and the network's final-layer weights, which allows the center and radius to be learned end-to-end as part of the model and yields intrinsic interpretability and visualizable outputs. We further develop an efficient training algorithm that jointly optimizes representation, margin, and final-layer parameters. Extensive experiments and ablation studies on image and tabular benchmarks demonstrate that IMD-AD empirically improves detection performance over several state-of-the-art baselines while providing interpretable decision diagnostics.", "date": "2026-03-07", "categories": []}
{"id": "2603.07142", "title": "PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection", "abstract": "Medical image anomaly detection faces unique challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures. Through systematic Grad-CAM analysis, we reveal that discriminative activation maps fail on medical data, unlike their success on industrial datasets, motivating the need for manifold-level modeling. We propose PDD (Manifold-Prior Diverse Distillation), a framework that unifies dual-teacher priors into a shared high-dimensional manifold and distills this knowledge into dual students with complementary behaviors. Specifically, frozen VMamba-Tiny and wide-ResNet50 encoders provide global contextual and local structural priors, respectively. Their features are unified through a Manifold Matching and Unification (MMU) module, while an Inter-Level Feature Adaption (InA) module enriches intermediate representations. The unified manifold is distilled into two students: one performs layer-wise distillation via InA for local consistency, while the other receives skip-projected representations through a Manifold Prior Affine (MPA) module to capture cross-layer dependencies. A diversity loss prevents representation collapse while maintaining detection sensitivity. Extensive experiments on multiple medical datasets demonstrate that PDD significantly outperforms existing state-of-the-art methods, achieving improvements of up to 11.8%, 5.1%, and 8.5% in AUROC on HeadCT, BrainMRI, and ZhangLab datasets, respectively, and 3.4% in F1 max on the Uni-Medical dataset, establishing new state-of-the-art performance in medical image anomaly detection. The implementation will be released at https://github.com/OxygenLu/PDD", "date": "2026-03-07", "categories": []}
{"id": "2603.07566", "title": "GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module", "abstract": "Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products.\n  Defect localization is a key task, that usually is achieved using a basic comparison between generated image and the original one, implementing some blob-analysis or image-editing algorithms, in the post-processing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted.\n  For these reasons, we propose a new architecture composed by two blocks.\n  The first block is a Generative Adversarial Network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects.\n  The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using pre-processing algorithms, formerly developed with blob-analysis and image-editing procedures.\n  To test our model we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network.", "date": "2026-03-08", "categories": []}
{"id": "2603.07577", "title": "Integration of deep generative Anomaly Detection algorithm in high-speed industrial line", "abstract": "Industrial visual inspection in pharmaceutical production requires high accuracy under strict constraints on cycle time, hardware footprint, and operational cost. Manual inline inspection is still common, but it is affected by operator variability and limited throughput. Classical rule-based computer vision pipelines are often rigid and difficult to scale to highly variable production scenarios. To address these limitations, we present a semi-supervised anomaly detection framework based on a generative adversarial architecture with a residual autoencoder and a dense bottleneck, specifically designed for online deployment on a high-speed Blow-Fill-Seal (BFS) line. The model is trained only on nominal samples and detects anomalies through reconstruction residuals, providing both classification and spatial localization via heatmaps. The training set contains 2,815,200 grayscale patches. Experiments on a real industrial test kit show high detection performance while satisfying timing constraints compatible with a 500 ms acquisition slot.", "date": "2026-03-08", "categories": []}
{"id": "2603.07864", "title": "An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series", "abstract": "Detecting structural instability and anomalies in high-dimensional financial time series is challenging due to complex temporal dependence and evolving cross-sectional structure. We propose ReGEN-TAD, an interpretable generative framework that integrates modern machine learning with econometric diagnostics for anomaly detection. The model combines joint forecasting and reconstruction within a refined convolutional--transformer architecture and aggregates complementary signals capturing predictive inconsistency, reconstruction degradation, latent distortion, and volatility shifts. Robust calibration yields a unified anomaly score without labeled data. Experiments on synthetic and financial panels demonstrate improved robustness to structured deviations while enabling economically coherent factor-level attribution.", "date": "2026-03-09", "categories": []}
{"id": "2603.07952", "title": "VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer", "abstract": "Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VLMs) such as CLIP: they build hand-crafted or learned prompt sets for normal and abnormal semantics, then compute image-text similarities for open-set discrimination. While effective, this paradigm depends on a text encoder and cross-modal alignment, which can lead to training instability and parameter redundancy. This work revisits the necessity of the text branch in ZSAD and presents VisualAD, a purely visual framework built on Vision Transformers. We introduce two learnable tokens within a frozen backbone to directly encode normality and abnormality. Through multi-layer self-attention, these tokens interact with patch tokens, gradually acquiring high-level notions of normality and anomaly while guiding patches to highlight anomaly-related cues. Additionally, we incorporate a Spatial-Aware Cross-Attention (SCA) module and a lightweight Self-Alignment Function (SAF): SCA injects fine-grained spatial information into the tokens, and SAF recalibrates patch features before anomaly scoring. VisualAD achieves state-of-the-art performance on 13 zero-shot anomaly detection benchmarks spanning industrial and medical domains, and adapts seamlessly to pretrained vision backbones such as the CLIP image encoder and DINOv2. Code: https://github.com/7HHHHH/VisualAD", "date": "2026-03-09", "categories": []}
{"id": "2603.08130", "title": "Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions", "abstract": "We present a novel Explainable methodology for Condition Monitoring, relying on healthy data only. Since faults are rare events, we propose to focus on learning the probability distribution of healthy observations only, and detect Anomalies at runtime. This objective is achieved via the definition of probabilistic measures of deviation from nominality, which allow to detect and anticipate faults. The Bayesian perspective underpinning our approach allows us to perform Uncertainty Quantification to inform decisions. At the same time, we provide descriptive tools to enhance the interpretability of the results, supporting the deployment of the proposed strategy also in safety-critical applications. The methodology is validated experimentally on two use cases: a publicly available benchmark for Predictive Maintenance, and a real-world Helicopter Transmission dataset collected over multiple years. In both applications, the method achieves competitive detection performance with respect to state-of-the-art anomaly detection methods.", "date": "2026-03-09", "categories": []}
{"id": "2603.08137", "title": "Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach", "abstract": "Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they struggle with two major challenges: 1) homophily disparity, where nodes exhibit varying homophily at both class and node levels; and 2) limited scalability, as many methods rely on costly whole-graph operations. To address them, we propose SAGAD, a Scalable and Adaptive framework for GAD. SAGAD precomputes multi-hop embeddings and applies reparameterized Chebyshev filters to extract low- and high-frequency information, enabling efficient training and capturing both homophilic and heterophilic patterns. To mitigate node-level homophily disparity, we introduce an Anomaly Context-Aware Adaptive Fusion, which adaptively fuses low- and high-pass embeddings using fusion coefficients conditioned on Rayleigh Quotient-guided anomalous subgraph structures for each node. To alleviate class-level disparity, we design a Frequency Preference Guidance Loss, which encourages anomalies to preserve more high-frequency information than normal nodes. SAGAD supports mini-batch training, achieves linear time and space complexity, and drastically reduces memory usage on large-scale graphs. Theoretically, SAGAD ensures asymptotic linear separability between normal and abnormal nodes under mild conditions. Extensive experiments on 10 benchmarks confirm SAGAD's superior accuracy and scalability over state-of-the-art methods.", "date": "2026-03-09", "categories": []}
{"id": "2603.08455", "title": "The Boiling Frog Threshold: Criticality and Blindness in World Model-Based Anomaly Detection Under Gradual Drift", "abstract": "When an RL agent's observations are gradually corrupted, at what drift rate does it \"wake up\" -- and what determines this boundary? We study world model-based self-monitoring under continuous observation drift across four MuJoCo environments, three detector families (z-score, variance, percentile), and three model capacities. We find that (1) a sharp detection threshold $\\varepsilon^*$ exists universally: below it, drift is absorbed as normal variation; above it, detection occurs rapidly. The threshold's existence and sigmoid shape are invariant across all detector families and model capacities, though its position depends on the interaction between detector sensitivity, noise floor structure, and environment dynamics. (2) Sinusoidal drift is completely undetectable by all detector families -- including variance and percentile detectors with no temporal smoothing -- establishing this as a world model property rather than a detector artifact. (3) Within each environment, $\\varepsilon^*$ follows a power law in detector parameters ($R^2 = 0.89$-$0.97$), but cross-environment prediction fails ($R^2 = 0.45$), revealing that the missing variable is environment-specific dynamics structure $\\partial \\mathrm{PE}/\\partial\\varepsilon$. (4) In fragile environments, agents collapse before any detector can fire (\"collapse before awareness\"), creating a fundamentally unmonitorable failure mode. Our results reframe $\\varepsilon^*$ from an emergent world model property to a three-way interaction between noise floor, detector, and environment dynamics, providing a more defensible and empirically grounded account of self-monitoring boundaries in RL agents.", "date": "2026-03-09", "categories": []}
{"id": "2504.14919", "title": "GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection", "abstract": "Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen categories by leveraging CLIP's zero-shot capabilities to match text prompts with visual features. A key challenge in ZSAD is learning general prompts stably and utilizing them effectively, while maintaining both generalizability and category specificity. Although general prompts have been explored in prior works, achieving their stable optimization and effective deployment remains a significant challenge. In this work, we propose GenCLIP, a novel framework that learns and leverages general prompts more effectively through multi-layer prompting and dual-branch inference. Multi-layer prompting integrates category-specific visual cues from different CLIP layers, enriching general prompts with more comprehensive and robust feature representations. By combining general prompts with multi-layer visual features, our method further enhances its generalization capability. To balance specificity and generalization, we introduce a dual-branch inference strategy, where a vision-enhanced branch captures fine-grained category-specific features, while a query-only branch prioritizes generalization. The complementary outputs from both branches improve the stability and reliability of anomaly detection across unseen categories. Additionally, we propose an adaptive text prompt filtering mechanism, which removes irrelevant or atypical class names not encountered during CLIP's training, ensuring that only meaningful textual inputs contribute to the final vision-language alignment.", "date": "2025-04-21", "categories": []}
{"id": "2507.15335", "title": "ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis", "abstract": "Industrial defect detection systems face critical limitations when confined to one-class anomaly detection paradigms, which assume uniform outlier distributions and struggle with data scarcity in real-world manufacturing environments. We present ExDD (Explicit Dual Distribution), a novel framework that transcends these limitations by explicitly modeling dual feature distributions. Our approach leverages parallel memory banks that capture the distinct statistical properties of both normality and anomalous patterns, addressing the fundamental flaw of uniform outlier assumptions. To overcome data scarcity, we employ latent diffusion models with domain-specific textual conditioning, generating in-distribution synthetic defects that preserve industrial context. Our neighborhood-aware ratio scoring mechanism elegantly fuses complementary distance metrics, amplifying signals in regions exhibiting both deviation from normality and similarity to known defect patterns. Experimental validation on KSDD2 demonstrates superior performance (94.2% I-AUROC, 97.7% P-AUROC), with optimal augmentation at 100 synthetic samples. https://github.com/aqeeelmirza/ExDD-Defect-Detection", "date": "2025-07-21", "categories": []}
{"id": "2603.06131", "title": "DQE: A Semantic-Aware Evaluation Metric for Time Series Anomaly Detection", "abstract": "Time series anomaly detection has achieved remarkable progress in recent years. However, evaluation practices have received comparatively less attention, despite their critical importance. Existing metrics exhibit several limitations: (1) bias toward point-level coverage, (2) insensitivity or inconsistency in near-miss detections, (3) inadequate penalization of false alarms, and (4) inconsistency caused by threshold or threshold-interval selection. These limitations can produce unreliable or counterintuitive results, hindering objective progress. In this work, we revisit the evaluation of time series anomaly detection from the perspective of detection semantics and propose a novel metric for more comprehensive assessment. We first introduce a partitioning strategy grounded in detection semantics, which decomposes the local temporal region of each anomaly into three functionally distinct subregions. Using this partitioning, we evaluate overall detection behavior across events and design finer-grained scoring mechanisms for each subregion, enabling more reliable and interpretable assessment. Through a systematic study of existing metrics, we identify an evaluation bias associated with threshold-interval selection and adopt an approach that aggregates detection qualities across the full threshold spectrum, thereby eliminating evaluation inconsistency. Extensive experiments on synthetic and real-world data demonstrate that our metric provides stable, discriminative, and interpretable evaluation, while achieving robust assessment compared with ten widely used metrics.", "date": "2026-03-06", "categories": []}
{"id": "2512.14106", "title": "HydroGEM: A Self Supervised Zero Shot Hybrid TCN Transformer Foundation Model for Continental Scale Streamflow Quality Control", "abstract": "Advances in sensor networks have enabled real-time stream discharge monitoring, yet persistent sensor malfunctions limit data utility. Manual quality control by expert hydrologists cannot scale with networks generating millions of measurements annually. We introduce HydroGEM, a foundation model for continental-scale streamflow quality control designed to support human expertise. HydroGEM uses self-supervised pretraining on 6.03 million clean sequences from 3,724 USGS stations to learn general hydrological representations, followed by fine-tuning with synthetic anomalies for detection and reconstruction. A hybrid TCN-Transformer architecture (14.2M parameters) captures both local and long-range temporal dependencies, while hierarchical normalization handles six orders of magnitude in discharge. On held-out observations from 799 stations with 18 synthetic anomaly types grounded in USGS standards, HydroGEM achieves F1=0.792 for detection and 68.7% reconstruction error reduction, outperforming the strongest baseline by 36.3%. For cross-national validation on 100 Environment and Climate Change Canada stations using tolerant evaluation with a plus or minus 24-hour buffer, HydroGEM achieves Tolerant F1=0.70 with 90.1% segment-level event detection, demonstrating cross-national generalization. The model maintains consistent detection across correction magnitudes and aligns with operational seasonal patterns, with peak flagging during winter ice-affected periods matching hydrologists' correction behavior. Architectural separation between simplified training anomalies and complex test anomalies confirms that performance reflects learned hydrometric principles rather than pattern memorization.", "date": "2025-12-16", "categories": []}
{"id": "2603.04723", "title": "From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security", "abstract": "Shoplifting is a growing operational and economic challenge for retailers, with incidents rising and losses increasing despite extensive video surveillance. Continuous human monitoring is infeasible, motivating automated, privacy-preserving, and resource-aware detection solutions. In this paper, we cast shoplifting detection as a pose-based, unsupervised video anomaly detection problem and introduce a periodic adaptation framework designed for on-site Internet of Things (IoT) deployment. Our approach enables edge devices in smart retail environments to adapt from streaming, unlabeled data, supporting scalable and low-latency anomaly detection across distributed camera networks. To support reproducibility, we introduce RetailS, a new large-scale real-world shoplifting dataset collected from a retail store under multi-day, multi-camera conditions, capturing unbiased shoplifting behavior in realistic IoT settings. For deployable operation, thresholds are selected using both F1 and H_PRS scores, the harmonic mean of precision, recall, and specificity, during data filtering and training. In periodic adaptation experiments, our framework consistently outperformed offline baselines on AUC-ROC and AUC-PR in 91.6% of evaluations, with each training update completing in under 30 minutes on edge-grade hardware, demonstrating the feasibility and reliability of our solution for IoT-enabled smart retail deployment.", "date": "2026-03-05", "categories": []}
{"id": "2603.05031", "title": "AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems", "abstract": "AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in production systems. The trouble is that a payload can pass every schema check and still trick a user: a button might say \"View invoice\" while its hidden action wipes an account, or a display widget might quietly bind to an internal salary field. Current defenses stop at syntax; they were never built to catch this kind of behavioral mismatch.\n  We built AegisUI to study exactly this gap. The framework generates structured UI payloads, injects realistic attacks into them, extracts numeric features, and benchmarks anomaly detectors end-to-end. We produced 4000 labeled payloads (3000 benign, 1000 malicious) spanning five application domains and five attack families: phishing interfaces, data leakage, layout abuse, manipulative UI, and workflow anomalies.\n  From each payload we extracted 18 features covering structural, semantic, binding, and session dimensions, then compared three detectors: Isolation Forest (unsupervised), a benign-trained autoencoder (semi-supervised), and Random Forest (supervised). On a stratified 80/20 split, Random Forest scored best overall (accuracy 0.931, precision 0.980, recall 0.740, F1 0.843, ROC-AUC 0.952). The autoencoder came second (F1 0.762, ROC-AUC 0.863) and needs no malicious labels at training time, which matters when deploying a new system that lacks attack history. Per-attack-type analysis showed that layout abuse is easiest to catch while manipulative UI payloads are hardest. All code, data, and configurations are released for full reproducibility.", "date": "2026-03-05", "categories": []}
{"id": "2410.20310", "title": "Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection", "abstract": "While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish from normal instances. This limitation highlights a fundamental gap in the reasoning capabilities of existing methods. We attribute this issue to the reliance of standard Graph Contrastive Learning (GCL) on easy negatives, which fosters the learning of simplistic decision boundaries. To address this issue, we propose ANOMIX, a framework that synthesizes informative hard negatives by linearly interpolating representations of normal and abnormal subgraphs. This graph mixup strategy intentionally populates the decision boundary with hard-to-detect samples. Through targeted experimental analysis, we demonstrate that ANOMIX successfully separates these boundary anomalies where state-of-the-art baselines fail, as shown by a clear distinction in the score distributions for these challenging cases. These findings suggest that synthesizing hard negatives via mixup is a potent strategy for refining GNN representation space, which in turn enhances its reasoning capacity for more robust and reliable graph anomaly detection. Code is available at https://github.com/missinghwan/ANOMIX.", "date": "2024-10-27", "categories": []}
{"id": "2411.19888", "title": "FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation", "abstract": "Anomaly segmentation is an essential capability for safety-critical robotics applications that must be aware of unexpected events. Normalizing flows (NFs), a class of generative models, are a promising approach for this task due to their ability to model the inlier data distribution efficiently. However, their performance falters in dynamic scenes, where complex, multi-modal data distributions cause them to struggle with identifying out-of-distribution samples, leaving a performance gap to leading discriminative methods. To address this limitation, we introduce FlowCLAS, a hybrid framework that enhances the traditional maximum likelihood objective of NFs with a discriminative, contrastive loss. Leveraging Outlier Exposure, this objective explicitly enforces a separation between normal and anomalous features in the latent space, retaining the probabilistic foundation of NFs while embedding the discriminative power they lack. The strength of this approach is demonstrated by FlowCLAS establishing new state-of-the-art (SOTA) performance across multiple challenging anomaly segmentation benchmarks for robotics, including Fishyscapes Lost & Found, Road Anomaly, SegmentMeIfYouCan-ObstacleTrack, and ALLO. Our experiments also show that this contrastive approach is more effective than other outlier-based training strategies for NFs, successfully bridging the performance gap to leading discriminative methods. Project page: https://trailab.github.io/FlowCLAS", "date": "2024-11-29", "categories": []}
{"id": "2602.04027", "title": "A Consensus-Bayesian Framework for Detecting Malicious Activity in Enterprise Directory Access Graphs", "abstract": "This work presents a consensus-based Bayesian framework to detect malicious user behavior in enterprise directory access graphs. By modeling directories as topics and users as agents within a multi-level interaction graph, we simulate access evolution using influence-weighted opinion dynamics. Logical dependencies between users are encoded in dynamic matrices Ci, and directory similarity is captured via a shared influence matrix W. Malicious behavior is injected as cross-component logical perturbations that violate structural norms of strongly connected components(SCCs). We apply theoretical guarantees from opinion dynamics literature to determine topic convergence and detect anomaly via scaled opinion variance. To quantify uncertainty, we introduce a Bayesian anomaly scoring mechanism that evolves over time, using both static and online priors. Simulations over synthetic access graphs validate our method, demonstrating its sensitivity to logical inconsistencies and robustness under dynamic perturbation.", "date": "2026-02-03", "categories": []}
{"id": "2603.03101", "title": "MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection", "abstract": "The CLIP model's outstanding generalization has driven recent success in Zero-Shot Anomaly Detection (ZSAD) for detecting anomalies in unseen categories. The core challenge in ZSAD is to specialize the model for anomaly detection tasks while preserving CLIP's powerful generalization capability. Existing approaches attempting to solve this challenge share the fundamental limitation of a patch-agnostic design that processes all patches monolithically without regard for their unique characteristics. To address this limitation, we propose MoECLIP, a Mixture-of-Experts (MoE) architecture for the ZSAD task, which achieves patch-level adaptation by dynamically routing each image patch to a specialized Low-Rank Adaptation (LoRA) expert based on its unique characteristics. Furthermore, to prevent functional redundancy among the LoRA experts, we introduce (1) Frozen Orthogonal Feature Separation (FOFS), which orthogonally separates the input feature space to force experts to focus on distinct information, and (2) a simplex equiangular tight frame (ETF) loss to regulate the expert outputs to form maximally equiangular representations. Comprehensive experimental results across 14 benchmark datasets spanning industrial and medical domains demonstrate that MoECLIP outperforms existing state-of-the-art methods. The code is available at https://github.com/CoCoRessa/MoECLIP.", "date": "2026-03-03", "categories": []}
{"id": "2603.03939", "title": "Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection", "abstract": "Multimodal industrial anomaly detection benefits from integrating RGB appearance with 3D surface geometry, yet existing \\emph{unsupervised} approaches commonly rely on memory banks, teacher-student architectures, or fragile fusion schemes, limiting robustness under noisy depth, weak texture, or missing modalities. This paper introduces \\textbf{CMDR-IAD}, a lightweight and modality-flexible unsupervised framework for reliable anomaly detection in 2D+3D multimodal as well as single-modality (2D-only or 3D-only) settings. \\textbf{CMDR-IAD} combines bidirectional 2D$\\leftrightarrow$3D cross-modal mapping to model appearance-geometry consistency with dual-branch reconstruction that independently captures normal texture and geometric structure. A two-part fusion strategy integrates these cues: a reliability-gated mapping anomaly highlights spatially consistent texture-geometry discrepancies, while a confidence-weighted reconstruction anomaly adaptively balances appearance and geometric deviations, yielding stable and precise anomaly localization even in depth-sparse or low-texture regions. On the MVTec 3D-AD benchmark, CMDR-IAD achieves state-of-the-art performance while operating without memory banks, reaching 97.3\\% image-level AUROC (I-AUROC), 99.6\\% pixel-level AUROC (P-AUROC), and 97.6\\% AUPRO. On a real-world polyurethane cutting dataset, the 3D-only variant attains 92.6\\% I-AUROC and 92.5\\% P-AUROC, demonstrating strong effectiveness under practical industrial conditions. These results highlight the framework's robustness, modality flexibility, and the effectiveness of the proposed fusion strategies for industrial visual inspection. Our source code is available at https://github.com/ECGAI-Research/CMDR-IAD/", "date": "2026-03-04", "categories": []}
{"id": "2503.13160", "title": "Language-guided Open-world Video Anomaly Detection under Weak Supervision", "abstract": "Video anomaly detection (VAD) aims to detect anomalies that deviate from what is expected. In open-world scenarios, the expected events may change as requirements change. For example, not wearing a mask may be considered abnormal during a flu outbreak but normal otherwise. However, existing methods assume that the definition of anomalies is invariable, and thus are not applicable to the open world. To address this, we propose a novel open-world VAD paradigm with variable definitions, allowing guided detection through user-provided natural language at inference time. This paradigm necessitates establishing a robust mapping from video and textual definition to anomaly scores. Therefore, we propose LaGoVAD (Language-guided Open-world Video Anomaly Detector), a model that dynamically adapts anomaly definitions under weak supervision with two regularization strategies: diversifying the relative durations of anomalies via dynamic video synthesis, and enhancing feature robustness through contrastive learning with negative mining. Training such adaptable models requires diverse anomaly definitions, but existing datasets typically provide labels without semantic descriptions. To bridge this gap, we collect PreVAD (Pre-training Video Anomaly Dataset), the largest and most diverse video anomaly dataset to date, featuring 35,279 annotated videos with multi-level category labels and descriptions that explicitly define anomalies. Zero-shot experiments on seven datasets demonstrate LaGoVAD's SOTA performance. Our dataset and code are released at https://github.com/Kamino666/LaGoVAD-PreVAD.", "date": "2025-03-17", "categories": []}
{"id": "2512.03101", "title": "ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification", "abstract": "The advance of Large Language Models (LLMs) has greatly stimulated research interest in developing multi-modal LLM (MLLM)-based visual anomaly detection (VAD) algorithms that can be deployed in complex environments. The challenge is that in these complex environments, the anomalies are sometimes highly contextual and also ambiguous, and thereby, uncertainty quantification (UQ) is a crucial capacity for an MLLM-based VAD system to succeed. In this paper, we introduce our UQ-supported MLLM-based VAD framework called ALARM. ALARM integrates UQ with quality-assurance techniques like reasoning chain, self-reflection, and MLLM ensemble for robust and accurate performance and is designed based on a rigorous probabilistic inference pipeline and computational process. Extensive empirical evaluations are conducted using the real-world smart-home benchmark data and wound image classification data, which shows ALARM's superior performance and its generic applicability across different domains for reliable decision-making.", "date": "2025-12-01", "categories": []}
{"id": "2603.02427", "title": "Learning to Pay Attention: Unsupervised Modeling of Attentive and Inattentive Respondents in Survey Data", "abstract": "The integrity of behavioral and social-science surveys depends on detecting inattentive respondents who provide random or low-effort answers. Traditional safeguards, such as attention checks, are often costly, reactive, and inconsistent. We propose a unified, label-free framework for inattentiveness detection that scores response coherence using complementary unsupervised views: geometric reconstruction (Autoencoders) and probabilistic dependency modeling (Chow-Liu trees). While we introduce a \"Percentile Loss\" objective to improve Autoencoder robustness against anomalies, our primary contribution is identifying the structural conditions that enable unsupervised quality control. Across nine heterogeneous real-world datasets, we find that detection effectiveness is driven less by model complexity than by survey structure: instruments with coherent, overlapping item batteries exhibit strong covariance patterns that allow even linear models to reliably separate attentive from inattentive respondents. This reveals a critical ``Psychometric-ML Alignment'': the same design principles that maximize measurement reliability (e.g., internal consistency) also maximize algorithmic detectability. The framework provides survey platforms with a scalable, domain-agnostic diagnostic tool that links data quality directly to instrument design, enabling auditing without additional respondent burden.", "date": "2026-03-02", "categories": []}
{"id": "2603.02629", "title": "Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective", "abstract": "The quest for incremental unified multimodal anomaly detection seeks to empower a single model with the ability to systematically detect anomalies across all categories and support incremental learning to accommodate emerging objects/categories. Central to this pursuit is resolving the catastrophic forgetting dilemma, which involves acquiring new knowledge while preserving prior learned knowledge. Despite some efforts to address this dilemma, a key oversight persists: ignoring the potential impact of spurious and redundant features on catastrophic forgetting. In this paper, we delve into the negative effect of spurious and redundant features on this dilemma in incremental unified frameworks, and reveal that under similar conditions, the multimodal framework developed by naive aggregation of unimodal architectures is more prone to forgetting. To address this issue, we introduce a novel denoising framework called IB-IUMAD, which exploits the complementary benefits of the Mamba decoder and information bottleneck fusion module: the former dedicated to disentangle inter-object feature coupling, preventing spurious feature interference between objects; the latter serves to filter out redundant features from the fused features, thus explicitly preserving discriminative information. A series of theoretical analyses and experiments on MVTec 3D-AD and Eyecandies datasets demonstrates the effectiveness and competitive performance of IB-IUMAD.", "date": "2026-03-03", "categories": []}
{"id": "2603.02964", "title": "Improving Anomaly Detection with Foundation-Model Synthesis and Wavelet-Domain Attention", "abstract": "Industrial anomaly detection faces significant challenges due to the scarcity of anomalous samples and the complexity of real-world anomalies. In this paper, we propose a foundation model-based anomaly synthesis pipeline (FMAS) that generates highly realistic anomalous samples without fine-tuning or class-specific training. Motivated by the distinct frequency-domain characteristics of anomalies, we introduce aWavelet Domain Attention Module (WDAM), which exploits adaptive sub-band processing to enhance anomaly feature extraction. The combination of FMAS and WDAM significantly improves anomaly detection sensitivity while maintaining computational efficiency. Comprehensive experiments on MVTec AD and VisA datasets demonstrate that WDAM, as a plug-and-play module, achieves substantial performance gains against existing baselines.", "date": "2026-03-03", "categories": []}
{"id": "2603.02974", "title": "Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection", "abstract": "DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most existing methods extract patch embeddings from ``normal'' images and model them independently, ignoring spatial and neighborhood relationships between patches. This implicitly assumes that self-attention and positional encodings sufficiently encode contextual information within each patch embedding. In addition, the normative distribution is often modeled as memory banks or prototype-based representations, which require storing large numbers of features and performing costly comparisons at inference time, leading to substantial memory and computational overhead. In this work, we address both limitations by proposing a simple and efficient framework that explicitly models spatial and contextual dependencies between patch embeddings using a 2D autoregressive (AR) model. Instead of storing embeddings or clustering prototypes, our approach learns a compact parametric model of the normative distribution via an AR convolutional neural network (CNN). At test time, anomaly detection reduces to a single forward pass through the network and enables fast and memory-efficient inference. We evaluate our method on the BMAD benchmark, which comprises three medical imaging datasets, and compare it against existing work including recent DINO-based methods. Experimental results demonstrate that explicitly modeling spatial dependencies achieves competitive anomaly detection performance while substantially reducing inference time and memory requirements. Code is available at the project page: https://eerdil.github.io/spatial-ar-dinov3-uad/.", "date": "2026-03-03", "categories": []}
{"id": "2404.13671", "title": "FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization", "abstract": "Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories. Existing approaches typically rely on the robust generalization capabilities of multimodal pretrained models, computing similarities between manually crafted textual features representing \"normal\" or \"abnormal\" semantics and image features to detect anomalies and localize anomalous patches. However, the generic descriptions of \"abnormal\" often fail to precisely match diverse types of anomalies across different object categories. Additionally, computing feature similarities for single patches struggles to pinpoint specific locations of anomalies with various sizes and scales. To address these issues, we propose a novel ZSAD method called FiLo, comprising two components: adaptively learned Fine-Grained Description (FG-Des) and position-enhanced High-Quality Localization (HQ-Loc). FG-Des introduces fine-grained anomaly descriptions for each category using Large Language Models (LLMs) and employs adaptively learned textual templates to enhance the accuracy and interpretability of anomaly detection. HQ-Loc, utilizing Grounding DINO for preliminary localization, position-enhanced text prompts, and Multi-scale Multi-shape Cross-modal Interaction (MMCI) module, facilitates more accurate localization of anomalies of different sizes and shapes. Experimental results on datasets like MVTec and VisA demonstrate that FiLo significantly improves the performance of ZSAD in both detection and localization, achieving state-of-the-art performance with an image-level AUC of 83.9% and a pixel-level AUC of 95.9% on the VisA dataset. Code is available at https://github.com/CASIA-IVA-Lab/FiLo.", "date": "2024-04-21", "categories": []}
{"id": "2404.17931", "title": "Critical Review for One-class Classification: recent advances and the reality behind them", "abstract": "This paper offers a comprehensive review of one-class classification (OCC), examining the technologies and methodologies employed in its implementation. It delves into various approaches utilized for OCC across diverse data types, such as feature data, image, video, time series, and others. Through a systematic review, this paper synthesizes promi-nent strategies used in OCC from its inception to its current advance-ments, with a particular emphasis on the promising application. Moreo-ver, the article criticizes the state-of-the-art (SOTA) image anomaly de-tection (AD) algorithms dominating one-class experiments. These algo-rithms include outlier exposure (binary classification) and pretrained model (multi-class classification), conflicting with the fundamental con-cept of learning from one class. Our investigation reveals that the top nine algorithms for one-class CIFAR10 benchmark are not OCC. We ar-gue that binary/multi-class classification algorithms should not be com-pared with OCC.", "date": "2024-04-27", "categories": []}
{"id": "2501.10067", "title": "FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization", "abstract": "Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, such as cold start. Zero-shot and few-shot anomaly detection do not require labeled samples from the target class in advance, making them a promising research direction. Existing zero-shot and few-shot approaches often leverage powerful multimodal models to detect and localize anomalies by comparing image-text similarity. However, their handcrafted generic descriptions fail to capture the diverse range of anomalies that may emerge in different objects, and simple patch-level image-text matching often struggles to localize anomalous regions of varying shapes and sizes. To address these issues, this paper proposes the FiLo++ method, which consists of two key components. The first component, Fused Fine-Grained Descriptions (FusDes), utilizes large language models to generate anomaly descriptions for each object category, combines both fixed and learnable prompt templates and applies a runtime prompt filtering method, producing more accurate and task-specific textual descriptions. The second component, Deformable Localization (DefLoc), integrates the vision foundation model Grounding DINO with position-enhanced text descriptions and a Multi-scale Deformable Cross-modal Interaction (MDCI) module, enabling accurate localization of anomalies with various shapes and sizes. In addition, we design a position-enhanced patch matching approach to improve few-shot anomaly detection performance. Experiments on multiple datasets demonstrate that FiLo++ achieves significant performance improvements compared with existing methods. Code will be available at https://github.com/CASIA-IVA-Lab/FiLo.", "date": "2025-01-17", "categories": []}
{"id": "2506.22837", "title": "xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection", "abstract": "The recently proposed xLSTM is a powerful model that leverages expressive multiplicative gating and residual connections, providing the temporal capacity needed for long-horizon forecasting and representation learning. This architecture has demonstrated success in time series forecasting, lossless compression, and even large-scale language modeling tasks, where its linear memory footprint and fast inference make it a viable alternative to Transformers. Despite its growing popularity, no prior work has explored xLSTM for anomaly detection. In this work, we fill this gap by proposing xLSTMAD, the first anomaly detection method that integrates a full encoder-decoder xLSTM architecture, purpose-built for multivariate time series data. Our encoder processes input sequences to capture historical context, while the decoder is devised in two separate variants of the method. In the forecasting approach, the decoder iteratively generates forecasted future values xLSTMAD-F, while the reconstruction approach reconstructs the input time series from its encoded counterpart xLSTMAD-R. We investigate the performance of two loss functions: Mean Squared Error (MSE), and Soft Dynamic Time Warping (SoftDTW) to consider local reconstruction fidelity and global sequence alignment, respectively. We evaluate our method on the comprehensive TSB-AD-M benchmark, which spans 17 real-world datasets, using state-of-the-art challenging metrics such as VUS-PR. In our results, xLSTM showcases state-of-the-art accuracy, outperforming 23 popular anomaly detection baselines. Our paper is the first work revealing the powerful modeling capabilities of xLSTM for anomaly detection, paving the way for exciting new developments on this subject. Our code is available at: https://github.com/Nyderx/xlstmad", "date": "2025-06-28", "categories": []}
{"id": "2510.07109", "title": "GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics", "abstract": "Consumer electronics (CE) connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while enabling the propagation of malicious code across the CE network, resulting in device failures. Existing deep learning-based traffic anomaly detection systems exhibit high accuracy in traditional network environments but are often overly complex and reliant on static infrastructure, necessitating manual configuration and management. To address these limitations, we propose a scalable network model that integrates Software-defined Networking (SDN) and Compute First Networking (CFN) for next-generation CE networks. In this network model, we propose a Graph Neural Networks-based Network Anomaly Detection framework (GNN-NAD) that integrates SDN-based CE networks and enables the CFN architecture. GNN-NAD uniquely fuses a static, vulnerability-aware attack graph with dynamic traffic features, providing a holistic view of network security. The core of the framework is a GNN model (GSAGE) for graph representation learning, followed by a Random Forest (RF) classifier. This design (GSAGE+RF) demonstrates superior performance compared to existing feature selection methods. Experimental evaluations on CE environment reveal that GNN-NAD achieves superior metrics in accuracy, recall, precision, and F1 score, even with small sample sizes, exceeding the performance of current network anomaly detection methods. This work advances the security and efficiency of next-generation intelligent CE networks.", "date": "2025-10-08", "categories": []}
{"id": "2603.00055", "title": "M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection", "abstract": "Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable decisions in fine-grained and structurally complex industrial scenarios, and lack effective self-corrective mechanisms. To address this issue, we propose M3-AD, a unified reflection-aware multimodal framework for industrial anomaly detection. M3-AD comprises two complementary data resources: M3-AD-FT, designed for reflection-aligned fine-tuning, and M3-AD-Bench, designed for systematic cross-category evaluation, together providing a foundation for reflection-aware learning and reliability assessment. Building upon this foundation, we propose RA-Monitor, which models reflection as a learnable decision revision process and guides models to perform controlled self-correction when initial judgments are unreliable, thereby improving decision robustness. Extensive experiments conducted on M3-AD-Bench demonstrate that RA-Monitor outperforms multiple open-source and commercial MLLMs in zero-shot anomaly detection and anomaly analysis tasks. Code will be released at https://github.com/Yanhui-Lee/M3-AD.", "date": "2026-02-10", "categories": []}
{"id": "2603.00550", "title": "Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning", "abstract": "Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. However, a key limitation persists in WS-VAD, as dense frame-level annotations are absent, which often leaves existing methods struggling to learn anomaly semantics effectively. To address this issue, we propose a novel framework named LAS-VAD, short for Learning Anomaly Semantics for WS-VAD, which integrates anomaly-connected component mechanism and intention awareness mechanism. The former is designed to assign video frames into distinct semantic groups within a video, and frame segments within the same group are deemed to share identical semantic information. The latter leverages an intention-aware strategy to distinguish between similar normal and abnormal behaviors (e.g., taking items and stealing). To further model the semantic information of anomalies, as anomaly occurrence is accompanied by distinct characteristic attributes (i.e., explosions are characterized by flames and thick smoke), we additionally incorporate anomaly attribute information to guide accurate detection. Extensive experiments on two benchmark datasets, XD-Violence and UCF-Crime, demonstrate that our LAS-VAD outperforms current state-of-the-art methods with remarkable gains.", "date": "2026-02-28", "categories": []}
{"id": "2603.01029", "title": "Vision-Language Feature Alignment for Road Anomaly Segmentation", "abstract": "Safe autonomous systems in complex environments require robust road anomaly segmentation to identify unknown obstacles. However, existing approaches often rely on pixel-level statistics to determine whether a region appears anomalous. This reliance leads to high false-positive rates on semantically normal background regions such as sky or vegetation, and poor recall of true Out-of-distribution (OOD) instances, thereby posing safety risks for robotic perception and decision-making. To address these challenges, we propose VL-Anomaly, a vision-language anomaly segmentation framework that incorporates semantic priors from pre-trained Vision-Language Models (VLMs). Specifically, we design a prompt learning-driven alignment module that adapts Mask2Forme's visual features to CLIP text embeddings of known categories, effectively suppressing spurious anomaly responses in background regions. At inference time, we further introduce a multi-source inference strategy that integrates text-guided similarity, CLIP-based image-text similarity and detector confidence, enabling more reliable anomaly prediction by leveraging complementary information sources. Extensive experiments demonstrate that VL-Anomaly achieves state-of-the-art performance on benchmark datasets including RoadAnomaly, SMIYC and Fishyscapes.Code is released on https://github.com/NickHezhuolin/VL-aligner-Road-anomaly-segment.", "date": "2026-03-01", "categories": []}
{"id": "2603.01713", "title": "Dual Distillation for Few-Shot Anomaly Detection", "abstract": "Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D$^2$4FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. We further propose a learn-to-weight mechanism that dynamically assesses the reference value of each support image conditioned on the query, optimizing anomaly detection performance. To evaluate our method, we curate a comprehensive benchmark dataset comprising 13,084 images across four organs, four imaging modalities, and five disease categories. Extensive experiments demonstrate that D$^2$4FAD significantly outperforms existing approaches, establishing a new state-of-the-art in few-shot medical anomaly detection. Code is available at https://github.com/ttttqz/D24FAD.", "date": "2026-03-02", "categories": []}
{"id": "2603.01806", "title": "GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection", "abstract": "Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomaly detection methods use a novel mechanism called truncated affinity maximization (TAM) to detect anomaly nodes without using any label information and achieve impressive results. TAM maximizes the affinities among the normal nodes while truncating the affinities of the anomalous nodes to identify the anomalies. However, existing TAM-based methods truncate suspicious nodes according to a rigid threshold that ignores the specificity and high-order affinities of different nodes. This inevitably causes inefficient truncations from both normal and anomalous nodes, limiting the effectiveness of anomaly detection. To this end, this paper proposes a novel truncation model combining contextual and global affinity to truncate the anomalous nodes. The core idea of the work is to use contextual truncation to decrease the affinity of anomalous nodes, while global truncation increases the affinity of normal nodes. Extensive experiments on massive real-world datasets show that our method surpasses peer methods in most graph anomaly detection tasks. In highlights, compared with previous state-of-the-art methods, the proposed method has +15\\% $\\sim$ +20\\% improvements in two famous real-world datasets, Amazon and YelpChi. Notably, our method works well in large datasets, Amazin-all and YelpChi-all, and achieves the best results, while most previous models cannot complete the tasks.", "date": "2026-03-02", "categories": []}
{"id": "2603.01841", "title": "Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies", "abstract": "Detecting anomalies in link streams that represent various kinds of interactions is an important research topic with crucial applications. Because of the lack of ground truth data, proposed methods are mostly evaluated through their ability to detect randomly injected links. In contrast with most proposed methods, that rely on complex approaches raising computational and/or interpretability issues, we show here that trivial graph features and classical learning techniques are sufficient to detect such anomalies extremely well. This basic approach has very low computational costs and it leads to easily interpretable results. It also has many other desirable properties that we study through an extensive set of experiments. We conclude that detection methods should now target more complex kinds of anomalies.", "date": "2026-03-02", "categories": []}
{"id": "2510.14896", "title": "Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection", "abstract": "Existing semi-supervised video anomaly detection (VAD) methods often struggle with detecting complex anomalies involving object interactions and generally lack explainability. To overcome these limitations, we propose a novel VAD framework leveraging Multimodal Large Language Models (MLLMs). Unlike previous MLLM-based approaches that make direct anomaly judgments at the frame level, our method focuses on extracting and interpreting object activity and interactions over time. By querying an MLLM with visual inputs of object pairs at different moments, we generate textual descriptions of the activity and interactions from nominal videos. These textual descriptions serve as a high-level representation of the activity and interactions of objects in a video. They are used to detect anomalies during test time by comparing them to textual descriptions found in nominal training videos. Our approach inherently provides explainability and can be combined with many traditional VAD methods to further enhance their interpretability. Extensive experiments on benchmark datasets demonstrate that our method not only detects complex interaction-based anomalies effectively but also achieves state-of-the-art performance on datasets without interaction anomalies.", "date": "2025-10-16", "categories": []}
{"id": "2511.18172", "title": "MEDIC: a network for monitoring data quality in collider experiments", "abstract": "Data Quality Monitoring (DQM) is a crucial component of particle physics experiments and ensures that the recorded data is of the highest quality, and suitable for subsequent physics analysis. Due to the extreme environmental conditions, unprecedented data volumes, and the sheer scale and complexity of the detectors, DQM orchestration has become a very challenging task. Therefore, the use of Machine Learning (ML) to automate anomaly detection, improve efficiency, and reduce human error in the process of collecting high-quality data is unavoidable. Since DQM relies on real experimental data, it is inherently tied to the specific detector substructure and technology in operation. In this work, a simulation-driven approach to DQM is proposed, enabling the study and development of data-quality methodologies in a controlled environment. Using a modified version of Delphes -- a fast, multi-purpose detector simulation -- the preliminary realization of a framework is demonstrated which leverages ML to identify detector anomalies as well as localize the malfunctioning components responsible. We introduce MEDIC (Monitoring for Event Data Integrity and Consistency), a neural network designed to learn detector behavior and perform DQM tasks to look for potential faults. Although the present implementation adopts a simplified setup for computational ease, where large detector regions are deliberately deactivated to mimic faults, this work represents an initial step toward a comprehensive ML-based DQM framework. The encouraging results underline the potential of simulation-driven studies as a foundation for developing more advanced, data-driven DQM systems for future particle detectors.", "date": "2025-11-22", "categories": []}
{"id": "2602.23595", "title": "Incremental dimension reduction for efficient and accurate visual anomaly detection", "abstract": "While nowadays visual anomaly detection algorithms use deep neural networks to extract salient features from images, the high dimensionality of extracted features makes it difficult to apply those algorithms to large data with 1000s of images. To address this issue, we present an incremental dimension reduction algorithm to reduce the extracted features. While our algorithm essentially computes truncated singular value decomposition of these features, other than processing all vectors at once, our algorithm groups the vectors into batches. At each batch, our algorithm updates the truncated singular values and vectors that represent all visited vectors, and reduces each batch by its own singular values and vectors so they can be stored in the memory with low overhead. After processing all batches, we re-transform these batch-wise singular vectors to the space spanned by the singular vectors of all features. We show that our algorithm can accelerate the training of state-of-the-art anomaly detection algorithm with close accuracy.", "date": "2026-02-27", "categories": []}
{"id": "2602.23662", "title": "Selective Denoising Diffusion Model for Time Series Anomaly Detection", "abstract": "Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity and demonstrating success. Diffusion models have recently attracted attention due to their advanced generative capabilities. Existing diffusion-based methods for TSAD rely on a conditional strategy, which reconstructs input instances from white noise with the aid of the conditioner. However, this poses challenges in accurately reconstructing the normal parts, resulting in suboptimal detection performance. In response, we propose a novel diffusion-based method, named AnomalyFilter, which acts as a selective filter that only denoises anomaly parts in the instance while retaining normal parts. To build such a filter, we mask Gaussian noise during the training phase and conduct the denoising process without adding noise to the instances. The synergy of the two simple components greatly enhances the performance of naive diffusion models. Extensive experiments on five datasets demonstrate that AnomalyFilter achieves notably low reconstruction error on normal parts, providing empirical support for its effectiveness in anomaly detection. AnomalyFilter represents a pioneering approach that focuses on the noise design of diffusion models specifically tailored for TSAD.", "date": "2026-02-27", "categories": []}
{"id": "2602.24021", "title": "Steering and Rectifying Latent Representation Manifolds in Frozen Multi-modal LLMs for Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) aims to identify abnormal events in videos. Traditional VAD methods generally suffer from the high costs of labeled data and full training, thus some recent works have explored leveraging frozen multi-modal large language models (MLLMs) in a tuning-free manner to perform VAD. However, their performance is limited as they directly inherit pre-training biases and cannot adapt internal representations to specific video contexts, leading to difficulties in handling subtle or ambiguous anomalies. To address these limitations, we propose a novel intervention framework, termed SteerVAD, which advances MLLM-based VAD by shifting from passively reading to actively steering and rectifying internal representations. Our approach first leverages the gradient-free representational separability analysis (RSA) to identify top attention heads as latent anomaly experts (LAEs) which are most discriminative for VAD. Then a hierarchical meta-controller (HMC) generates dynamic rectification signals by jointly conditioning on global context and these LAE outputs. The signals execute targeted, anisotropic scaling directly upon the LAE representation manifolds, amplifying anomaly-relevant dimensions while suppressing inherent biases. Extensive experiments on mainstream benchmarks demonstrate our method achieves state-of-the-art performance among tuning-free approaches requiring only 1% of training data, establishing it as a powerful new direction for video anomaly detection. The code will be released upon the publication.", "date": "2026-02-27", "categories": []}
{"id": "2602.24209", "title": "An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks", "abstract": "Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous nature of IoT data, stemming from differences in device capabilities, data formats, and communication constraints, poses significant challenges to maintaining both global model performance and privacy. In the context of IoT-based anomaly detection, unsupervised FL offers a promising means to identify abnormal behavior without centralized data aggregation. Nevertheless, feature heterogeneity across devices complicates model training and optimization, hindering effective implementation. In this study we propose an efficient unsupervised FL framework that enhances anomaly detection by leveraging shared features from two distinct IoT datasets: one focused on anomaly detection and the other on device identification, while preserving dataset-specific features. To improve transparency and interpretability, we employ explainable AI techniques, such as SHAP, to identify key features influencing local model decisions. Experiments conducted on real-world IoT datasets demonstrate that the proposed method significantly outperforms conventional FL approaches in anomaly detection accuracy. This work underscores the potential of using shared features from complementary datasets to optimize unsupervised federated learning and achieve superior anomaly detection results in decentralized IoT environments.", "date": "2026-02-27", "categories": []}
{"id": "2307.09861", "title": "BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection", "abstract": "Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, the lack of labeled samples for HSIs leads to poor generalization of existing HAD methods. This paper starts the first attempt to study a new and generalizable background learning problem without labeled samples. We present a novel solution BSDM (background suppression diffusion model) for HAD, which can simultaneously learn latent background distributions and generalize to different datasets for suppressing complex background. It is featured in three aspects: (1) For the complex background of HSIs, we design pseudo background noise and learn the potential background distribution in it with a diffusion model (DM). (2) For the generalizability problem, we apply a statistical offset module so that the BSDM adapts to datasets of different domains without labeling samples. (3) For achieving background suppression, we innovatively improve the inference process of DM by feeding the original HSIs into the denoising network, which removes the background as noise. Our work paves a new background suppression way for HAD that can improve HAD performance without the prerequisite of manually labeled data. Assessments and generalization experiments of four HAD methods on several real HSI datasets demonstrate the above three unique properties of the proposed method. The code is available at https://github.com/majitao-xd/BSDM-HAD.", "date": "2023-07-19", "categories": []}
{"id": "2512.02686", "title": "ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data", "abstract": "Anomaly segmentation seeks to detect and localize unknown or out-of-distribution (OoD) objects that fall outside predefined semantic classes a capability essential for safe autonomous driving. However, the scarcity and limited diversity of anomaly data severely constrain model generalization in open-world environments. Existing approaches mitigate this issue through synthetic data generation, either by copy-pasting external objects into driving scenes or by leveraging text-to-image diffusion models to inpaint anomalous regions. While these methods improve anomaly diversity, they often lack contextual coherence and physical realism, resulting in domain gaps between synthetic and real data. In this paper, we present ClimaDrive, a semantics-guided image-to-image framework for synthesizing semantically coherent, weather-diverse, and physically plausible OoD driving data. ClimaDrive unifies structure-guided multi-weather generation with prompt-driven anomaly inpainting, enabling the creation of visually realistic training data. Based on this framework, we construct ClimaOoD, a large-scale benchmark spanning six representative driving scenarios under both clear and adverse weather conditions. Extensive experiments on four state-of-the-art methods show that training with ClimaOoD leads to robust improvements in anomaly segmentation. Across all methods, AUROC, AP, and FPR95 show notable gains, with FPR95 dropping from 3.97 to 3.52 for RbA on Fishyscapes LAF. These results demonstrate that ClimaOoD enhances model robustness, offering valuable training data for better generalization in open-world anomaly detection.", "date": "2025-12-02", "categories": []}
{"id": "2602.15457", "title": "Benchmarking IoT Time-Series AD with Event-Level Augmentations", "abstract": "Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for model selection in practice. We introduce an evaluation protocol with unified event-level augmentations that simulate real-world issues: calibrated sensor dropout, linear and log drift, additive noise, and window shifts. We also perform sensor-level probing via mask-as-missing zeroing with per-channel influence estimation to support root-cause analysis. We evaluate 14 representative models on five public anomaly datasets (SWaT, WADI, SMD, SKAB, TEP) and two industrial datasets (steam turbine, nuclear turbogenerator) using unified splits and event aggregation. There is no universal winner: graph-structured models transfer best under dropout and long events (e.g., on SWaT under additive noise F1 drops 0.804->0.677 for a graph autoencoder, 0.759->0.680 for a graph-attention variant, and 0.762->0.756 for a hybrid graph attention model); density/flow models work well on clean stationary plants but can be fragile to monotone drift; spectral CNNs lead when periodicity is strong; reconstruction autoencoders become competitive after basic sensor vetting; predictive/hybrid dynamics help when faults break temporal dependencies but remain window-sensitive. The protocol also informs design choices: on SWaT under log drift, replacing normalizing flows with Gaussian density reduces high-stress F1 from ~0.75 to ~0.57, and fixing a learned DAG gives a small clean-set gain (~0.5-1.0 points) but increases drift sensitivity by ~8x.", "date": "2026-02-17", "categories": []}
{"id": "2601.01016", "title": "Improving Variational Autoencoder using Random Fourier Transformation: An Aviation Safety Anomaly Detection Case-Study", "abstract": "In this study, we focus on the training process and inference improvements of deep neural networks (DNNs), specifically Autoencoders (AEs) and Variational Autoencoders (VAEs), using Random Fourier Transformation (RFT). We further explore the role of RFT in model training behavior using Frequency Principle (F-Principle) analysis and show that models with RFT turn to learn low frequency and high frequency at the same time, whereas conventional DNNs start from low frequency and gradually learn (if successful) high-frequency features. We focus on reconstruction-based anomaly detection using autoencoder and variational autoencoder and investigate the RFT's role. We also introduced a trainable variant of RFT that uses the existing computation graph to train the expansion of RFT instead of it being random. We showcase our findings with two low-dimensional synthetic datasets for data representation, and an aviation safety dataset, called Dashlink, for high-dimensional reconstruction-based anomaly detection. The results indicate the superiority of models with Fourier transformation compared to the conventional counterpart and remain inconclusive regarding the benefits of using trainable Fourier transformation in contrast to the Random variant.", "date": "2026-01-03", "categories": []}
{"id": "2602.03594", "title": "TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection", "abstract": "Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) leverages vision-language models (VLMs). However, CLIP's coarse image-text alignment limits both localization and detection due to (i) spatial misalignment and (ii) weak sensitivity to fine-grained anomalies; prior works compensate with complex auxiliary modules yet largely overlook the choice of backbone. We revisit the backbone and use TIPS-a VLM trained with spatially aware objectives. While TIPS alleviates CLIP's issues, it exposes a distributional gap between global and local features. We address this with decoupled prompts-fixed for image-level detection and learnable for pixel-level localization-and by injecting local evidence into the global score. Without CLIP-specific tricks, our TIPS-based pipeline improves image-level performance by 1.1-3.9% and pixel-level by 1.5-6.9% across seven industrial datasets, delivering strong generalization with a lean architecture. Code is available at github.com/AlirezaSalehy/Tipsomaly.", "date": "2026-02-03", "categories": []}
{"id": "2602.21766", "title": "RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms", "abstract": "Time-series data vary widely across domains, making a universal anomaly detector impractical. Methods that perform well on one dataset often fail to transfer because what counts as an anomaly is context dependent. The key challenge is to design a method that performs well in specific contexts while remaining adaptable across domains with varying data complexities. We present the Robust and Adaptive Model Selection for Time-Series Anomaly Detection RAMSeS framework. RAMSeS comprises two branches: (i) a stacking ensemble optimized with a genetic algorithm to leverage complementary detectors. (ii) An adaptive model-selection branch identifies the best single detector using techniques including Thompson sampling, robustness testing with generative adversarial networks, and Monte Carlo simulations. This dual strategy exploits the collective strength of multiple models and adapts to dataset-specific characteristics. We evaluate RAMSeS and show that it outperforms prior methods on F1.", "date": "2026-02-25", "categories": []}
{"id": "2602.03596", "title": "SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network", "abstract": "Machine learning-based anomaly detection systems are increasingly being adopted in 5G Core networks to monitor complex, high-volume traffic. However, most existing approaches are evaluated under strong assumptions that rarely hold in operational environments, notably the availability of independent and identically distributed (IID) data and the absence of adaptive attackers. In this work, we study the problem of detecting 5G attacks in the wild, focusing on realistic deployment settings. We propose a set of Security-Aware Guidelines for Evaluating anomaly detectors in 5G Core Network (SAGE-5GC), driven by domain knowledge and consideration of potential adversarial threats. Using a realistic 5G Core dataset, we first train several anomaly detectors and assess their baseline performance against standard 5GC control-plane cyberattacks targeting PFCP-based network services. We then extend the evaluation to adversarial settings, where an attacker tries to manipulate the observable features of the network traffic to evade detection, under the constraint that the intended functionality of the malicious traffic is preserved. Starting from a selected set of controllable features, we analyze model sensitivity and adversarial robustness through randomized perturbations. Finally, we introduce a practical optimization strategy based on genetic algorithms that operates exclusively on attacker-controllable features and does not require prior knowledge of the underlying detection model. Our experimental results show that adversarially crafted attacks can substantially degrade detection performance, underscoring the need for robust, security-aware evaluation methodologies for anomaly detection in 5G networks deployed in the wild.", "date": "2026-02-03", "categories": []}
{"id": "2602.20468", "title": "CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection", "abstract": "Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover, learned dynamic graphs can overfit noise without a stable anchor, causing false alarms or misses. To address these challenges, we propose the CGSTA framework with two key innovations. First, Dynamic Layered Graph Construction (DLGC) forms local, regional, and global views of variable relations for each sliding window; rather than contrasting whole windows, Contrastive Discrimination across Scales (CDS) contrasts graph representations within each view and aligns the same window across views to make learning structure-aware. Second, Stability-Aware Alignment (SAA) maintains a per-scale stable reference learned from normal data and guides the current window's fast-changing graphs toward it to suppress noise. We fuse the multi-scale and temporal features and use a conditional density estimator to produce per-time-step anomaly scores. Across four benchmarks, CGSTA delivers optimal performance on PSM and WADI, and is comparable to the baseline methods on SWaT and SMAP.", "date": "2026-02-24", "categories": []}
{"id": "2602.17048", "title": "StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection", "abstract": "Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it relies on a single extreme response, it discards most information about how anomaly evidence is distributed and structured across the image, often causing normal and anomalous scores to overlap.\n  We propose StructCore, a training-free, structure-aware image-level scoring method that goes beyond max pooling. Given an anomaly score map, StructCore computes a low-dimensional structural descriptor phi(S) that captures distributional and spatial characteristics, and refines image-level scoring via a diagonal Mahalanobis calibration estimated from train-good samples, without modifying pixel-level localization.\n  StructCore achieves image-level AUROC scores of 99.6% on MVTec AD and 98.4% on VisA, demonstrating robust image-level anomaly detection by exploiting structural signatures missed by max pooling.", "date": "2026-02-19", "categories": []}
{"id": "2602.18793", "title": "From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GAD methods often focus on the learning paradigms of \"one-model-for-one-dataset\", requiring dataset-specific training for each dataset to achieve optimal performance. However, this paradigm suffers from significant limitations, such as high computational and data costs, limited generalization and transferability to new datasets, and challenges in privacy-sensitive scenarios where access to full datasets or sufficient labels is restricted. To address these limitations, we propose a novel generalist GAD paradigm that aims to develop a unified model capable of detecting anomalies on multiple unseen datasets without extensive retraining/fine-tuning or dataset-specific customization. To this end, we propose ARC, a few-shot generalist GAD method that leverages in-context learning and requires only a few labeled normal samples at inference time. Specifically, ARC consists of three core modules: a feature Alignment module to unify and align features across datasets, a Residual GNN encoder to capture dataset-agnostic anomaly representations, and a cross-attentive in-Context learning module to score anomalies using few-shot normal context. Building on ARC, we further introduce ARC_zero for the zero-shot generalist GAD setting, which selects representative pseudo-normal nodes via a pseudo-context mechanism and thus enables fully label-free inference on unseen datasets. Extensive experiments on 17 real-world graph datasets demonstrate that both ARC and ARC_zero effectively detect anomalies, exhibit strong generalization ability, and perform efficiently under few-shot and zero-shot settings.", "date": "2026-02-21", "categories": []}
{"id": "2602.19068", "title": "TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection", "abstract": "Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (e.g., forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks-such as time series anomaly detection (TSAD), which aims to identify rare, irregular patterns. This limitation arises because such abnormal patterns can closely resemble the regular patterns when presented in the same time/frequency domain. To address this issue, we introduce TimeRadar, an innovative TSFM built in a fractional time-frequency domain to support generalist TSAD across diverse unseen datasets. Our key insight is that rotating a time series into a data-dependent fractional time-frequency representation can adaptively differentiate the normal and abnormal signals across different datasets. To this end, a novel component, namely Fractionally modulated Time-Frequency Reconstruction (FTFRecon), is proposed in TimeRadar to leverage a learnable fractional order to rotate the time series to the most pronounced angle between a continuous time and frequency domain for accurate data reconstruction. This provides adaptive data reconstruction in an optimal time-frequency domain for each data input, enabling effective differentiation of the unbounded abnormal patterns from the regular ones across datasets, including unseen datasets. To allow TimeRadar to model local abnormality that is not captured by the global data reconstruction, we further introduce a Contextual Deviation Learning (CDL) component to model the local deviation of the input relative to its contextual time series data in the rotatable domain.", "date": "2026-02-22", "categories": []}
{"id": "2602.19611", "title": "RAID: Retrieval-Augmented Anomaly Detection", "abstract": "Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on image reconstruction or template retrieval but face a fundamental challenge: matching between test images and normal templates inevitably introduces noise due to intra-class variations, imperfect correspondences, and limited templates. Observing that Retrieval-Augmented Generation (RAG) leverages retrieved samples directly in the generation process, we reinterpret UAD through this lens and introduce \\textbf{RAID}, a retrieval-augmented UAD framework designed for noise-resilient anomaly detection and localization. Unlike standard RAG that enriches context or knowledge, we focus on using retrieved normal samples to guide noise suppression in anomaly map generation. RAID retrieves class-, semantic-, and instance-level representations from a hierarchical vector database, forming a coarse-to-fine pipeline. A matching cost volume correlates the input with retrieved exemplars, followed by a guided Mixture-of-Experts (MoE) network that leverages the retrieved samples to adaptively suppress matching noise and produce fine-grained anomaly maps. RAID achieves state-of-the-art performance across full-shot, few-shot, and multi-dataset settings on MVTec, VisA, MPDD, and BTAD benchmarks. \\href{https://github.com/Mingxiu-Cai/RAID}{https://github.com/Mingxiu-Cai/RAID}.", "date": "2026-02-23", "categories": []}
{"id": "2505.15847", "title": "Graph Neural Networks Based Anomalous RSSI Detection", "abstract": "In today's world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks.\n  It is essential to monitor these networks to ensure smooth operations by detecting and correcting link failures or abnormal network behaviour proactively, which can otherwise cause interruptions in business operations.\n  This paper presents a novel method for detecting anomalies in wireless links using graph neural networks. The proposed approach involves converting time series data into graphs and training a new graph neural network architecture based on graph attention networks that successfully detects anomalies at the level of individual measurements of the time series data. The model provides competitive results compared to the state of the art while being computationally more efficient with ~171 times fewer trainable parameters.", "date": "2025-05-19", "categories": []}
{"id": "2602.17028", "title": "Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles", "abstract": "Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essential for ensuring system reliability and enabling preventive maintenance. However, most existing methods are reactive: they detect anomalies only after they occur and lack the capability to provide proactive early warning signals. In this paper, we propose FATE (Forecasting Anomalies with Time-series Ensembles), a novel unsupervised framework for detecting Precursors-of-Anomaly (PoA) by quantifying predictive uncertainty from a diverse ensemble of time-series forecasting models. Unlike prior approaches that rely on reconstruction errors or require ground-truth labels, FATE anticipates future values and leverages ensemble disagreement to signal early signs of potential anomalies without access to target values at inference time. To rigorously evaluate PoA detection, we introduce Precursor Time-series Aware Precision and Recall (PTaPR), a new metric that extends the traditional Time-series Aware Precision and Recall (TaPR) by jointly assessing segment-level accuracy, within-segment coverage, and temporal promptness of early predictions. This enables a more holistic assessment of early warning capabilities that existing metrics overlook. Experiments on five real-world benchmark datasets show that FATE achieves an average improvement of 19.9 percentage points in PTaPR AUC and 20.02 percentage points in early detection F1 score, outperforming baselines while requiring no anomaly labels. These results demonstrate the effectiveness and practicality of FATE for real-time unsupervised early warning in complex time-series environments.", "date": "2026-02-19", "categories": []}
{"id": "2602.16681", "title": "VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection", "abstract": "Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime.", "date": "2026-02-18", "categories": []}
{"id": "2502.17812", "title": "Can Multimodal LLMs Perform Time Series Anomaly Detection?", "abstract": "Time series anomaly detection (TSAD) has been a long-standing pillar problem in Web-scale systems and online infrastructures, such as service reliability monitoring, system fault diagnosis, and performance optimization. Large language models (LLMs) have demonstrated unprecedented capabilities in time series analysis, the potential of multimodal LLMs (MLLMs), particularly vision-language models, in TSAD remains largely under-explored. One natural way for humans to detect time series anomalies is through visualization and textual description. It motivates our research question: Can multimodal LLMs perform time series anomaly detection? Existing studies often oversimplify the problem by treating point-wise anomalies as special cases of range-wise ones or by aggregating point anomalies to approximate range-wise scenarios. They limit our understanding for realistic scenarios such as multi-granular anomalies and irregular time series. To address the gap, we build a VisualTimeAnomaly benchmark to comprehensively investigate zero-shot capabilities of MLLMs for TSAD, progressively from point-, range-, to variate-wise anomalies, and extends to irregular sampling conditions. Our study reveals several key insights in multimodal MLLMs for TSAD. Built on these findings, we propose a MLLMs-based multi-agent framework TSAD-Agents to achieve automatic TSAD. Our framework comprises scanning, planning, detection, and checking agents that synergistically collaborate to reason, plan, and self-reflect to enable automatic TSAD. These agents adaptively invoke tools such as traditional methods and MLLMs and dynamically switch between text and image modalities to optimize detection performance.", "date": "2025-02-25", "categories": []}
{"id": "2602.15315", "title": "Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models", "abstract": "Zero-shot anomaly detection (ZSAD) has gained increasing attention in medical imaging as a way to identify abnormalities without task-specific supervision, but most advances remain limited to 2D datasets. Extending ZSAD to 3D medical images has proven challenging, with existing methods relying on slice-wise features and vision-language models, which fail to capture volumetric structure. In this paper, we introduce a fully training-free framework for ZSAD in 3D brain MRI that constructs localized volumetric tokens by aggregating multi-axis slices processed by 2D foundation models. These 3D patch tokens restore cubic spatial context and integrate directly with distance-based, batch-level anomaly detection pipelines. The framework provides compact 3D representations that are practical to compute on standard GPUs and require no fine-tuning, prompts, or supervision. Our results show that training-free, batch-based ZSAD can be effectively extended from 2D encoders to full 3D MRI volumes, offering a simple and robust approach for volumetric anomaly detection.", "date": "2026-02-17", "categories": []}
{"id": "2503.02112", "title": "Building Machine Learning Challenges for Anomaly Detection in Science", "abstract": "Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.", "date": "2025-03-03", "categories": []}
{"id": "2602.02929", "title": "RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection", "abstract": "Advanced Persistent Threats (APTs) are sophisticated, long-term cyberattacks that are difficult to detect because they operate stealthily and often blend into normal system behavior. This paper presents a neuro-symbolic anomaly detection framework that combines a Graph Autoencoder (GAE) with rare pattern mining to identify APT-like activities in system-level provenance data. Our approach first constructs a process behavioral graph using k-Nearest Neighbors based on feature similarity, then learns normal relational structure using a Graph Autoencoder. Anomaly candidates are identified through deviations between observed and reconstructed graph structure. To further improve detection, we integrate an rare pattern mining module that discovers infrequent behavioral co-occurrences and uses them to boost anomaly scores for processes exhibiting rare signatures. We evaluate the proposed method on the DARPA Transparent Computing datasets and show that rare-pattern boosting yields substantial gains in anomaly ranking quality over the baseline GAE. Compared with existing unsupervised approaches on the same benchmark, our single unified model consistently outperforms individual context-based detectors and achieves performance competitive with ensemble aggregation methods that require multiple separate detectors. These results highlight the value of coupling graph-based representation learning with classical pattern mining to improve both effectiveness and interpretability in provenance-based security anomaly detection.", "date": "2026-02-03", "categories": []}
{"id": "2602.13288", "title": "Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasets", "abstract": "Anomaly detection is important for keeping cloud systems reliable and stable. Deep learning has improved time-series anomaly detection, but most models are evaluated on one dataset at a time. This raises questions about whether these models can handle different types of telemetry, especially in large-scale and high-dimensional environments.\n  In this study, we evaluate four deep learning models, GRU, TCN, Transformer, and TSMixer. We also include Isolation Forest as a classical baseline. The models are tested across four telemetry datasets: the Numenta Anomaly Benchmark, Microsoft Cloud Monitoring dataset, Exathlon dataset, and IBM Console dataset. These datasets differ in structure, dimensionality, and labelling strategy. They include univariate time series, synthetic multivariate workloads, and real-world production telemetry with over 100,000 features.\n  We use a unified training and evaluation pipeline across all datasets. The evaluation includes NAB-style metrics to capture early detection behaviour for datasets where anomalies persist over contiguous time intervals. This enables window-based scoring in settings where anomalies occur over contiguous time intervals, even when labels are recorded at the point level. The unified setup enables consistent analysis of model behaviour under shared scoring and calibration assumptions.\n  Our results demonstrate that anomaly detection performance in cloud systems is governed not only by model architecture, but critically by calibration stability and feature-space geometry. By releasing our preprocessing pipelines, benchmark configuration, and evaluation artifacts, we aim to support reproducible and deployment-aware evaluation of anomaly detection systems for cloud environments.", "date": "2026-02-07", "categories": []}
{"id": "2602.13649", "title": "Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series", "abstract": "Time series chain (TSC) is a recently introduced concept that captures the evolving patterns in large scale time series. Informally, a time series chain is a temporally ordered set of subsequences, in which consecutive subsequences in the chain are similar to one another, but the last and the first subsequences maybe be dissimilar. Time series chain has the great potential to reveal latent unusual evolving trend in the time series, or identify precursor of important events in a complex system. Unfortunately, existing definitions of time series chains only consider finding chains in a single time series. As a result, they are likely to miss unexpected evolving patterns in interrupted time series, or across two related time series. To address this limitation, in this work, we introduce a new definition called \\textit{Joint Time Series Chain}, which is specially designed for the task of finding unexpected evolving trend across interrupted time series or two related time series. Our definition focuses on mitigating the robustness issues caused by the gap or interruption in the time series. We further propose an effective ranking criterion to identify the best chain. We demonstrate that our proposed approach outperforms existing TSC work in locating unusual evolving patterns through extensive empirical evaluations. We further demonstrate the utility of our work with a real-life manufacturing application from Intel. Our source code is publicly available at the supporting page https://github.com/lizhang-ts/JointTSC .", "date": "2026-02-14", "categories": []}
{"id": "2602.14251", "title": "Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection", "abstract": "Tabular anomaly detection is often handled by single detectors or static ensembles, even though strong performance on tabular data typically comes from heterogeneous model families (e.g., tree ensembles, deep tabular networks, and tabular foundation models) that frequently disagree under distribution shift, missingness, and rare-anomaly regimes. We propose MAD, a Multi-Agent Debating framework that treats this disagreement as a first-class signal and resolves it through a mathematically grounded coordination layer. Each agent is a machine learning (ML)-based detector that produces a normalized anomaly score, confidence, and structured evidence, augmented by a large language model (LLM)-based critic. A coordinator converts these messages into bounded per-agent losses and updates agent influence via an exponentiated-gradient rule, yielding both a final debated anomaly score and an auditable debate trace. MAD is a unified agentic framework that can recover existing approaches, such as mixture-of-experts gating and learning-with-expert-advice aggregation, by restricting the message space and synthesis operator. We establish regret guarantees for the synthesized losses and show how conformal calibration can wrap the debated score to control false positives under exchangeability. Experiments on diverse tabular anomaly benchmarks show improved robustness over baselines and clearer traces of model disagreement", "date": "2026-02-15", "categories": []}
{"id": "2508.14746", "title": "MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection", "abstract": "LLM-generated reasoning graphs, referred to as mission-specific graphs (MSGs), are increasingly used for video anomaly detection (VAD) and recognition (VAR). However, they are typically treated as fixed despite being generic and distribution-deficient. Conventional graph structure refinement (GSR) methods are ill-suited to this setting, as they rely on learning structural distributions that are absent in LLM-generated graphs. We propose HDC-constrained Graph Structure Refinement (HDC-GSR), a new paradigm that directly optimizes a decodable, task-aligned graph representation in a single hyperdimensional space without distribution modeling. Leveraging Hyperdimensional Computing (HDC), our framework encodes graphs via binding and bundling operations, aligns the resulting graph code with downstream loss, and decodes edge contributions to refine the structure. We instantiate this approach as MissionHD for weakly supervised VAD/VAR and demonstrate consistent performance gains on benchmark datasets.", "date": "2025-08-20", "categories": []}
{"id": "2602.12592", "title": "Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems", "abstract": "Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as black boxes, offering only binary outputs without any explanation, such as identifying anomaly type and origin. To address this challenge, we propose Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a unified, causality-informed architecture that jointly performs anomaly detection along with the explanation why it is detected as an anomaly, including root cause localization, anomaly type classification, and anomaly shape characterization. Experimental results in power systems demonstrate that PICODE achieves competitive detection performance while offering improved interpretability and reduced reliance on labeled data or external causal graphs. We provide theoretical results demonstrating the alignment between the shape of anomaly functions and the changes in the weights of the extracted causal graphs.", "date": "2026-02-13", "categories": []}
{"id": "2602.12622", "title": "Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection", "abstract": "Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy-preserving framework for distributed IoT environments, current federated principal component analysis (PCA) methods lack the integration of personalization and robustness, which are critical for effective anomaly detection. To address these limitations, we propose an efficient personalized federated PCA (FedEP) method for anomaly detection in IoT networks. The proposed model achieves personalization through introducing local representations with the $\\ell_1$-norm for element-wise sparsity, while maintaining robustness via enforcing local models with the $\\ell_{2,1}$-norm for row-wise sparsity. To solve this non-convex problem, we develop a manifold optimization algorithm based on the alternating direction method of multipliers (ADMM) with rigorous theoretical convergence guarantees. Experimental results confirm that the proposed FedEP outperforms the state-of-the-art FedPG, achieving excellent F1-scores and accuracy in various IoT security scenarios. Our code will be available at \\href{https://github.com/xianchaoxiu/FedEP}{https://github.com/xianchaoxiu/FedEP}.", "date": "2026-02-13", "categories": []}
{"id": "2602.12976", "title": "Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling", "abstract": "In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are unlabeled, making it challenging to identify events, particularly anomalies. This task becomes even more formidable in nonstationary environments where model performance can deteriorate over time due to concept drift. To address these challenges, this paper presents a novel method, VAE++ESDD, which employs incremental learning and two-level ensembling: an ensemble of Variational AutoEncoder(VAEs) for anomaly prediction, along with an ensemble of concept drift detectors. Each drift detector utilizes a statistical-based concept drift mechanism. To evaluate the effectiveness of VAE++ESDD, we conduct a comprehensive experimental study using real-world and synthetic datasets characterized by severely or extremely low anomalous rates and various drift characteristics. Our study reveals that the proposed method significantly outperforms both strong baselines and state-of-the-art methods.", "date": "2026-02-13", "categories": []}
{"id": "2404.12134", "title": "Warped Time Series Anomaly Detection", "abstract": "This paper addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines.Notable challenges arise from the fact that a task performed multiple times may exhibit different duration in each repetition and that the time series reported by the sensors are irregularly sampled because of data gaps. The anomaly detection approach presented in this paper consists of three stages.The first stage identifies the repetitive cycles in the lengthy time series and segments them into individual time series corresponding to one task cycle, while accounting for possible temporal distortions.The second stage computes a prototype for the cycles using a GPU-based barycenter  algorithm, specifically tailored for very large time series.The third stage uses the prototype to detect abnormal cycles by computing an anomaly score for each cycle.The overall approach, named WarpEd Time Series ANomaly Detection (WETSAND), makes use of the Dynamic Time Warping algorithm and its variants because they are suited to the distorted nature of the time series.The experiments show that \\wetsand scales to large signals, computes human-friendly prototypes, works with very little data, and outperforms some general purpose anomaly detection approaches such as autoencoders.", "date": "2024-04-18", "categories": []}
{"id": "2511.00540", "title": "Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era", "abstract": "Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement of IAD algorithms is severely hindered by the limitations of existing public benchmarks. Current datasets often suffer from restricted category diversity and insufficient scale, leading to performance saturation and poor model transferability in complex, real-world scenarios. To bridge this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark. It comprises 198,950 high-resolution images across 160 distinct object categories. The dataset ensures unprecedented diversity by covering 28 industries, 24 material types, 22 color variations, and 27 defect types. Our extensive experimental analysis highlights the substantial challenges posed by this benchmark: state-of-the-art multi-class unsupervised anomaly detection methods suffer significant performance degradation (ranging from 10% to 20%) when scaled from 30 to 160 categories. Conversely, we demonstrate that zero-shot and few-shot IAD models exhibit remarkable robustness to category scale-up, maintaining consistent performance and significantly enhancing generalization across diverse industrial contexts. This unprecedented scale positions Real-IAD Variety as an essential resource for training and evaluating next-generation foundation IAD models.", "date": "2025-11-01", "categories": []}
{"id": "2602.07011", "title": "MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation", "abstract": "As industrial manufacturing scales, automating fine-grained product image analysis has become critical for quality control. However, existing approaches are hindered by limited dataset coverage and poor model generalization across diverse and complex anomaly patterns. To address these challenges, we introduce MAU-Set, a comprehensive dataset for Multi-type industrial Anomaly Understanding. It spans multiple industrial domains and features a hierarchical task structure, ranging from binary classification to complex reasoning. Alongside this dataset, we establish a rigorous evaluation protocol to facilitate fair and comprehensive model assessment. Building upon this foundation, we further present MAU-GPT, a domain-adapted multimodal large model specifically designed for industrial anomaly understanding. It incorporates a novel AMoE-LoRA mechanism that unifies anomaly-aware and generalist experts adaptation, enhancing both understanding and reasoning across diverse defect classes. Extensive experiments show that MAU-GPT consistently outperforms prior state-of-the-art methods across all domains, demonstrating strong potential for scalable and automated industrial inspection.", "date": "2026-01-31", "categories": []}
{"id": "2602.11539", "title": "Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling", "abstract": "Reactive anomaly detection methods, which are commonly deployed to identify anomalies after they occur based on observed deviations, often fall short in applications that demand timely intervention, such as industrial monitoring, finance, and cybersecurity. Proactive anomaly detection, by contrast, aims to detect early warning signals before failures fully manifest, but existing methods struggle with handling heterogeneous multivariate data and maintaining precision under noisy or unpredictable conditions. In this work, we introduce two proactive anomaly detection frameworks: the Forward Forecasting Model (FFM) and the Backward Reconstruction Model (BRM). Both models leverage a hybrid architecture combining Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Transformer encoders to model directional temporal dynamics. FFM forecasts future sequences to anticipate disruptions, while BRM reconstructs recent history from future context to uncover early precursors. Anomalies are flagged based on forecasting error magnitudes and directional embedding discrepancies. Our models support both continuous and discrete multivariate features, enabling robust performance in real-world settings. Extensive experiments on four benchmark datasets, MSL, SMAP, SMD, and PSM, demonstrate that FFM and BRM outperform state-of-the-art baselines across detection metrics and significantly improve the timeliness of anomaly anticipation. These properties make our approach well-suited for deployment in time-sensitive domains requiring proactive monitoring.", "date": "2026-02-12", "categories": []}
{"id": "2510.21584", "title": "Automated Quality Control for Language Documentation: Detecting Phonotactic Inconsistencies in a Kokborok Wordlist", "abstract": "Lexical data collection in language documentation often contains transcription errors and undocumented borrowings that can mislead linguistic analysis. We present unsupervised anomaly detection methods to identify phonotactic inconsistencies in wordlists, applying them to a multilingual dataset of Kokborok varieties with Bangla. Using character-level and syllable-level phonotactic features, our algorithms identify potential transcription errors and borrowings. While precision and recall remain modest due to the subtle nature of these anomalies, syllable-aware features significantly outperform character-level baselines. The high-recall approach provides fieldworkers with a systematic method to flag entries requiring verification, supporting data quality improvement in low-resourced language documentation.", "date": "2025-10-24", "categories": []}
{"id": "2512.09446", "title": "Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation", "abstract": "Recent vision language models (VLMs) like CLIP have demonstrated impressive anomaly detection performance under significant distribution shift by utilizing high-level semantic information through text prompts. However, these models often neglect fine-grained details, such as which kind of anomalies, like \"hole\", \"cut\", \"scratch\" that could provide more specific insight into the nature of anomalies. We argue that recognizing fine-grained anomaly types 1) enriches the representation of \"abnormal\" with structured semantics, narrowing the gap between coarse anomaly signals and fine-grained defect categories; 2) enables manufacturers to understand the root causes of the anomaly and implement more targeted and appropriate corrective measures quickly. While incorporating such detailed semantic information is crucial, designing handcrafted prompts for each defect type is both time-consuming and susceptible to human bias. For this reason, we introduce DAPO, a novel approach for Defect-aware Prompt Optimization based on progressive tuning for the zero-shot multi-type and binary anomaly detection and segmentation under distribution shifts. Our approach aligns anomaly-relevant image features with their corresponding text semantics by learning hybrid defect-aware prompts with both fixed textual anchors and learnable token embeddings. We conducted experiments on public benchmarks (MPDD, VisA, MVTec-AD, MAD, and Real-IAD) and an internal dataset. The results suggest that compared to the baseline models, DAPO achieves a 3.7% average improvement in AUROC and average precision metrics at the image level under distribution shift, and a 6.5% average improvement in localizing novel anomaly types under zero-shot settings.", "date": "2025-12-10", "categories": []}
{"id": "2602.10549", "title": "Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance", "abstract": "Weakly supervised multimodal video anomaly detection has gained significant attention, yet the potential of the text modality remains under-explored. Text provides explicit semantic information that can enhance anomaly characterization and reduce false alarms. However, extracting effective text features is challenging due to the inability of general-purpose language models to capture anomaly-specific nuances and the scarcity of relevant descriptions. Furthermore, multimodal fusion often suffers from redundancy and imbalance. To address these issues, we propose a novel text-guided framework. First, we introduce an in-context learning-based multi-stage text augmentation mechanism to generate high-quality anomaly text samples for fine-tuning the text feature extractor. Second, we design a multi-scale bottleneck Transformer fusion module that uses compressed bottleneck tokens to progressively integrate information across modalities, mitigating redundancy and imbalance. Experiments on UCF-Crime and XD-Violence demonstrate state-of-the-art performance.", "date": "2026-02-11", "categories": []}
{"id": "2602.10708", "title": "Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes", "abstract": "The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have shown promising performance, their black-box nature limits their reliability and deployment in real-world applications. Although some recent methods have made attempts to provide explanations for anomaly detection results, they either provide explanations without referencing normal graphs, or rely on abstract latent vectors as prototypes rather than concrete graphs from the dataset. To address these limitations, we propose Prototype-based Graph-Level Anomaly Detection (ProtoGLAD), an interpretable unsupervised framework that provides explanation for each detected anomaly by explicitly contrasting with its nearest normal prototype graph. It employs a point-set kernel to iteratively discover multiple normal prototype graphs and their associated clusters from the dataset, then identifying graphs distant from all discovered normal clusters as anomalies. Extensive experiments on multiple real-world datasets demonstrate that ProtoGLAD achieves competitive anomaly detection performance compared to state-of-the-art GLAD methods while providing better human-interpretable prototype-based explanations.", "date": "2026-02-11", "categories": []}
{"id": "2602.10806", "title": "DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples", "abstract": "Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods rely on category-specific training, which limits their flexibility in few-shot scenarios. In this paper, we propose DMP-3DAD, a training-free framework for cross-category 3D anomaly detection based on multi-view realistic depth map projection. Specifically, by converting point clouds into a fixed set of realistic depth images, our method leverages a frozen CLIP visual encoder to extract multi-view representations and performs anomaly detection via weighted feature similarity, which does not require any fine-tuning or category-dependent adaptation. Extensive experiments on the ShapeNetPart dataset demonstrate that DMP-3DAD achieves state-of-the-art performance under few-shot setting. The results show that the proposed approach provides a simple yet effective solution for practical cross-category 3D anomaly detection.", "date": "2026-02-11", "categories": []}
{"id": "2602.10888", "title": "Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids", "abstract": "We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.", "date": "2026-02-11", "categories": []}
{"id": "2602.09593", "title": "Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?", "abstract": "Deep generative models with tractable and analytically computable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-based scoring. We demonstrate that, unlike in the image domain where deep generative models frequently assign higher likelihoods to anomalous data, such counterintuitive behavior occurs far less often in tabular settings. We first introduce a domain-agnostic formulation that enables consistent detection and evaluation of the counterintuitive phenomenon, addressing the absence of precise definition. Through extensive experiments on 47 tabular datasets and 10 CV/NLP embedding datasets in ADBench, benchmarked against 13 baseline models, we demonstrate that the phenomenon, as defined, is consistently rare in general tabular data. We further investigate this phenomenon from both theoretical and empirical perspectives, focusing on the roles of data dimensionality and difference in feature correlation. Our results suggest that likelihood-only detection with normalizing flows offers a practical and reliable approach for anomaly detection in tabular domains.", "date": "2026-02-10", "categories": []}
{"id": "2602.09690", "title": "Contextual and Seasonal LSTMs for Time Series Anomaly Detection", "abstract": "Univariate time series (UTS), where each timestamp records a single variable, serve as crucial indicators in web systems and cloud servers. Anomaly detection in UTS plays an essential role in both data mining and system reliability management. However, existing reconstruction-based and prediction-based methods struggle to capture certain subtle anomalies, particularly small point anomalies and slowly rising anomalies. To address these challenges, we propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs). CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns, thereby strengthening the detection of subtle anomalies. By integrating both time-domain and frequency-domain representations, CS-LSTMs achieve more accurate modeling of periodic trends and anomaly localization. Extensive evaluations on public benchmark datasets demonstrate that CS-LSTMs consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.", "date": "2026-02-10", "categories": []}
{"id": "2602.09985", "title": "Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings", "abstract": "As autonomous vehicles are rolled out, measures must be taken to ensure their safe operation. In order to supervise a system that is already in operation, monitoring frameworks are frequently employed. These run continuously online in the background, supervising the system status and recording anomalies. This work proposes an online monitoring framework to detect anomalies in object state representations. Thereby, a key challenge is creating a framework for anomaly detection without anomaly labels, which are usually unavailable for unknown anomalies. To address this issue, this work applies a self-supervised embedding method to translate object data into a latent representation space. For this, a JEPA-based self-supervised prediction task is constructed, allowing training without anomaly labels and the creation of rich object embeddings. The resulting expressive JEPA embeddings serve as input for established anomaly detection methods, in order to identify anomalies within object state representations. This framework is particularly useful for applications in real-world environments, where new or unknown anomalies may occur during operation for which there are no labels available. Experiments performed on the publicly available, real-world nuScenes dataset illustrate the framework's capabilities.", "date": "2026-02-10", "categories": []}
{"id": "2506.21550", "title": "mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale", "abstract": "Anomaly detection in multivariate time series is essential across domains such as healthcare, cybersecurity, and industrial monitoring, yet remains fundamentally challenging due to high-dimensional dependencies, the presence of cross-correlations between time-dependent variables, and the scarcity of labeled anomalies. We introduce mTSBench, the largest benchmark to date for multivariate time series anomaly detection and model selection, consisting of 344 labeled time series across 19 datasets from a wide range of application domains. We comprehensively evaluate 24 anomaly detectors, including the only two publicly available large language model-based methods for multivariate time series. Consistent with prior findings, we observe that no single detector dominates across datasets, motivating the need for effective model selection. We benchmark three recent model selection methods and find that even the strongest of them remain far from optimal. Our results highlight the outstanding need for robust, generalizable selection strategies. We open-source the benchmark at https://plan-lab.github.io/mtsbench to encourage future research.", "date": "2025-06-26", "categories": []}
{"id": "2510.18781", "title": "Decoupled Complementary Spectral-Spatial Learning for Background Representation Enhancement in Hyperspectral Anomaly Detection", "abstract": "A recent class of hyperspectral anomaly detection methods can be trained once on background datasets and then deployed universally without per-scene retraining or parameter tuning, showing strong efficiency and robustness. Building upon this paradigm, we propose a decoupled complementary spectral--spatial learning framework for background representation enhancement. The framework follows a two-stage training strategy: (1) we first train a spectral enhancement network via reverse distillation to obtain robust background spectral representations; and (2) we then freeze the spectral branch as a teacher and train a spatial branch as a complementary student (the \"rebellious student\") to capture spatial patterns overlooked by the teacher. Complementary learning is achieved through decorrelation objectives that reduce representational redundancy between the two branches, together with reconstruction regularization to prevent the student from learning irrelevant noise. After training, the framework jointly enhances background representations from both spectral and spatial perspectives, and the resulting enhanced features can be plugged into parameter-free, training-free detectors (e.g., the Reed--Xiaoli (RX) detector) for test-time deployment without per-scene retraining or parameter tuning. Experiments on the HAD100 benchmark demonstrate substantial improvements over representative baselines with modest computational overhead, validating the effectiveness of the proposed complementary learning paradigm. Our code is publicly available at https://github.com/xjpp2016/FERS.", "date": "2025-10-21", "categories": []}
{"id": "2602.06859", "title": "Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts", "abstract": "Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to enable generalization to unseen graph datasets. However, existing zero-shot GAD methods largely ignore intrinsic geometric differences across diverse anomaly patterns, substantially limiting their cross-domain generalization. In this work, we reveal that anomaly detectability is highly dependent on the underlying geometric properties and that embedding graphs from different domains into a single static curvature space can distort the structural signatures of anomalies. To address the challenge that a single curvature space cannot capture geometry-dependent graph anomaly patterns, we propose GAD-MoRE, a novel framework for zero-shot Generalizable Graph Anomaly Detection with a Mixture of Riemannian Experts architecture. Specifically, to ensure that each anomaly pattern is modeled in the Riemannian space where it is most detectable, GAD-MoRE employs a set of specialized Riemannian expert networks, each operating in a distinct curvature space. To align raw node features with curvature-specific anomaly characteristics, we introduce an anomaly-aware multi-curvature feature alignment module that projects inputs into parallel Riemannian spaces, enabling the capture of diverse geometric characteristics. Finally, to facilitate better generalization beyond seen patterns, we design a memory-based dynamic router that adaptively assigns each input to the most compatible expert based on historical reconstruction performance on similar anomalies. Extensive experiments in the zero-shot setting demonstrate that GAD-MoRE significantly outperforms state-of-the-art generalist GAD baselines, and even surpasses strong competitors that are few-shot fine-tuned with labeled data from the target domain.", "date": "2026-02-06", "categories": []}
{"id": "2602.07694", "title": "Semantic-Deviation-Anchored Multi-Branch Fusion for Unsupervised Anomaly Detection and Localization in Unstructured Conveyor-Belt Coal Scenes", "abstract": "Reliable foreign-object anomaly detection and pixel-level localization in conveyor-belt coal scenes are essential for safe and intelligent mining operations. This task is particularly challenging due to the highly unstructured environment: coal and gangue are randomly piled, backgrounds are complex and variable, and foreign objects often exhibit low contrast, deformation, occlusion, resulting in coupling with their surroundings. These characteristics weaken the stability and regularity assumptions that many anomaly detection methods rely on in structured industrial settings, leading to notable performance degradation. To support evaluation and comparison in this setting, we construct \\textbf{CoalAD}, a benchmark for unsupervised foreign-object anomaly detection with pixel-level localization in coal-stream scenes. We further propose a complementary-cue collaborative perception framework that extracts and fuses complementary anomaly evidence from three perspectives: object-level semantic composition modeling, semantic-attribution-based global deviation analysis, and fine-grained texture matching. The fused outputs provide robust image-level anomaly scoring and accurate pixel-level localization. Experiments on CoalAD demonstrate that our method outperforms widely used baselines across the evaluated image-level and pixel-level metrics, and ablation studies validate the contribution of each component. The code is available at https://github.com/xjpp2016/USAD.", "date": "2026-02-07", "categories": []}
{"id": "2602.07798", "title": "CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection", "abstract": "Detecting anomalies in tabular data is critical for many real-world applications, such as credit card fraud detection. With the rapid advancements in large language models (LLMs), state-of-the-art performance in tabular anomaly detection has been achieved by converting tabular data into text and fine-tuning LLMs. However, these methods randomly order columns during conversion, without considering the causal relationships between them, which is crucial for accurately detecting anomalies. In this paper, we present CausalTaD, a method that injects causal knowledge into LLMs for tabular anomaly detection. We first identify the causal relationships between columns and reorder them to align with these causal relationships. This reordering can be modeled as a linear ordering problem. Since each column contributes differently to the causal relationships, we further propose a reweighting strategy to assign different weights to different columns to enhance this effect. Experiments across more than 30 datasets demonstrate that our method consistently outperforms the current state-of-the-art methods. The code for CausalTAD is available at https://github.com/350234/CausalTAD.", "date": "2026-02-08", "categories": []}
{"id": "2602.08467", "title": "Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization", "abstract": "Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The vast majority of existing anomaly diagnosis methods offer limited theoretical insights, especially for anomaly localization, which is a vital but largely unexplored area. The aim of this contribution is to study the learning process of a Transformer when applied to MTS by revealing connections to statistical time series methods. Based on these theoretical insights, we propose the Attention Low-Rank Transformer (ALoRa-T) model, which applies low-rank regularization to self-attention, and we introduce the Attention Low-Rank score, effectively capturing the temporal characteristics of anomalies. Finally, to enable anomaly localization, we propose the ALoRa-Loc method, a novel approach that associates anomalies to specific variables by quantifying interrelationships among time series. Extensive experiments and real data analysis, show that the proposed methodology significantly outperforms state-of-the-art methods in both detection and localization tasks.", "date": "2026-02-09", "categories": []}
{"id": "2602.08638", "title": "LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection", "abstract": "As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations. A key challenge in unsupervised TSAD is that many anomalies are too subtle to exhibit detectable deviation in any single view (e.g., time domain), and instead manifest as inconsistencies across multiple views like time, frequency, and a mixture of resolutions. However, most cross-view methods rely on feature or score fusion and do not enforce analysis-synthesis consistency, meaning the frequency branch is not required to reconstruct the time signal through an inverse transform, and vice versa. In this paper, we present Learnable Fusion of Tri-view Tokens (LEFT), a unified unsupervised TSAD framework that models anomalies as inconsistencies across complementary representations. LEFT learns feature tokens from three views of the same input time series: frequency-domain tokens that embed periodicity information, time-domain tokens that capture local dynamics, and multi-scale tokens that learns abnormal patterns at varying time series granularities. By learning a set of adaptive Nyquist-constrained spectral filters, the original time series is rescaled into multiple resolutions and then encoded, allowing these multi-scale tokens to complement the extracted frequency- and time-domain information. When generating the fused representation, we introduce a novel objective that reconstructs fine-grained targets from coarser multi-scale structure, and put forward an innovative time-frequency cycle consistency constraint to explicitly regularize cross-view agreement. Experiments on real-world benchmarks show that LEFT yields the best detection accuracy against SOTA baselines, while achieving a 5x reduction on FLOPs and 8x speed-up for training.", "date": "2026-02-09", "categories": []}
{"id": "2602.08792", "title": "Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems", "abstract": "The pantograph-catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However, electrical arcing at this interface poses serious risks, including accelerated wear of contact components, degraded system performance, and potential service disruptions. Detecting arcing events at the pantograph-catenary interface is challenging due to their transient nature, noisy operating environment, data scarcity, and the difficulty of distinguishing arcs from other similar transient phenomena. To address these challenges, we propose a novel multimodal framework that combines high-resolution image data with force measurements to more accurately and robustly detect arcing events. First, we construct two arcing detection datasets comprising synchronized visual and force measurements. One dataset is built from data provided by the Swiss Federal Railways (SBB), and the other is derived from publicly available videos of arcing events in different railway systems and synthetic force data that mimic the characteristics observed in the real dataset. Leveraging these datasets, we propose MultiDeepSAD, an extension of the DeepSAD algorithm for multiple modalities with a new loss formulation. Additionally, we introduce tailored pseudo-anomaly generation techniques specific to each data type, such as synthetic arc-like artifacts in images and simulated force irregularities, to augment training data and improve the discriminative ability of the model. Through extensive experiments and ablation studies, we demonstrate that our framework significantly outperforms baseline approaches, exhibiting enhanced sensitivity to real arcing events even under domain shifts and limited availability of real arcing observations.", "date": "2026-02-09", "categories": []}
{"id": "2506.00956", "title": "Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection", "abstract": "In this paper, we introduce a new benchmark for continual learning in anomaly detection, aimed at better reflecting real-world deployment scenarios. Our benchmark, Continual-MEGA, includes a large and diverse dataset that significantly expands existing evaluation settings by combining carefully curated existing datasets with our newly proposed dataset, ContinualAD. In addition to standard continual learning with expanded quantity, we propose a novel scenario that measures zero-shot generalization to unseen classes, those not observed during continual adaptation. This setting poses a new problem setting that continual adaptation also enhances zero-shot performance. We also present a unified baseline algorithm that improves robustness in few-shot detection and maintains strong generalization. Through extensive evaluations, we report three key findings: (1) existing methods show substantial room for improvement, particularly in pixel-level defect localization; (2) our proposed method consistently outperforms prior approaches; and (3) the newly introduced ContinualAD dataset enhances the performance of strong anomaly detection models. We release the benchmark and code in https://github.com/Continual-Mega/Continual-Mega.", "date": "2025-06-01", "categories": []}
{"id": "2602.06179", "title": "Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging", "abstract": "Pelvic diseases in women of reproductive age represent a major global health burden, with diagnosis frequently delayed due to high anatomical variability, complicating MRI interpretation. Existing AI approaches are largely disease-specific and lack real-time compatibility, limiting generalizability and clinical integration. To address these challenges, we establish a benchmark framework for disease- and parameter-agnostic, real-time-compatible unsupervised anomaly detection in pelvic MRI. The method uses a residual variational autoencoder trained exclusively on healthy sagittal T2-weighted scans acquired across diverse imaging protocols to model normal pelvic anatomy. During inference, reconstruction error heatmaps indicate deviations from learned healthy structure, enabling detection of pathological regions without labeled abnormal data. The model is trained on 294 healthy scans and augmented with diffusion-generated synthetic data to improve robustness. Quantitative evaluation on the publicly available Uterine Myoma MRI Dataset yields an average area-under-the-curve (AUC) value of 0.736, with 0.828 sensitivity and 0.692 specificity. Additional inter-observer clinical evaluation extends analysis to endometrial cancer, endometriosis, and adenomyosis, revealing the influence of anatomical heterogeneity and inter-observer variability on performance interpretation. With a reconstruction time of approximately 92.6 frames per second, the proposed framework establishes a baseline for unsupervised anomaly detection in the female pelvis and supports future integration into real-time MRI. Code is available upon request (https://github.com/AniKnu/UADPelvis), prospective data sets are available for academic collaboration.", "date": "2026-02-05", "categories": []}
{"id": "2602.06330", "title": "Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection", "abstract": "Efficient and robust Out-of-Distribution (OOD) detection is paramount for safety-critical applications.However, existing methods still execute full-scale inference on low-level statistical noise. This computational mismatch not only incurs resource waste but also induces semantic hallucination, where deep networks forcefully interpret physical anomalies as high-confidence semantic features.To address this, we propose the Cascaded Early Rejection (CER) framework, which realizes hierarchical filtering for anomaly detection via a coarse-to-fine logic.CER comprises two core modules: 1)Structural Energy Sieve (SES), which establishes a non-parametric barrier at the network entry using the Laplacian operator to efficiently intercept physical signal anomalies; and 2) the Semantically-aware Hyperspherical Energy (SHE) detector, which decouples feature magnitude from direction in intermediate layers to identify fine-grained semantic deviations. Experimental results demonstrate that CER not only reduces computational overhead by 32% but also achieves a significant performance leap on the CIFAR-100 benchmark:the average FPR95 drastically decreases from 33.58% to 22.84%, and AUROC improves to 93.97%. Crucially, in real-world scenarios simulating sensor failures, CER exhibits performance far exceeding state-of-the-art methods. As a universal plugin, CER can be seamlessly integrated into various SOTA models to provide performance gains.", "date": "2026-02-06", "categories": []}
{"id": "2602.06443", "title": "TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents", "abstract": "We address the problem of runtime trajectory anomaly detection, a critical capability for enabling trustworthy LLM agents. Current safety measures predominantly focus on static input/output filtering. However, we argue that ensuring LLM agents reliability requires auditing the intermediate execution process. In this work, we formulate the task of Trajectory Anomaly Detection. The goal is not merely detection, but precise error localization. This capability is essential for enabling efficient rollback-and-retry. To achieve this, we construct TrajBench, a dataset synthesized via a perturb-and-complete strategy to cover diverse procedural anomalies. Using this benchmark, we investigate the capability of models in process supervision. We observe that general-purpose LLMs, even with zero-shot prompting, struggle to identify and localize these anomalies. This reveals that generalized capabilities do not automatically translate to process reliability. To address this, we propose TrajAD, a specialized verifier trained with fine-grained process supervision. Our approach outperforms baselines, demonstrating that specialized supervision is essential for building trustworthy agents.", "date": "2026-02-06", "categories": []}
{"id": "2502.08262", "title": "GenIAS: Generator for Instantiating Anomalies in time Series", "abstract": "Synthetic anomaly injection is a recent and promising approach for time series anomaly detection (TSAD), but existing methods rely on ad hoc, hand-crafted strategies applied to raw time series that fail to capture diverse and complex anomalous patterns, particularly in multivariate settings. We propose a synthetic anomaly generation method named Generator for Instantiating Anomalies in Time Series (GenIAS), which generates realistic and diverse anomalies via a novel learnable perturbation in the latent space of a variational autoencoder. This enables abnormal patterns to be injected across different temporal segments at varying scales based on variational reparameterization. To generate anomalies that align with normal patterns while remaining distinguishable, we introduce a learning strategy that jointly learns the perturbation scale and compact latent representations via a tunable prior, which improves the distinguishability of generated anomalies, as supported by our theoretical analysis. Extensive experiments show that GenIAS produces more diverse and realistic anomalies, and that detection models trained with these anomalies outperform 17 baseline methods on 9 popular TSAD benchmarks.", "date": "2025-02-12", "categories": []}
{"id": "2602.04917", "title": "Multi-Aspect Mining and Anomaly Detection for Heterogeneous Tensor Streams", "abstract": "Analysis and anomaly detection in event tensor streams consisting of timestamps and multiple attributes - such as communication logs(time, IP address, packet length)- are essential tasks in data mining. While existing tensor decomposition and anomaly detection methods provide useful insights, they face the following two limitations. (i) They cannot handle heterogeneous tensor streams, which comprises both categorical attributes(e.g., IP address) and continuous attributes(e.g., packet length). They typically require either discretizing continuous attributes or treating categorical attributes as continuous, both of which distort the underlying statistical properties of the data.Furthermore, incorrect assumptions about the distribution family of continuous attributes often degrade the model's performance. (ii) They discretize timestamps, failing to track the temporal dynamics of streams(e.g., trends, abnormal events), which makes them ineffective for detecting anomalies at the group level, referred to as 'group anomalies' (e.g, DoS attacks). To address these challenges, we propose HeteroComp, a method for continuously summarizing heterogeneous tensor streams into 'components' representing latent groups in each attribute and their temporal dynamics, and detecting group anomalies. Our method employs Gaussian process priors to model unknown distributions of continuous attributes, and temporal dynamics, which directly estimate probability densities from data. Extracted components give concise but effective summarization, enabling accurate group anomaly detection. Extensive experiments on real datasets demonstrate that HeteroComp outperforms the state-of-the-art algorithms for group anomaly detection accuracy, and its computational time does not depend on the data stream length.", "date": "2026-02-04", "categories": []}
{"id": "2602.05232", "title": "Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenges persist. At the model level, most methods adopt a transductive learning paradigm, which assumes static graph structures, making them unsuitable for dynamic, evolving networks. At the data level, the extreme class imbalance, where anomalous nodes are rare, leads to biased models that fail to generalize to unseen anomalies. These challenges are interdependent: static transductive frameworks limit effective data augmentation, while imbalance exacerbates model distortion in inductive learning settings. To address these challenges, we propose a novel data-centric framework that integrates dynamic graph modeling with balanced anomaly synthesis. Our framework features: (1) a discrete ego-graph diffusion model, which captures the local topology of anomalies to generate ego-graphs aligned with anomalous structural distribution, and (2) a curriculum anomaly augmentation mechanism, which dynamically adjusts synthetic data generation during training, focusing on underrepresented anomaly patterns to improve detection and generalization. Experiments on five datasets demonstrate that the effectiveness of our framework.", "date": "2026-02-05", "categories": []}
{"id": "2602.05238", "title": "PatchFlow: Leveraging a Flow-Based Model with Patch Features", "abstract": "Die casting plays a crucial role across various industries due to its ability to craft intricate shapes with high precision and smooth surfaces. However, surface defects remain a major issue that impedes die casting quality control. Recently, computer vision techniques have been explored to automate and improve defect detection. In this work, we combine local neighbor-aware patch features with a normalizing flow model and bridge the gap between the generic pretrained feature extractor and industrial product images by introducing an adapter module to increase the efficiency and accuracy of automated anomaly detection. Compared to state-of-the-art methods, our approach reduces the error rate by 20\\% on the MVTec AD dataset, achieving an image-level AUROC of 99.28\\%. Our approach has also enhanced performance on the VisA dataset , achieving an image-level AUROC of 96.48\\%. Compared to the state-of-the-art models, this represents a 28.2\\% reduction in error. Additionally, experiments on a proprietary die casting dataset yield an accuracy of 95.77\\% for anomaly detection, without requiring any anomalous samples for training. Our method illustrates the potential of leveraging computer vision and deep learning techniques to advance inspection capabilities for the die casting industry", "date": "2026-02-05", "categories": []}
{"id": "2602.05426", "title": "Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications", "abstract": "Traditional deep learning models often lack annotated data, especially in cross-domain applications such as anomaly detection, which is critical for early disease diagnosis in medicine and defect detection in industry. To address this challenge, we propose Multi-AD, a convolutional neural network (CNN) model for robust unsupervised anomaly detection across medical and industrial images. Our approach employs the squeeze-and-excitation (SE) block to enhance feature extraction via channel-wise attention, enabling the model to focus on the most relevant features and detect subtle anomalies. Knowledge distillation (KD) transfers informative features from the teacher to the student model, enabling effective learning of the differences between normal and anomalous data. Then, the discriminator network further enhances the model's capacity to distinguish between normal and anomalous data. At the inference stage, by integrating multi-scale features, the student model can detect anomalies of varying sizes. The teacher-student (T-S) architecture ensures consistent representation of high-dimensional features while adapting them to enhance anomaly detection. Multi-AD was evaluated on several medical datasets, including brain MRI, liver CT, and retina OCT, as well as industrial datasets, such as MVTec AD, demonstrating strong generalization across multiple domains. Experimental results demonstrated that our approach consistently outperformed state-of-the-art models, achieving the best average AUROC for both image-level (81.4% for medical and 99.6% for industrial) and pixel-level (97.0% for medical and 98.4% for industrial) tasks, making it effective for real-world applications.", "date": "2026-02-05", "categories": []}
{"id": "2601.11073", "title": "Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud", "abstract": "Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and affects the well-being of online communities. However, existing detection methods based on graph neural networks (GNNs) struggle with two persistent challenges: (1) long-tailed data distributions, which obscure rare but critical fraudulent cases, and (2) fraud camouflage, where malicious transactions mimic benign behaviors to evade detection. To fill these gaps, we propose HIMVH, a Hippocampus-Inspired Multi-View Hypergraph learning model for web finance fraud detection. Specifically, drawing inspiration from the scene conflict monitoring role of the hippocampus, we design a cross-view inconsistency perception module that captures subtle discrepancies and behavioral heterogeneity across multiple transaction views. This module enables the model to identify subtle cross-view conflicts for detecting online camouflaged fraudulent behaviors. Furthermore, inspired by the match-mismatch novelty detection mechanism of the CA1 region, we introduce a novelty-aware hypergraph learning module that measures feature deviations from neighborhood expectations and adaptively reweights messages, thereby enhancing sensitivity to online rare fraud patterns in the long-tailed settings. Extensive experiments on six web-based financial fraud datasets demonstrate that HIMVH achieves 6.42% improvement in AUC, 9.74% in F1 and 39.14% in AP on average over 15 SOTA models.", "date": "2026-01-16", "categories": []}
{"id": "2601.22913", "title": "Multi-Cue Anomaly Detection and Localization under Data Contamination", "abstract": "Visual anomaly detection in real-world industrial settings faces two major limitations. First, most existing methods are trained on purely normal data or on unlabeled datasets assumed to be predominantly normal, presuming the absence of contamination, an assumption that is rarely satisfied in practice. Second, they assume no access to labeled anomaly samples, limiting the model from learning discriminative characteristics of true anomalies. Therefore, these approaches often struggle to distinguish anomalies from normal instances, resulting in reduced detection and weak localization performance. In real-world applications, where training data are frequently contaminated with anomalies, such methods fail to deliver reliable performance. In this work, we propose a robust anomaly detection framework that integrates limited anomaly supervision into the adaptive deviation learning paradigm. We introduce a composite anomaly score that combines three complementary components: a deviation score capturing statistical irregularity, an entropy-based uncertainty score reflecting predictive inconsistency, and a segmentation-based score highlighting spatial abnormality. This unified scoring mechanism enables accurate detection and supports gradient-based localization, providing intuitive and explainable visual evidence of anomalous regions. Following the few-anomaly paradigm, we incorporate a small set of labeled anomalies during training while simultaneously mitigating the influence of contaminated samples through adaptive instance weighting. Extensive experiments on the MVTec and VisA benchmarks demonstrate that our framework outperforms state-of-the-art baselines and achieves strong detection and localization performance, interpretability, and robustness under various levels of data contamination.", "date": "2026-01-30", "categories": []}
{"id": "2602.03870", "title": "DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Features", "abstract": "Unsupervised anomaly detection (AD) in medical images aims to identify abnormal regions without relying on pixel-level annotations, which is crucial for scalable and label-efficient diagnostic systems. In this paper, we propose a novel anomaly detection framework based on DINO-V3 representations, termed DINO-AD, which leverages self-supervised visual features for precise and interpretable anomaly localization. Specifically, we introduce an embedding similarity matching strategy to select a semantically aligned support image and a foreground-aware K-means clustering module to model the distribution of normal features. Anomaly maps are then computed by comparing the query features with clustered normal embeddings through cosine similarity. Experimental results on both the Brain and Liver datasets demonstrate that our method achieves superior quantitative performance compared with state-of-the-art approaches, achieving AUROC scores of up to 98.71. Qualitative results further confirm that our framework produces clearer and more accurate anomaly localization. Extensive ablation studies validate the effectiveness of each proposed component, highlighting the robustness and generalizability of our approach.", "date": "2026-01-31", "categories": []}
{"id": "2602.04643", "title": "MTS-JEPA: Multi-Resolution Joint-Embedding Predictive Architecture for Time-Series Anomaly Prediction", "abstract": "Multivariate time series underpin modern critical infrastructure, making the prediction of anomalies a vital necessity for proactive risk mitigation. While Joint-Embedding Predictive Architectures (JEPA) offer a promising framework for modeling the latent evolution of these systems, their application is hindered by representation collapse and an inability to capture precursor signals across varying temporal scales. To address these limitations, we propose MTS-JEPA, a specialized architecture that integrates a multi-resolution predictive objective with a soft codebook bottleneck. This design explicitly decouples transient shocks from long-term trends, and utilizes the codebook to capture discrete regime transitions. Notably, we find this constraint also acts as an intrinsic regularizer to ensure optimization stability. Empirical evaluations on standard benchmarks confirm that our approach effectively prevents degenerate solutions and achieves state-of-the-art performance under the early-warning protocol.", "date": "2026-02-04", "categories": []}
{"id": "2501.08628", "title": "Transformer-based Multivariate Time Series Anomaly Localization", "abstract": "With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decision-making. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the Space-Time Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and space-time statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.", "date": "2025-01-15", "categories": []}
{"id": "2510.10915", "title": "LPCVAE: A Conditional VAE with Long-Term Dependency and Probabilistic Time-Frequency Fusion for Time Series Anomaly Detection", "abstract": "Time series anomaly detection(TSAD) is a critical task in signal processing field, ensuring the reliability of complex systems. Reconstruction-based methods dominate in TSAD. Among these methods, VAE-based methods have achieved promising results. Existing VAE-based methods suffer from the limitation of single-window feature and insufficient leveraging of long-term time and frequency information. We propose a Conditional Variational AutoEncoder with Long-term dependency and Probabilistic time-frequency fusion, named LPCVAE. LPCVAE introduces LSTM to capture long-term dependencies beyond windows. It further incorporates a Product-of-Experts (PoE) mechanism for adaptive and distribution-level probabilistic fusion. This design effectively mitigates time-frequency information loss. Extensive experiments on four public datasets demonstrate it outperforms state-of-the-art methods. The results confirm that integrating long-term time and frequency representations with adaptive fusion yields a robust and efficient solution for TSAD.", "date": "2025-10-13", "categories": []}
{"id": "2602.01635", "title": "COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection", "abstract": "Time series anomaly detection is a critical task across various industrial domains. However, capturing temporal dependencies and multivariate correlations within patch-level representation learning remains underexplored, and reliance on single-scale patterns limits the detection of anomalies across different temporal ranges. Furthermore, focusing on normal data representations makes models vulnerable to distribution shifts at inference time. To address these limitations, we propose Codebook-based Online-adaptive Multi-scale Embedding for Time-series anomaly detection (COMET), which consists of three key components: (1) Multi-scale Patch Encoding captures temporal dependencies and inter-variable correlations across multiple patch scales. (2) Vector-Quantized Coreset learns representative normal patterns via codebook and detects anomalies with a dual-score combining quantization error and memory distance. (3) Online Codebook Adaptation generates pseudo-labels based on codebook entries and dynamically adapts the model at inference through contrastive learning. Experiments on five benchmark datasets demonstrate that COMET achieves the best performance in 36 out of 45 evaluation metrics, validating its effectiveness across diverse environments.", "date": "2026-02-02", "categories": []}
{"id": "2602.02925", "title": "Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Space", "abstract": "Detecting rare and diverse anomalies in highly imbalanced datasets-such as Advanced Persistent Threats (APTs) in cybersecurity-remains a fundamental challenge for machine learning systems. Active learning offers a promising direction by strategically querying an oracle to minimize labeling effort, yet conventional approaches often fail to exploit the intrinsic geometric structure of the feature space for model refinement. In this paper, we introduce SDA2E, a Sparse Dual Adversarial Attention-based AutoEncoder designed to learn compact and discriminative latent representations from imbalanced, high-dimensional data. We further propose a similarity-guided active learning framework that integrates three novel strategies to refine decision boundaries efficiently: mormal-like expansion, which enriches the training set with points similar to labeled normals to improve reconstruction fidelity; anomaly-like prioritization, which boosts ranking accuracy by focusing on points resembling known anomalies; and a hybrid strategy that combines both for balanced model refinement and ranking. A key component of our framework is a new similarity measure, Normalized Matching 1s (SIM_NM1), tailored for sparse binary embeddings. We evaluate SDA2E extensively across 52 imbalanced datasets, including multiple DARPA Transparent Computing scenarios, and benchmark it against 15 state-of-the-art anomaly detection methods. Results demonstrate that SDA2E consistently achieves superior ranking performance (nDCG up to 1.0 in several cases) while reducing the required labeled data by up to 80% compared to passive training. Statistical tests confirm the significance of these improvements. Our work establishes a robust, efficient, and statistically validated framework for anomaly detection that is particularly suited to cybersecurity applications such as APT detection.", "date": "2026-02-02", "categories": []}
{"id": "2602.03530", "title": "Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning", "abstract": "Logical anomalies are violations of predefined constraints on object quantity, spatial layout, and compositional relationships in industrial images. While prior work largely treats anomaly detection as a binary decision, such formulations cannot indicate which logical rule is broken and therefore offer limited value for quality assurance. We introduce Logical Anomaly Classification (LAC), a task that unifies anomaly detection and fine-grained violation classification in a single inference step. To tackle LAC, we propose LogiCls, a vision-language framework that decomposes complex logical constraints into a sequence of verifiable subqueries. We further present a data-centric instruction synthesis pipeline that generates chain-of-thought (CoT) supervision for these subqueries, coupling precise grounding annotations with diverse image-text augmentations to adapt vision language models (VLMs) to logic-sensitive reasoning. Training is stabilized by a difficulty-aware resampling strategy that emphasizes challenging subqueries and long tail constraint types. Extensive experiments demonstrate that LogiCls delivers robust, interpretable, and accurate industrial logical anomaly classification, providing both the predicted violation categories and their evidence trails.", "date": "2026-02-03", "categories": []}
{"id": "2602.03673", "title": "Referring Industrial Anomaly Segmentation", "abstract": "Industrial Anomaly Detection (IAD) is vital for manufacturing, yet traditional methods face significant challenges: unsupervised approaches yield rough localizations requiring manual thresholds, while supervised methods overfit due to scarce, imbalanced data. Both suffer from the \"One Anomaly Class, One Model\" limitation. To address this, we propose Referring Industrial Anomaly Segmentation (RIAS), a paradigm leveraging language to guide detection. RIAS generates precise masks from text descriptions without manual thresholds and uses universal prompts to detect diverse anomalies with a single model. We introduce the MVTec-Ref dataset to support this, designed with diverse referring expressions and focusing on anomaly patterns, notably with 95% small anomalies. We also propose the Dual Query Token with Mask Group Transformer (DQFormer) benchmark, enhanced by Language-Gated Multi-Level Aggregation (LMA) to improve multi-scale segmentation. Unlike traditional methods using redundant queries, DQFormer employs only \"Anomaly\" and \"Background\" tokens for efficient visual-textual integration. Experiments demonstrate RIAS's effectiveness in advancing IAD toward open-set capabilities. Code: https://github.com/swagger-coder/RIAS-MVTec-Ref.", "date": "2026-02-03", "categories": []}
{"id": "2602.03678", "title": "ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling", "abstract": "Log files record computational events that reflect system state and behavior, making them a primary source of operational insights in modern computer systems. Automated anomaly detection on logs is therefore critical, yet most established methods rely on log parsers that collapse messages into discrete templates, discarding variable values and semantic content. We propose ContraLog, a parser-free and self-supervised method that reframes log anomaly detection as predicting continuous message embeddings rather than discrete template IDs. ContraLog combines a message encoder that produces rich embeddings for individual log messages with a sequence encoder to model temporal dependencies within sequences. The model is trained with a combination of masked language modeling and contrastive learning to predict masked message embeddings based on the surrounding context. Experiments on the HDFS, BGL, and Thunderbird benchmark datasets empirically demonstrate effectiveness on complex datasets with diverse log messages. Additionally, we find that message embeddings generated by ContraLog carry meaningful information and are predictive of anomalies even without sequence context. These results highlight embedding-level prediction as an approach for log anomaly detection, with potential applicability to other event sequences.", "date": "2026-02-03", "categories": []}
{"id": "2407.15424", "title": "Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention", "abstract": "With the widespread deployment of video surveillance devices and the demand for intelligent system development, video anomaly detection (VAD) has become an important part of constructing intelligent surveillance systems. Expanding the discriminative boundary between normal and abnormal events to enhance performance is the common goal and challenge of VAD. To address this problem, we propose a Bidirectional Skip-frame Prediction (BiSP) network based on a dual-stream autoencoder, from the perspective of learning the intra-domain disparity between different features. The BiSP skips frames in the training phase to achieve the forward and backward frame prediction respectively, and in the testing phase, it utilizes bidirectional consecutive frames to co-predict the same intermediate frames, thus expanding the degree of disparity between normal and abnormal events. The BiSP designs the variance channel attention and context spatial attention from the perspectives of movement patterns and object scales, respectively, thus ensuring the maximization of the disparity between normal and abnormal in the feature extraction and delivery with different dimensions. Extensive experiments from four benchmark datasets demonstrate the effectiveness of the proposed BiSP, which substantially outperforms state-of-the-art competing methods.", "date": "2024-07-22", "categories": []}
{"id": "2503.21169", "title": "VADMamba: Exploring State Space Models for Fast Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) methods are mostly CNN-based or Transformer-based, achieving impressive results, but the focus on detection accuracy often comes at the expense of inference speed. The emergence of state space models in computer vision, exemplified by the Mamba model, demonstrates improved computational efficiency through selective scans and showcases the great potential for long-range modeling. Our study pioneers the application of Mamba to VAD, dubbed VADMamba, which is based on multi-task learning for frame prediction and optical flow reconstruction. Specifically, we propose the VQ-Mamba Unet (VQ-MaU) framework, which incorporates a Vector Quantization (VQ) layer and Mamba-based Non-negative Visual State Space (NVSS) block. Furthermore, two individual VQ-MaU networks separately predict frames and reconstruct corresponding optical flows, further boosting accuracy through a clip-level fusion evaluation strategy. Experimental results validate the efficacy of the proposed VADMamba across three benchmark datasets, demonstrating superior performance in inference speed compared to previous work. Code is available at https://github.com/jLooo/VADMamba.", "date": "2025-03-27", "categories": []}
{"id": "2505.22259", "title": "Domain Adaptation of Attention Heads for Zero-shot Anomaly Detection", "abstract": "Zero-shot anomaly detection (ZSAD) enables anomaly detection without normal samples from target categories, addressing scenarios where task-specific training data is unavailable. However, existing ZSAD methods either neglect adaptation of vision-language models to anomaly detection or implement only partial adaptation. This paper proposes Head-adaptive CLIP (HeadCLIP), which effectively adapts both text and image encoders. HeadCLIP employs learnable prompts in the text encoder to generalize normality and abnormality concepts, and introduces learnable head weights in the image encoder to dynamically adjust attention head features for task-specific adaptation. A joint anomaly score is further proposed to leverage adapted pixel-level information for enhanced image-level detection. Experiments on 17 datasets across industrial and medical domains demonstrate that HeadCLIP outperforms existing ZSAD methods at both pixel and image levels, achieving improvements of up to 4.9\\%p in pixel-level mean anomaly detection score (mAD) and 3.7%p in image-level mAD in the industrial domain, with comparable gains (3.2%p, 3.2%p) in the medical domain. Code and pretrained weights are available at https://github.com/kiyoonjeong0305/HeadCLIP.", "date": "2025-05-28", "categories": []}
{"id": "2602.00124", "title": "Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance", "abstract": "The detection of anomalies is crucial to ensuring the safety and security of maritime vessel traffic surveillance. Although autoencoders are popular for anomaly detection, their effectiveness in identifying collective and contextual anomalies is limited, especially in the maritime domain, where anomalies depend on vessel-specific contexts derived from self-reported AIS messages. To address these limitations, we propose a novel solution: the context-aware autoencoder. By integrating context-specific thresholds, our method improves detection accuracy and reduces computational cost. We compare four context-aware autoencoder variants and a conventional autoencoder using a case study focused on fishing status anomalies in maritime surveillance. Results demonstrate the significant impact of context on reconstruction loss and anomaly detection. The context-aware autoencoder outperforms others in detecting anomalies in time series data. By incorporating context-specific thresholds and recognizing the importance of context in anomaly detection, our approach offers a promising solution to improve accuracy in maritime vessel traffic surveillance systems.", "date": "2026-01-27", "categories": []}
{"id": "2602.00126", "title": "D3R-Net: Dual-Domain Denoising Reconstruction Network for Robust Industrial Anomaly Detection", "abstract": "Unsupervised anomaly detection (UAD) is a key ingredient of automated visual inspection in modern manufacturing. The reconstruction-based methods appeal because they have basic architectural design and they process data quickly but they produce oversmoothed results for high-frequency details. As a result, subtle defects are partially reconstructed rather than highlighted, which limits segmentation accuracy. We build on this line of work and introduce D3R-Net, a Dual-Domain Denoising Reconstruction framework that couples a self-supervised 'healing' task with frequency-aware regularization. During training, the network receives synthetically corrupted normal images and is asked to reconstruct the clean targets, which prevents trivial identity mapping and pushes the model to learn the manifold of defect-free textures. In addition to the spatial mean squared error, we employ a Fast Fourier Transform (FFT) magnitude loss that encourages consistency in the frequency domain. The implementation also allows an optional structural similarity (SSIM) term, which we study in an ablation. On the MVTec AD Hazelnut benchmark, D3R-Net with the FFT loss improves localization consistency over a spatial-only baseline: PRO AUC increases from 0.603 to 0.687, while image-level ROC AUC remains robust. Evaluated across fifteen MVTec categories, the FFT variant raises the average pixel ROC AUC from 0.733 to 0.751 and PRO AUC from 0.417 to 0.468 compared to the MSE-only baseline, at roughly 20 FPS on a single GPU. The network is trained from scratch and uses a lightweight convolutional autoencoder backbone, providing a practical alternative to heavy pre-trained feature embedding methods.", "date": "2026-01-27", "categories": []}
{"id": "2602.00522", "title": "MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval", "abstract": "Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data distribution, resulting in high training or inference cost and limited cross-domain stability. To address these limitations, we propose Memory-Retrieval Anomaly Detection method (MRAD), a unified framework that replaces parametric fitting with a direct memory retrieval. The train-free base model, MRAD-TF, freezes the CLIP image encoder and constructs a two-level memory bank (image-level and pixel-level) from auxiliary data, where feature-label pairs are explicitly stored as keys and values. During inference, anomaly scores are obtained directly by similarity retrieval over the memory bank. Based on the MRAD-TF, we further propose two lightweight variants as enhancements: (i) MRAD-FT fine-tunes the retrieval metric with two linear layers to enhance the discriminability between normal and anomaly; (ii) MRAD-CLIP injects the normal and anomalous region priors from the MRAD-FT as dynamic biases into CLIP's learnable text prompts, strengthening generalization to unseen categories. Across 16 industrial and medical datasets, the MRAD framework consistently demonstrates superior performance in anomaly classification and segmentation, under both train-free and training-based settings. Our work shows that fully leveraging the empirical distribution of raw data, rather than relying only on model fitting, can achieve stronger anomaly detection performance. The code will be publicly released at https://github.com/CROVO1026/MRAD.", "date": "2026-01-31", "categories": []}
{"id": "2602.00672", "title": "Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSAD", "abstract": "Research in time series anomaly detection (TSAD) has largely focused on developing increasingly sophisticated, hard-to-train, and expensive-to-infer neural architectures. We revisit this paradigm and show that a simple linear autoregressive anomaly score with the closed-form solution provided by ordinary least squares (OLS) regression consistently matches or outperforms state-of-the-art deep detectors. From a theoretical perspective, we show that linear models capture a broad class of anomaly types, estimating a finite-history Gaussian process conditional density. From a practical side, across extensive univariate and multivariate benchmarks, the proposed approach achieves superior accuracy while requiring orders of magnitude fewer computational resources. Thus, future research should consistently include strong linear baselines and, more importantly, develop new benchmarks with richer temporal structures pinpointing the advantages of deep learning models.", "date": "2026-01-31", "categories": []}
{"id": "2602.01060", "title": "TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detection", "abstract": "Existing generative models for unsupervised anomalous sound detection are limited by their inability to fully capture the complex feature distribution of normal sounds, while the potential of powerful diffusion models in this domain remains largely unexplored. To address this challenge, we propose a novel framework, TLDiffGAN, which consists of two complementary branches. One branch incorporates a latent diffusion model into the GAN generator for adversarial training, thereby making the discriminator's task more challenging and improving the quality of generated samples. The other branch leverages pretrained audio model encoders to extract features directly from raw audio waveforms for auxiliary discrimination. This framework effectively captures feature representations of normal sounds from both raw audio and Mel spectrograms. Moreover, we introduce a TMixup spectrogram augmentation technique to enhance sensitivity to subtle and localized temporal patterns that are often overlooked. Extensive experiments on the DCASE 2020 Challenge Task 2 dataset demonstrate the superior detection performance of TLDiffGAN, as well as its strong capability in anomalous time-frequency localization.", "date": "2026-02-01", "categories": []}
{"id": "2602.02124", "title": "Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies", "abstract": "Drug-induced toxicity remains a leading cause of failure in preclinical development and early clinical trials. Detecting adverse effects at an early stage is critical to reduce attrition and accelerate the development of safe medicines. Histopathological evaluation remains the gold standard for toxicity assessment, but it relies heavily on expert pathologists, creating a bottleneck for large-scale screening. To address this challenge, we introduce an AI-based anomaly detection framework for histopathological whole-slide images (WSIs) in rodent livers from toxicology studies. The system identifies healthy tissue and known pathologies (anomalies) for which training data is available. In addition, it can detect rare pathologies without training data as out-of-distribution (OOD) findings. We generate a novel dataset of pixelwise annotations of healthy tissue and known pathologies and use this data to fine-tune a pre-trained Vision Transformer (DINOv2) via Low-Rank Adaptation (LoRA) in order to do tissue segmentation. Finally, we extract features for OOD detection using the Mahalanobis distance. To better account for class-dependent variability in histological data, we propose the use of class-specific thresholds. We optimize the thresholds using the mean of the false negative and false positive rates, resulting in only 0.16\\% of pathological tissue classified as healthy and 0.35\\% of healthy tissue classified as pathological. Applied to mouse liver WSIs with known toxicological findings, the framework accurately detects anomalies, including rare OOD morphologies. This work demonstrates the potential of AI-driven histopathology to support preclinical workflows, reduce late-stage failures, and improve efficiency in drug development.", "date": "2026-02-02", "categories": []}
{"id": "2501.05130", "title": "Contrastive Representation Modeling for Anomaly Detection", "abstract": "Distance-based anomaly detection methods rely on compact in-distribution (ID) embeddings that are well separated from anomalies. However, conventional contrastive learning strategies often struggle to achieve this balance, either promoting excessive variance among inliers or failing to preserve the diversity of outliers. We begin by analyzing the challenges of representation learning for anomaly detection and identify three essential properties for the pretext task: (1) compact clustering of inliers, (2) strong separation between inliers and anomalies, and (3) preservation of diversity among synthetic outliers. Building on this, we propose a structured contrastive objective that redefines positive and negative relationships during training, promoting these properties without requiring explicit anomaly labels. We extend this framework with a patch-based learning and evaluation strategy specifically designed to improve the detection of localized anomalies in industrial settings. Our approach demonstrates significantly faster convergence and improved performance compared to standard contrastive methods. It matches or surpasses anomaly detection methods on both semantic and industrial benchmarks, including methods that rely on discriminative training or explicit anomaly labels.", "date": "2025-01-09", "categories": []}
{"id": "2601.22399", "title": "Score-based Integrated Gradient for Root Cause Explanations of Outliers", "abstract": "Identifying the root causes of outliers is a fundamental problem in causal inference and anomaly detection. Traditional approaches based on heuristics or counterfactual reasoning often struggle under uncertainty and high-dimensional dependencies. We introduce SIREN, a novel and scalable method that attributes the root causes of outliers by estimating the score functions of the data likelihood. Attribution is computed via integrated gradients that accumulate score contributions along paths from the outlier toward the normal data distribution. Our method satisfies three of the four classic Shapley value axioms - dummy, efficiency, and linearity - as well as an asymmetry axiom derived from the underlying causal structure. Unlike prior work, SIREN operates directly on the score function, enabling tractable and uncertainty-aware root cause attribution in nonlinear, high-dimensional, and heteroscedastic causal models. Extensive experiments on synthetic random graphs and real-world cloud service and supply chain datasets show that SIREN outperforms state-of-the-art baselines in both attribution accuracy and computational efficiency.", "date": "2026-01-29", "categories": []}
{"id": "2601.22492", "title": "PromptMAD: Cross-Modal Prompting for Multi-Class Visual Anomaly Localization", "abstract": "Visual anomaly detection in multi-class settings poses significant challenges due to the diversity of object categories, the scarcity of anomalous examples, and the presence of camouflaged defects. In this paper, we propose PromptMAD, a cross-modal prompting framework for unsupervised visual anomaly detection and localization that integrates semantic guidance through vision-language alignment. By leveraging CLIP-encoded text prompts describing both normal and anomalous class-specific characteristics, our method enriches visual reconstruction with semantic context, improving the detection of subtle and textural anomalies. To further address the challenge of class imbalance at the pixel level, we incorporate Focal loss function, which emphasizes hard-to-detect anomalous regions during training. Our architecture also includes a supervised segmentor that fuses multi-scale convolutional features with Transformer-based spatial attention and diffusion iterative refinement, yielding precise and high-resolution anomaly maps. Extensive experiments on the MVTec-AD dataset demonstrate that our method achieves state-of-the-art pixel-level performance, improving mean AUC to 98.35% and AP to 66.54%, while maintaining efficiency across diverse categories.", "date": "2026-01-30", "categories": []}
{"id": "2601.22800", "title": "Trackly: A Unified SaaS Platform for User Behavior Analytics and Real Time Rule Based Anomaly Detection", "abstract": "Understanding user behavior is essential for improving digital experiences, optimizing business conversions, and mitigating threats like account takeovers, fraud, and bot attacks. Most platforms separate product analytics and security, creating fragmented visibility and delayed threat detection. Trackly, a scalable SaaS platform, unifies comprehensive user behavior analytics with real time, rule based anomaly detection. It tracks sessions, IP based geo location, device browser fingerprints, and granular events such as page views, add to cart, and checkouts. Suspicious activities logins from new devices or locations, impossible travel (Haversine formula), rapid bot like actions, VPN proxy usage, or multiple accounts per IP are flagged via configurable rules with weighted risk scoring, enabling transparent, explainable decisions. A real time dashboard provides global session maps, DAU MAU, bounce rates, and session durations. Integration is simplified with a lightweight JavaScript SDK and secure REST APIs. Implemented on a multi tenant microservices stack (ASP.NET Core, MongoDB, RabbitMQ, Next.js), Trackly achieved 98.1% accuracy, 97.7% precision, and 2.25% false positives on synthetic datasets, proving its efficiency for SMEs and ecommerce.", "date": "2026-01-30", "categories": []}
{"id": "2504.14250", "title": "Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of annotations and (2) homophily disparity at node and class levels. In this paper, we introduce Anomaly-Aware Pre-Training and Fine-Tuning (APF), a targeted and effective framework to mitigate the above challenges in GAD. In the pre-training stage, APF incorporates node-specific subgraphs selected via the Rayleigh Quotient, a label-free anomaly metric, into the learning objective to enhance anomaly awareness. It further introduces two learnable spectral polynomial filters to jointly learn dual representations that capture both general semantics and subtle anomaly cues. During fine-tuning, a gated fusion mechanism adaptively integrates pre-trained representations across nodes and dimensions, while an anomaly-aware regularization loss encourages abnormal nodes to preserve more anomaly-relevant information. Furthermore, we theoretically show that APF tends to achieve linear separability under mild conditions. Comprehensive experiments on 10 benchmark datasets validate the superior performance of APF in comparison to state-of-the-art baselines.", "date": "2025-04-19", "categories": []}
{"id": "2505.03911", "title": "Explaining Anomalies with Tensor Networks", "abstract": "Tensor networks, a class of variational quantum many-body wave functions have attracted considerable research interest across many disciplines, including classical machine learning. Recently, Aizpurua et al. demonstrated explainable anomaly detection with matrix product states on a discrete-valued cyber-security task, using quantum-inspired methods to gain insight into the learned model and detected anomalies. Here, we extend this framework to real-valued data domains. We furthermore introduce tree tensor networks for the task of explainable anomaly detection. We demonstrate these methods with three benchmark problems, show adequate predictive performance compared to several baseline models and both tensor network architectures' ability to explain anomalous samples. We thereby extend the application of tensor networks to a broader class of potential problems and open a pathway for future extensions to more complex tensor network architectures.", "date": "2025-05-06", "categories": []}
{"id": "2507.04490", "title": "Dealing with Uncertainty in Contextual Anomaly Detection", "abstract": "Contextual anomaly detection (CAD) aims to identify anomalies in a target (behavioral) variable conditioned on a set of contextual variables that influence the normalcy of the target variable but are not themselves indicators of anomaly. In many anomaly detection tasks, there exist contextual variables that influence the normalcy of the target variable but are not themselves indicators of anomaly. In this work, we propose a novel framework for CAD, normalcy score (NS), that explicitly models both the aleatoric and epistemic uncertainties. Built on heteroscedastic Gaussian process regression, our method regards the Z-score as a random variable, providing confidence intervals that reflect the reliability of the anomaly assessment. Through experiments on benchmark datasets and a real-world application in cardiology, we demonstrate that NS outperforms state-of-the-art CAD methods in both detection accuracy and interpretability. Moreover, confidence intervals enable an adaptive, uncertainty-driven decision-making process, which may be very important in domains such as healthcare.", "date": "2025-07-06", "categories": []}
{"id": "2509.18751", "title": "MOMEMTO: Patch-based Memory Gate Model in Time Series Foundation Model", "abstract": "Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization capability increase, these models tend to over-generalize, often reconstructing unseen anomalies accurately. Prior works have attempted to mitigate this by incorporating a memory architecture that stores prototypes of normal patterns. Nevertheless, these approaches suffer from high training costs and have yet to be effectively integrated with time series foundation models (TFMs). To address these challenges, we propose MOMEMTO, an improved variant of TFM for anomaly detection, enhanced with a patch-based memory module to mitigate over-generalization. The memory module is designed to capture representative normal patterns from multiple domains and enables a single model to be jointly fine-tuned across multiple datasets through a multi-domain training strategy. MOMEMTO initializes memory items with latent representations from a pre-trained encoder, organizes them into patch-level units, and updates them via an attention mechanism. We evaluate our method using 23 univariate benchmark datasets. Experimental results demonstrate that MOMEMTO, as a single model, achieves higher scores on AUC and VUS metrics compared to baseline methods, and further enhances the performance of its backbone TFM, particularly in few-shot learning scenarios.", "date": "2025-09-23", "categories": []}
{"id": "2510.21296", "title": "An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination", "abstract": "Unsupervised anomaly detection (AD) methods typically assume clean training data, yet real-world datasets often contain undetected or mislabeled anomalies, leading to significant performance degradation. Existing solutions require access to the training pipelines, data or prior knowledge of the proportions of anomalies in the data, limiting their real-world applicability. To address this challenge, we propose EPHAD, a simple yet effective test-time adaptation framework that updates the outputs of AD models trained on contaminated datasets using evidence gathered at test time. Our approach integrates the prior knowledge captured by the AD model trained on contaminated datasets with evidence derived from multimodal foundation models like Contrastive Language-Image Pre-training (CLIP), classical AD methods like the Local Outlier Factor or domain-specific knowledge. We illustrate the intuition behind EPHAD using a synthetic toy example and validate its effectiveness through comprehensive experiments across eight visual AD datasets, twenty-six tabular AD datasets, and a real-world industrial AD dataset. Additionally, we conduct an ablation study to analyse hyperparameter influence and robustness to varying contamination levels, demonstrating the versatility and robustness of EPHAD across diverse AD models and evidence pairs. To ensure reproducibility, our code is publicly available at https://github.com/sukanyapatra1997/EPHAD.", "date": "2025-10-24", "categories": []}
{"id": "2601.21050", "title": "SMKC: Sketch Based Kernel Correlation Images for Variable Cardinality Time Series Anomaly Detection", "abstract": "Conventional anomaly detection in multivariate time series relies on the assumption that the set of observed variables remains static. In operational environments, however, monitoring systems frequently experience sensor churn. Signals may appear, disappear, or be renamed, creating data windows where the cardinality varies and may include values unseen during training. To address this challenge, we propose SMKC, a framework that decouples the dynamic input structure from the anomaly detector. We first employ permutation-invariant feature hashing to sketch raw inputs into a fixed size state sequence. We then construct a hybrid kernel image to capture global temporal structure through pairwise comparisons of the sequence and its derivatives. The model learns normal patterns using masked reconstruction and a teacher-student prediction objective. Our evaluation reveals that robust log-distance channels provide the primary discriminative signal, whereas cosine representations often fail to capture sufficient contrast. Notably, we find that a detector using random projections and nearest neighbors on the SMKC representation performs competitively with fully trained baselines without requiring gradient updates. This highlights the effectiveness of the representation itself and offers a practical cold-start solution for resource-constrained deployments.", "date": "2026-01-28", "categories": []}
{"id": "2601.21171", "title": "AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection", "abstract": "Graph anomaly detection aims to identify abnormal patterns in networks, but faces significant challenges from label scarcity and extreme class imbalance. While graph contrastive learning offers a promising unsupervised solution, existing methods suffer from two critical limitations: random augmentations break semantic consistency in positive pairs, while naive negative sampling produces trivial, uninformative contrasts. We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework that addresses both limitations through principled counterfactual reasoning. By combining information-theoretic active selection with counterfactual generation, our approach identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside normal negative counterparts that provide hard contrasts, while restricting expensive counterfactual generation to a strategically selected subset. This design reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Experiments on nine benchmark datasets, including real-world financial transaction graphs from GADBench, show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.", "date": "2026-01-29", "categories": []}
{"id": "2601.21446", "title": "Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders", "abstract": "Illicit financial activities such as money laundering often manifest through recurrent topological patterns in transaction networks. Detecting these patterns automatically remains challenging due to the scarcity of labeled real-world data and strict privacy constraints. To address this, we investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data. The analysis consists of two phases: (i) data generation, where synthetic samples are created for seven well-known illicit activity patterns using parametrized generators that preserve structural consistency while introducing realistic variability; and (ii) model training and validation, where separate GAEs are trained on each pattern without explicit labels, relying solely on reconstruction error as an indicator of learned structure. We compare three GAE implementations based on three distinct convolutional layers: Graph Convolutional (GAE-GCN), GraphSAGE (GAE-SAGE), and Graph Attention Network (GAE-GAT). Experimental results show that GAE-GCN achieves the most consistent reconstruction performance across patterns, while GAE-SAGE and GAE-GAT exhibit competitive results only in few specific patterns. These findings suggest that graph-based representation learning on synthetic data provides a viable path toward developing AI-driven tools for detecting illicit behaviors, overcoming the limitations of financial datasets.", "date": "2026-01-29", "categories": []}
{"id": "2504.06039", "title": "Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies", "abstract": "Capsule endoscopy is a method to capture images of the gastrointestinal tract and screen for diseases which might remain hidden if investigated with standard endoscopes. Due to the limited size of a video capsule, embedding AI models directly into the capsule demands careful consideration of the model size and thus complicates anomaly detection in this field. Furthermore, the scarcity of available data in this domain poses an ongoing challenge to achieving effective anomaly detection. Thus, this work introduces an ensemble strategy to address this challenge in anomaly detection tasks in video capsule endoscopies, requiring only a small number of individual neural networks during both the training and inference phases. Ensemble learning combines the predictions of multiple independently trained neural networks. This has shown to be highly effective in enhancing both the accuracy and robustness of machine learning models. However, this comes at the cost of higher memory usage and increased computational effort, which quickly becomes prohibitive in many real-world applications. Instead of applying the same training algorithm to each individual network, we propose using various loss functions, drawn from the anomaly detection field, to train each network. The methods are validated on the two largest publicly available datasets for video capsule endoscopy images, the Galar and the Kvasir-Capsule dataset. We achieve an AUC score of 76.86% on the Kvasir-Capsule and an AUC score of 76.98% on the Galar dataset. Our approach outperforms current baselines with significantly fewer parameters across all models, which is a crucial step towards incorporating artificial intelligence into capsule endoscopies.", "date": "2025-04-08", "categories": []}
{"id": "2601.03664", "title": "Stochastic Voronoi Ensembles for Anomaly Detection", "abstract": "Anomaly detection aims to identify data instances that deviate significantly from majority of data, which has been widely used in fraud detection, network security, and industrial quality control. Existing methods struggle with datasets exhibiting varying local densities: distance-based methods miss local anomalies, while density-based approaches require careful parameter selection and incur quadratic time complexity. We observe that local anomalies, though indistinguishable under global analysis, become conspicuous when the data space is decomposed into restricted regions and each region is examined independently. Leveraging this geometric insight, we propose SVEAD (Stochastic Voronoi Ensembles Anomaly Detector), which constructs ensemble random Voronoi diagrams and scores points by normalized cell-relative distances weighted by local scale. The proposed method achieves linear time complexity and constant space complexity. Experiments on 45 datasets demonstrate that SVEAD outperforms 12 state-of-the-art approaches.", "date": "2026-01-07", "categories": []}
{"id": "2601.19992", "title": "BayPrAnoMeta: Bayesian Proto-MAML for Few-Shot Industrial Image Anomaly Detection", "abstract": "Industrial image anomaly detection is a challenging problem owing to extreme class imbalance and the scarcity of labeled defective samples, particularly in few-shot settings. We propose BayPrAnoMeta, a Bayesian generalization of Proto-MAML for few-shot industrial image anomaly detection. Unlike existing Proto-MAML approaches that rely on deterministic class prototypes and distance-based adaptation, BayPrAnoMeta replaces prototypes with task-specific probabilistic normality models and performs inner-loop adaptation via a Bayesian posterior predictive likelihood. We model normal support embeddings with a Normal-Inverse-Wishart (NIW) prior, producing a Student-$t$ predictive distribution that enables uncertainty-aware, heavy-tailed anomaly scoring and is essential for robustness in extreme few-shot settings. We further extend BayPrAnoMeta to a federated meta-learning framework with supervised contrastive regularization for heterogeneous industrial clients and prove convergence to stationary points of the resulting nonconvex objective. Experiments on the MVTec AD benchmark demonstrate consistent and significant AUROC improvements over MAML, Proto-MAML, and PatchCore-based methods in few-shot anomaly detection settings.", "date": "2026-01-27", "categories": []}
{"id": "2601.20333", "title": "Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chaining", "abstract": "Deep topological data analysis (TDA) offers a principled framework for capturing structural invariants such as connectivity and cycles that persist across scales, making it a natural fit for anomaly segmentation (AS). Unlike thresholdbased binarisation, which produces brittle masks under distribution shift, TDA allows anomalies to be characterised as disruptions to global structure rather than local fluctuations. We introduce TopoOT, a topology-aware optimal transport (OT) framework that integrates multi-filtration persistence diagrams (PDs) with test-time adaptation (TTA). Our key innovation is Optimal Transport Chaining, which sequentially aligns PDs across thresholds and filtrations, yielding geodesic stability scores that identify features consistently preserved across scales. These stabilityaware pseudo-labels supervise a lightweight head trained online with OT-consistency and contrastive objectives, ensuring robust adaptation under domain shift. Across standard 2D and 3D anomaly detection benchmarks, TopoOT achieves state-of-the-art performance, outperforming the most competitive methods by up to +24.1% mean F1 on 2D datasets and +10.2% on 3D AS benchmarks.", "date": "2026-01-28", "categories": []}
{"id": "2505.12825", "title": "Theoretical Investigation on Inductive Bias of Isolation Forest", "abstract": "Isolation Forest (iForest) stands out as a widely-used unsupervised anomaly detector, primarily owing to its remarkable runtime efficiency and superior performance in large-scale tasks. Despite its widespread adoption, a theoretical foundation explaining iForest's success remains unclear. This paper focuses on the inductive bias of iForest, which theoretically elucidates under what circumstances and to what extent iForest works well. The key is to formulate the growth process of iForest, where the split dimensions and split values are randomly selected. We model the growth process of iForest as a random walk, enabling us to derive the expected depth function, which is the outcome of iForest, using transition probabilities. The case studies reveal key inductive biases: iForest exhibits lower sensitivity to central anomalies while demonstrating greater parameter adaptability compared to $k$-Nearest Neighbor. Our study provides a theoretical understanding of the effectiveness of iForest and establishes a foundation for further theoretical exploration.", "date": "2025-05-19", "categories": []}
{"id": "2601.19255", "title": "LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection", "abstract": "Time series anomaly detection is critical for supply chain management to take proactive operations, but faces challenges: classical unsupervised anomaly detection based on exploiting data patterns often yields results misaligned with business requirements and domain knowledge, while manual expert analysis cannot scale to millions of products in the supply chain. We propose a framework that leverages large language models (LLMs) to systematically encode human expertise into interpretable, logic-based rules for detecting anomaly patterns in supply chain time series data. Our approach operates in three stages: 1) LLM-based labeling of training data instructed by domain knowledge, 2) automated generation and iterative improvements of symbolic rules through LLM-driven optimization, and 3) rule augmentation with business-relevant anomaly categories supported by LLMs to enhance interpretability. The experiment results showcase that our approach outperforms the unsupervised learning methods in both detection accuracy and interpretability. Furthermore, compared to direct LLM deployment for time series anomaly detection, our approach provides consistent, deterministic results with low computational latency and cost, making it ideal for production deployment. The proposed framework thus demonstrates how LLMs can bridge the gap between scalable automation and expert-driven decision-making in operational settings.", "date": "2026-01-27", "categories": []}
{"id": "2601.19833", "title": "A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection", "abstract": "In this paper, we address the problem of class-generalizable anomaly detection, where the objective is to develop a unified model by focusing our learning on the available normal data and a small amount of anomaly data in order to detect the completely unseen anomalies, also referred to as the out-of-distribution (OOD) classes. Adding to this challenge is the fact that the anomaly data is rare and costly to label. To achieve this, we propose a multidirectional meta-learning algorithm -- at the inner level, the model aims to learn the manifold of the normal data (representation); at the outer level, the model is meta-tuned with a few anomaly samples to maximize the softmax confidence margin between the normal and anomaly samples (decision surface calibration), treating normals as in-distribution (ID) and anomalies as out-of-distribution (OOD). By iteratively repeating this process over multiple episodes of predominantly normal and a small number of anomaly samples, we realize a multidirectional meta-learning framework. This two-level optimization, enhanced by multidirectional training, enables stronger generalization to unseen anomaly classes.", "date": "2026-01-27", "categories": []}
{"id": "2511.14832", "title": "How to pick the best anomaly detector?", "abstract": "Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is empirically shown to robustly select the most sensitive anomaly detection model given the data. Focusing on weakly-supervised, classifier-based anomaly detection methods, we show that the ARGOS metric outperforms other model selection metrics previously used in the literature, in particular the binary cross-entropy loss. We explore several realistic applications, including hyperparameter tuning as well as architecture and feature selection, and in all cases we demonstrate that ARGOS is robust to the noisy conditions of anomaly detection.", "date": "2025-11-18", "categories": []}
{"id": "2601.17301", "title": "Tabular Foundation Models are Strong Graph Anomaly Detectors", "abstract": "Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing GAD methods follow a \"one model per dataset\" paradigm, leading to high computational costs, substantial data demands, and poor generalization when transferred to new datasets. This calls for a foundation model that enables a \"one-for-all\" GAD solution capable of detecting anomalies across diverse graphs without retraining. Yet, achieving this is challenging due to the large structural and feature heterogeneity across domains. In this paper, we propose TFM4GAD, a simple yet effective framework that adapts tabular foundation models (TFMs) for graph anomaly detection. Our key insight is that the core challenges of foundation GAD, handling heterogeneous features, generalizing across domains, and operating with scarce labels, are the exact problems that modern TFMs are designed to solve via synthetic pre-training and powerful in-context learning. The primary challenge thus becomes structural: TFMs are agnostic to graph topology. TFM4GAD bridges this gap by \"flattening\" the graph, constructing an augmented feature table that enriches raw node features with Laplacian embeddings, local and global structural characteristics, and anomaly-sensitive neighborhood aggregations. This augmented table is processed by a TFM in a fully in-context regime. Extensive experiments on multiple datasets with various TFM backbones reveal that TFM4GAD surprisingly achieves significant performance gains over specialized GAD models trained from scratch. Our work offers a new perspective and a practical paradigm for leveraging TFMs as powerful, generalist graph anomaly detectors.", "date": "2026-01-24", "categories": []}
{"id": "2601.17396", "title": "GO-OSC and VASH: Geometry-Aware Representation Learning for Early Degradation Detection in Oscillatory Systems", "abstract": "Early-stage degradation in oscillatory systems often manifests as geometric distortions of the dynamics, such as phase jitter, frequency drift, or loss of coherence, long before changes in signal energy are detectable. In this regime, classical energy-based diagnostics and unconstrained learned representations are structurally insensitive, leading to delayed or unstable detection. We introduce GO-OSC, a geometry-aware representation learning framework for oscillatory time series that enforces a canonical and identifiable latent parameterization, enabling stable comparison and aggregation across short, unlabeled windows. Building on this representation, we define a family of invariant linear geometric probes that target degradation-relevant directions in latent space. We provide theoretical results showing that under early phase-only degradation, energy-based statistics have zero first-order detection power, whereas geometric probes achieve strictly positive sensitivity. Our analysis characterizes when and why linear probing fails under non-identifiable representations and shows how canonicalization restores statistical detectability. Experiments on synthetic benchmarks and real vibration datasets validate the theory, demonstrating earlier detection, improved data efficiency, and robustness to operating condition changes.", "date": "2026-01-24", "categories": []}
{"id": "2601.17405", "title": "HAAF: Hierarchical Adaptation and Alignment of Foundation Models for Few-Shot Pathology Anomaly Detection", "abstract": "Precision pathology relies on detecting fine-grained morphological abnormalities within specific Regions of Interest (ROIs), as these local, texture-rich cues - rather than global slide contexts - drive expert diagnostic reasoning. While Vision-Language (V-L) models promise data efficiency by leveraging semantic priors, adapting them faces a critical Granularity Mismatch, where generic representations fail to resolve such subtle defects. Current adaptation methods often treat modalities as independent streams, failing to ground semantic prompts in ROI-specific visual contexts. To bridge this gap, we propose the Hierarchical Adaptation and Alignment Framework (HAAF). At its core is a novel Cross-Level Scaled Alignment (CLSA) mechanism that enforces a sequential calibration order: visual features first inject context into text prompts to generate content-adaptive descriptors, which then spatially guide the visual encoder to spotlight anomalies. Additionally, a dual-branch inference strategy integrates semantic scores with geometric prototypes to ensure stability in few-shot settings. Experiments on four benchmarks show HAAF significantly outperforms state-of-the-art methods and effectively scales with domain-specific backbones (e.g., CONCH) in low-resource scenarios.", "date": "2026-01-24", "categories": []}
{"id": "2601.17786", "title": "Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations", "abstract": "Text anomaly detection (TAD) plays a critical role in various language-driven real-world applications, including harmful content moderation, phishing detection, and spam review filtering. While two-step \"embedding-detector\" TAD methods have shown state-of-the-art performance, their effectiveness is often limited by the use of a single embedding model and the lack of adaptability across diverse datasets and anomaly types. To address these limitations, we propose to exploit the embeddings from multiple pretrained language models and integrate them into $MCA^2$, a multi-view TAD framework. $MCA^2$ adopts a multi-view reconstruction model to effectively extract normal textual patterns from multiple embedding perspectives. To exploit inter-view complementarity, a contrastive collaboration module is designed to leverage and strengthen the interactions across different views. Moreover, an adaptive allocation module is developed to automatically assign the contribution weight of each view, thereby improving the adaptability to diverse datasets. Extensive experiments on 10 benchmark datasets verify the effectiveness of $MCA^2$ against strong baselines. The source code of $MCA^2$ is available at https://github.com/yankehan/MCA2.", "date": "2026-01-25", "categories": []}
{"id": "2510.21935", "title": "AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing", "abstract": "Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robust statements about any observed outliers. While there is a wealth of literature on anomaly detection via dimensionality reduction, most methods do not produce outputs compatible with quantifiable claims of scientific discovery. In this work we directly address these challenges, presenting the first step towards a unified pipeline for novelty detection adapted for the rigorous statistical demands of science. We introduce AutoSciDACT (Automated Scientific Discovery with Anomalous Contrastive Testing), a general-purpose pipeline for detecting novelty in scientific data. AutoSciDACT begins by creating expressive low-dimensional data representations using a contrastive pre-training, leveraging the abundance of high-quality simulated data in many scientific domains alongside expertise that can guide principled data augmentation strategies. These compact embeddings then enable an extremely sensitive machine learning-based two-sample test using the New Physics Learning Machine (NPLM) framework, which identifies and statistically quantifies deviations in observed data relative to a reference distribution (null hypothesis). We perform experiments across a range of astronomical, physical, biological, image, and synthetic datasets, demonstrating strong sensitivity to small injections of anomalous data across all domains.", "date": "2025-10-24", "categories": []}
{"id": "2510.26501", "title": "Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters", "abstract": "Continuous electrocardiogram (ECG) monitoring via wearable devices is vital for early cardiovascular disease detection. However, deploying deep learning models on resource-constrained microcontrollers faces reliability challenges, particularly from Out-of-Distribution (OOD) pathologies and noise. Standard classifiers often yield high-confidence errors on such data. Existing OOD detection methods either neglect computational constraints or address noise and unseen classes separately. This paper investigates Unsupervised Anomaly Detection (UAD) as a lightweight, upstream filtering mechanism. We perform a Neural Architecture Search (NAS) on six UAD approaches, including Deep Support Vector Data Description (Deep SVDD), input reconstruction with (Variational-)Autoencoders (AE/VAE), Masked Anomaly Detection (MAD), Normalizing Flows (NFs) and Denoising Diffusion Probabilistic Models (DDPM) under strict hardware constraints ($\\leq$512k parameters), suitable for microcontrollers. Evaluating on the PTB-XL and BUT QDB datasets, we demonstrate that a NAS-optimized Deep SVDD offers the superior Pareto efficiency between detection performance and model size. In a simulated deployment, this lightweight filter improves the accuracy of a diagnostic classifier by up to 21.0 percentage points, demonstrating that optimized UAD filters can safeguard ECG analysis on wearables.", "date": "2025-10-30", "categories": []}
{"id": "2601.16381", "title": "VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection", "abstract": "Few-Shot Anomaly Detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual semantics to complement visual data, they predominantly rely on features pre-trained on natural scenes, thereby neglecting the granular, domain-specific semantics essential for industrial inspection. Furthermore, prevalent fusion strategies often resort to superficial concatenation, failing to address the inherent semantic misalignment between visual and textual modalities, which compromises robustness against cross-modal interference. To bridge these gaps, this study proposes VTFusion, a vision-text multimodal fusion framework tailored for FSAD. The framework rests on two core designs. First, adaptive feature extractors for both image and text modalities are introduced to learn task-specific representations, bridging the domain gap between pre-trained models and industrial data; this is further augmented by generating diverse synthetic anomalies to enhance feature discriminability. Second, a dedicated multimodal prediction fusion module is developed, comprising a fusion block that facilitates rich cross-modal information exchange and a segmentation network that generates refined pixel-level anomaly maps under multimodal guidance. VTFusion significantly advances FSAD performance, achieving image-level AUROCs of 96.8% and 86.2% in the 2-shot scenario on the MVTec AD and VisA datasets, respectively. Furthermore, VTFusion achieves an AUPRO of 93.5% on a real-world dataset of industrial automotive plastic parts introduced in this paper, further demonstrating its practical applicability in demanding industrial scenarios.", "date": "2026-01-23", "categories": []}
{"id": "2509.25856", "title": "PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection", "abstract": "Industrial anomaly detection is increasingly relying on foundation models, aiming for strong out-of-distribution generalization and rapid adaptation in real-world deployments. Notably, past studies have primarily focused on textual prompt tuning, leaving the intrinsic visual counterpart fragmented into processing steps specific to each foundation model. We aim to address this limitation by proposing a unified patch-focused framework, Patch-Exclusive Anomaly Detection (PatchEAD), enabling training-free anomaly detection that is compatible with diverse foundation models. The framework constructs visual prompting techniques, including an alignment module and foreground masking. Our experiments show superior few-shot and batch zero-shot performance compared to prior work, despite the absence of textual features. Our study further examines how backbone structure and pretrained characteristics affect patch-similarity robustness, providing actionable guidance for selecting and configuring foundation models for real-world visual inspection. These results confirm that a well-unified patch-only framework can enable quick, calibration-light deployment without the need for carefully engineered textual prompts.", "date": "2025-09-30", "categories": []}
{"id": "2601.15304", "title": "An Explainable Market Integrity Monitoring System with Multi-Source Attention Signals and Transparent Scoring", "abstract": "Market integrity monitoring is difficult because suspicious price/volume behavior can arise from many benign mechanisms, while modern detection systems often rely on opaque models that are hard to audit and communicate. We present AIMM-X, an explainable monitoring pipeline that combines market microstructure-style signals derived from OHLCV time series with multi-source public attention signals (e.g., news and online discussion proxies) to surface time windows that merit analyst review. The system detects candidate anomalous windows using transparent thresholding and aggregation, then assigns an interpretable integrity score decomposed into a small set of additive components, allowing practitioners to trace why a window was flagged and which factors drove the score. We provide an end-to-end, reproducible implementation that downloads data, constructs attention features, builds unified panels, detects windows, computes component signals, and generates summary figures/tables. Our goal is not to label manipulation, but to provide a practical, auditable screening tool that supports downstream investigation by compliance teams, exchanges, or researchers.", "date": "2026-01-10", "categories": []}
{"id": "2601.15453", "title": "DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection", "abstract": "Few-normal shot anomaly detection (FNSAD) aims to detect abnormal regions in images using only a few normal training samples, making the task highly challenging due to limited supervision and the diversity of potential defects. Recent approaches leverage vision-language models such as CLIP with prompt-based learning to align image and text features. However, existing methods often exhibit weak discriminability between normal and abnormal prompts and lack principled scoring mechanisms for patch-level anomalies. We propose a deviation-guided prompt learning framework that integrates the semantic power of vision-language models with the statistical reliability of deviation-based scoring. Specifically, we replace fixed prompt prefixes with learnable context vectors shared across normal and abnormal prompts, while anomaly-specific suffix tokens enable class-aware alignment. To enhance separability, we introduce a deviation loss with Top-K Multiple Instance Learning (MIL), modeling patch-level features as Gaussian deviations from the normal distribution. This allows the network to assign higher anomaly scores to patches with statistically significant deviations, improving localization and interpretability. Experiments on the MVTecAD and VISA benchmarks demonstrate superior pixel-level detection performance compared to PromptAD and other baselines. Ablation studies further validate the effectiveness of learnable prompts, deviation-based scoring, and the Top-K MIL strategy.", "date": "2026-01-21", "categories": []}
{"id": "2601.14305", "title": "An Optimized Decision Tree-Based Framework for Explainable IoT Anomaly Detection", "abstract": "The increase in the number of Internet of Things (IoT) devices has tremendously increased the attack surface of cyber threats thus making a strong intrusion detection system (IDS) with a clear explanation of the process essential towards resource-constrained environments. Nevertheless, current IoT IDS systems are usually traded off with detection quality, model elucidability, and computational effectiveness, thus the deployment on IoT devices. The present paper counteracts these difficulties by suggesting an explainable AI (XAI) framework based on an optimized Decision Tree classifier with both local and global importance methods: SHAP values that estimate feature attribution using local explanations, and Morris sensitivity analysis that identifies the feature importance in a global view. The proposed system attains the state of art on the test performance with 99.91% accuracy, F1-score of 99.51% and Cohen Kappa of 0.9960 and high stability is confirmed by a cross validation mean accuracy of 98.93%. Efficiency is also enhanced in terms of computations to provide faster inferences compared to those that are generalized in ensemble models. SrcMac has shown as the most significant predictor in feature analyses according to SHAP and Morris methods. Compared to the previous work, our solution eliminates its major drawback lack because it allows us to apply it to edge devices and, therefore, achieve real-time processing, adhere to the new regulation of transparency in AI, and achieve high detection rates on attacks of dissimilar classes. This combination performance of high accuracy, explainability, and low computation make the framework useful and reliable as a resource-constrained IoT security problem in real environments.", "date": "2026-01-18", "categories": []}
{"id": "2601.14336", "title": "Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization", "abstract": "Log anomaly detection is essential for system reliability, but it is extremely challenging to do considering it involves class imbalance. Additionally, the models trained in one domain are not applicable to other domains, necessitating the need for cross-domain adaptation (such as HDFS and Linux). Traditional detection models often fail to generalize due to significant data drift and the inherent absence of labeled anomalies in new target domains. To handle the above challenges, we proposed a new end-to-end framework based on a meta-learning approach. Our methodology first gets the data ready by combining a Drain3 log parsing mechanism with a dynamic drift-based labeling technique that uses semantic and fuzzy matching to move existing anomaly knowledge from one source to another. BERT-based semantic embeddings are obtained, and the feature selection is invoked to reduce the dimensionality. Later, Model Agnostic Meta-Learning (MAML) and Prototypical Networks models are trained to adapt quickly and effectively. The SMOTE oversampling method is employed to handle imbalances in the data. All the results are obtained by employing the leave-one-out source method, and the corresponding mean F1 scores are reported. Our empirical findings validate that the proposed meta-learning-driven approach yielded the highest mean F1 score and proved to be effective for cross-domain settings.", "date": "2026-01-20", "categories": []}
{"id": "2601.15177", "title": "Dynamic Management of a Deep Learning-Based Anomaly Detection System for 5G Networks", "abstract": "Fog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.", "date": "2026-01-21", "categories": []}
{"id": "2411.10150", "title": "Outliers resistant image classification by anomaly detection", "abstract": "Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to variations in environmental conditions and unpredictable behavior when processing objects that are not included in the training dataset. As it is impractical to add all possible subjects in the training sample, an alternative solution is necessary. This study proposes a model that simultaneously performs classification and anomaly detection, employing metric learning to generate vector representations of images in a multidimensional space, followed by classification using cross-entropy. For experimentation, a dataset of over 327,000 images was prepared. Experiments were conducted with various computer vision model architectures, and the outcomes of each approach were compared.", "date": "2024-11-15", "categories": []}
{"id": "2505.01969", "title": "MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection", "abstract": "3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1\\% and 9.3\\% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The code is available at https://github.com/iCAN-SZU/MC3D-AD.", "date": "2025-05-04", "categories": []}
{"id": "2510.09616", "title": "Causal Digital Twins for Cyber-Physical Security: A Framework for Robust Anomaly Detection in Industrial Control Systems", "abstract": "Industrial Control Systems (ICS) in water distribution and treatment face cyber-physical attacks exploiting network and physical vulnerabilities. Current water system anomaly detection methods rely on correlations, yielding high false alarms and poor root cause analysis. We propose a Causal Digital Twin (CDT) framework for water infrastructures, combining causal inference with digital twin modeling. CDT supports association for pattern detection, intervention for system response, and counterfactual analysis for water attack prevention. Evaluated on water-related datasets SWaT, WADI, and HAI, CDT shows 90.8\\% compliance with physical constraints and structural Hamming distance 0.133 $\\pm$ 0.02. F1-scores are $0.944 \\pm 0.014$ (SWaT), $0.902 \\pm 0.021$ (WADI), $0.923 \\pm 0.018$ (HAI, $p<0.0024$). CDT reduces false positives by 74\\%, achieves 78.4\\% root cause accuracy, and enables counterfactual defenses reducing attack success by 73.2\\%. Real-time performance at 3.2 ms latency ensures safe and interpretable operation for medium-scale water systems.", "date": "2025-09-16", "categories": []}
{"id": "2512.21650", "title": "Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulation", "abstract": "Multimodal Unsupervised Anomaly Detection (UAD) is critical for quality assurance in smart manufacturing, particularly in complex processes like robotic welding. However, existing methods often suffer from process-logic blindness, treating process modalities (e.g., real-time video, audio, and sensors) and result modalities (e.g., post-weld images) as symmetric feature sources, thereby ignoring the inherent unidirectional physical generative logic. Furthermore, the heterogeneity gap between high-dimensional visual data and low-dimensional sensor signals frequently leads to critical process context being drowned out. In this paper, we propose Physic-HM, a multimodal UAD framework that explicitly incorporates physical inductive bias to model the process-to-result dependency. Specifically, our framework incorporates two key innovations: a Sensor-Guided PHM Modulation mechanism that utilizes low-dimensional sensor signals as context to guide high-dimensional audio-visual feature extraction, and a Physic-Hierarchical architecture that enforces a unidirectional generative mapping to identify anomalies that violate physical consistency. Extensive experiments on Weld-4M benchmark demonstrate that Physic-HM achieves a SOTA I-AUROC of 90.7%. The source code of Physic-HM will be released after the paper is accepted.", "date": "2025-12-25", "categories": []}
{"id": "2601.09147", "title": "SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection", "abstract": "Zero-Shot Anomaly Detection (ZSAD) leverages Vision-Language Models (VLMs) to enable supervision-free industrial inspection. However, existing ZSAD paradigms are constrained by single visual backbones, which struggle to balance global semantic generalization with fine-grained structural discriminability. To bridge this gap, we propose Synergistic Semantic-Visual Prompting (SSVP), that efficiently fuses diverse visual encodings to elevate model's fine-grained perception. Specifically, SSVP introduces the Hierarchical Semantic-Visual Synergy (HSVS) mechanism, which deeply integrates DINOv3's multi-scale structural priors into the CLIP semantic space. Subsequently, the Vision-Conditioned Prompt Generator (VCPG) employs cross-modal attention to guide dynamic prompt generation, enabling linguistic queries to precisely anchor to specific anomaly patterns. Furthermore, to address the discrepancy between global scoring and local evidence, the Visual-Text Anomaly Mapper (VTAM) establishes a dual-gated calibration paradigm. Extensive evaluations on seven industrial benchmarks validate the robustness of our method; SSVP achieves state-of-the-art performance with 93.0% Image-AUROC and 92.2% Pixel-AUROC on MVTec-AD, significantly outperforming existing zero-shot approaches.", "date": "2026-01-14", "categories": []}
{"id": "2601.10993", "title": "Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection", "abstract": "Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime-without any information about anomalous instances in the training data-is challenging. A recently observed phenomenon, known as the inlier-memorization (IM) effect, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate active learning to selectively acquire informative labels, and propose IMBoost, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a warm-up phase that induces and promotes the IM effect, and 2) a polarization phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.", "date": "2026-01-16", "categories": []}
{"id": "2601.12337", "title": "Turbo-GoDec: Exploiting the Cluster Sparsity Prior for Hyperspectral Anomaly Detection", "abstract": "As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily relt on two prior assumption: low-rank background and sparse anomaly, along with additional spatial assumptions of the background. However, most methods only utilize the sparsity prior assumption for anomalies and rarely expand on this hypothesis. From observations of hyperspectral images, we find that anomalous pixels exhibit certain spatial distribution characteristics: they often manifest as small, clustered groups in space, which we refer to as cluster sparsity of anomalies. Then, we combined the cluster sparsity prior with the classical GoDec algorithm, incorporating the cluster sparsity prior into the S-step of GoDec. This resulted in a new hyperspectral anomaly detection method, which we called Turbo-GoDec. In this approach, we modeled the cluster sparsity prior of anomalies using a Markov random field and computed the marginal probabilities of anomalies through message passing on a factor graph. Locations with high anomalous probabilities were treated as the sparse component in the Turbo-GoDec. Experiments are conducted on three real hyperspectral image (HSI) datasets which demonstrate the superior performance of the proposed Turbo-GoDec method in detecting small-size anomalies comparing with the vanilla GoDec (LSMAD) and state-of-the-art anomaly detection methods. The code is available at https://github.com/jiahuisheng/Turbo-GoDec.", "date": "2026-01-18", "categories": []}
{"id": "2601.12379", "title": "Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection", "abstract": "Hyperspectral images (HSIs) are a type of image that contains abundant spectral information. As a type of real-world data, the high-dimensional spectra in hyperspectral images are actually determined by only a few factors, such as chemical composition and illumination. Thus, spectra in hyperspectral images are highly likely to satisfy the manifold hypothesis. Based on the hyperspectral manifold hypothesis, we propose a novel hyperspectral anomaly detection method (named ScoreAD) that leverages the time-dependent gradient field of the data distribution (i.e., the score), as learned by a score-based generative model (SGM). Our method first trains the SGM on the entire set of spectra from the hyperspectral image. At test time, each spectrum is passed through a perturbation kernel, and the resulting perturbed spectrum is fed into the trained SGM to obtain the estimated score. The manifold hypothesis of HSIs posits that background spectra reside on one or more low-dimensional manifolds. Conversely, anomalous spectra, owing to their unique spectral signatures, are considered outliers that do not conform to the background manifold. Based on this fundamental discrepancy in their manifold distributions, we leverage a generative SGM to achieve hyperspectral anomaly detection. Experiments on the four hyperspectral datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/jiahuisheng/ScoreAD.", "date": "2026-01-18", "categories": []}
{"id": "2601.12426", "title": "Graph Attention Networks with Physical Constraints for Anomaly Detection", "abstract": "Water distribution systems (WDSs) face increasing cyber-physical risks, which make reliable anomaly detection essential. Many data-driven models ignore network topology and are hard to interpret, while model-based ones depend strongly on parameter accuracy. This work proposes a hydraulic-aware graph attention network using normalized conservation law violations as features. It combines mass and energy balance residuals with graph attention and bidirectional LSTM to learn spatio-temporal patterns. A multi-scale module aggregates detection scores from node to network level. On the BATADAL dataset, it reaches $F1=0.979$, showing $3.3$pp gain and high robustness under $15\\%$ parameter noise.", "date": "2026-01-18", "categories": []}
{"id": "2601.12660", "title": "Toward Faithful Explanations in Acoustic Anomaly Detection", "abstract": "Interpretability is essential for user trust in real-world anomaly detection applications. However, deep learning models, despite their strong performance, often lack transparency. In this work, we study the interpretability of autoencoder-based models for audio anomaly detection, by comparing a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability. We applied several attribution methods, including error maps, saliency maps, SmoothGrad, Integrated Gradients, GradSHAP, and Grad-CAM. Although MAE shows a slightly lower detection, it consistently provides more faithful and temporally precise explanations, suggesting a better alignment with true anomalies. To assess the relevance of the regions highlighted by the explanation method, we propose a perturbation-based faithfulness metric that replaces them with their reconstructions to simulate normal input. Our findings, based on experiments in a real industrial scenario, highlight the importance of incorporating interpretability into anomaly detection pipelines and show that masked training improves explanation quality without compromising performance.", "date": "2026-01-19", "categories": []}
{"id": "2601.12745", "title": "A Graph Prompt Fine-Tuning Method for WSN Spatio-Temporal Correlation Anomaly Detection", "abstract": "Anomaly detection of multi-temporal modal data in Wireless Sensor Network (WSN) can provide an important guarantee for reliable network operation. Existing anomaly detection methods in multi-temporal modal data scenarios have the problems of insufficient extraction of spatio-temporal correlation features, high cost of anomaly sample category annotation, and imbalance of anomaly samples. In this paper, a graph neural network anomaly detection backbone network incorporating spatio-temporal correlation features and a multi-task self-supervised training strategy of \"pre-training - graph prompting - fine-tuning\" are designed for the characteristics of WSN graph structure data. First, the anomaly detection backbone network is designed by improving the Mamba model based on a multi-scale strategy and inter-modal fusion method, and combining it with a variational graph convolution module, which is capable of fully extracting spatio-temporal correlation features in the multi-node, multi-temporal modal scenarios of WSNs. Secondly, we design a three-subtask learning \"pre-training\" method with no-negative comparative learning, prediction, and reconstruction to learn generic features of WSN data samples from unlabeled data, and design a \"graph prompting-fine-tuning\" mechanism to guide the pre-trained self-supervised learning. The model is fine-tuned through the \"graph prompting-fine-tuning\" mechanism to guide the pre-trained self-supervised learning model to complete the parameter fine-tuning, thereby reducing the training cost and enhancing the detection generalization performance. The F1 metrics obtained from experiments on the public dataset and the actual collected dataset are up to 91.30% and 92.31%, respectively, which provides better detection performance and generalization ability than existing methods designed by the method.", "date": "2026-01-19", "categories": []}
{"id": "2601.12839", "title": "Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanations", "abstract": "Detecting anomalous trajectories in decentralized crypto networks is fundamentally challenged by extreme label scarcity and the adaptive evasion strategies of illicit actors. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they struggle to internalize multi hop, logic driven motifs such as fund dispersal and layering that characterize sophisticated money laundering, limiting their forensic accountability under regulations like the FATF Travel Rule. To address this limitation, we propose Relational Domain Logic Integration (RDLI), a framework that embeds expert derived heuristics as differentiable, logic aware latent signals within representation learning. Unlike static rule based approaches, RDLI enables the detection of complex transactional flows that evade standard message passing. To further account for market volatility, we incorporate a Retrieval Grounded Context (RGC) module that conditions anomaly scoring on regulatory and macroeconomic context, mitigating false positives caused by benign regime shifts. Under extreme label scarcity (0.01%), RDLI outperforms state of the art GNN baselines by 28.9% in F1 score. A micro expert user study further confirms that RDLI path level explanations significantly improve trustworthiness, perceived usefulness, and clarity compared to existing methods, highlighting the importance of integrating domain logic with contextual grounding for both accuracy and explainability.", "date": "2026-01-19", "categories": []}
{"id": "2601.13546", "title": "ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution", "abstract": "LLM-driven Anomaly Detection (AD) helps enhance the understanding and explanatory abilities of anomalous behaviors in Time Series (TS). Existing methods face challenges of inadequate reasoning ability, deficient multi-turn dialogue capability, and narrow generalization. To this end, we 1) propose a multi-agent-based TS Evolution algorithm named TSEvol. On top of it, we 2) introduce the AD reasoning and multi-turn dialogue Dataset TSEData-20K and contribute the Chatbot family for AD, including ChatAD-Llama3-8B, Qwen2.5-7B, and Mistral-7B. Furthermore, 3) we propose the TS Kahneman-Tversky Optimization (TKTO) to enhance ChatAD's cross-task generalization capability. Lastly, 4) we propose a LLM-driven Learning-based AD Benchmark LLADBench to evaluate the performance of ChatAD and nine baselines across seven datasets and tasks. Our three ChatAD models achieve substantial gains, up to 34.50% in accuracy, 34.71% in F1, and a 37.42% reduction in false positives. Besides, via KTKO, our optimized ChatAD achieves competitive performance in reasoning and cross-task generalization on classification, forecasting, and imputation.", "date": "2026-01-20", "categories": []}
{"id": "2601.13644", "title": "Towards Token-Level Text Anomaly Detection", "abstract": "Despite significant progress in text anomaly detection for web applications such as spam filtering and fake news detection, existing methods are fundamentally limited to document-level analysis, unable to identify which specific parts of a text are anomalous. We introduce token-level anomaly detection, a novel paradigm that enables fine-grained localization of anomalies within text. We formally define text anomalies at both document and token-levels, and propose a unified detection framework that operates across multiple levels. To facilitate research in this direction, we collect and annotate three benchmark datasets spanning spam, reviews and grammar errors with token-level labels. Experimental results demonstrate that our framework get better performance than other 6 baselines, opening new possibilities for precise anomaly localization in text. All the codes and data are publicly available on https://github.com/charles-cao/TokenCore.", "date": "2026-01-20", "categories": []}
{"id": "2407.05934", "title": "Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels", "abstract": "Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their own communities but also create a ripple effect on neighbors throughout the graph structure. Detecting anomalous nodes in complex graphs has been a challenging task. While existing GAD methods assume all labels are correct, real-world scenarios often involve inaccurate annotations. These noisy labels can severely degrade GAD performance because, with anomalies representing a minority class, even a small number of mislabeled instances can disproportionately interfere with detection models. Cutting edges to mitigate the negative effects of noisy labels is a good option; however, it has both positive and negative influences and also presents an issue of weak supervision. To perform effective GAD with noisy labels, we propose REinforced Graph Anomaly Detector (REGAD) by pruning the edges of candidate nodes potentially with mistaken labels. Moreover, we design the performance feedback based on strategically crafted confident labels to guide the cutting process, ensuring optimal results. Specifically, REGAD contains two novel components. (i) A tailored policy network, which involves two-step actions to remove negative effect propagation step by step. (ii) A policy-in-the-loop mechanism to identify suitable edge removal strategies that control the propagation of noise on the graph and estimate the updated structure to obtain reliable pseudo labels iteratively. Experiments on three real-world datasets demonstrate that REGAD outperforms all baselines under different noisy ratios.", "date": "2024-07-08", "categories": []}
{"id": "2601.06204", "title": "Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification", "abstract": "Intelligent anomaly detection in dynamic visual environments requires reconciling real-time performance with semantic interpretability. Conventional approaches address only fragments of this challenge. Reconstruction-based models capture low-level deviations without contextual reasoning, object detectors provide speed but limited semantics, and large vision-language systems deliver interpretability at prohibitive computational cost. This work introduces a cascading multi-agent framework that unifies these complementary paradigms into a coherent and interpretable architecture. Early modules perform reconstruction-gated filtering and object-level assessment, while higher-level reasoning agents are selectively invoked to interpret semantically ambiguous events. The system employs adaptive escalation thresholds and a publish-subscribe communication backbone, enabling asynchronous coordination and scalable deployment across heterogeneous hardware. Extensive evaluation on large-scale monitoring data demonstrates that the proposed cascade achieves a threefold reduction in latency compared to direct vision-language inference, while maintaining high perceptual fidelity (PSNR = 38.3 dB, SSIM = 0.965) and consistent semantic labeling. The framework advances beyond conventional detection pipelines by combining early-exit efficiency, adaptive multi-agent reasoning, and explainable anomaly attribution, establishing a reproducible and energy-efficient foundation for scalable intelligent visual monitoring.", "date": "2026-01-08", "categories": []}
{"id": "2601.11254", "title": "FTDMamba: Frequency-Assisted Temporal Dilation Mamba for Unmanned Aerial Vehicle Video Anomaly Detection", "abstract": "Recent advances in video anomaly detection (VAD) mainly focus on ground-based surveillance or unmanned aerial vehicle (UAV) videos with static backgrounds, whereas research on UAV videos with dynamic backgrounds remains limited. Unlike static scenarios, dynamically captured UAV videos exhibit multi-source motion coupling, where the motion of objects and UAV-induced global motion are intricately intertwined. Consequently, existing methods may misclassify normal UAV movements as anomalies or fail to capture true anomalies concealed within dynamic backgrounds. Moreover, many approaches do not adequately address the joint modeling of inter-frame continuity and local spatial correlations across diverse temporal scales. To overcome these limitations, we propose the Frequency-Assisted Temporal Dilation Mamba (FTDMamba) network for UAV VAD, including two core components: (1) a Frequency Decoupled Spatiotemporal Correlation Module, which disentangles coupled motion patterns and models global spatiotemporal dependencies through frequency analysis; and (2) a Temporal Dilation Mamba Module, which leverages Mamba's sequence modeling capability to jointly learn fine-grained temporal dynamics and local spatial structures across multiple temporal receptive fields. Additionally, unlike existing UAV VAD datasets which focus on static backgrounds, we construct a large-scale Moving UAV VAD dataset (MUVAD), comprising 222,736 frames with 240 anomaly events across 12 anomaly types. Extensive experiments demonstrate that FTDMamba achieves state-of-the-art (SOTA) performance on two public static benchmarks and the new MUVAD dataset. The code and MUVAD dataset will be available at: https://github.com/uavano/FTDMamba.", "date": "2026-01-16", "categories": []}
{"id": "2601.11415", "title": "Zero-Shot Detection of Elastic Transient Morphology Across Physical Systems", "abstract": "We test whether a representation learned from interferometric strain transients in gravitational-wave observatories can act as a frozen morphology-sensitive operator for unseen sensors, provided the target signals preserve coherent elastic transient structure. Using a neural encoder trained exclusively on non-Gaussian instrumental glitches, we perform strict zero-shot anomaly analysis on rolling-element bearings without retraining, fine-tuning, or target-domain labels.\n  On the IMS-NASA run-to-failure dataset, the operator yields a monotonic health index HI(t) = s0.99(t)/tau normalized to an early-life reference distribution, enabling fixed false-alarm monitoring at 1-q = 1e-3 with tau = Q0.999(P0). In discrete fault regimes (CWRU), it achieves strong window-level discrimination (AUC_win about 0.90) and file-level separability approaching unity (AUC_file about 0.99). Electrically dominated vibration signals (VSB) show weak, non-selective behavior, delineating a physical boundary for transfer.\n  Under a matched IMS controlled-split protocol, a generic EfficientNet-B0 encoder pretrained on ImageNet collapses in the intermittent regime (Lambda_tail about 2), while the interferometric operator retains strong extreme-event selectivity (Lambda_tail about 860), indicating that the effect is not a generic property of CNN features. Controlled morphology-destruction transformations selectively degrade performance despite per-window normalization, consistent with sensitivity to coherent time-frequency organization rather than marginal amplitude statistics.", "date": "2026-01-16", "categories": []}
{"id": "2601.10193", "title": "GFM4GA: Graph Foundation Model for Group Anomaly Detection", "abstract": "Group anomaly detection is crucial in many network applications, but faces challenges due to diverse anomaly patterns. Motivated by the success of large language models (LLMs) in natural language processing, graph foundation models (GFMs) is proposed to handle few-shot learning task with fewer labeling efforts. GFMs have been successfully applied to detection of individual anomalies but cannot be generalized to group anomalies, as group anomaly patterns must be detected as a whole and individuals in an abnormal group can look rather normal. Therefore, we propose GFM4GA, a novel graph foundation model for group anomaly detection. The pipeline is pretrained via dual-level contrastive learning based on feature-based estimation and group extraction, to capture potential group anomaly structure and feature inconsistencies. In the downstream tasks, the pipeline is finetuned in parameter-constrained and group-anomaly-proportion weighted few-shot settings, and its adaptive ability to unseen group anomalies expanded via group contexts determined by labeled anomaly neighbors. Experiments show that GFM4GA surpasses group anomaly detectors and GFMs for individual anomalies, achieving average improvements of 2.85% in AUROC and 2.55% in AUPRC.", "date": "2026-01-15", "categories": []}
{"id": "2601.10269", "title": "Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders", "abstract": "This paper introduces an unsupervised health-monitoring framework for turbofan engines that does not require run-to-failure labels. First, operating-condition effects in NASA CMAPSS sensor streams are removed via regression-based normalisation; then a Long Short-Term Memory (LSTM) autoencoder is trained only on the healthy portion of each trajectory. Persistent reconstruction error, estimated using an adaptive data-driven threshold, triggers real-time alerts without hand-tuned rules. Benchmark results show high recall and low false-alarm rates across multiple operating regimes, demonstrating that the method can be deployed quickly, scale to diverse fleets, and serve as a complementary early-warning layer to Remaining Useful Life models.", "date": "2026-01-15", "categories": []}
{"id": "2601.00005", "title": "Evaluating Anomaly Detectors for Simulated Highly Imbalanced Industrial Classification Problems", "abstract": "Machine learning offers potential solutions to current issues in industrial systems in areas such as quality control and predictive maintenance, but also faces unique barriers in industrial applications. An ongoing challenge is extreme class imbalance, primarily due to the limited availability of faulty data during training. This paper presents a comprehensive evaluation of anomaly detection algorithms using a problem-agnostic simulated dataset that reflects real-world engineering constraints. Using a synthetic dataset with a hyper-spherical based anomaly distribution in 2D and 10D, we benchmark 14 detectors across training datasets with anomaly rates between 0.05% and 20% and training sizes between 1 000 and 10 000 (with a testing dataset size of 40 000) to assess performance and generalization error. Our findings reveal that the best detector is highly dependant on the total number of faulty examples in the training dataset, with additional healthy examples offering insignificant benefits in most cases. With less than 20 faulty examples, unsupervised methods (kNN/LOF) dominate; but around 30-50 faulty examples, semi-supervised (XGBOD) and supervised (SVM/CatBoost) detectors, we see large performance increases. While semi-supervised methods do not show significant benefits with only two features, the improvements are evident at ten features. The study highlights the performance drop on generalization of anomaly detection methods on smaller datasets, and provides practical insights for deploying anomaly detection in industrial environments.", "date": "2025-12-07", "categories": []}
{"id": "2509.05327", "title": "Layer-Wise Anomaly Detection in Directed Energy Deposition using High-Fidelity Fringe Projection Profilometry", "abstract": "Directed energy deposition (DED), a metal additive manufacturing process, is highly susceptible to process-induced defects such as geometric deviations, lack of fusion, and poor surface finish. This work presents a build-height-synchronized fringe projection system for in-situ, layer-wise surface reconstruction of laser-DED components, achieving a reconstruction accuracy of ${\\pm}$46 ${\\mu}$m. From the reconstructed 3D morphology, two complementary geometry-based point cloud metrics are introduced: local point density, which highlights poor surface finish, and normal-change rate, which identifies lack-of-fusion features. These methods enable automated, annotation-free identification of common deposition anomalies directly from reconstructed surfaces, without the need for manual labeling. By directly linking geometric deviation to defect formation, the approach enables precise anomaly localization and advances the feasibility of closed-loop process control. This work establishes fringe projection as a practical tool for micrometer-scale monitoring in DED, bridging the gap between process signatures and part geometry for certifiable additive manufacturing.", "date": "2025-08-31", "categories": []}
{"id": "2601.08022", "title": "Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion", "abstract": "Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data. While recent ZSAD approaches leverage additional modalities such as language to generate fine-grained prompts for localisation, vision-only methods remain limited to image-level classification, lacking spatial precision. In this work, we introduce a simple yet effective training-free vision-only ZSAD framework that circumvents the need for fine-grained prompts by leveraging the inversion of a pretrained Denoising Diffusion Implicit Model (DDIM). Specifically, given an input image and a generic text description (e.g., \"an image of an [object class]\"), we invert the image to obtain latent representations and initiate the denoising process from a fixed intermediate timestep to reconstruct the image. Since the underlying diffusion model is trained solely on normal data, this process yields a normal-looking reconstruction. The discrepancy between the input image and the reconstructed one highlights potential anomalies. Our method achieves state-of-the-art performance on VISA dataset, demonstrating strong localisation capabilities without auxiliary modalities and facilitating a shift away from prompt dependence for zero-shot anomaly detection research. Code is available at https://github.com/giddyyupp/DIVAD.", "date": "2026-01-12", "categories": []}
{"id": "2601.08155", "title": "Instance-Aligned Captions for Explainable Video Anomaly Detection", "abstract": "Explainable video anomaly detection (VAD) is crucial for safety-critical applications, yet even with recent progress, much of the research still lacks spatial grounding, making the explanations unverifiable. This limitation is especially pronounced in multi-entity interactions, where existing explainable VAD methods often produce incomplete or visually misaligned descriptions, reducing their trustworthiness. To address these challenges, we introduce instance-aligned captions that link each textual claim to specific object instances with appearance and motion attributes. Our framework captures who caused the anomaly, what each entity was doing, whom it affected, and where the explanationis grounded, enabling verifiable and actionable reasoning. We annotate eight widely used VAD benchmarks and extend the 360-degree egocentric dataset, VIEW360, with 868 additional videos, eight locations, and four new anomaly types, creating VIEW360+, a comprehensive testbed for explainable VAD. Experiments show that our instance-level spatially grounded captions reveal significant limitations in current LLM- and VLM-based methods while providing a robust benchmark for future research in trustworthy and interpretable anomaly detection.", "date": "2026-01-13", "categories": []}
{"id": "2601.08659", "title": "TRACE: Reconstruction-Based Anomaly Detection in Ensemble and Time-Dependent Simulations", "abstract": "Detecting anomalies in high-dimensional, time-dependent simulation data is challenging due to complex spatial and temporal dynamics. We study reconstruction-based anomaly detection for ensemble data from parameterized K\\'arm\\'an vortex street simulations using convolutional autoencoders. We compare a 2D autoencoder operating on individual frames with a 3D autoencoder that processes short temporal stacks. The 2D model identifies localized spatial irregularities in single time steps, while the 3D model exploits spatio-temporal context to detect anomalous motion patterns and reduces redundant detections across time. We further evaluate volumetric time-dependent data and find that reconstruction errors are strongly influenced by the spatial distribution of mass, with highly concentrated regions yielding larger errors than dispersed configurations. Our results highlight the importance of temporal context for robust anomaly detection in dynamic simulations.", "date": "2026-01-13", "categories": []}
{"id": "2402.14469", "title": "Reimagining Anomalies: What If Anomalies Were Normal?", "abstract": "Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates multiple alternative modifications for each anomaly, capturing diverse concepts of anomalousness. Each modification is trained to be perceived as normal by the anomaly detector. The method provides a semantic explanation of the mechanism that triggered the detector, allowing users to explore ``what-if scenarios.'' Qualitative and quantitative analyses across various image datasets demonstrate that applying this method to state-of-the-art detectors provides high-quality semantic explanations.", "date": "2024-02-22", "categories": []}
{"id": "2504.17709", "title": "Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning", "abstract": "Intelligent condition monitoring of wind turbines is essential for reducing downtimes. Machine learning models trained on wind turbine operation data are commonly used to detect anomalies and, eventually, operation faults. However, data-driven normal behavior models (NBMs) require a substantial amount of training data, as NBMs trained with scarce data may result in unreliable fault detection. To overcome this limitation, we present a novel generative deep transfer learning approach to make SCADA samples from one wind turbine lacking training data resemble SCADA data from wind turbines with representative training data. Through CycleGAN-based domain mapping, our method enables the application of an NBM trained on an existing wind turbine to a new one with severely limited data. We demonstrate our approach on field data mapping SCADA samples across 7 substantially different WTs. Our findings show significantly improved fault detection in wind turbines with scarce data. Our method achieves the most similar anomaly scores to an NBM trained with abundant data, outperforming NBMs trained on scarce training data with improvements of +10.3% in F1-score when 1 month of training data is available and +16.8% when 2 weeks are available. The domain mapping approach outperforms conventional fine-tuning at all considered degrees of data scarcity, ranging from 1 to 8 weeks of training data. The proposed technique enables earlier and more reliable fault detection in newly installed wind farms, demonstrating a novel and promising research direction to improve anomaly detection when faced with training data scarcity.", "date": "2025-04-24", "categories": []}
{"id": "2511.20044", "title": "RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction", "abstract": "Anomaly prediction (AP) in multivariate time series (MTS) is crucial to ensure system dependability. Existing methods either focus solely on whether an anomaly is imminent without providing precise predictions for the future anomaly, or performing predictions directly on historical data, which is easily drowned out by the normal patterns. To address the challenges in AP task, we propose RED-F, a novel framework comprised of the Reconstruction-Elimination Model (REM) and the Dual-stream Contrastive Forecasting Model (DFM). We utilize REM to construct a baseline of normal patterns from historical data, providing a foundation for subsequent predictions of anomalies. Then DFM simultaneously predicts both the constructed normal pattern and the current window, employing a contrastive forecast that transforms the difficult AP task into a simpler, more robust task of relative trajectory comparison by computing the divergence between these two predictions. To enable the forecasting model to generate a prediction not easily obscured by normal patterns, we propose a Multi-Series Prediction (MSP) training objective to enhance its sensitivity to the current window. Extensive experiments on multiple real-world datasets demonstrate the superior capability of RED-F in anomaly prediction tasks. Our code is available at http://github.com/PenyChen/RED-F.", "date": "2025-11-25", "categories": []}
{"id": "2601.06186", "title": "Time-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Quality", "abstract": "Supporting Go/No-Go decisions prior to launch requires assessing real-time telemetry data against redline limits established during the design qualification phase. Family data from ground testing or previous flights is commonly used to detect initiating failure modes and their timing; however, this approach relies heavily on engineering judgment and is more error-prone for new launch vehicles. To address these limitations, we utilize Long-Term Short-Term Memory (LSTM) networks for supervised classification of time-series anomalies. Although, initial training labels derived from simulated anomaly data may be suboptimal due to variations in anomaly strength, anomaly settling times, and other factors. In this work, we propose a novel statistical detector based on the Mahalanobis distance and forward-backward detection fractions to adjust the supervised training labels. We demonstrate our method on digital twin simulations of a ground-stage propulsion system with 20.8 minutes of operation per trial and O(10^8) training timesteps. The statistical data relabeling improved precision and recall of the LSTM classifier by 7% and 22% respectively.", "date": "2026-01-07", "categories": []}
{"id": "2601.05552", "title": "One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection", "abstract": "Universal visual anomaly detection (AD) aims to identify anomaly images and segment anomaly regions towards open and dynamic scenarios, following zero- and few-shot paradigms without any dataset-specific fine-tuning. We have witnessed significant progress in widely use of visual-language foundational models in recent approaches. However, current methods often struggle with complex prompt engineering, elaborate adaptation modules, and challenging training strategies, ultimately limiting their flexibility and generality. To address these issues, this paper rethinks the fundamental mechanism behind visual-language models for AD and presents an embarrassingly simple, general, and effective framework for Universal vision Anomaly Detection (UniADet). Specifically, we first find language encoder is used to derive decision weights for anomaly classification and segmentation, and then demonstrate that it is unnecessary for universal AD. Second, we propose an embarrassingly simple method to completely decouple classification and segmentation, and decouple cross-level features, i.e., learning independent weights for different tasks and hierarchical features. UniADet is highly simple (learning only decoupled weights), parameter-efficient (only 0.002M learnable parameters), general (adapting a variety of foundation models), and effective (surpassing state-of-the-art zero-/few-shot by a large margin and even full-shot AD methods for the first time) on 14 real-world AD benchmarks covering both industrial and medical domains. We will make the code and model of UniADet available at https://github.com/gaobb/UniADet.", "date": "2026-01-09", "categories": []}
{"id": "2601.05759", "title": "Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms", "abstract": "Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform characteristics. This study reframes P-wave arrival detection as a self-supervised anomaly detection task to evaluate how architectural variations regulate the trade-off between reconstruction fidelity and anomaly discrimination. Through a comprehensive grid search of 492 Variational Autoencoder configurations, we show that while skip connections minimize reconstruction error (Mean Absolute Error approximately 0.0012), they induce \"overgeneralization\", allowing the model to reconstruct noise and masking the detection signal. In contrast, attention mechanisms prioritize global context over local detail and yield the highest detection performance with an area-under-the-curve of 0.875. The attention-based Variational Autoencoder achieves an area-under-the-curve of 0.91 in the 0 to 40-kilometer near-source range, demonstrating high suitability for immediate early warning applications. These findings establish that architectural constraints favoring global context over pixel-perfect reconstruction are essential for robust, self-supervised P-wave detection.", "date": "2026-01-09", "categories": []}
{"id": "2601.05852", "title": "Kidney Cancer Detection Using 3D-Based Latent Diffusion Models", "abstract": "In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and leverages weak supervision with only case-level pseudo-labels. We benchmark our approach against state-of-the-art supervised segmentation and detection models. This study demonstrates the feasibility and promise of 3D latent diffusion for weakly supervised anomaly detection. While the current results do not yet match supervised baselines, they reveal key directions for improving reconstruction fidelity and lesion localization. Our findings provide an important step toward annotation-efficient, generative modeling of complex abdominal anatomy.", "date": "2026-01-09", "categories": []}
{"id": "2601.05984", "title": "Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks", "abstract": "The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Communities of Interest (CoIs) provide a promising paradigm for organising heterogeneous IoT sensor networks by grouping devices with similar operational and environmental characteristics. This work presents an anomaly detection framework based on the CoI paradigm by grouping sensors into communities using a fused similarity matrix that incorporates temporal correlations via Spearman coefficients, spatial proximity using Gaussian distance decay, and elevation similarities. For each community, representative stations based on the best silhouette are selected and three autoencoder architectures (BiLSTM, LSTM, and MLP) are trained using Bayesian hyperparameter optimization with expanding window cross-validation and tested on stations from the same cluster and the best representative stations of other clusters. The models are trained on normal temperature patterns of the data and anomalies are detected through reconstruction error analysis. Experimental results show a robust within-community performance across the evaluated configurations, while variations across communities are observed. Overall, the results support the applicability of community-based model sharing in reducing computational overhead and to analyse model generalisability across IoT sensor networks.", "date": "2026-01-09", "categories": []}
{"id": "2601.05988", "title": "CyberGFM: Graph Foundation Models for Lateral Movement Detection in Enterprise Networks", "abstract": "Representing networks as a graph and training a link prediction model using benign connections is an effective method of anomaly-based intrusion detection. Existing works using this technique have shown great success using temporal graph neural networks and skip-gram-based approaches on random walks. However, random walk-based approaches are unable to incorporate rich edge data, while the GNN-based approaches require large amounts of memory to train. In this work, we propose extending the original insight from random walk-based skip-grams--that random walks through a graph are analogous to sentences in a corpus--to the more modern transformer-based foundation models. Using language models that take advantage of GPU optimizations, we can quickly train a graph foundation model to predict missing tokens in random walks through a network of computers. The graph foundation model is then finetuned for link prediction and used as a network anomaly detector. This new approach allows us to combine the efficiency of random walk-based methods and the rich semantic representation of deep learning methods. This system, which we call CyberGFM, achieved state-of-the-art results on three widely used network anomaly detection datasets, delivering a up to 2$\\times$ improvement in average precision. We found that CyberGFM outperforms all prior works in unsupervised link prediction for network anomaly detection, using the same number of parameters, and with equal or better efficiency than the previous best approaches.", "date": "2026-01-09", "categories": []}
{"id": "2601.01856", "title": "GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection", "abstract": "Feature-based anomaly detection is widely adopted in industrial inspection due to the strong representational power of large pre-trained vision encoders. While most existing methods focus on improving within-category anomaly scoring, practical deployments increasingly require task-agnostic operation under continual category expansion, where the category identity is unknown at test time. In this setting, overall performance is often dominated by expert selection, namely routing an input to an appropriate normality model before any head-specific scoring is applied. However, routing rules that compare head-specific anomaly scores across independently constructed heads are unreliable in practice, as score distributions can differ substantially across categories in scale and tail behavior.\n  We propose GCR, a lightweight mixture-of-experts framework for stabilizing task-agnostic continual anomaly detection through geometry-consistent routing. GCR routes each test image directly in a shared frozen patch-embedding space by minimizing an accumulated nearest-prototype distance to category-specific prototype banks, and then computes anomaly maps only within the routed expert using a standard prototype-based scoring rule. By separating cross-head decision making from within-head anomaly scoring, GCR avoids cross-head score comparability issues without requiring end-to-end representation learning.\n  Experiments on MVTec AD and VisA show that geometry-consistent routing substantially improves routing stability and mitigates continual performance collapse, achieving near-zero forgetting while maintaining competitive detection and localization performance. These results indicate that many failures previously attributed to representation forgetting can instead be explained by decision-rule instability in cross-head routing. Code is available at https://github.com/jw-chae/GCR", "date": "2026-01-05", "categories": []}
{"id": "2510.13197", "title": "Text Anomaly Detection with Simplified Isolation Kernel", "abstract": "Two-step approaches combining pre-trained large language model embeddings and anomaly detectors demonstrate strong performance in text anomaly detection by leveraging rich semantic representations. However, high-dimensional dense embeddings extracted by large language models pose challenges due to substantial memory requirements and high computation time. To address this challenge, we introduce the Simplified Isolation Kernel (SIK), which maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. SIK has linear time complexity and significantly reduces space complexity through its innovative boundary-focused feature mapping. Experiments across 7 datasets demonstrate that SIK achieves better detection performance than 11 state-of-the-art (SOTA) anomaly detection algorithms while maintaining computational efficiency and low memory cost. All code and demonstrations are available at https://github.com/charles-cao/SIK.", "date": "2025-10-15", "categories": []}
{"id": "2601.02440", "title": "Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss", "abstract": "Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and long-tailed anomaly score distributions (LTD). This imbalance skews model training and degrades detection performance, especially for minority instances. To address this issue, we propose a novel importance-weighted loss designed specifically for anomaly detection. Compared to the previous method for LTD in classification, our method does not require prior knowledge of normal data classes. Instead, we introduce a weighted loss function that incorporates importance sampling to align the distribution of anomaly scores with a target Gaussian, ensuring a balanced representation of normal data. Extensive experiments on three benchmark image datasets and three real-world hyperspectral imaging datasets demonstrate the robustness of our approach in mitigating LTD-induced bias. Our method improves anomaly detection performance by 0.043, highlighting its effectiveness in real-world applications.", "date": "2026-01-05", "categories": []}
{"id": "2601.02511", "title": "LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection", "abstract": "Detecting anomalies in time series data is crucial for finance, healthcare, sensor networks, and industrial monitoring applications. However, time series anomaly detection often suffers from sparse labels, complex temporal patterns, and costly expert annotation. We propose a unified framework that integrates Large Language Model (LLM)-based potential functions for reward shaping with Reinforcement Learning (RL), Variational Autoencoder (VAE)-enhanced dynamic reward scaling, and active learning with label propagation. An LSTM-based RL agent leverages LLM-derived semantic rewards to guide exploration, while VAE reconstruction errors add unsupervised anomaly signals. Active learning selects the most uncertain samples, and label propagation efficiently expands labeled data. Evaluations on Yahoo-A1 and SMD benchmarks demonstrate that our method achieves state-of-the-art detection accuracy under limited labeling budgets and operates effectively in data-constrained settings. This study highlights the promise of combining LLMs with RL and advanced unsupervised techniques for robust, scalable anomaly detection in real-world applications.", "date": "2026-01-05", "categories": []}
{"id": "2601.03085", "title": "Real-Time Adaptive Anomaly Detection in Industrial IoT Environments", "abstract": "To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today's industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements.", "date": "2026-01-06", "categories": []}
{"id": "2404.00645", "title": "Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security", "abstract": "This research introduces an innovative security enhancement approach, employing advanced image analysis and soft computing. The focus is on an intelligent surveillance system that detects unauthorized individuals in restricted areas by analyzing attire. Traditional security measures face challenges in monitoring unauthorized access. Leveraging YOLOv8, an advanced object detection algorithm, our system identifies authorized personnel based on their attire in CCTV footage. The methodology involves training the YOLOv8 model on a comprehensive dataset of uniform patterns, ensuring precise recognition in specific regions. Soft computing techniques enhance adaptability to dynamic environments and varying lighting conditions. This research contributes to image analysis and soft computing, providing a sophisticated security solution. Emphasizing uniform-based anomaly detection, it establishes a foundation for robust security systems in restricted areas. The outcomes highlight the potential of YOLOv8-based surveillance in ensuring safety in sensitive locations.", "date": "2024-03-31", "categories": []}
{"id": "2509.00050", "title": "Applying Deep Learning to Anomaly Detection of Russian Satellite Activity for Indications Prior to Military Activity", "abstract": "We apply deep learning techniques for anomaly detection to analyze activity of Russian-owned resident space objects (RSO) prior to the Ukraine invasion and assess the results for any findings that can be used as indications and warnings (I&W) of aggressive military behavior for future conflicts. Through analysis of anomalous activity, an understanding of possible tactics and procedures can be established to assess the existence of statistically significant changes in Russian RSO pattern of life/pattern of behavior (PoL/PoB) using publicly available two-line element (TLE) data. This research looks at statistical and deep learning approaches to assess anomalous activity. The deep learning methods assessed are isolation forest (IF), traditional autoencoder (AE), variational autoencoder (VAE), Kolmogorov Arnold Network (KAN), and a novel anchor-loss based autoencoder (Anchor AE). Each model is used to establish a baseline of on-orbit activity based on a five-year data sample. The primary investigation period focuses on the six months leading up to the invasion date of February 24, 2022. Additional analysis looks at RSO activity during an active combat period by sampling TLE data after the invasion date. The deep learning autoencoder models identify anomalies based on reconstruction errors that surpass a threshold sigma. To capture the nuance and unique characteristics of each RSO an individual model was trained for each observed space object. The research made an effort to prioritize explainability and interpretability of the model results thus each observation was assessed for anomalous behavior of the individual six orbital elements versus analyzing the input data as a single monolithic observation. The results demonstrate not only statistically significant anomalies of Russian RSO activity but also details anomalous findings to the individual orbital element.", "date": "2025-08-24", "categories": []}
{"id": "2601.00883", "title": "Outlier Detection Using Vector Cosine Similarity by Adding a Dimension", "abstract": "We propose a new outlier detection method for multi-dimensional data. The method detects outliers based on vector cosine similarity, using a new dataset constructed by adding a dimension with zero values to the original data. When a point in the new dataset is selected as the measured point, an observation point is created as the origin, differing only in the new dimension by having a non-zero value compared to the measured point. Vectors are then formed from the observation point to the measured point and to other points in the dataset. By comparing the cosine similarities of these vectors, abnormal data can be identified. An optimized implementation (MDOD) is available on PyPI: https://pypi.org/project/mdod/.", "date": "2025-12-31", "categories": []}
{"id": "2601.01358", "title": "A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data", "abstract": "The detection of rare cell types in single-cell transcriptomics data is crucial for elucidating disease pathogenesis and tissue development dynamics. However, a critical gap that persists in current methods is their inability to provide an explanation based on genes for each cell they have detected as rare. We identify three primary sources of this deficiency. First, the anomaly detectors often function as \"black boxes\", designed to detect anomalies but unable to explain why a cell is anomalous. Second, the standard analytical framework hinders interpretability by relying on dimensionality reduction techniques, such as Principal Component Analysis (PCA), which transform meaningful gene expression data into abstract, uninterpretable features. Finally, existing explanation algorithms cannot be readily applied to this domain, as single-cell data is characterized by high dimensionality, noise, and substantial sparsity. To overcome these limitations, we introduce a framework for explainable anomaly detection in single-cell transcriptomics data which not only identifies individual anomalies, but also provides a visual explanation based on genes that makes an instance anomalous. This framework has two key ingredients that are not existed in current methods applied in this domain. First, it eliminates the PCA step which is deemed to be an essential component in previous studies. Second, it employs the state-of-art anomaly detector and explainer as the efficient and effective means to find each rare cell and the relevant gene subspace in order to provide explanations for each rare cell as well as the typical normal cell associated with the rare cell's closest normal cells.", "date": "2026-01-04", "categories": []}
{"id": "2601.01403", "title": "A Graph-based Framework for Online Time Series Anomaly Detection Using Model Ensemble", "abstract": "With the increasing volume of streaming data in industrial systems, online anomaly detection has become a critical task. The diverse and rapidly evolving data patterns pose significant challenges for online anomaly detection. Many existing anomaly detection methods are designed for offline settings or have difficulty in handling heterogeneous streaming data effectively. This paper proposes GDME, an unsupervised graph-based framework for online time series anomaly detection using model ensemble. GDME maintains a dynamic model pool that is continuously updated by pruning underperforming models and introducing new ones. It utilizes a dynamic graph structure to represent relationships among models and employs community detection on the graph to select an appropriate subset for ensemble. The graph structure is also used to detect concept drift by monitoring structural changes, allowing the framework to adapt to evolving streaming data. Experiments on seven heterogeneous time series demonstrate that GDME outperforms existing online anomaly detection methods, achieving improvements of up to 24%. In addition, its ensemble strategy provides superior detection performance compared with both individual models and average ensembles, with competitive computational efficiency.", "date": "2026-01-04", "categories": []}
{"id": "2601.01701", "title": "Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT", "abstract": "Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, with the advent of digital twins and data-driven decision-making, several statistical and machine-learning methods have been proposed. However, these methods face several challenges, such as dependence on only real sensor datasets, limited labeled data, high false alarm rates, and privacy concerns. To address these problems, we propose a suite of digital twin-integrated federated learning (DTFL) methods that enhance global model performance while preserving data privacy and communication efficiency. Specifically, we present five novel approaches: Digital Twin-Based Meta-Learning (DTML), Federated Parameter Fusion (FPF), Layer-wise Parameter Exchange (LPE), Cyclic Weight Adaptation (CWA), and Digital Twin Knowledge Distillation (DTKD). Each method introduces a unique mechanism to combine synthetic and real-world knowledge, balancing generalization with communication overhead. We conduct an extensive experiment using a publicly available cyber-physical anomaly detection dataset. For a target accuracy of 80%, CWA reaches the target in 33 rounds, FPF in 41 rounds, LPE in 48 rounds, and DTML in 87 rounds, whereas the standard FedAvg baseline and DTKD do not reach the target within 100 rounds. These results highlight substantial communication-efficiency gains (up to 62% fewer rounds than DTML and 31% fewer than LPE) and demonstrate that integrating DT knowledge into FL accelerates convergence to operationally meaningful accuracy thresholds for IIoT anomaly detection.", "date": "2026-01-05", "categories": []}
{"id": "2601.02037", "title": "Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling", "abstract": "Multivariate time-series (MTS) anomaly detection is critical in domains such as service monitor, IoT, and network security. While multi-model methods based on selection or ensembling outperform single-model ones, they still face limitations: (i) selection methods rely on a single chosen model and are sensitive to the strategy; (ii) ensembling methods often combine all models or are restricted to univariate data; and (iii) most methods depend on fixed data dimensionality, limiting scalability. To address these, we propose DMPEAD, a Dynamic Model Pool and Ensembling framework for MTS Anomaly Detection. The framework first (i) constructs a diverse model pool via parameter transfer and diversity metric, then (ii) updates it with a meta-model and similarity-based strategy for adaptive pool expansion, subset selection, and pool merging, finally (iii) ensembles top-ranked models through proxy metric ranking and top-k aggregation in the selected subset, outputting the final anomaly detection result. Extensive experiments on 8 real-world datasets show that our model outperforms all baselines, demonstrating superior adaptability and scalability.", "date": "2026-01-05", "categories": []}
{"id": "2601.02324", "title": "Hunting for \"Oddballs\" with Machine Learning: Detecting Anomalous Exoplanets Using a Deep-Learned Low-Dimensional Representation of Transit Spectra with Autoencoders", "abstract": "This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the Atmospheric Big Challenge (ABC) database, a publicly available dataset with over 100,000 simulated exoplanet spectra, to construct an anomaly detection scenario by defining CO2-rich atmospheres as anomalies and CO2-poor atmospheres as the normal class. We benchmarked four different anomaly detection strategies: Autoencoder Reconstruction Loss, One-Class Support Vector Machine (1 class-SVM), K-means Clustering, and Local Outlier Factor (LOF). Each method was evaluated in both the original spectral space and the autoencoder's latent space using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) metrics. To test the performance of the different methods under realistic conditions, we introduced Gaussian noise levels ranging from 10 to 50 ppm. Our results indicate that anomaly detection is consistently more effective when performed within the latent space across all noise levels. Specifically, K-means clustering in the latent space emerged as a stable and high-performing method. We demonstrate that this anomaly detection approach is robust to noise levels up to 30 ppm (consistent with realistic space-based observations) and remains viable even at 50 ppm when leveraging latent space representations. On the other hand, the performance of the anomaly detection methods applied directly in the raw spectral space degrades significantly with increasing the level of noise. This suggests that autoencoder-driven dimensionality reduction offers a robust methodology for flagging chemically anomalous targets in large-scale surveys where exhaustive retrievals are computationally prohibitive.", "date": "2026-01-05", "categories": []}
{"id": "2310.18961", "title": "AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection", "abstract": "Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/organs, can vary significantly. Recently large pre-trained vision-language models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition ability in various vision tasks, including anomaly detection. However, their ZSAD performance is weak since the VLMs focus more on modeling the class semantics of the foreground objects rather than the abnormality/normality in the images. In this paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIP for accurate ZSAD across different domains. The key insight of AnomalyCLIP is to learn object-agnostic text prompts that capture generic normality and abnormality in an image regardless of its foreground objects. This allows our model to focus on the abnormal image regions rather than the object semantics, enabling generalized normality and abnormality recognition on diverse types of objects. Large-scale experiments on 17 real-world anomaly detection datasets show that AnomalyCLIP achieves superior zero-shot performance of detecting and segmenting anomalies in datasets of highly diverse class semantics from various defect inspection and medical imaging domains. Code will be made available at https://github.com/zqhang/AnomalyCLIP.", "date": "2023-10-29", "categories": []}
{"id": "2601.00327", "title": "HarmoniAD: Harmonizing Local Structures and Global Semantics for Anomaly Detection", "abstract": "Anomaly detection is crucial in industrial product quality inspection. Failing to detect tiny defects often leads to serious consequences. Existing methods face a structure-semantics trade-off: structure-oriented models (such as frequency-based filters) are noise-sensitive, while semantics-oriented models (such as CLIP-based encoders) often miss fine details. To address this, we propose HarmoniAD, a frequency-guided dual-branch framework. Features are first extracted by the CLIP image encoder, then transformed into the frequency domain, and finally decoupled into high- and low-frequency paths for complementary modeling of structure and semantics. The high-frequency branch is equipped with a fine-grained structural attention module (FSAM) to enhance textures and edges for detecting small anomalies, while the low-frequency branch uses a global structural context module (GSCM) to capture long-range dependencies and preserve semantic consistency. Together, these branches balance fine detail and global semantics. HarmoniAD further adopts a multi-class joint training strategy, and experiments on MVTec-AD, VisA, and BTAD show state-of-the-art performance with both sensitivity and robustness.", "date": "2026-01-01", "categories": []}
{"id": "2601.00446", "title": "A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection", "abstract": "Time series anomaly detection is essential for the reliable operation of complex systems, but most existing methods require extensive task-specific training. We explore whether time series foundation models (TSFMs), pretrained on large heterogeneous data, can serve as universal backbones for anomaly detection. Through systematic experiments across multiple benchmarks, we compare zero-shot inference, full model adaptation, and parameter-efficient fine-tuning (PEFT) strategies. Our results demonstrate that TSFMs outperform task-specific baselines, achieving notable gains in AUC-PR and VUS-PR, particularly under severe class imbalance. Moreover, PEFT methods such as LoRA, OFT, and HRA not only reduce computational cost but also match or surpass full fine-tuning in most cases, indicating that TSFMs can be efficiently adapted for anomaly detection, even when pretrained for forecasting. These findings position TSFMs as promising general-purpose models for scalable and efficient time series anomaly detection.", "date": "2026-01-01", "categories": []}
{"id": "2510.22693", "title": "VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree", "abstract": "Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sampling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularityaware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and redundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https://github.com/wenlongli10/VADTree.", "date": "2025-10-26", "categories": []}
{"id": "2511.12899", "title": "FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI", "abstract": "Due to the diversity of brain anatomy and the scarcity of annotated data, supervised anomaly detection for brain MRI remains challenging, driving the development of unsupervised anomaly detection (UAD) approaches. Current UAD methods typically utilize artificially generated noise perturbations on healthy MRIs to train generative models for normal anatomy reconstruction, enabling anomaly detection via residual maps. However, such simulated anomalies lack the biophysical fidelity and morphological complexity characteristic of true clinical lesions. To advance UAD in brain MRI, we conduct the first systematic frequency-domain analysis of pathological signatures, revealing two key properties: (1) anomalies exhibit unique frequency patterns distinguishable from normal anatomy, and (2) low-frequency signals maintain consistent representations across healthy scans. These insights motivate our Frequency-Decomposition Preprocessing (FDP) framework, the first UAD method to leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation. FDP can integrate seamlessly with existing anomaly simulation techniques, consistently enhancing detection performance across diverse architectures while maintaining diagnostic fidelity. Experimental results demonstrate that FDP consistently improves anomaly detection performance when integrated with existing methods. Notably, FDP achieves a 17.63% increase in DICE score with LDM while maintaining robust improvements across multiple baselines. The code is available at https://github.com/ls1rius/MRI_FDP.", "date": "2025-11-17", "categories": []}
{"id": "2512.23380", "title": "A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers", "abstract": "Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to handle the interactions between these modalities. Applying multimodal sentiment analysis to log anomaly detection, we propose CoLog, a framework that collaboratively encodes logs utilizing various modalities. CoLog utilizes collaborative transformers and multi-head impressed attention to learn interactions among several modalities, ensuring comprehensive anomaly detection. To handle the heterogeneity caused by these interactions, CoLog incorporates a modality adaptation layer, which adapts the representations from different log modalities. This methodology enables CoLog to learn nuanced patterns and dependencies within the data, enhancing its anomaly detection capabilities. Extensive experiments demonstrate CoLog's superiority over existing state-of-the-art methods. Furthermore, in detecting both point and collective anomalies, CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets for log-based anomaly detection. The comprehensive detection capabilities of CoLog make it highly suitable for cybersecurity, system monitoring, and operational efficiency. CoLog represents a significant advancement in log anomaly detection, providing a sophisticated and effective solution to point and collective anomaly detection through a unified framework and a solution to the complex challenges automatic log data analysis poses. We also provide the implementation of CoLog at https://github.com/NasirzadehMoh/CoLog.", "date": "2025-12-29", "categories": []}
{"id": "2512.23986", "title": "Anomaly detection in satellite imagery through temporal inpainting", "abstract": "Detecting surface changes from satellite imagery is critical for rapid disaster response and environmental monitoring, yet remains challenging due to the complex interplay between atmospheric noise, seasonal variations, and sensor artifacts. Here we show that deep learning can leverage the temporal redundancy of satellite time series to detect anomalies at unprecedented sensitivity, by learning to predict what the surface should look like in the absence of change. We train an inpainting model built upon the SATLAS foundation model to reconstruct the last frame of a Sentinel-2 time series from preceding acquisitions, using globally distributed training data spanning diverse climate zones and land cover types. When applied to regions affected by sudden surface changes, the discrepancy between prediction and observation reveals anomalies that traditional change detection methods miss. We validate our approach on earthquake-triggered surface ruptures from the 2023 Turkey-Syria earthquake sequence, demonstrating detection of a rift feature in Tepehan with higher sensitivity and specificity than temporal median or Reed-Xiaoli anomaly detectors. Our method reaches detection thresholds approximately three times lower than baseline approaches, providing a path towards automated, global-scale monitoring of surface changes from freely available multi-spectral satellite data.", "date": "2025-12-30", "categories": []}
{"id": "2305.07328", "title": "Enhance Multi-Scale Spatial-Temporal Coherence for Configurable Video Anomaly Detection", "abstract": "The development of unsupervised Video Anomaly Detection (VAD) relies on technologies in the field of signal processing. Since the anomaly is quite ambiguous and unbounded, different detection demands may often be raised even in one scenario. Thus, we propose to design the configurable VAD with flexible solutions targeting to solve the issue that previous methods have to train their models from scratch and waste resources when detection demands even change slightly. Moreover, we also design a dataset with good compatibility to evaluate the VAD performance when changes happen in detection demands. Besides, videos contain important information regarding continuous changes in the object's appearance and motion. Thus, we also propose a module to establish the multi-scale spatial-temporal coherence, which improves the accuracy and has the ability to dynamically adjust and accurately capture spatial-temporal normal patterns. Experiments show that our method not only models coherence effectively but also has better configurable ability.", "date": "2023-05-12", "categories": []}
{"id": "2502.21012", "title": "FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection", "abstract": "Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over data privacy. The limited class diversity inherent to one-class classification tasks, combined with distribution biases caused by variations in products across and within clients, poses significant challenges for preserving data privacy with federated UAD. Thus, this article proposes an efficient federated learning method with dynamic memory and memory-reduce for unsupervised image anomaly detection, called FedDyMem. Considering all client data belongs to a single class (i.e., normal sample) in UAD and the distribution of intra-class features demonstrates significant skewness, FedDyMem facilitates knowledge sharing between the client and server through the client's dynamic memory bank instead of model parameters. In the local clients, a memory generator and a metric loss are employed to improve the consistency of the feature distribution for normal samples, leveraging the local model to update the memory bank dynamically. For efficient communication and data privacy, a memory-reduce method based on weighted averages is proposed to significantly decrease the scale of memory banks. This reduced representation inherently, thereby mitigating the risk of data reconstruction. On the server, global memory is constructed and distributed to individual clients through k-means aggregation. Experiments conducted on six industrial and medical datasets, comprising a mixture of six products or health screening types derived from eleven public datasets, demonstrate the effectiveness of FedDyMem.", "date": "2025-02-28", "categories": []}
{"id": "2511.13204", "title": "RefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detection", "abstract": "Weakly-Supervised Video Anomaly Detection aims to identify anomalous events using only video-level labels, balancing annotation efficiency with practical applicability. However, existing methods often oversimplify the anomaly space by treating all abnormal events as a single category, overlooking the diverse semantic and temporal characteristics intrinsic to real-world anomalies. Inspired by how humans perceive anomalies, by jointly interpreting temporal motion patterns and semantic structures underlying different anomaly types, we propose RefineVAD, a novel framework that mimics this dual-process reasoning. Our framework integrates two core modules. The first, Motion-aware Temporal Attention and Recalibration (MoTAR), estimates motion salience and dynamically adjusts temporal focus via shift-based attention and global Transformer-based modeling. The second, Category-Oriented Refinement (CORE), injects soft anomaly category priors into the representation space by aligning segment-level features with learnable category prototypes through cross-attention. By jointly leveraging temporal dynamics and semantic structure, explicitly models both \"how\" motion evolves and \"what\" semantic category it resembles. Extensive experiments on WVAD benchmark validate the effectiveness of RefineVAD and highlight the importance of integrating semantic context to guide feature refinement toward anomaly-relevant patterns.", "date": "2025-11-17", "categories": []}
{"id": "2512.22291", "title": "Multi-Head Spectral-Adaptive Graph Anomaly Detection", "abstract": "Graph anomaly detection technology has broad applications in financial fraud and risk control. However, existing graph anomaly detection methods often face significant challenges when dealing with complex and variable abnormal patterns, as anomalous nodes are often disguised and mixed with normal nodes, leading to the coexistence of homophily and heterophily in the graph domain. Recent spectral graph neural networks have made notable progress in addressing this issue; however, current techniques typically employ fixed, globally shared filters. This 'one-size-fits-all' approach can easily cause over-smoothing, erasing critical high-frequency signals needed for fraud detection, and lacks adaptive capabilities for different graph instances. To solve this problem, we propose a Multi-Head Spectral-Adaptive Graph Neural Network (MHSA-GNN). The core innovation is the design of a lightweight hypernetwork that, conditioned on a 'spectral fingerprint' containing structural statistics and Rayleigh quotient features, dynamically generates Chebyshev filter parameters tailored to each instance. This enables a customized filtering strategy for each node and its local subgraph. Additionally, to prevent mode collapse in the multi-head mechanism, we introduce a novel dual regularization strategy that combines teacher-student contrastive learning (TSC) to ensure representation accuracy and Barlow Twins diversity loss (BTD) to enforce orthogonality among heads. Extensive experiments on four real-world datasets demonstrate that our method effectively preserves high-frequency abnormal signals and significantly outperforms existing state-of-the-art methods, especially showing excellent robustness on highly heterogeneous datasets.", "date": "2025-12-25", "categories": []}
{"id": "2512.23227", "title": "Anomaly Detection by Effectively Leveraging Synthetic Images", "abstract": "Anomaly detection plays a vital role in industrial manufacturing. Due to the scarcity of real defect images, unsupervised approaches that rely solely on normal images have been extensively studied. Recently, diffusion-based generative models brought attention to training data synthesis as an alternative solution. In this work, we focus on a strategy to effectively leverage synthetic images to maximize the anomaly detection performance. Previous synthesis strategies are broadly categorized into two groups, presenting a clear trade-off. Rule-based synthesis, such as injecting noise or pasting patches, is cost-effective but often fails to produce realistic defect images. On the other hand, generative model-based synthesis can create high-quality defect images but requires substantial cost. To address this problem, we propose a novel framework that leverages a pre-trained text-guided image-to-image translation model and image retrieval model to efficiently generate synthetic defect images. Specifically, the image retrieval model assesses the similarity of the generated images to real normal images and filters out irrelevant outputs, thereby enhancing the quality and relevance of the generated defect images. To effectively leverage synthetic images, we also introduce a two stage training strategy. In this strategy, the model is first pre-trained on a large volume of images from rule-based synthesis and then fine-tuned on a smaller set of high-quality images. This method significantly reduces the cost for data collection while improving the anomaly detection performance. Experiments on the MVTec AD dataset demonstrate the effectiveness of our approach.", "date": "2025-12-29", "categories": []}
{"id": "2502.21022", "title": "When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity", "abstract": "This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significantly in the presence of a domain shift, difficult to avoid in a real-world setting. While UDA has contributed to solving this issue in binary and multi-class classification, such a strategy is ill-posed in UAD. This might be explained by the unsupervised nature of the two tasks, namely, domain adaptation and anomaly detection. Herein, we first formulate this problem that we call the two-fold unsupervised curse. Then, we propose a pioneering solution to this curse, considered intractable so far, by assuming that anomalies are rare. Specifically, we leverage clustering techniques to identify a dominant cluster in the target feature space. Posed as the normal cluster, the latter is aligned with the source normal features. Concretely, given a one-class source set and an unlabeled target set composed mostly of normal data and some anomalies, we fit the source features within a hypersphere while jointly aligning them with the features of the dominant cluster from the target set. The paper provides extensive experiments and analysis on common adaptation benchmarks for anomaly detection, demonstrating the relevance of both the newly introduced paradigm and the proposed approach. The code will be made publicly available.", "date": "2025-02-28", "categories": []}
{"id": "2503.17903", "title": "GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model", "abstract": "Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture long-range dependencies efficiently and neglect the spectral information. Recently, selective state space models, particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design a View-Fused Mamba (VFM) module with a Mamba-Transformer-style architecture to efficiently fuse information from different graph views with a selective state mechanism. We also design a Spectrum-Guided Mamba (SGM) module with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refinement process, considering the spectral information for UGLAD. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-Fu/GLADMamba.", "date": "2025-03-23", "categories": []}
{"id": "2504.11781", "title": "ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model", "abstract": "Unsupervised anomaly detection in hyperspectral images (HSI), aiming to detect unknown targets from backgrounds, is challenging for earth surface monitoring. However, current studies are hindered by steep computational costs due to the high-dimensional property of HSI and dense sampling-based training paradigm, constraining their rapid deployment. Our key observation is that, during training, not all samples within the same homogeneous area are indispensable, whereas ingenious sampling can provide a powerful substitute for reducing costs. Motivated by this, we propose an Asymmetrical Consensus State Space Model (ACMamba) to significantly reduce computational costs without compromising accuracy. Specifically, we design an asymmetrical anomaly detection paradigm that utilizes region-level instances as an efficient alternative to dense pixel-level samples. In this paradigm, a low-cost Mamba-based module is introduced to discover global contextual attributes of regions that are essential for HSI reconstruction. Additionally, we develop a consensus learning strategy from the optimization perspective to simultaneously facilitate background reconstruction and anomaly compression, further alleviating the negative impact of anomaly reconstruction. Theoretical analysis and extensive experiments across eight benchmarks verify the superiority of ACMamba, demonstrating a faster speed and stronger performance over the state-of-the-art.", "date": "2025-04-16", "categories": []}
{"id": "2512.21459", "title": "CCAD: Compressed Global Feature Conditioned Anomaly Detection", "abstract": "Anomaly detection holds considerable industrial significance, especially in scenarios with limited anomalous data. Currently, reconstruction-based and unsupervised representation-based approaches are the primary focus. However, unsupervised representation-based methods struggle to extract robust features under domain shift, whereas reconstruction-based methods often suffer from low training efficiency and performance degradation due to insufficient constraints. To address these challenges, we propose a novel method named Compressed Global Feature Conditioned Anomaly Detection (CCAD). CCAD synergizes the strengths of both paradigms by adapting global features as a new modality condition for the reconstruction model. Furthermore, we design an adaptive compression mechanism to enhance both generalization and training efficiency. Extensive experiments demonstrate that CCAD consistently outperforms state-of-the-art methods in terms of AUC while achieving faster convergence. In addition, we contribute a reorganized and re-annotated version of the DAGM 2007 dataset with new annotations to further validate our method's effectiveness. The code for reproducing main results is available at https://github.com/chloeqxq/CCAD.", "date": "2025-12-25", "categories": []}
{"id": "2512.21924", "title": "Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning", "abstract": "Detection of various lesions in brain MRI is clinically critical, but challenging due to the diversity of lesions and variability in imaging conditions. Current unsupervised learning methods detect anomalies mainly through reconstructing abnormal images into pseudo-healthy images (PHIs) by normal samples learning and then analyzing differences between images. However, these unsupervised models face two significant limitations: restricted generalizability to multi-modality and multi-center MRIs due to their reliance on the specific imaging information in normal training data, and constrained performance due to abnormal residuals propagated from input images to reconstructed PHIs. To address these limitations, two novel modules are proposed, forming a new PHI reconstruction framework. Firstly, the disentangled representation module is proposed to improve generalizability by decoupling brain MRI into imaging information and essential imaging-invariant anatomical images, ensuring that the reconstruction focuses on the anatomy. Specifically, brain anatomical priors and a differentiable one-hot encoding operator are introduced to constrain the disentanglement results and enhance the disentanglement stability. Secondly, the edge-to-image restoration module is designed to reconstruct high-quality PHIs by restoring the anatomical representation from the high-frequency edge information of anatomical images, and then recoupling the disentangled imaging information. This module not only suppresses abnormal residuals in PHI by reducing abnormal pixels input through edge-only input, but also effectively reconstructs normal regions using the preserved structural details in the edges. Evaluated on nine public datasets (4,443 patients' MRIs from multiple centers), our method outperforms 17 SOTA methods, achieving absolute improvements of +18.32% in AP and +13.64% in DSC.", "date": "2025-12-26", "categories": []}
{"id": "2512.21264", "title": "AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI", "abstract": "Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical workflows. Existing single-class or multi-class anomaly detection (AD) models typically rely on fixed modality configurations, require repetitive training, or fail to generalize to unseen modality combinations, limiting their clinical scalability. In this work, we present a unified Any-Modality AD framework that performs robust anomaly detection and localization under arbitrary MRI modality availability. The framework integrates a dual-pathway DINOv2 encoder with a feature distribution alignment mechanism that statistically aligns incomplete-modality features with full-modality representations, enabling stable inference even with severe modality dropout. To further enhance semantic consistency, we introduce an Intrinsic Normal Prototypes (INPs) extractor and an INP-guided decoder that reconstruct only normal anatomical patterns while naturally amplifying abnormal deviations. Through randomized modality masking and indirect feature completion during training, the model learns to adapt to all modality configurations without re-training. Extensive experiments on BraTS2018, MU-Glioma-Post, and Pretreat-MetsToBrain-Masks demonstrate that our approach consistently surpasses state-of-the-art industrial and medical AD baselines across 7 modality combinations, achieving superior generalization. This study establishes a scalable paradigm for multimodal medical AD under real-world, imperfect modality conditions. Our source code is available at https://github.com/wuchangw/AnyAD.", "date": "2025-12-24", "categories": []}
{"id": "2512.17601", "title": "HeadHunt-VAD: Hunting Robust Anomaly-Sensitive Heads in MLLM for Tuning-Free Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) aims to locate events that deviate from normal patterns in videos. Traditional approaches often rely on extensive labeled data and incur high computational costs. Recent tuning-free methods based on Multimodal Large Language Models (MLLMs) offer a promising alternative by leveraging their rich world knowledge. However, these methods typically rely on textual outputs, which introduces information loss, exhibits normalcy bias, and suffers from prompt sensitivity, making them insufficient for capturing subtle anomalous cues. To address these constraints, we propose HeadHunt-VAD, a novel tuning-free VAD paradigm that bypasses textual generation by directly hunting robust anomaly-sensitive internal attention heads within the frozen MLLM. Central to our method is a Robust Head Identification module that systematically evaluates all attention heads using a multi-criteria analysis of saliency and stability, identifying a sparse subset of heads that are consistently discriminative across diverse prompts. Features from these expert heads are then fed into a lightweight anomaly scorer and a temporal locator, enabling efficient and accurate anomaly detection with interpretable outputs. Extensive experiments show that HeadHunt-VAD achieves state-of-the-art performance among tuning-free methods on two major VAD benchmarks while maintaining high efficiency, validating head-level probing in MLLMs as a powerful and practical solution for real-world anomaly detection.", "date": "2025-12-19", "categories": []}
{"id": "2512.20086", "title": "Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection", "abstract": "Spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in structured domains such as road traffic and public transportation, where spatial entities can be naturally represented as fixed nodes. In contrast, many real-world systems including maritime traffic lack such fixed anchors, making the construction of spatio-temporal graphs a fundamental challenge. Anomaly detection in these non-grid environments is particularly difficult due to the absence of canonical reference points, the sparsity and irregularity of trajectories, and the fact that anomalies may manifest at multiple granularities. In this work, we introduce a novel benchmark dataset for anomaly detection in the maritime domain, extending the Open Maritime Traffic Analysis Dataset (OMTAD) into a benchmark tailored for graph-based anomaly detection. Our dataset enables systematic evaluation across three different granularities: node-level, edge-level, and graph-level anomalies. We plan to employ two specialized LLM-based agents: \\emph{Trajectory Synthesizer} and \\emph{Anomaly Injector} to construct richer interaction contexts and generate semantically meaningful anomalies. We expect this benchmark to promote reproducibility and to foster methodological advances in anomaly detection for non-grid spatio-temporal systems.", "date": "2025-12-23", "categories": []}
{"id": "2512.20417", "title": "Chain-of-Anomaly Thoughts with Large Vision-Language Models", "abstract": "Automated video surveillance with Large Vision-Language Models is limited by their inherent bias towards normality, often failing to detect crimes. While Chain-of-Thought reasoning strategies show significant potential for improving performance in language tasks, the lack of inductive anomaly biases in their reasoning further steers the models towards normal interpretations. To address this, we propose Chain-of-Anomaly-Thoughts (CoAT), a multi-agent reasoning framework that introduces inductive criminal bias in the reasoning process through a final, anomaly-focused classification layer. Our method significantly improves Anomaly Detection, boosting F1-score by 11.8 p.p. on challenging low-resolution footage and Anomaly Classification by 3.78 p.p. in high-resolution videos.", "date": "2025-12-23", "categories": []}
{"id": "2512.20432", "title": "High Dimensional Data Decomposition for Anomaly Detection of Textured Images", "abstract": "In the realm of diverse high-dimensional data, images play a significant role across various processes of manufacturing systems where efficient image anomaly detection has emerged as a core technology of utmost importance. However, when applied to textured defect images, conventional anomaly detection methods have limitations including non-negligible misidentification, low robustness, and excessive reliance on large-scale and structured datasets. This paper proposes a texture basis integrated smooth decomposition (TBSD) approach, which is targeted at efficient anomaly detection in textured images with smooth backgrounds and sparse anomalies. Mathematical formulation of quasi-periodicity and its theoretical properties are investigated for image texture estimation. TBSD method consists of two principal processes: the first process learns the texture basis functions to effectively extract quasi-periodic texture patterns; the subsequent anomaly detection process utilizes that texture basis as prior knowledge to prevent texture misidentification and capture potential anomalies with high accuracy.The proposed method surpasses benchmarks with less misidentification, smaller training dataset requirement, and superior anomaly detection performance on both simulation and real-world datasets.", "date": "2025-12-23", "categories": []}
{"id": "2509.16625", "title": "Self-Supervised Learning of Graph Representations for Network Intrusion Detection", "abstract": "Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged graph neural networks for network intrusion detection, they often decouple representation learning from anomaly detection, limiting the utility of the embeddings for identifying attacks. We propose GraphIDS, a self-supervised intrusion detection model that unifies these two stages by learning local graph representations of normal communication patterns through a masked autoencoder. An inductive graph neural network embeds each flow with its local topological context to capture typical network behavior, while a Transformer-based encoder-decoder reconstructs these embeddings, implicitly learning global co-occurrence patterns via self-attention without requiring explicit positional information. During inference, flows with unusually high reconstruction errors are flagged as potential intrusions. This end-to-end framework ensures that embeddings are directly optimized for the downstream task, facilitating the recognition of malicious traffic. On diverse NetFlow benchmarks, GraphIDS achieves up to 99.98% PR-AUC and 99.61% macro F1-score, outperforming baselines by 5-25 percentage points.", "date": "2025-09-20", "categories": []}
{"id": "2512.17296", "title": "Towards Pixel-Wise Anomaly Location for High-Resolution PCBA via Self-Supervised Image Reconstruction", "abstract": "Automated defect inspection of assembled Printed Circuit Board Assemblies (PCBA) is quite challenging due to the insufficient labeled data, micro-defects with just a few pixels in visually-complex and high-resolution images. To address these challenges, we present HiSIR-Net, a High resolution, Self-supervised Reconstruction framework for pixel-wise PCBA localization. Our design combines two lightweight modules that make this practical on real 4K-resolution boards: (i) a Selective Input-Reconstruction Gate (SIR-Gate) that lets the model decide where to trust reconstruction versus the original input, thereby reducing irrelevant reconstruction artifacts and false alarms; and (ii) a Region-level Optimized Patch Selection (ROPS) scheme with positional cues to select overlapping patch reconstructions coherently across arbitrary resolutions. Organically integrating these mechanisms yields clean, high-resolution anomaly maps with low false positive (FP) rate. To bridge the gap in high-resolution PCBA datasets, we further contribute a self-collected dataset named SIPCBA-500 of 500 images. We conduct extensive experiments on our SIPCBA-500 as well as public benchmarks, demonstrating the superior localization performance of our method while running at practical speed. Full code and dataset will be made available upon acceptance.", "date": "2025-12-19", "categories": []}
{"id": "2512.18219", "title": "Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching", "abstract": "Anomaly detection or outlier is one of the challenging subjects in unsupervised learning . This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics . For this purpose , we first pre-train the ResNet-18 network on the ImageNet and then fine-tune it on the MVTech-AD dataset . Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods . Our model , Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.", "date": "2025-12-20", "categories": []}
{"id": "2512.18673", "title": "Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs", "abstract": "This paper proposes a structure-aware driven scheduling graph modeling method to improve the accuracy and representation capability of anomaly identification in scheduling behaviors of complex systems. The method first designs a structure-guided scheduling graph construction mechanism that integrates task execution stages, resource node states, and scheduling path information to build dynamically evolving scheduling behavior graphs, enhancing the model's ability to capture global scheduling relationships. On this basis, a multi-scale graph semantic aggregation module is introduced to achieve semantic consistency modeling of scheduling features through local adjacency semantic integration and global topology alignment, thereby strengthening the model's capability to capture abnormal features in complex scenarios such as multi-task concurrency, resource competition, and stage transitions. Experiments are conducted on a real scheduling dataset with multiple scheduling disturbance paths set to simulate different types of anomalies, including structural shifts, resource changes, and task delays. The proposed model demonstrates significant performance advantages across multiple metrics, showing a sensitive response to structural disturbances and semantic shifts. Further visualization analysis reveals that, under the combined effect of structure guidance and semantic aggregation, the scheduling behavior graph exhibits stronger anomaly separability and pattern representation, validating the effectiveness and adaptability of the method in scheduling anomaly detection tasks.", "date": "2025-12-21", "categories": []}
{"id": "2512.18774", "title": "Label-Informed Outlier Detection Based on Granule Density", "abstract": "Outlier detection, crucial for identifying unusual patterns with significant implications across numerous applications, has drawn considerable research interest. Existing semi-supervised methods typically treat data as purely numerical and} in a deterministic manner, thereby neglecting the heterogeneity and uncertainty inherent in complex, real-world datasets. This paper introduces a label-informed outlier detection method for heterogeneous data based on Granular Computing and Fuzzy Sets, namely Granule Density-based Outlier Factor (GDOF). Specifically, GDOF first employs label-informed fuzzy granulation to effectively represent various data types and develops granule density for precise density estimation. Subsequently, granule densities from individual attributes are integrated for outlier scoring by assessing attribute relevance with a limited number of labeled outliers. Experimental results on various real-world datasets show that GDOF stands out in detecting outliers in heterogeneous data with a minimal number of labeled outliers. The integration of Fuzzy Sets and Granular Computing in GDOF offers a practical framework for outlier detection in complex and diverse data types. All relevant datasets and source codes are publicly available for further research. This is the author's accepted manuscript of a paper published in IEEE Transactions on Fuzzy Systems. The final version is available at https://doi.org/10.1109/TFUZZ.2024.3514853", "date": "2025-12-21", "categories": []}
{"id": "2512.18826", "title": "Hyperbolic Graph Embeddings: a Survey and an Evaluation on Anomaly Detection", "abstract": "This survey reviews hyperbolic graph embedding models, and evaluate them on anomaly detection, highlighting their advantages over Euclidean methods in capturing complex structures. Evaluating models like \\textit{HGCAE}, \\textit{\\(\\mathcal{P}\\)-VAE}, and \\textit{HGCN} demonstrates high performance, with \\textit{\\(\\mathcal{P}\\)-VAE} achieving an F1-score of 94\\% on the \\textit{Elliptic} dataset and \\textit{HGCAE} scoring 80\\% on \\textit{Cora}. In contrast, Euclidean methods like \\textit{DOMINANT} and \\textit{GraphSage} struggle with complex data. The study emphasizes the potential of hyperbolic spaces for improving anomaly detection, and provides an open-source library to foster further research in this field.", "date": "2025-12-21", "categories": []}
{"id": "2512.18977", "title": "Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets", "abstract": "Outlier detection aims to find samples that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-supervised methods focus on numerical data and neglect the heterogeneity of data information. In this paper, we propose a consistency-guided outlier detection algorithm (COD) for heterogeneous data with the fuzzy rough set theory in a semi-supervised manner. First, a few labeled outliers are leveraged to construct label-informed fuzzy similarity relations. Next, the consistency of the fuzzy decision system is introduced to evaluate attributes' contributions to knowledge classification. Subsequently, we define the outlier factor based on the fuzzy similarity class and predict outliers by integrating the classification consistency and the outlier factor. The proposed algorithm is extensively evaluated on 15 freshly proposed datasets. Experimental results demonstrate that COD is better than or comparable with the leading outlier detectors. This manuscript is the accepted author version of a paper published by Elsevier. The final published version is available at https://doi.org/10.1016/j.asoc.2024.112070", "date": "2025-12-22", "categories": []}
{"id": "2512.18978", "title": "Outlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropy", "abstract": "Outlier detection is a critical task in data mining, aimed at identifying objects that significantly deviate from the norm. Semi-supervised methods improve detection performance by leveraging partially labeled data but typically overlook the uncertainty and heterogeneity of real-world mixed-attribute data. This paper introduces a semi-supervised outlier detection method, namely fuzzy rough sets-based outlier detection (FROD), to effectively handle these challenges. Specifically, we first utilize a small subset of labeled data to construct fuzzy decision systems, through which we introduce the attribute classification accuracy based on fuzzy approximations to evaluate the contribution of attribute sets in outlier detection. Unlabeled data is then used to compute fuzzy relative entropy, which provides a characterization of outliers from the perspective of uncertainty. Finally, we develop the detection algorithm by combining attribute classification accuracy with fuzzy relative entropy. Experimental results on 16 public datasets show that FROD is comparable with or better than leading detection algorithms. All datasets and source codes are accessible at https://github.com/ChenBaiyang/FROD. This manuscript is the accepted author version of a paper published by Elsevier. The final published version is available at https://doi.org/10.1016/j.ijar.2025.109373", "date": "2025-12-22", "categories": []}
{"id": "2512.19383", "title": "Real-Time Machine Learning for Embedded Anomaly Detection", "abstract": "The spread of a resource-constrained Internet of Things (IoT) environment and embedded devices has put pressure on the real-time detection of anomalies occurring at the edge. This survey presents an overview of machine-learning methods aimed specifically at on-device anomaly detection with extremely strict constraints for latency, memory, and power consumption. Lightweight algorithms such as Isolation Forest, One-Class SVM, recurrent architectures, and statistical techniques are compared here according to the realities of embedded implementation. Our survey brings out significant trade-offs of accuracy and computational efficiency of detection, as well as how hardware constraints end up fundamentally redefining algorithm choice. The survey is completed with a set of practical recommendations on the choice of the algorithm depending on the equipment profiles and new trends in TinyML, which can help close the gap between detection capabilities and embedded reality. The paper serves as a strategic roadmap for engineers deploying anomaly detection in edge environments that are constrained by bandwidth and may be safety-critical.", "date": "2025-12-22", "categories": []}
{"id": "2512.01365", "title": "Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection", "abstract": "Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.", "date": "2025-12-01", "categories": []}
{"id": "2505.24431", "title": "Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation", "abstract": "3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating in-situ anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of 80.2% and 90.0%, respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. The code is available at https://github.com/ZZZBBBZZZ/PASDF to support further research.", "date": "2025-05-30", "categories": []}
{"id": "2512.14615", "title": "Hierarchical Persistence Velocity for Network Anomaly Detection: Theory and Applications to Cryptocurrency Markets", "abstract": "We introduce the Overlap-Weighted Hierarchical Normalized Persistence Velocity (OW-HNPV), a novel topological data analysis method for detecting anomalies in time-varying networks. Unlike existing methods that measure cumulative topological presence, we introduce the first velocity-based perspective on persistence diagrams, measuring the rate at which features appear and disappear, automatically downweighting noise through overlap-based weighting. We also prove that OW-HNPV is mathematically stable. It behaves in a controlled, predictable way, even when comparing persistence diagrams from networks with different feature types. Applied to Ethereum transaction networks (May 2017-May 2018), OW-HNPV demonstrates superior performance for cryptocurrency anomaly detection, achieving up to 10.4% AUC gain over baseline models for 7-day price movement predictions. Compared with established methods, including Vector of Averaged Bettis (VAB), persistence landscapes, and persistence images, velocity-based summaries excel at medium- to long-range forecasting (4-7 days), with OW-HNPV providing the most consistent and stable performance across prediction horizons. Our results show that modeling topological velocity is crucial for detecting structural anomalies in dynamic networks.", "date": "2025-12-16", "categories": []}
{"id": "2512.14708", "title": "SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis", "abstract": "Current deep learning approaches for physiological signal monitoring suffer from static topologies and constant energy consumption. We introduce SGEMAS (Self-Growing Ephemeral Multi-Agent System), a bio-inspired architecture that treats intelligence as a dynamic thermodynamic process. By coupling a structural plasticity mechanism (agent birth death) to a variational free energy objective, the system naturally evolves to minimize prediction error with extreme sparsity. An ablation study on the MIT-BIH Arrhythmia Database reveals that adding a multi-scale instability index to the agent dynamics significantly improves performance. In a challenging inter-patient, zero-shot setting, the final SGEMAS v3.3 model achieves a mean AUC of 0.570 +- 0.070, outperforming both its simpler variants and a standard autoencoder baseline. This result validates that a physics-based, energy-constrained model can achieve robust unsupervised anomaly detection, offering a promising direction for efficient biomedical AI.", "date": "2025-12-08", "categories": []}
{"id": "2512.15319", "title": "Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection", "abstract": "Few-shot anomaly detection (FSAD) denotes the identification of anomalies within a target category with a limited number of normal samples. Existing FSAD methods largely rely on pre-trained feature representations to detect anomalies, but the inherent domain gap between pre-trained representations and target FSAD scenarios is often overlooked. This study proposes a Prototypical Learning Guided Context-Aware Segmentation Network (PCSNet) to address the domain gap, thereby improving feature descriptiveness in target scenarios and enhancing FSAD performance. In particular, PCSNet comprises a prototypical feature adaption (PFA) sub-network and a context-aware segmentation (CAS) sub-network. PFA extracts prototypical features as guidance to ensure better feature compactness for normal data while distinct separation from anomalies. A pixel-level disparity classification loss is also designed to make subtle anomalies more distinguishable. Then a CAS sub-network is introduced for pixel-level anomaly localization, where pseudo anomalies are exploited to facilitate the training process. Experimental results on MVTec and MPDD demonstrate the superior FSAD performance of PCSNet, with 94.9% and 80.2% image-level AUROC in an 8-shot scenario, respectively. Real-world applications on automotive plastic part inspection further demonstrate that PCSNet can achieve promising results with limited training samples. Code is available at https://github.com/yuxin-jiang/PCSNet.", "date": "2025-12-17", "categories": []}
{"id": "2512.15323", "title": "MECAD: A multi-expert architecture for continual anomaly detection", "abstract": "In this paper we propose MECAD, a novel approach for continual anomaly detection using a multi-expert architecture. Our system dynamically assigns experts to object classes based on feature similarity and employs efficient memory management to preserve the knowledge of previously seen classes. By leveraging an optimized coreset selection and a specialized replay buffer mechanism, we enable incremental learning without requiring full model retraining. Our experimental evaluation on the MVTec AD dataset demonstrates that the optimal 5-expert configuration achieves an average AUROC of 0.8259 across 15 diverse object categories while significantly reducing knowledge degradation compared to single-expert approaches. This framework balances computational efficiency, specialized knowledge retention, and adaptability, making it well-suited for industrial environments with evolving product types.", "date": "2025-12-17", "categories": []}
{"id": "2512.15326", "title": "A Masked Reverse Knowledge Distillation Method Incorporating Global and Local Information for Image Anomaly Detection", "abstract": "Knowledge distillation is an effective image anomaly detection and localization scheme. However, a major drawback of this scheme is its tendency to overly generalize, primarily due to the similarities between input and supervisory signals. In order to address this issue, this paper introduces a novel technique called masked reverse knowledge distillation (MRKD). By employing image-level masking (ILM) and feature-level masking (FLM), MRKD transforms the task of image reconstruction into image restoration. Specifically, ILM helps to capture global information by differentiating input signals from supervisory signals. On the other hand, FLM incorporates synthetic feature-level anomalies to ensure that the learned representations contain sufficient local information. With these two strategies, MRKD is endowed with stronger image context capture capacity and is less likely to be overgeneralized. Experiments on the widely-used MVTec anomaly detection dataset demonstrate that MRKD achieves impressive performance: image-level 98.9% AU-ROC, pixel-level 98.4% AU-ROC, and 95.3% AU-PRO. In addition, extensive ablation experiments have validated the superiority of MRKD in mitigating the overgeneralization problem.", "date": "2025-12-17", "categories": []}
{"id": "2512.15685", "title": "A Multivariate Statistical Framework for Detection, Classification and Pre-localization of Anomalies in Water Distribution Networks", "abstract": "This paper presents a unified framework, for the detection, classification, and preliminary localization of anomalies in water distribution networks using multivariate statistical analysis. The approach, termed SICAMS (Statistical Identification and Classification of Anomalies in Mahalanobis Space), processes heterogeneous pressure and flow sensor data through a whitening transformation to eliminate spatial correlations among measurements. Based on the transformed data, the Hotelling's $T^2$ statistic is constructed, enabling the formulation of anomaly detection as a statistical hypothesis test of network conformity to normal operating conditions. It is shown that Hotelling's $T^2$ statistic can serve as an integral indicator of the overall \"health\" of the system, exhibiting correlation with total leakage volume, and thereby enabling approximate estimation of water losses via a regression model. A heuristic algorithm is developed to analyze the $T^2$ time series and classify detected anomalies into abrupt leaks, incipient leaks, and sensor malfunctions. Furthermore, a coarse leak localization method is proposed, which ranks sensors according to their statistical contribution and employs Laplacian interpolation to approximate the affected region within the network. Application of the proposed framework to the BattLeDIM L-Town benchmark dataset demonstrates high sensitivity and reliability in leak detection, maintaining robust performance even under multiple leaks. These capabilities make the method applicable to real-world operational environments without the need for a calibrated hydraulic model.", "date": "2025-12-17", "categories": []}
{"id": "2505.13496", "title": "ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model", "abstract": "Modern software systems generate extensive heterogeneous log data with dynamic formats, fragmented event sequences, and varying temporal patterns, making anomaly detection both crucial and challenging. To address these complexities, we propose ADALog, an adaptive, unsupervised anomaly detection framework designed for practical applicability across diverse real-world environments. Unlike traditional methods reliant on log parsing, strict sequence dependencies, or labeled data, ADALog operates on individual unstructured logs, extracts intra-log contextual relationships, and performs adaptive thresholding on normal data. The proposed approach utilizes a transformer-based, pretrained bidirectional encoder with a masked language modeling task, fine-tuned on normal logs to capture domain-specific syntactic and semantic patterns essential for accurate anomaly detection. Anomalies are identified via token-level reconstruction probabilities, aggregated into log-level scores, with adaptive percentile-based thresholding calibrated only on normal data. This allows the model to dynamically adapt to evolving system behaviors while avoiding rigid, heuristic-based thresholds common in traditional systems. We evaluate ADALog on benchmark datasets BGL, Thunderbird, and Spirit, showing strong generalization and competitive performance compared to state-of-the-art supervised and unsupervised methods. Additional ablation studies examine the effects of masking, fine-tuning, and token positioning on model behavior and interpretability.", "date": "2025-05-15", "categories": []}
{"id": "2512.13735", "title": "DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series", "abstract": "Multivariate time series anomaly detection (MTSAD) aims to accurately identify and localize complex abnormal patterns in the large-scale industrial control systems. While existing approaches excel in recognizing the distinct patterns under the low-dimensional scenarios, they often fail to robustly capture long-range spatiotemporal dependencies when learning representations from the high-dimensional noisy time series. To address these limitations, we propose DARTs, a robust long short-term dual-path framework with window-aware spatiotemporal soft fusion mechanism, which can be primarily decomposed into three complementary components. Specifically, in the short-term path, we introduce a Multi-View Sparse Graph Learner and a Diffusion Multi-Relation Graph Unit that collaborate to adaptively capture hierarchical discriminative short-term spatiotemporal patterns in the high-noise time series. While in the long-term path, we design a Multi-Scale Spatiotemporal Graph Constructor to model salient long-term dynamics within the high-dimensional representation space. Finally, a window-aware spatiotemporal soft-fusion mechanism is introduced to filter the residual noise while seamlessly integrating anomalous patterns. Extensive qualitative and quantitative experimental results across mainstream datasets demonstrate the superiority and robustness of our proposed DARTs. A series of ablation studies are also conducted to explore the crucial design factors of our proposed components. Our code and model will be made publicly open soon.", "date": "2025-12-14", "categories": []}
{"id": "2512.14604", "title": "LLmFPCA-detect: LLM-powered Multivariate Functional PCA for Anomaly Detection in Sparse Longitudinal Texts", "abstract": "Sparse longitudinal (SL) textual data arises when individuals generate text repeatedly over time (e.g., customer reviews, occasional social media posts, electronic medical records across visits), but the frequency and timing of observations vary across individuals. These complex textual data sets have immense potential to inform future policy and targeted recommendations. However, because SL text data lack dedicated methods and are noisy, heterogeneous, and prone to anomalies, detecting and inferring key patterns is challenging. We introduce LLmFPCA-detect, a flexible framework that pairs LLM-based text embeddings with functional data analysis to detect clusters and infer anomalies in large SL text datasets. First, LLmFPCA-detect embeds each piece of text into an application-specific numeric space using LLM prompts. Sparse multivariate functional principal component analysis (mFPCA) conducted in the numeric space forms the workhorse to recover primary population characteristics, and produces subject-level scores which, together with baseline static covariates, facilitate data segmentation, unsupervised anomaly detection and inference, and enable other downstream tasks. In particular, we leverage LLMs to perform dynamic keyword profiling guided by the data segments and anomalies discovered by LLmFPCA-detect, and we show that cluster-specific functional PC scores from LLmFPCA-detect, used as features in existing pipelines, help boost prediction performance. We support the stability of LLmFPCA-detect with experiments and evaluate it on two different applications using public datasets, Amazon customer-review trajectories, and Wikipedia talk-page comment streams, demonstrating utility across domains and outperforming state-of-the-art baselines.", "date": "2025-12-16", "categories": []}
{"id": "2405.13199", "title": "Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model", "abstract": "The emergence of tau PET imaging over the last decade has enabled Alzheimer's disease (AD) researchers to examine tau pathology in vivo and more effectively characterize the disease trajectories of AD. Current tau PET analysis methods, however, typically perform inferences on large cortical ROIs and are limited in the detection of localized tau pathology that varies across subjects. In this work, we propose a novel bilateral-guided deterministic diffusion sampling method to perform anomaly detection from tau PET imaging data. By including individualized brain structure and cognitively normal (CN) template conditions, our model computes a voxel-level anomaly map based on the deterministically sampled pseudo-healthy reconstruction. We train our model on ADNI CN subjects (n=380) and evaluate anomaly localization performance on the left MCI/AD subjects (n=154) and the preclinical subjects of the A4 clinical trial (n=447). We further train a CNN classifier on the derived 3D anomaly maps from ADNI, including CN and MCI/AD, to classify subjects into two groups and test classification performance on A4. We demonstrate that our method outperforms baselines in anomaly localization. Additionally, we show that our method can successfully group preclinical subjects with significantly different cognitive functions, highlighting the potential of our approach for application in preclinical screening tests. The code will be publicly available.", "date": "2024-05-21", "categories": []}
{"id": "2510.20268", "title": "GMFVAD: Using Grained Multi-modal Feature to Improve Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) is a challenging task that detects anomalous frames in continuous surveillance videos. Most previous work utilizes the spatio-temporal correlation of visual features to distinguish whether there are abnormalities in video snippets. Recently, some works attempt to introduce multi-modal information, like text feature, to enhance the results of video anomaly detection. However, these works merely incorporate text features into video snippets in a coarse manner, overlooking the significant amount of redundant information that may exist within the video snippets. Therefore, we propose to leverage the diversity among multi-modal information to further refine the extracted features, reducing the redundancy in visual features, and we propose Grained Multi-modal Feature for Video Anomaly Detection (GMFVAD). Specifically, we generate more grained multi-modal feature based on the video snippet, which summarizes the main content, and text features based on the captions of original video will be introduced to further enhance the visual features of highlighted portions. Experiments show that the proposed GMFVAD achieves state-of-the-art performance on four mainly datasets. Ablation experiments also validate that the improvement of GMFVAD is due to the reduction of redundant information.", "date": "2025-10-23", "categories": []}
{"id": "2511.08987", "title": "WDT-MD: Wavelet Diffusion Transformers for Microaneurysm Detection in Fundus Images", "abstract": "Microaneurysms (MAs), the earliest pathognomonic signs of Diabetic Retinopathy (DR), present as sub-60 $\\mu m$ lesions in fundus images with highly variable photometric and morphological characteristics, rendering manual screening not only labor-intensive but inherently error-prone. While diffusion-based anomaly detection has emerged as a promising approach for automated MA screening, its clinical application is hindered by three fundamental limitations. First, these models often fall prey to \"identity mapping\", where they inadvertently replicate the input image. Second, they struggle to distinguish MAs from other anomalies, leading to high false positives. Third, their suboptimal reconstruction of normal features hampers overall performance. To address these challenges, we propose a Wavelet Diffusion Transformer framework for MA Detection (WDT-MD), which features three key innovations: a noise-encoded image conditioning mechanism to avoid \"identity mapping\" by perturbing image conditions during training; pseudo-normal pattern synthesis via inpainting to introduce pixel-level supervision, enabling discrimination between MAs and other anomalies; and a wavelet diffusion Transformer architecture that combines the global modeling capability of diffusion Transformers with multi-scale wavelet analysis to enhance reconstruction of normal retinal features. Comprehensive experiments on the IDRiD and e-ophtha MA datasets demonstrate that WDT-MD outperforms state-of-the-art methods in both pixel-level and image-level MA detection. This advancement holds significant promise for improving early DR screening.", "date": "2025-11-12", "categories": []}
{"id": "2511.13115", "title": "A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features", "abstract": "3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data. However, existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly. To address this problem, we propose a novel Rotationally Invariant Features (RIF) framework for 3D AD. Firstly, to remove the adverse effect of variations on point cloud data, we develop a Point Coordinate Mapping (PCM) technique, which maps each point into a rotationally invariant space to maintain consistency of representation. Then, to learn robust and discriminative features, we design a lightweight Convolutional Transform Feature Network (CTF-Net) to extract rotationally invariant features for the memory bank. To improve the ability of the feature extractor, we introduce the idea of transfer learning to pre-train the feature extractor with 3D data augmentation. Experimental results show that the proposed method achieves the advanced performance on the Anomaly-ShapeNet dataset, with an average P-AUROC improvement of 17.7\\%, and also gains the best performance on the Real3D-AD dataset, with an average P-AUROC improvement of 1.6\\%. The strong generalization ability of RIF has been verified by combining it with traditional feature extraction methods on anomaly detection tasks, demonstrating great potential for industrial applications.", "date": "2025-11-17", "categories": []}
{"id": "2512.11997", "title": "Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion", "abstract": "System logs are a critical resource for monitoring and managing distributed systems, providing insights into failures and anomalous behavior. Traditional log analysis techniques, including template-based and sequence-driven approaches, often lose important semantic information or struggle with ambiguous log patterns. To address this, we present EnrichLog, a training-free, entry-based anomaly detection framework that enriches raw log entries with both corpus-specific and sample-specific knowledge. EnrichLog incorporates contextual information, including historical examples and reasoning derived from the corpus, to enable more accurate and interpretable anomaly detection. The framework leverages retrieval-augmented generation to integrate relevant contextual knowledge without requiring retraining. We evaluate EnrichLog on four large-scale system log benchmark datasets and compare it against five baseline methods. Our results show that EnrichLog consistently improves anomaly detection performance, effectively handles ambiguous log entries, and maintains efficient inference. Furthermore, incorporating both corpus- and sample-specific knowledge enhances model confidence and detection accuracy, making EnrichLog well-suited for practical deployments.", "date": "2025-12-12", "categories": []}
{"id": "2512.13497", "title": "On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing", "abstract": "In modern manufacturing, Visual Anomaly Detection (VAD) is essential for automated inspection and consistent product quality. Yet, increasingly dynamic and flexible production environments introduce key challenges: First, frequent product changes in small-batch and on-demand manufacturing require rapid model updates. Second, legacy edge hardware lacks the resources to train and run large AI models. Finally, both anomalous and normal training data are often scarce, particularly for newly introduced product variations. We investigate on-device continual learning for unsupervised VAD with localization, extending the PatchCore to incorporate online learning for real-world industrial scenarios. The proposed method leverages a lightweight feature extractor and an incremental coreset update mechanism based on k-center selection, enabling rapid, memory-efficient adaptation from limited data while eliminating costly cloud retraining. Evaluations on an industrial use case are conducted using a testbed designed to emulate flexible production with frequent variant changes in a controlled environment. Our method achieves a 12% AUROC improvement over the baseline, an 80% reduction in memory usage, and faster training compared to batch retraining. These results confirm that our method delivers accurate, resource-efficient, and adaptive VAD suitable for dynamic and smart manufacturing.", "date": "2025-12-15", "categories": []}
{"id": "2512.13560", "title": "3D Human-Human Interaction Anomaly Detection", "abstract": "Human-centric anomaly detection (AD) has been primarily studied to specify anomalous behaviors in a single person. However, as humans by nature tend to act in a collaborative manner, behavioral anomalies can also arise from human-human interactions. Detecting such anomalies using existing single-person AD models is prone to low accuracy, as these approaches are typically not designed to capture the complex and asymmetric dynamics of interactions. In this paper, we introduce a novel task, Human-Human Interaction Anomaly Detection (H2IAD), which aims to identify anomalous interactive behaviors within collaborative 3D human actions. To address H2IAD, we then propose Interaction Anomaly Detection Network (IADNet), which is formalized with a Temporal Attention Sharing Module (TASM). Specifically, in designing TASM, we share the encoded motion embeddings across both people such that collaborative motion correlations can be effectively synchronized. Moreover, we notice that in addition to temporal dynamics, human interactions are also characterized by spatial configurations between two people. We thus introduce a Distance-Based Relational Encoding Module (DREM) to better reflect social cues in H2IAD. The normalizing flow is eventually employed for anomaly scoring. Extensive experiments on human-human motion benchmarks demonstrate that IADNet outperforms existing Human-centric AD baselines in H2IAD.", "date": "2025-12-15", "categories": []}
{"id": "2410.23834", "title": "Denoising Diffusion Models for Anomaly Localization in Medical Images", "abstract": "This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.", "date": "2024-10-31", "categories": []}
{"id": "2505.07119", "title": "Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression", "abstract": "Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challenges due to limited computational power and bandwidth of edge devices. This study investigates how to perform VAD effectively under such constraints by leveraging compact, efficient processing strategies. We evaluate several data compression techniques, examining the tradeoff between system latency and detection accuracy. Experiments on the MVTec AD benchmark demonstrate that significant compression can be achieved with minimal loss in anomaly detection performance compared to uncompressed data. Current results show up to 80% reduction in end-to-end inference time, including edge processing, transmission, and server computation.", "date": "2025-05-11", "categories": []}
{"id": "2511.07299", "title": "VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models", "abstract": "Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional methods that focus solely on detecting and localizing anomalies. However, existing approaches often neglect the deeper causal relationships and interactions between objects, which are critical for understanding anomalous behaviors. In this paper, we propose VADER, an LLM-driven framework for Video Anomaly unDErstanding, which integrates keyframe object Relation features with visual cues to enhance anomaly comprehension from video. Specifically, VADER first applies an Anomaly Scorer to assign per-frame anomaly scores, followed by a Context-AwarE Sampling (CAES) strategy to capture the causal context of each anomalous event. A Relation Feature Extractor and a COntrastive Relation Encoder (CORE) jointly model dynamic object interactions, producing compact relational representations for downstream reasoning. These visual and relational cues are integrated with LLMs to generate detailed, causally grounded descriptions and support robust anomaly-related question answering. Experiments on multiple real-world VAU benchmarks demonstrate that VADER achieves strong results across anomaly description, explanation, and causal reasoning tasks, advancing the frontier of explainable video anomaly analysis.", "date": "2025-11-10", "categories": []}
{"id": "2512.11052", "title": "An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees", "abstract": "We study outlier (a.k.a., anomaly) detection for single-pass non-stationary streaming data. In the well-studied offline or batch outlier detection problem, traditional methods such as kernel One-Class SVM (OCSVM) are both computationally heavy and prone to large false-negative (Type II) errors under non-stationarity. To remedy this, we introduce SONAR, an efficient SGD-based OCSVM solver with strongly convex regularization. We show novel theoretical guarantees on the Type I/II errors of SONAR, superior to those known for OCSVM, and further prove that SONAR ensures favorable lifelong learning guarantees under benign distribution shifts. In the more challenging problem of adversarial non-stationary data, we show that SONAR can be used within an ensemble method and equipped with changepoint detection to achieve adaptive guarantees, ensuring small Type I/II errors on each phase of data. We validate our theoretical findings on synthetic and real-world datasets.", "date": "2025-12-11", "categories": []}
{"id": "2512.11284", "title": "RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection", "abstract": "Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.", "date": "2025-12-12", "categories": []}
{"id": "2512.11401", "title": "Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection", "abstract": "Industrial anomaly detection is a challenging open-set task that aims to identify unknown anomalous patterns deviating from normal data distribution. To avoid the significant memory consumption and limited generalizability brought by building separate models per class, we focus on developing a unified framework for multi-class anomaly detection. However, under this challenging setting, conventional reconstruction-based networks often suffer from an identity mapping problem, where they directly replicate input features regardless of whether they are normal or anomalous, resulting in detection failures. To address this issue, this study proposes a novel framework termed Collaborative Reconstruction and Repair (CRR), which transforms the reconstruction to repairation. First, we optimize the decoder to reconstruct normal samples while repairing synthesized anomalies. Consequently, it generates distinct representations for anomalous regions and similar representations for normal areas compared to the encoder's output. Second, we implement feature-level random masking to ensure that the representations from decoder contain sufficient local information. Finally, to minimize detection errors arising from the discrepancies between feature representations from the encoder and decoder, we train a segmentation network supervised by synthetic anomaly masks, thereby enhancing localization performance. Extensive experiments on industrial datasets that CRR effectively mitigates the identity mapping issue and achieves state-of-the-art performance in multi-class industrial anomaly detection.", "date": "2025-12-12", "categories": []}
{"id": "2506.14933", "title": "Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection", "abstract": "The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.", "date": "2025-06-17", "categories": []}
{"id": "2511.02541", "title": "Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data", "abstract": "Shearography is a non-destructive testing method for detecting subsurface defects, offering high sensitivity and full-field inspection capabilities. However, its industrial adoption remains limited due to the need for expert interpretation. To reduce reliance on labeled data and manual evaluation, this study explores unsupervised learning methods for automated anomaly detection in shearographic images. Three architectures are evaluated: a fully connected autoencoder, a convolutional autoencoder, and a student-teacher feature matching model. All models are trained solely on defect-free data. A controlled dataset was developed using a custom specimen with reproducible defect patterns, enabling systematic acquisition of shearographic measurements under both ideal and realistic deformation conditions. Two training subsets were defined: one containing only undistorted, defect-free samples, and one additionally including globally deformed, yet defect-free, data. The latter simulates practical inspection conditions by incorporating deformation-induced fringe patterns that may obscure localized anomalies. The models are evaluated in terms of binary classification and, for the student-teacher model, spatial defect localization. Results show that the student-teacher approach achieves superior classification robustness and enables precise localization. Compared to the autoencoder-based models, it demonstrates improved separability of feature representations, as visualized through t-SNE embeddings. Additionally, a YOLOv8 model trained on labeled defect data serves as a reference to benchmark localization quality. This study underscores the potential of unsupervised deep learning for scalable, label-efficient shearographic inspection in industrial environments.", "date": "2025-11-04", "categories": []}
{"id": "2512.03346", "title": "Hierarchical Attention for Sparse Volumetric Anomaly Detection in Subclinical Keratoconus", "abstract": "The detection of weak, spatially distributed anomalies in volumetric medical imaging remains challenging due to the difficulty of integrating subtle signals across non-adjacent regions. This study presents a controlled comparison of sixteen architectures spanning convolutional, hybrid, and transformer families for subclinical keratoconus detection from three-dimensional anterior segment optical coherence tomography (AS-OCT). The results demonstrate that hierarchical architectures achieve 21-23% higher sensitivity and specificity, particularly in the difficult subclinical regime, outperforming both convolutional neural networks (CNNs) and global-attention Vision Transformer (ViT) baselines. Mechanistic analyses indicate that this advantage arises from spatial scale alignment: hierarchical windowing produces effective receptive fields matched to the intermediate extent of subclinical abnormalities, avoiding the excessive locality observed in convolutional models and the diffuse integration characteristic of pure global attention. Attention-distance measurements show that subclinical cases require longer spatial integration than healthy or overtly pathological volumes, with hierarchical models exhibiting lower variance and more anatomically coherent focus. Representational similarity further indicates that hierarchical attention learns a distinct feature space that balances local structure sensitivity with flexible long-range interactions. Auxiliary age and sex prediction tasks demonstrate moderately high cross-task consistency, supporting the generalizability of these inductive principles. The findings provide design guidance for volumetric anomaly detection and highlight hierarchical attention as a principled approach for early pathological change analysis in medical imaging.", "date": "2025-12-03", "categories": []}
{"id": "2503.01260", "title": "OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest", "abstract": "With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective evaluation for TAD performance. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual points are inadequate. Existing TAD evaluators typically employ point-based or event-based metrics to capture the temporal context. However, point-based evaluators tend to overestimate detectors that excel only in detecting long anomalies, while event-based evaluators are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR (Operator Interest-based Precision and Recall metrics), a novel TAD evaluator with area-based metrics. It models the process of operators receiving detector alarms and handling anomalies, utilizing area under the operator interest curve to evaluate TAD performance. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluators. Through experiments conducted on the special scenario dataset and five real-world datasets, we demonstrate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations.", "date": "2025-03-03", "categories": []}
{"id": "2503.12883", "title": "Early Detection of Forest Calamities in Homogeneous Stands -- Deep Learning Applied to Bark-Beetle Outbreaks", "abstract": "Climate change has increased the vulnerability of forests to insect-related damage, resulting in widespread forest loss in Central Europe and highlighting the need for effective, continuous monitoring systems. Remote sensing based forest health monitoring, oftentimes, relies on supervised machine learning algorithms that require labeled training data. Monitoring temporal patterns through time series analysis offers a potential alternative for earlier detection of disturbance but requires substantial storage resources. This study investigates the potential of a Deep Learning algorithm based on a Long Short Term Memory (LSTM) Autoencoder for the detection of anomalies in forest health (e.g. bark beetle outbreaks), utilizing Sentinel-2 time series data. This approach is an alternative to supervised machine learning methods, avoiding the necessity for labeled training data. Furthermore, it is more memory-efficient than other time series analysis approaches, as a robust model can be created using only a 26-week-long time series as input. In this study, we monitored pure stands of spruce in Thuringia, Germany, over a 7-year period from 2018 to the end of 2024. Our best model achieved a detection accuracy of 87% on test data and was able to detect 61% of all anomalies at a very early stage (more than a month before visible signs of forest degradation). Compared to another widely used time series break detection algorithm - BFAST (Breaks For Additive Season and Trend), our approach consistently detected higher percentage of anomalies at an earlier stage. These findings suggest that LSTM-based Autoencoders could provide a promising, resource-efficient approach to forest health monitoring, enabling more timely responses to emerging threats.", "date": "2025-03-17", "categories": []}
{"id": "2512.07863", "title": "SetAD: Semi-Supervised Anomaly Learning in Contextual Sets", "abstract": "Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such {point- or pair-centric} view not only overlooks the contextual nature of anomalies, which are defined by their deviation from a collective group, but also fails to exploit the rich supervisory signals that can be generated from the combinatorial composition of sets. Consequently, such models struggle to exploit the high-order interactions within the data, which are critical for learning discriminative representations. To address these limitations, we propose SetAD, a novel framework that reframes semi-supervised AD as a Set-level Anomaly Detection task. SetAD employs an attention-based set encoder trained via a graded learning objective, where the model learns to quantify the degree of anomalousness within an entire set. This approach directly models the complex group-level interactions that define anomalies. Furthermore, to enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism, which assesses a point's anomaly score by aggregating its normalized deviations from peer behavior across multiple, diverse contextual sets. Extensive experiments on 10 real-world datasets demonstrate that SetAD significantly outperforms state-of-the-art models. Notably, we show that our model's performance consistently improves with increasing set size, providing strong empirical support for the set-based formulation of anomaly detection.", "date": "2025-11-26", "categories": []}
{"id": "2512.07864", "title": "Pattern Recognition of Ozone-Depleting Substance Exports in Global Trade Data", "abstract": "New methods are needed to monitor environmental treaties, like the Montreal Protocol, by reviewing large, complex customs datasets. This paper introduces a framework using unsupervised machine learning to systematically detect suspicious trade patterns and highlight activities for review. Our methodology, applied to 100,000 trade records, combines several ML techniques. Unsupervised Clustering (K-Means) discovers natural trade archetypes based on shipment value and weight. Anomaly Detection (Isolation Forest and IQR) identifies rare \"mega-trades\" and shipments with commercially unusual price-per-kilogram values. This is supplemented by Heuristic Flagging to find tactics like vague shipment descriptions. These layers are combined into a priority score, which successfully identified 1,351 price outliers and 1,288 high-priority shipments for customs review. A key finding is that high-priority commodities show a different and more valuable value-to-weight ratio than general goods. This was validated using Explainable AI (SHAP), which confirmed vague descriptions and high value as the most significant risk predictors. The model's sensitivity was validated by its detection of a massive spike in \"mega-trades\" in early 2021, correlating directly with the real-world regulatory impact of the US AIM Act. This work presents a repeatable unsupervised learning pipeline to turn raw trade data into prioritized, usable intelligence for regulatory groups.", "date": "2025-11-26", "categories": []}
{"id": "2512.08277", "title": "FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection", "abstract": "Log-based anomaly detection (LAD) is critical for ensuring the reliability of large-scale distributed systems. However, most existing LAD approaches assume centralized training, which is often impractical due to privacy constraints and the decentralized nature of system logs. While federated learning (FL) offers a promising alternative, there is a lack of dedicated testbeds tailored to the needs of LAD in federated settings. To address this, we present FedLAD, a unified platform for training and evaluating LAD models under FL constraints. FedLAD supports plug-and-play integration of diverse LAD models, benchmark datasets, and aggregation strategies, while offering runtime support for validation logging (self-monitoring), parameter tuning (self-configuration), and adaptive strategy control (self-adaptation). By enabling reproducible and scalable experimentation, FedLAD bridges the gap between FL frameworks and LAD requirements, providing a solid foundation for future research. Project code is publicly available at: https://github.com/AA-cityu/FedLAD.", "date": "2025-12-09", "categories": []}
{"id": "2512.08885", "title": "Explainable Anomaly Detection for Industrial IoT Data Streams", "abstract": "Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under limited computational resources. While data stream mining (DSM) addresses this challenge, most methods assume fully supervised settings, yet in practice, ground-truth labels are often delayed or unavailable. This paper presents a collaborative DSM framework that integrates unsupervised anomaly detection with interactive, human-in-the-loop learning to support maintenance decisions. We employ an online Isolation Forest and enhance interpretability using incremental Partial Dependence Plots and a feature importance score, derived from deviations of Individual Conditional Expectation curves from a fading average, enabling users to dynamically reassess feature relevance and adjust anomaly thresholds. We describe the real-time implementation and provide initial results for fault detection in a Jacquard loom unit. Ongoing work targets continuous monitoring to predict and explain imminent bearing failures.", "date": "2025-12-09", "categories": []}
{"id": "2502.16412", "title": "A Survey on Industrial Anomalies Synthesis", "abstract": "This paper comprehensively reviews anomaly synthesis methodologies. Existing surveys focus on limited techniques, missing an overall field view and understanding method interconnections. In contrast, our study offers a unified review, covering about 40 representative methods across Hand-crafted, Distribution-hypothesis-based, Generative models (GM)-based, and Vision-language models (VLM)-based synthesis. We introduce the first industrial anomaly synthesis (IAS) taxonomy. Prior works lack formal classification or use simplistic taxonomies, hampering structured comparisons and trend identification. Our taxonomy provides a fine-grained framework reflecting methodological progress and practical implications, grounding future research. Furthermore, we explore cross-modality synthesis and large-scale VLM. Previous surveys overlooked multimodal data and VLM in anomaly synthesis, limiting insights into their advantages. Our survey analyzes their integration, benefits, challenges, and prospects, offering a roadmap to boost IAS with multimodal learning. More resources are available at https://github.com/M-3LAB/awesome-anomaly-synthesis.", "date": "2025-02-23", "categories": []}
{"id": "2505.16650", "title": "Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images", "abstract": "Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having different computational capacities. For this reason, in this work we propose an image-based representation of network traffic which allows to realize a compact summary of the current network conditions with 1-second time windows. The proposed representation highlights the presence of anomalies thus reducing the need for complex processing architectures. Finally, we present an unsupervised learning approach which effectively detects the presence of anomalies. The code and the dataset are available at https://github.com/michaelneri/image-based-network-traffic-anomaly-detection.", "date": "2025-05-22", "categories": []}
{"id": "2506.15448", "title": "Semi-supervised Graph Anomaly Detection via Robust Homophily Learning", "abstract": "Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current methods in this line posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of two novel modules, adaptive frequency response filters (AdaFreq) and graph normality alignment (GNA). AdaFreq learns a set of adaptive spectral filters that capture different frequency components of the labeled normal nodes with varying homophily in the channel-wise and cross-channel views of node attributes. GNA is introduced to enforce consistency between the channel-wise and cross-channel homophily representations to robustify the normality learned by the filters in the two views. Experiments on eight real-world GAD datasets show that RHO can effectively learn varying, often under-represented, homophily in the small normal node set and substantially outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/RHO.", "date": "2025-06-18", "categories": []}
{"id": "2512.06840", "title": "CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles", "abstract": "Video anomaly detection (VAD) has long been studied as a crucial problem in public security and crime prevention. In recent years, weakly-supervised VAD (WVAD) have attracted considerable attention due to their easy annotation process and promising research results. While existing WVAD methods tackle mainly on static datasets, the possibility that the domain of data can vary has been neglected. To adapt such domain-shift, the continual learning (CL) perspective is required because otherwise additional training only with new coming data could easily cause performance degradation for previous data, i.e., forgetting. Therefore, we propose a brand-new approach, called Continual Anomaly Detection with Ensembles (CADE) that is the first work combining CL and WVAD viewpoints. Specifically, CADE uses the Dual-Generator(DG) to address data imbalance and label uncertainty in WVAD. We also found that forgetting exacerbates the \"incompleteness'' where the model becomes biased towards certain anomaly modes, leading to missed detections of various anomalies. To address this, we propose to ensemble Multi-Discriminator (MD) that capture missed anomalies in past scenes due to forgetting, using multiple models. Extensive experiments show that CADE significantly outperforms existing VAD methods on the common multi-scene VAD datasets, such as ShanghaiTech and Charlotte Anomaly datasets.", "date": "2025-12-07", "categories": []}
{"id": "2512.06845", "title": "Pseudo Anomalies Are All You Need: Diffusion-Based Generation for Weakly-Supervised Video Anomaly Detection", "abstract": "Deploying video anomaly detection in practice is hampered by the scarcity and collection cost of real abnormal footage. We address this by training without any real abnormal videos while evaluating under the standard weakly supervised split, and we introduce PA-VAD, a generation-driven approach that learns a detector from synthesized pseudo-abnormal videos paired with real normal videos, using only a small set of real normal images to drive synthesis. For synthesis, we select class-relevant initial images with CLIP and refine textual prompts with a vision-language model to improve fidelity and scene consistency before invoking a video diffusion model. For training, we mitigate excessive spatiotemporal magnitude in synthesized anomalies by an domain-aligned regularized module that combines domain alignment and memory usage-aware updates. Extensive experiments show that our approach reaches 98.2% on ShanghaiTech and 82.5% on UCF-Crime, surpassing the strongest real-abnormal method on ShanghaiTech by +0.6% and outperforming the UVAD state-of-the-art on UCF-Crime by +1.9%. The results demonstrate that high-accuracy anomaly detection can be obtained without collecting real anomalies, providing a practical path toward scalable deployment.", "date": "2025-12-07", "categories": []}
{"id": "2503.04504", "title": "AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM", "abstract": "Video anomaly detection (VAD) is crucial for video analysis and surveillance in computer vision. However, existing VAD models rely on learned normal patterns, which makes them difficult to apply to diverse environments. Consequently, users should retrain models or develop separate AI models for new environments, which requires expertise in machine learning, high-performance hardware, and extensive data collection, limiting the practical usability of VAD. To address these challenges, this study proposes customizable video anomaly detection (C-VAD) technique and the AnyAnomaly model. C-VAD considers user-defined text as an abnormal event and detects frames containing a specified event in a video. We effectively implemented AnyAnomaly using a context-aware visual question answering without fine-tuning the large vision language model. To validate the effectiveness of the proposed model, we constructed C-VAD datasets and demonstrated the superiority of AnyAnomaly. Furthermore, our approach showed competitive results on VAD benchmarks, achieving state-of-the-art performance on UBnormal and UCF-Crime and surpassing other methods in generalization across all datasets. Our code is available online at github.com/SkiddieAhn/Paper-AnyAnomaly.", "date": "2025-03-06", "categories": []}
{"id": "2512.05531", "title": "IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection", "abstract": "Anomaly detection on data streams presents significant challenges, requiring methods to maintain high detection accuracy among evolving distributions while ensuring real-time efficiency. Here we introduce $\\mathcal{IDK}$-$\\mathcal{S}$, a novel $\\mathbf{I}$ncremental $\\mathbf{D}$istributional $\\mathbf{K}$ernel for $\\mathbf{S}$treaming anomaly detection that effectively addresses these challenges by creating a new dynamic representation in the kernel mean embedding framework. The superiority of $\\mathcal{IDK}$-$\\mathcal{S}$ is attributed to two key innovations. First, it inherits the strengths of the Isolation Distributional Kernel, an offline detector that has demonstrated significant performance advantages over foundational methods like Isolation Forest and Local Outlier Factor due to the use of a data-dependent kernel. Second, it adopts a lightweight incremental update mechanism that significantly reduces computational overhead compared to the naive baseline strategy of performing a full model retraining. This is achieved without compromising detection accuracy, a claim supported by its statistical equivalence to the full retrained model. Our extensive experiments on thirteen benchmarks demonstrate that $\\mathcal{IDK}$-$\\mathcal{S}$ achieves superior detection accuracy while operating substantially faster, in many cases by an order of magnitude, than existing state-of-the-art methods.", "date": "2025-12-05", "categories": []}
{"id": "2512.05540", "title": "SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection", "abstract": "The core problem in multi-view anomaly detection is to represent local neighborhoods of normal instances consistently across all views. Recent approaches consider a representation of local neighborhood in each view independently, and then capture the consistent neighbors across all views via a learning process. They suffer from two key issues. First, there is no guarantee that they can capture consistent neighbors well, especially when the same neighbors are in regions of varied densities in different views, resulting in inferior detection accuracy. Second, the learning process has a high computational cost of $\\mathcal{O}(N^2)$, rendering them inapplicable for large datasets. To address these issues, we propose a novel method termed \\textbf{S}pherical \\textbf{C}onsistent \\textbf{N}eighborhoods \\textbf{E}nsemble (SCoNE). It has two unique features: (a) the consistent neighborhoods are represented with multi-view instances directly, requiring no intermediate representations as used in existing approaches; and (b) the neighborhoods have data-dependent properties, which lead to large neighborhoods in sparse regions and small neighborhoods in dense regions. The data-dependent properties enable local neighborhoods in different views to be represented well as consistent neighborhoods, without learning. This leads to $\\mathcal{O}(N)$ time complexity. Empirical evaluations show that SCoNE has superior detection accuracy and runs orders-of-magnitude faster in large datasets than existing approaches.", "date": "2025-12-05", "categories": []}
{"id": "2410.16888", "title": "Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning", "abstract": "Time series anomaly prediction plays an essential role in many real-world scenarios, such as environmental prevention and prompt maintenance of cyber-physical systems. However, existing time series anomaly prediction methods mainly require supervised training with plenty of manually labeled data, which are difficult to obtain in practice. Besides, unseen anomalies can occur during inference, which could differ from the labeled training data and make these models fail to predict such new anomalies. In this paper, we study a novel problem of unsupervised time series anomaly prediction. We provide a theoretical analysis and propose Importance-based Generative Contrastive Learning (IGCL) to address the aforementioned problems. IGCL distinguishes between normal and anomaly precursors, which are generated by our anomaly precursor pattern generation module. To address the efficiency issues caused by the potential complex anomaly precursor combinations, we propose a memory bank with importance-based scores to adaptively store representative anomaly precursors and generate more complicated anomaly precursors. Extensive experiments on seven benchmark datasets show our method outperforms state-of-the-art baselines on unsupervised time series anomaly prediction problems.", "date": "2024-10-22", "categories": []}
{"id": "2503.08293", "title": "A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows", "abstract": "The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.", "date": "2025-03-11", "categories": []}
{"id": "2512.04635", "title": "Federated Learning for Anomaly Detection in Maritime Movement Data", "abstract": "This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed.", "date": "2025-12-04", "categories": []}
{"id": "2509.10419", "title": "Run-Time Monitoring of ERTMS/ETCS Control Flow by Process Mining", "abstract": "Ensuring the resilience of computer-based railways is increasingly crucial to account for uncertainties and changes due to the growing complexity and criticality of those systems. Although their software relies on strict verification and validation processes following well-established best-practices and certification standards, anomalies can still occur at run-time due to residual faults, system and environmental modifications that were unknown at design-time, or other emergent cyber-threat scenarios. This paper explores run-time control-flow anomaly detection using process mining to enhance the resilience of ERTMS/ETCS L2 (European Rail Traffic Management System / European Train Control System Level 2). Process mining allows learning the actual control flow of the system from its execution traces, thus enabling run-time monitoring through online conformance checking. In addition, anomaly localization is performed through unsupervised machine learning to link relevant deviations to critical system components. We test our approach on a reference ERTMS/ETCS L2 scenario, namely the RBC/RBC Handover, to show its capability to detect and localize anomalies with high accuracy, efficiency, and explainability.", "date": "2025-09-12", "categories": []}
{"id": "2512.03187", "title": "Neighborhood density estimation using space-partitioning based hashing schemes", "abstract": "This work introduces FiRE/FiRE.1, a novel sketching-based algorithm for anomaly detection to quickly identify rare cell sub-populations in large-scale single-cell RNA sequencing data. This method demonstrated superior performance against state-of-the-art techniques. Furthermore, the thesis proposes Enhash, a fast and resource-efficient ensemble learner that uses projection hashing to detect concept drift in streaming data, proving highly competitive in time and accuracy across various drift types.", "date": "2025-12-02", "categories": []}
{"id": "2512.03243", "title": "Novelty detection on path space", "abstract": "We frame novelty detection on path space as a hypothesis testing problem with signature-based test statistics. Using transportation-cost inequalities of Gasteratos and Jacquier (2023), we obtain tail bounds for false positive rates that extend beyond Gaussian measures to laws of RDE solutions with smooth bounded vector fields, yielding estimates of quantiles and p-values. Exploiting the shuffle product, we derive exact formulae for smooth surrogates of conditional value-at-risk (CVaR) in terms of expected signatures, leading to new one-class SVM algorithms optimising smooth CVaR objectives. We then establish lower bounds on type-$\\mathrm{II}$ error for alternatives with finite first moment, giving general power bounds when the reference measure and the alternative are absolutely continuous with respect to each other. Finally, we evaluate numerically the type-$\\mathrm{I}$ error and statistical power of signature-based test statistic, using synthetic anomalous diffusion data and real-world molecular biology data.", "date": "2025-12-02", "categories": []}
{"id": "2512.04016", "title": "TARA Test-by-Adaptive-Ranks for Quantum Anomaly Detection with Conformal Prediction Guarantees", "abstract": "Quantum key distribution (QKD) security fundamentally relies on the ability to distinguish genuine quantum correlations from classical eavesdropper simulations, yet existing certification methods lack rigorous statistical guarantees under finite-sample conditions and adversarial scenarios. We introduce TARA (Test by Adaptive Ranks), a novel framework combining conformal prediction with sequential martingale testing for quantum anomaly detection that provides distribution-free validity guarantees. TARA offers two complementary approaches. TARA k, based on Kolmogorov Smirnov calibration against local hidden variable (LHV) null distributions, achieving ROC AUC = 0.96 for quantum-classical discrimination. And TARA-m, employing betting martingales for streaming detection with anytime valid type I error control that enables real time monitoring of quantum channels. We establish theoretical guarantees proving that under (context conditional) exchangeability, conformal p-values remain uniformly distributed even for strongly contextual quantum data, confirming that quantum contextuality does not break conformal prediction validity a result with implications beyond quantum certification to any application of distribution-free methods to nonclassical data. Extensive validation on both IBM Torino (superconducting, CHSH = 2.725) and IonQ Forte Enterprise (trapped ion, CHSH = 2.716) quantum processors demonstrates cross-platform robustness, achieving 36% security margins above the classical CHSH bound of 2. Critically, our framework reveals a methodological concern affecting quantum certification more broadly: same-distribution calibration can inflate detection performance by up to 44 percentage points compared to proper cross-distribution calibration, suggesting that prior quantum certification studies using standard train test splits may have systematically overestimated adversarial robustness.", "date": "2025-12-03", "categories": []}
{"id": "2405.15727", "title": "Anomalous Change Point Detection Using Probabilistic Predictive Coding", "abstract": "Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods are often constrained to univariate data, face scalability challenges with large datasets due to computational demands, and experience reduced performance with high-dimensional or intricate data, as well as hidden anomalies. Furthermore, they often lack interpretability and adaptability to domain-specific knowledge, which limits their versatility across different fields. In this work, we propose a deep learning-based CPD/AD method called Probabilistic Predictive Coding (PPC) that jointly learns to encode sequential data to low-dimensional latent space representations and to predict the subsequent data representations as well as the corresponding prediction uncertainties. The model parameters are optimized with maximum likelihood estimation by comparing these predictions with the true encodings. At the time of application, the true and predicted encodings are used to determine the probability of conformance, an interpretable and meaningful anomaly score. Furthermore, our approach has linear time complexity, scalability issues are prevented, and the method can easily be adjusted to a wide range of data types and intricate applications. We demonstrate the effectiveness and adaptability of our proposed method across synthetic time series experiments, image data, and real-world magnetic resonance spectroscopic imaging data.", "date": "2024-05-24", "categories": []}
{"id": "2509.07451", "title": "Synthetic Data Generation with Lorenzetti for Time Series Anomaly Detection in High-Energy Physics Calorimeters", "abstract": "Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential, yet challenging due to scarce labels, unknown anomaly types, and complex correlations across dimensions. To address the scarcity and unreliability of labelled data, we use the Lorenzetti Simulator to generate synthetic events with injected calorimeter anomalies. We then assess the sensitivity of several time series anomaly detection methods, including transformer-based and other deep learning models. The approach employed here is generic and applicable to different detector designs and defects.", "date": "2025-09-09", "categories": []}
{"id": "2510.26643", "title": "MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection", "abstract": "Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic interest and the large number of methods proposed in the literature, recent benchmarks and evaluation studies demonstrated that no overall best anomaly detection methods exist when applied to very heterogeneous time series datasets. Therefore, the only scalable and viable solution to solve anomaly detection over very different time series collected from diverse domains is to propose a model selection method that will select, based on time series characteristics, the best anomaly detection methods to run. Existing AutoML solutions are, unfortunately, not directly applicable to time series anomaly detection, and no evaluation of time series-based approaches for model selection exists. Towards that direction, this paper studies the performance of time series classification methods used as model selection for anomaly detection. In total, we evaluate 234 model configurations derived from 16 base classifiers across more than 1980 time series, and we propose the first extensive experimental evaluation of time series classification as model selection for anomaly detection. Our results demonstrate that model selection methods outperform every single anomaly detection method while being in the same order of magnitude regarding execution time. This evaluation is the first step to demonstrate the accuracy and efficiency of time series classification algorithms for anomaly detection, and represents a strong baseline that can then be used to guide the model selection step in general AutoML pipelines. Preprint version of an article accepted at the VLDB Journal.", "date": "2025-10-30", "categories": []}
{"id": "2512.02520", "title": "On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection", "abstract": "Zero-shot anomaly classification and segmentation (AC/AS) aim to detect anomalous samples and regions without any training data, a capability increasingly crucial in industrial inspection and medical imaging. This dissertation aims to investigate the core challenges of zero-shot AC/AS and presents principled solutions rooted in theory and algorithmic design.\n  We first formalize the problem of consistent anomalies, a failure mode in which recurring similar anomalies systematically bias distance-based methods. By analyzing the statistical and geometric behavior of patch representations from pre-trained Vision Transformers, we identify two key phenomena - similarity scaling and neighbor-burnout - that describe how relationships among normal patches change with and without consistent anomalies in settings characterized by highly similar objects.\n  We then introduce CoDeGraph, a graph-based framework for filtering consistent anomalies built on the similarity scaling and neighbor-burnout phenomena. Through multi-stage graph construction, community detection, and structured refinement, CoDeGraph effectively suppresses the influence of consistent anomalies.\n  Next, we extend this framework to 3D medical imaging by proposing a training-free, computationally efficient volumetric tokenization strategy for MRI data. This enables a genuinely zero-shot 3D anomaly detection pipeline and shows that volumetric anomaly segmentation is achievable without any 3D training samples.\n  Finally, we bridge batch-based and text-based zero-shot methods by demonstrating that CoDeGraph-derived pseudo-masks can supervise prompt-driven vision-language models. Together, this dissertation provides theoretical understanding and practical solutions for the zero-shot AC/AS problem.", "date": "2025-12-02", "categories": []}
{"id": "2512.02705", "title": "FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detection", "abstract": "Graph-based Anomaly Detection models have gained widespread adoption in recent years, identifying suspicious nodes by aggregating neighborhood information. However, most existing studies overlook the pervasive issues of missing and adversarially obscured node attributes, which can undermine aggregation stability and prediction reliability. To mitigate this, we propose FGC-Comp, a lightweight, classifier-agnostic, and deployment-friendly attribute completion module-designed to enhance neighborhood aggregation under incomplete attributes. We partition each node's neighbors into three label-based groups, apply group-specific transforms to the labeled groups while a node-conditioned gate handles unknowns, fuse messages via residual connections, and train end-to-end with a binary classification objective to improve aggregation stability and prediction reliability under missing attributes. Experiments on two real-world fraud datasets validate the effectiveness of the approach with negligible computational overhead.", "date": "2025-12-02", "categories": []}
{"id": "2512.02726", "title": "AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping", "abstract": "Auditors rely on Journal Entry Tests (JETs) to detect anomalies in tax-related ledger records, but rule-based methods generate overwhelming false positives and struggle with subtle irregularities. We investigate whether large language models (LLMs) can serve as anomaly detectors in double-entry bookkeeping. Benchmarking SoTA LLMs such as LLaMA and Gemma on both synthetic and real-world anonymized ledgers, we compare them against JETs and machine learning baselines. Our results show that LLMs consistently outperform traditional rule-based JETs and classical ML baselines, while also providing natural-language explanations that enhance interpretability. These results highlight the potential of \\textbf{AI-augmented auditing}, where human auditors collaborate with foundation models to strengthen financial integrity.", "date": "2025-12-02", "categories": []}
{"id": "2508.10044", "title": "Large Language Models for Power System Security: A Novel Multi-Modal Approach for Anomaly Detection in Energy Management Systems", "abstract": "This paper elaborates on an extensive security framework specifically designed for energy management systems (EMSs), which effectively tackles the dynamic environment of cybersecurity vulnerabilities and/or system problems (SPs), accomplished through the incorporation of novel methodologies. A comprehensive multi-point attack/error model is initially proposed to systematically identify vulnerabilities throughout the entire EMS data processing pipeline, including post state estimation (SE) stealth attacks, EMS database manipulation, and human-machine interface (HMI) display corruption according to the real-time database (RTDB) storage. This framework acknowledges the interconnected nature of modern attack vectors, which utilize various phases of supervisory control and data acquisition (SCADA) data flow. Then, generative AI (GenAI)-based anomaly detection systems (ADSs) for EMSs are proposed for the first time in the power system domain to handle the scenarios. Further, a set-of-mark generative intelligence (SoM-GI) framework, which leverages multimodal analysis by integrating visual markers with rules considering the GenAI capabilities, is suggested to overcome inherent spatial reasoning limitations. The SoM-GI methodology employs systematic visual indicators to enable accurate interpretation of segmented HMI displays and detect visual anomalies that numerical methods fail to identify. Validation on the IEEE 14-Bus system shows the framework's effectiveness across scenarios, while visual analysis identifies inconsistencies. This integrated approach combines numerical analysis with visual pattern recognition and linguistic rules to protect against cyber threats and system errors.", "date": "2025-08-12", "categories": []}
{"id": "2509.22544", "title": "HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) is crucial for intelligent surveillance, but a significant challenge lies in identifying complex anomalies, which are events defined by intricate relationships and temporal dependencies among multiple entities rather than by isolated actions. While self-supervised learning (SSL) methods effectively model low-level spatiotemporal patterns, they often struggle to grasp the semantic meaning of these interactions. Conversely, large language models (LLMs) offer powerful contextual reasoning but are computationally expensive for frame-by-frame analysis and lack fine-grained spatial localization. We introduce HyCoVAD, Hybrid Complex Video Anomaly Detection, a hybrid SSL-LLM model that combines a multi-task SSL temporal analyzer with LLM validator. The SSL module is built upon an nnFormer backbone which is a transformer-based model for image segmentation. It is trained with multiple proxy tasks, learns from video frames to identify those suspected of anomaly. The selected frames are then forwarded to the LLM, which enriches the analysis with semantic context by applying structured, rule-based reasoning to validate the presence of anomalies. Experiments on the challenging ComplexVAD dataset show that HyCoVAD achieves a 72.5% frame-level AUC, outperforming existing baselines by 12.5% while reducing LLM computation. We release our interaction anomaly taxonomy, adaptive thresholding protocol, and code to facilitate future research in complex VAD scenarios.", "date": "2025-09-26", "categories": []}
{"id": "2511.11165", "title": "Explainable Deep Convolutional Multi-Type Anomaly Detection", "abstract": "Explainable anomaly detection methods often have the capability to identify and spatially localise anomalies within an image but lack the capability to differentiate the type of anomaly. Furthermore, they often require the costly training and maintenance of separate models for each object category. The lack of specificity is a significant research gap because identifying the type of anomaly (e.g., \"Crack\" vs. \"Scratch\") is crucial for accurate diagnosis that facilitates cost-saving operational decisions across diverse application domains. While some recent large-scale Vision-Language Models (VLMs) have begun to address this, they are computationally intensive and memory-heavy, restricting their use in real-time or embedded systems. We propose MultiTypeFCDD, a simple and lightweight convolutional framework designed as a practical alternative for explainable multi-type anomaly detection. MultiTypeFCDD uses only image-level labels to learn and produce multi-channel heatmaps, where each channel is trained to correspond to a specific anomaly type. The model functions as a single, unified framework capable of differentiating anomaly types across multiple object categories, eliminating the need to train and manage separate models for each object category. We evaluated our proposed method on the Real-IAD dataset and it delivers competitive results (96.4% I-AUROC) at just over 1% the size of state-of-the-art VLM models used for similar tasks. This makes it a highly practical and viable solution for real-world applications where computational resources are tightly constrained.", "date": "2025-11-14", "categories": []}
{"id": "2512.00229", "title": "TIE: A Training-Inversion-Exclusion Framework for Visually Interpretable and Uncertainty-Guided Out-of-Distribution Detection", "abstract": "Deep neural networks often struggle to recognize when an input lies outside their training experience, leading to unreliable and overconfident predictions. Building dependable machine learning systems therefore requires methods that can both estimate predictive \\textit{uncertainty} and detect \\textit{out-of-distribution (OOD)} samples in a unified manner. In this paper, we propose \\textbf{TIE: a Training--Inversion--Exclusion} framework for visually interpretable and uncertainty-guided anomaly detection that jointly addresses these challenges through iterative refinement. TIE extends a standard $n$-class classifier to an $(n+1)$-class model by introducing a garbage class initialized with Gaussian noise to represent outlier inputs. Within each epoch, TIE performs a closed-loop process of \\textit{training, inversion, and exclusion}, where highly uncertain inverted samples reconstructed from the just-trained classifier are excluded into the garbage class. Over successive iterations, the inverted samples transition from noisy artifacts into visually coherent class prototypes, providing transparent insight into how the model organizes its learned manifolds. During inference, TIE rejects OOD inputs by either directly mapping them to the garbage class or producing low-confidence, uncertain misclassifications within the in-distribution classes that are easily separable, all without relying on external OOD datasets. A comprehensive threshold-based evaluation using multiple OOD metrics and performance measures such as \\textit{AUROC}, \\textit{AUPR}, and \\textit{FPR@95\\%TPR} demonstrates that TIE offers a unified and interpretable framework for robust anomaly detection and calibrated uncertainty estimation (UE) achieving near-perfect OOD detection with \\textbf{\\(\\!\\approx\\!\\) 0 FPR@95\\%TPR} when trained on MNIST or FashionMNIST and tested against diverse unseen datasets.", "date": "2025-11-28", "categories": []}
{"id": "2512.00251", "title": "SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks", "abstract": "The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service (DoS) attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional Generative Adversarial Network framework enhanced with Sinkhorn Divergence, tailored for robust anomaly detection in IoT edge environments. The framework incorporates CTGAN-based synthetic data augmentation to address class imbalance and leverages Sinkhorn Divergence as a geometry-aware loss function to improve training stability and reduce mode collapse. The model is evaluated on exploitative attack subsets from the CICDDoS2019 dataset and compared against baseline deep learning and GAN-based approaches. Results show that SD-CGAN achieves superior detection accuracy, precision, recall, and F1-score while maintaining computational efficiency suitable for deployment in edge-enabled IoT environments.", "date": "2025-11-28", "categories": []}
{"id": "2512.00310", "title": "ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rays", "abstract": "Unsupervised anomaly detection aims to identify anomalies without pixel-level annotations. Synthetic anomaly-based methods exhibit a unique capacity to introduce controllable irregularities with known masks, enabling explicit supervision during training. However, existing methods often produce synthetic anomalies that are visually distinct from real pathological patterns and ignore anatomical structure. This paper presents a novel Anatomy-aware Realistic Texture-based Anomaly Synthesis framework (ART-ASyn) for chest X-rays that generates realistic and anatomically consistent lung opacity related anomalies using texture-based augmentation guided by our proposed Progressive Binary Thresholding Segmentation method (PBTSeg) for lung segmentation. The generated paired samples of synthetic anomalies and their corresponding precise pixel-level anomaly mask for each normal sample enable explicit segmentation supervision. In contrast to prior work limited to one-class classification, ART-ASyn is further evaluated for zero-shot anomaly segmentation, demonstrating generalizability on an unseen dataset without target-domain annotations. Code availability is available at https://github.com/angelacao-hub/ART-ASyn.", "date": "2025-11-29", "categories": []}
{"id": "2512.00844", "title": "FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity", "abstract": "Microservices have transformed software architecture through the creation of modular and independent services. However, they introduce operational complexities in service integration and system management that makes swift and accurate anomaly detection and localisation challenging. Despite the complex, dynamic, and interconnected nature of microservice architectures, prior works that investigate metrics for anomaly detection rarely include explicit information about time-varying interdependencies. And whilst prior works on fault localisation typically do incorporate information about dependencies between microservices, they scale poorly to real world large-scale deployments due to their reliance on computationally expensive causal inference. To address these challenges we propose FC-ADL, an end-to-end scalable approach for detecting and localising anomalous changes from microservice metrics based on the neuroscientific concept of functional connectivity. We show that by efficiently characterising time-varying changes in dependencies between microservice metrics we can both detect anomalies and provide root cause candidates without incurring the significant overheads of causal and multivariate approaches. We demonstrate that our approach can achieve top detection and localisation performance across a wide degree of different fault scenarios when compared to state-of-the-art approaches. Furthermore, we illustrate the scalability of our approach by applying it to Alibaba's extremely large real-world microservice deployment.", "date": "2025-11-30", "categories": []}
{"id": "2512.01427", "title": "Language-Guided Open-World Anomaly Segmentation", "abstract": "Open-world and anomaly segmentation methods seek to enable autonomous driving systems to detect and segment both known and unknown objects in real-world scenes. However, existing methods do not assign semantically meaningful labels to unknown regions, and distinguishing and learning representations for unknown classes remains difficult. While open-vocabulary segmentation methods show promise in generalizing to novel classes, they require a fixed inference vocabulary and thus cannot be directly applied to anomaly segmentation where unknown classes are unconstrained. We propose Clipomaly, the first CLIP-based open-world and anomaly segmentation method for autonomous driving. Our zero-shot approach requires no anomaly-specific training data and leverages CLIP's shared image-text embedding space to both segment unknown objects and assign human-interpretable names to them. Unlike open-vocabulary methods, our model dynamically extends its vocabulary at inference time without retraining, enabling robust detection and naming of anomalies beyond common class definitions such as those in Cityscapes. Clipomaly achieves state-of-the-art performance on established anomaly segmentation benchmarks while providing interpretability and flexibility essential for practical deployment.", "date": "2025-12-01", "categories": []}
{"id": "2512.01534", "title": "Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis", "abstract": "Deep unsupervised anomaly detection in brain magnetic resonance imaging offers a promising route to identify pathological deviations without requiring lesion-specific annotations. Yet, fragmented evaluations, heterogeneous datasets, and inconsistent metrics have hindered progress toward clinical translation. Here, we present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging. The training cohort comprised 2,976 T1 and 2,972 T2-weighted scans from healthy individuals across six scanners, with ages ranging from 6 to 89 years. Validation used 92 scans to tune hyperparameters and estimate unbiased thresholds. Testing encompassed 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts. Across all algorithms, the Dice-based segmentation performance varied between 0.03 and 0.65, indicating substantial variability. To assess robustness, we systematically evaluated the impact of different scanners, lesion types and sizes, as well as demographics (age, sex). Reconstruction-based methods, particularly diffusion-inspired approaches, achieved the strongest lesion segmentation performance, while feature-based methods showed greater robustness under distributional shifts. However, systematic biases, such as scanner-related effects, were observed for the majority of algorithms, including that small and low-contrast lesions were missed more often, and that false positives varied with age and sex. Increasing healthy training data yields only modest gains, underscoring that current unsupervised anomaly detection frameworks are limited algorithmically rather than by data availability. Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation, including image native pretraining, principled deviation measures, fairness-aware modeling, and robust domain adaptation.", "date": "2025-12-01", "categories": []}
{"id": "2512.01672", "title": "ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models", "abstract": "Anomaly detection (AD) is a fundamental task of critical importance across numerous domains. Current systems increasingly operate in rapidly evolving environments that generate diverse yet interconnected data modalities -- such as time series, system logs, and tabular records -- as exemplified by modern IT systems. Effective AD methods in such environments must therefore possess two critical capabilities: (1) the ability to handle heterogeneous data formats within a unified framework, allowing the model to process and detect multiple modalities in a consistent manner during anomalous events; (2) a strong generalization ability to quickly adapt to new scenarios without extensive retraining. However, most existing methods fall short of these requirements, as they typically focus on single modalities and lack the flexibility to generalize across domains. To address this gap, we introduce a novel paradigm: In-Context Anomaly Detection (ICAD), where anomalies are defined by their dissimilarity to a relevant reference set of normal samples. Under this paradigm, we propose ICAD-LLM, a unified AD framework leveraging Large Language Models' in-context learning abilities to process heterogeneous data within a single model. Extensive experiments demonstrate that ICAD-LLM achieves competitive performance with task-specific AD methods and exhibits strong generalization to previously unseen tasks, which substantially reduces deployment costs and enables rapid adaptation to new environments. To the best of our knowledge, ICAD-LLM is the first model capable of handling anomaly detection tasks across diverse domains and modalities.", "date": "2025-12-01", "categories": []}
{"id": "2502.12978", "title": "Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference", "abstract": "In real-world applications, anomaly detection (AD) often operates without access to anomalous data, necessitating semi-supervised methods that rely solely on normal data. Among these methods, deep k-nearest neighbor (deep kNN) AD stands out for its interpretability and flexibility, leveraging distance-based scoring in deep latent spaces.Despite its strong performance, deep kNN lacks a mechanism to quantify uncertainty-an essential feature for critical applications such as industrial inspection. To address this limitation, we propose a statistical framework that quantifies the significance of detected anomalies in the form of p-values, thereby enabling control over false positive rates at a user-specified significance level (e.g.,0.05). A central challenge lies in managing selection bias, which we tackle using Selective Inference-a principled method for conducting inference conditioned on data-driven selections. We evaluate our method on diverse datasets and demonstrate that it provides reliable AD well-suited for industrial use cases.", "date": "2025-02-18", "categories": []}
{"id": "2508.03143", "title": "SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance", "abstract": "Synthesizing realistic and spatially precise anomalies is essential for enhancing the robustness of industrial anomaly detection systems. While recent diffusion-based methods have demonstrated strong capabilities in modeling complex defect patterns, they often struggle with spatial controllability and fail to maintain fine-grained regional fidelity. To overcome these limitations, we propose SARD (Segmentation-Aware anomaly synthesis via Region-constrained Diffusion with discriminative mask Guidance), a novel diffusion-based framework specifically designed for anomaly generation. Our approach introduces a Region-Constrained Diffusion (RCD) process that preserves the background by freezing it and selectively updating only the foreground anomaly regions during the reverse denoising phase, thereby effectively reducing background artifacts. Additionally, we incorporate a Discriminative Mask Guidance (DMG) module into the discriminator, enabling joint evaluation of both global realism and local anomaly fidelity, guided by pixel-level masks. Extensive experiments on the MVTec-AD and BTAD datasets show that SARD surpasses existing methods in segmentation accuracy and visual quality, setting a new state-of-the-art for pixel-level anomaly synthesis.", "date": "2025-08-05", "categories": []}
{"id": "2511.21842", "title": "Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison", "abstract": "The rapid expansion of Internet of Things (IoT) deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly Detection Systems (ADS) in identifying emerging and zero-day threats, this study investigates the effectiveness of two unsupervised anomaly detection techniques, Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM), using the TON_IoT thermostat dataset. A comprehensive evaluation was performed based on standard metrics (accuracy, precision, recall, and F1-score) alongside critical resource utilization metrics such as inference time, model size, and peak RAM usage. Experimental results revealed that IF consistently outperformed OC-SVM, achieving higher detection accuracy, superior precision, and recall, along with a significantly better F1-score. Furthermore, Isolation Forest demonstrated a markedly superior computational footprint, making it more suitable for deployment on resource-constrained IoT edge devices. These findings underscore Isolation Forest's robustness in high-dimensional and imbalanced IoT environments and highlight its practical viability for real-time anomaly detection.", "date": "2025-11-26", "categories": []}
{"id": "2511.21932", "title": "Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection", "abstract": "Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.", "date": "2025-11-26", "categories": []}
{"id": "2511.22078", "title": "ARES: Anomaly Recognition Model For Edge Streams", "abstract": "Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time. Anomaly detection in this context has the objective of identifying unusual temporal connections within the graph structure. Detecting edge anomalies in real time is crucial for mitigating potential risks. Unlike traditional anomaly detection, this task is particularly challenging due to concept drifts, large data volumes, and the need for real-time response. To face these challenges, we introduce ARES, an unsupervised anomaly detection framework for edge streams. ARES combines Graph Neural Networks (GNNs) for feature extraction with Half-Space Trees (HST) for anomaly scoring. GNNs capture both spike and burst anomalous behaviors within streams by embedding node and edge properties in a latent space, while HST partitions this space to isolate anomalies efficiently. ARES operates in an unsupervised way without the need for prior data labeling. To further validate its detection capabilities, we additionally incorporate a simple yet effective supervised thresholding mechanism. This approach leverages statistical dispersion among anomaly scores to determine the optimal threshold using a minimal set of labeled data, ensuring adaptability across different domains. We validate ARES through extensive evaluations across several real-world cyber-attack scenarios, comparing its performance against existing methods while analyzing its space and time complexity.", "date": "2025-11-27", "categories": []}
{"id": "2511.22595", "title": "AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection", "abstract": "Zero-shot industrial anomaly detection (ZSAD) methods typically yield coarse anomaly maps as vision transformers (ViTs) extract patch-level features only. To solve this, recent solutions attempt to predict finer anomalies using features from ZSAD, but they still struggle to recover fine-grained anomalies without missed detections, mainly due to the gap between randomly synthesized training anomalies and real ones. We observe that anomaly score maps exactly provide complementary spatial cues that are largely absent from ZSAD's image features, a fact overlooked before.\n  Inspired by this, we propose an anomaly-aware refiner (AnoRefiner) that can be plugged into most ZSAD models and improve patch-level anomaly maps to the pixel level. First, we design an anomaly refinement decoder (ARD) that progressively enhances image features using anomaly score maps, reducing the reliance on synthetic anomaly data. Second, motivated by the mass production paradigm, we propose a progressive group-wise test-time training (PGT) strategy that trains ARD in each product group for the refinement process in the next group, while staying compatible with any ZSAD method.\n  Experiments on the MVTec AD and VisA datasets show that AnoRefiner boosts various ZSAD models by up to a 5.2\\% gain in pixel-AP metrics, which can also be directly observed in many visualizations. The code will be available at https://github.com/HUST-SLOW/AnoRefiner.", "date": "2025-11-27", "categories": []}
{"id": "2511.21378", "title": "Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data", "abstract": "Handling contaminated data poses a critical challenge in anomaly detection, as traditional models assume training on purely normal data. Conventional methods mitigate contamination by relying on fixed contamination ratios, but discrepancies between assumed and actual ratios can severely degrade performance, especially in noisy environments where normal and abnormal data distributions overlap. To address these limitations, we propose Adaptive and Aggressive Rejection (AAR), a novel method that dynamically excludes anomalies using a modified z-score and Gaussian mixture model-based thresholds. AAR effectively balances the trade-off between preserving normal data and excluding anomalies by integrating hard and soft rejection strategies. Extensive experiments on two image datasets and thirty tabular datasets demonstrate that AAR outperforms the state-of-the-art method by 0.041 AUROC. By providing a scalable and reliable solution, AAR enhances robustness against contaminated datasets, paving the way for broader real-world applications in domains such as security and healthcare.", "date": "2025-11-26", "categories": []}
{"id": "2505.21962", "title": "A2Seek: Towards Reasoning-Centric Benchmark for Aerial Anomaly Understanding", "abstract": "While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios. To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning. Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of \"Where\" anomalies occur and \"Why\" they happen in aerial frames. To this end, A2Seek-R1 first employs a graph-of-thought (GoT)-guided supervised fine-tuning approach to activate the model's latent reasoning capabilities on A2Seek. Then, we introduce Aerial Group Relative Policy Optimization (A-GRPO) to design rule-based reward functions tailored to aerial scenarios. Furthermore, we propose a novel \"seeking\" mechanism that simulates UAV flight behavior by directing the model's attention to informative regions. Extensive experiments demonstrate that A2Seek-R1 achieves up to a 22.04% improvement in AP for prediction accuracy and a 13.9% gain in mIoU for anomaly localization, exhibiting strong generalization across complex environments and out-of-distribution scenarios. Our dataset and code are released at https://2-mo.github.io/A2Seek/.", "date": "2025-05-28", "categories": []}
{"id": "2506.06836", "title": "Harnessing Vision-Language Models for Time Series Anomaly Detection", "abstract": "Time-series anomaly detection (TSAD) has played a vital role in a variety of fields, including healthcare, finance, and sensor-based condition monitoring. Prior methods, which mainly focus on training domain-specific models on numerical data, lack the visual-temporal understanding capacity that human experts have to identify contextual anomalies. To fill this gap, we explore a solution based on vision language models (VLMs). Recent studies have shown the ability of VLMs for visual understanding tasks, yet their direct application to time series has fallen short on both accuracy and efficiency. To harness the power of VLMs for TSAD, we propose a two-stage solution, with (1) ViT4TS, a vision-screening stage built on a relatively lightweight pre-trained vision encoder, which leverages 2D time series representations to accurately localize candidate anomalies; (2) VLM4TS, a VLM-based stage that integrates global temporal context and VLM's visual understanding capacity to refine the detection upon the candidates provided by ViT4TS. We show that without any time-series training, VLM4TS outperforms time-series pre-trained and from-scratch baselines in most cases, yielding a 24.6% improvement in F1-max score over the best baseline. Moreover, VLM4TS also consistently outperforms existing language model-based TSAD methods and is on average 36x more efficient in token usage.", "date": "2025-06-07", "categories": []}
{"id": "2508.00777", "title": "Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection", "abstract": "Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.", "date": "2025-08-01", "categories": []}
{"id": "2508.02860", "title": "Comparative Evaluation of Kolmogorov-Arnold Autoencoders and Orthogonal Autoencoders for Fault Detection with Varying Training Set Sizes", "abstract": "Kolmogorov-Arnold Networks (KANs) have recently emerged as a flexible and parameter-efficient alternative to conventional neural networks. Unlike standard architectures that use fixed node-based activations, KANs place learnable functions on edges, parameterized by different function families. While they have shown promise in supervised settings, their utility in unsupervised fault detection remains largely unexplored. This study presents a comparative evaluation of KAN-based autoencoders (KAN-AEs) for unsupervised fault detection in chemical processes. We investigate four KAN-AE variants, each based on a different KAN implementation (EfficientKAN, FastKAN, FourierKAN, and WavKAN), and benchmark them against an Orthogonal Autoencoder (OAE) on the Tennessee Eastman Process. Models are trained on normal operating data across 13 training set sizes and evaluated on 21 fault types, using Fault Detection Rate (FDR) as the performance metric. WavKAN-AE achieves the highest overall FDR ($\\geq$92\\%) using just 4,000 training samples and remains the top performer, even as other variants are trained on larger datasets. EfficientKAN-AE reaches $\\geq$90\\% FDR with only 500 samples, demonstrating robustness in low-data settings. FastKAN-AE becomes competitive at larger scales ($\\geq$50,000 samples), while FourierKAN-AE consistently underperforms. The OAE baseline improves gradually but requires substantially more data to match top KAN-AE performance. These results highlight the ability of KAN-AEs to combine data efficiency with strong fault detection performance. Their use of structured basis functions suggests potential for improved model transparency, making them promising candidates for deployment in data-constrained industrial settings.", "date": "2025-08-04", "categories": []}
{"id": "2511.20169", "title": "ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories", "abstract": "Anomaly detection (AD) aims to identify defects using normal-only training data. Existing anomaly detection benchmarks (e.g., MVTec-AD with 15 categories) cover only a narrow range of categories, limiting the evaluation of cross-context generalization and scalability. We introduce ADNet, a large-scale, multi-domain benchmark comprising 380 categories aggregated from 49 publicly available datasets across Electronics, Industry, Agrifood, Infrastructure, and Medical domains. The benchmark includes a total of 196,294 RGB images, consisting of 116,192 normal samples for training and 80,102 test images, of which 60,311 are anomalous. All images are standardized with MVTec-style pixel-level annotations and structured text descriptions spanning both spatial and visual attributes, enabling multimodal anomaly detection tasks. Extensive experiments reveal a clear scalability challenge: existing state-of-the-art methods achieve 90.6% I-AUROC in one-for-one settings but drop to 78.5% when scaling to all 380 categories in a multi-class setting. To address this, we propose Dinomaly-m, a context-guided Mixture-of-Experts extension of Dinomaly that expands decoder capacity without increasing inference cost. It achieves 83.2% I-AUROC and 93.1% P-AUROC, demonstrating superior performance over existing approaches. ADNet is designed as a standardized and extensible benchmark, supporting the community in expanding anomaly detection datasets across diverse domains and providing a scalable foundation for future anomaly detection foundation models. Dataset: https://grainnet.github.io/ADNet", "date": "2025-11-25", "categories": []}
{"id": "2511.20270", "title": "DRL-Guided Neural Batch Sampling for Semi-Supervised Pixel-Level Anomaly Detection", "abstract": "Anomaly detection in industrial visual inspection is challenging due to the scarcity of defective samples. Most existing methods rely on unsupervised reconstruction using only normal data, often resulting in overfitting and poor detection of subtle defects. We propose a semi-supervised deep reinforcement learning framework that integrates a neural batch sampler, an autoencoder, and a predictor. The RL-based sampler adaptively selects informative patches by balancing exploration and exploitation through a composite reward. The autoencoder generates loss profiles highlighting abnormal regions, while the predictor performs segmentation in the loss-profile space. This interaction enables the system to effectively learn both normal and defective patterns with limited labeled data. Experiments on the MVTec AD dataset demonstrate that our method achieves higher accuracy and better localization of subtle anomalies than recent state-of-the-art approaches while maintaining low complexity, yielding an average improvement of 0.15 in F1_max and 0.06 in AUC, with a maximum gain of 0.37 in F1_max in the best case.", "date": "2025-11-25", "categories": []}
{"id": "2511.20480", "title": "Ranking-Enhanced Anomaly Detection Using Active Learning-Assisted Attention Adversarial Dual AutoEncoders", "abstract": "Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised learning methods require extensive labeled data, which is often scarce in real-world cybersecurity environments. In this paper, we propose an innovative approach that leverages AutoEncoders for unsupervised anomaly detection, augmented by active learning to iteratively improve the detection of APT anomalies. By selectively querying an oracle for labels on uncertain or ambiguous samples, we minimize labeling costs while improving detection rates, enabling the model to improve its detection accuracy with minimal data while reducing the need for extensive manual labeling. We provide a detailed formulation of the proposed Attention Adversarial Dual AutoEncoder-based anomaly detection framework and show how the active learning loop iteratively enhances the model. The framework is evaluated on real-world imbalanced provenance trace databases produced by the DARPA Transparent Computing program, where APT-like attacks constitute as little as 0.004\\% of the data. The datasets span multiple operating systems, including Android, Linux, BSD, and Windows, and cover two attack scenarios. The results have shown significant improvements in detection rates during active learning and better performance compared to other existing approaches.", "date": "2025-11-25", "categories": []}
{"id": "2501.12104", "title": "Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection", "abstract": "Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addresses this challenge. However, most S-T frameworks rely solely on pre-trained teacher networks to guide student networks in learning multi-scale similar features, overlooking the potential of the student networks to enhance learning through multi-scale feature fusion. In this study, we propose a novel model named PFADSeg, which integrates a pre-trained teacher network, a denoising student network with multi-scale feature fusion, and a guided anomaly segmentation network into a unified framework. By adopting a unique teacher-encoder and student-decoder denoising mode, the model improves the student network's ability to learn from teacher network features. Furthermore, an adaptive feature fusion mechanism is introduced to train a self-supervised segmentation network that synthesizes anomaly masks autonomously, significantly increasing detection performance. Rigorous evaluations on the widely-used MVTec AD dataset demonstrate that PFADSeg exhibits excellent performance, achieving an image-level AUC of 98.9%, a pixel-level mean precision of 76.4%, and an instance-level mean precision of 78.7%.", "date": "2025-01-21", "categories": []}
{"id": "2506.23802", "title": "Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations", "abstract": "In this work we introduce a novel adaptive anomaly detection framework specifically designed for monitoring sequential random finite set (RFS) observations. Our approach effectively distinguishes between In-Control data (normal) and Out-Of-Control data (anomalies) by detecting deviations from the expected statistical behavior of the process. The primary contributions of this study include the development of an innovative RFS-based framework that not only learns the normal behavior of the data-generating process online but also dynamically adapts to behavioral shifts to accurately identify abnormal point patterns. To achieve this, we introduce a new class of RFS-based posterior distributions, named Power Discounting Posteriors (PD), which facilitate adaptation to systematic changes in data while enabling anomaly detection of point pattern data through a novel predictive posterior density function. The effectiveness of the proposed approach is demonstrated by extensive qualitative and quantitative simulation experiments.", "date": "2025-06-30", "categories": []}
{"id": "2509.03277", "title": "PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection", "abstract": "In this paper, we aim to transfer CLIP's robust 2D generalization capabilities to identify 3D anomalies across unseen objects of highly diverse class semantics. To this end, we propose a unified framework to comprehensively detect and segment 3D anomalies by leveraging both point- and pixel-level information. We first design PointAD, which leverages point-pixel correspondence to represent 3D anomalies through their associated rendering pixel representations. This approach is referred to as implicit 3D representation, as it focuses solely on rendering pixel anomalies but neglects the inherent spatial relationships within point clouds. Then, we propose PointAD+ to further broaden the interpretation of 3D anomalies by introducing explicit 3D representation, emphasizing spatial abnormality to uncover abnormal spatial relationships. Hence, we propose G-aggregation to involve geometry information to enable the aggregated point representations spatially aware. To simultaneously capture rendering and spatial abnormality, PointAD+ proposes hierarchical representation learning, incorporating implicit and explicit anomaly semantics into hierarchical text prompts: rendering prompts for the rendering layer and geometry prompts for the geometry layer. A cross-hierarchy contrastive alignment is further introduced to promote the interaction between the rendering and geometry layers, facilitating mutual anomaly learning. Finally, PointAD+ integrates anomaly semantics from both layers to capture the generalized anomaly semantics. During the test, PointAD+ can integrate RGB information in a plug-and-play manner and further improve its detection performance. Extensive experiments demonstrate the superiority of PointAD+ in ZS 3D anomaly detection across unseen objects with highly diverse class semantics, achieving a holistic understanding of abnormality.", "date": "2025-09-03", "categories": []}
{"id": "2511.15393", "title": "EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts", "abstract": "The brain age is a key indicator of brain health. While electroencephalography (EEG) is a practical tool for this task, existing models struggle with the common challenge of imperfect medical data, such as learning a ``normal'' baseline from weakly supervised, healthy-only cohorts. This is a critical anomaly detection task for identifying disease, but standard models are often black boxes lacking an interpretable structure. We propose EVA-Net, a novel framework that recasts brain age as an interpretable anomaly detection problem. EVA-Net uses an efficient, sparsified-attention Transformer to model long EEG sequences. To handle noise and variability in imperfect data, it employs a Variational Information Bottleneck to learn a robust, compressed representation. For interpretability, this representation is aligned to a continuous prototype network that explicitly learns the normative healthy aging manifold. Trained on 1297 healthy subjects, EVA-Net achieves state-of-the-art accuracy. We validated its anomaly detection capabilities on an unseen cohort of 27 MCI and AD patients. This pathological group showed significantly higher brain-age gaps and a novel Prototype Alignment Error, confirming their deviation from the healthy manifold. EVA-Net provides an interpretable framework for healthcare intelligence using imperfect medical data.", "date": "2025-11-19", "categories": []}
{"id": "2511.17584", "title": "LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning", "abstract": "Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed graphs (TAGs), in which node information is expressed in natural language, remain underexplored, largely due to the absence of standardized benchmark datasets. In this work, we introduce TAG-AD, a comprehensive benchmark for anomaly node detection on TAGs. TAG-AD leverages large language models (LLMs) to generate realistic anomalous node texts directly in the raw text space, producing anomalies that are semantically coherent yet contextually inconsistent and thus more reflective of real-world irregularities. In addition, TAG-AD incorporates multiple other anomaly types, enabling thorough and reproducible evaluation of graph anomaly detection (GAD) methods. With these datasets, we further benchmark existing unsupervised GNN-based GAD methods as well as zero-shot LLMs for GAD.\n  As part of our zero-shot detection setup, we propose a retrieval-augmented generation (RAG)-assisted, LLM-based zero-shot anomaly detection framework. The framework mitigates reliance on brittle, hand-crafted prompts by constructing a global anomaly knowledge base and distilling it into reusable analysis frameworks. Our experimental results reveal a clear division of strengths: LLMs are particularly effective at detecting contextual anomalies, whereas GNN-based methods remain superior for structural anomaly detection. Moreover, RAG-assisted prompting achieves performance comparable to human-designed prompts while eliminating manual prompt engineering, underscoring the practical value of our RAG-assisted zero-shot LLM anomaly detection framework.", "date": "2025-11-16", "categories": []}
{"id": "2511.18116", "title": "PromptMoE: Generalizable Zero-Shot Anomaly Detection via Visually-Guided Prompt Mixtures", "abstract": "Zero-Shot Anomaly Detection (ZSAD) aims to identify and localize anomalous regions in images of unseen object classes. While recent methods based on vision-language models like CLIP show promise, their performance is constrained by existing prompt engineering strategies. Current approaches, whether relying on single fixed, learnable, or dense dynamic prompts, suffer from a representational bottleneck and are prone to overfitting on auxiliary data, failing to generalize to the complexity and diversity of unseen anomalies. To overcome these limitations, we propose $\\mathtt{PromptMoE}$. Our core insight is that robust ZSAD requires a compositional approach to prompt learning. Instead of learning monolithic prompts, $\\mathtt{PromptMoE}$ learns a pool of expert prompts, which serve as a basis set of composable semantic primitives, and a visually-guided Mixture-of-Experts (MoE) mechanism to dynamically combine them for each instance. Our framework materializes this concept through a Visually-Guided Mixture of Prompt (VGMoP) that employs an image-gated sparse MoE to aggregate diverse normal and abnormal expert state prompts, generating semantically rich textual representations with strong generalization. Extensive experiments across 15 datasets in industrial and medical domains demonstrate the effectiveness and state-of-the-art performance of $\\mathtt{PromptMoE}$.", "date": "2025-11-22", "categories": []}
{"id": "2511.18627", "title": "Functional Localization Enforced Deep Anomaly Detection Using Fundus Images", "abstract": "Reliable detection of retinal diseases from fundus images is challenged by the variability in imaging quality, subtle early-stage manifestations, and domain shift across datasets. In this study, we systematically evaluated a Vision Transformer (ViT) classifier under multiple augmentation and enhancement strategies across several heterogeneous public datasets, as well as the AEyeDB dataset, a high-quality fundus dataset created in-house and made available for the research community. The ViT demonstrated consistently strong performance, with accuracies ranging from 0.789 to 0.843 across datasets and diseases. Diabetic retinopathy and age-related macular degeneration were detected reliably, whereas glaucoma remained the most frequently misclassified disease. Geometric and color augmentations provided the most stable improvements, while histogram equalization benefited datasets dominated by structural subtlety. Laplacian enhancement reduced performance across different settings.\n  On the Papila dataset, the ViT with geometric augmentation achieved an AUC of 0.91, outperforming previously reported convolutional ensemble baselines (AUC of 0.87), underscoring the advantages of transformer architectures and multi-dataset training. To complement the classifier, we developed a GANomaly-based anomaly detector, achieving an AUC of 0.76 while providing inherent reconstruction-based explainability and robust generalization to unseen data. Probabilistic calibration using GUESS enabled threshold-independent decision support for future clinical implementation.", "date": "2025-11-23", "categories": []}
{"id": "2511.18698", "title": "Multimodal Real-Time Anomaly Detection and Industrial Applications", "abstract": "This paper presents the design, implementation, and evolution of a comprehensive multimodal room-monitoring system that integrates synchronized video and audio processing for real-time activity recognition and anomaly detection. We describe two iterations of the system: an initial lightweight implementation using YOLOv8, ByteTrack, and the Audio Spectrogram Transformer (AST), and an advanced version that incorporates multi-model audio ensembles, hybrid object detection, bidirectional cross-modal attention, and multi-method anomaly detection. The evolution demonstrates significant improvements in accuracy, robustness, and industrial applicability. The advanced system combines three audio models (AST, Wav2Vec2, and HuBERT) for comprehensive audio understanding, dual object detectors (YOLO and DETR) for improved accuracy, and sophisticated fusion mechanisms for enhanced cross-modal learning. Experimental evaluation shows the system's effectiveness in general monitoring scenarios as well as specialized industrial safety applications, achieving real-time performance on standard hardware while maintaining high accuracy.", "date": "2025-11-24", "categories": []}
{"id": "2511.18766", "title": "Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment", "abstract": "Unsupervised visual anomaly detection from multi-view images presents a significant challenge: distinguishing genuine defects from benign appearance variations caused by viewpoint changes. Existing methods, often designed for single-view inputs, treat multiple views as a disconnected set of images, leading to inconsistent feature representations and a high false-positive rate. To address this, we introduce ViewSense-AD (VSAD), a novel framework that learns viewpoint-invariant representations by explicitly modeling geometric consistency across views. At its core is our Multi-View Alignment Module (MVAM), which leverages homography to project and align corresponding feature regions between neighboring views. We integrate MVAM into a View-Align Latent Diffusion Model (VALDM), enabling progressive and multi-stage alignment during the denoising process. This allows the model to build a coherent and holistic understanding of the object's surface from coarse to fine scales. Furthermore, a lightweight Fusion Refiner Module (FRM) enhances the global consistency of the aligned features, suppressing noise and improving discriminative power. Anomaly detection is performed by comparing multi-level features from the diffusion model against a learned memory bank of normal prototypes. Extensive experiments on the challenging RealIAD and MANTA datasets demonstrate that VSAD sets a new state-of-the-art, significantly outperforming existing methods in pixel, view, and sample-level visual anomaly proving its robustness to large viewpoint shifts and complex textures.", "date": "2025-11-24", "categories": []}
{"id": "2511.16920", "title": "DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution", "abstract": "Anomaly generation is often framed as few-shot fine-tuning with anomalous samples, which contradicts the scarcity that motivates generation and tends to overfit category priors. We tackle the setting where no real anomaly samples or training are available. We propose Delta-Denoising (DeltaDeno), a training-free zero-shot anomaly generation method that localizes and edits defects by contrasting two diffusion branches driven by a minimal prompt pair under a shared schedule. By accumulating per-step denoising deltas into an image-specific localization map, we obtain a mask to guide the latent inpainting during later diffusion steps and preserve the surrounding context while generating realistic local defects. To improve stability and control, DeltaDeno performs token-level prompt refinement that aligns shared content and strengthens anomaly tokens, and applies a spatial attention bias restricted to anomaly tokens in the predicted region. Experiments on public datasets show that DeltaDeno achieves great generation, realism and consistent gains in downstream detection performance. Code will be made publicly available.", "date": "2025-11-21", "categories": []}
{"id": "2511.16929", "title": "CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection", "abstract": "Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviours. While deep learning has emerged as the dominant approach, several key challenges remain unresolved. First, sub-trajectory anomaly detection, capable of pinpointing the precise segments where anomalies occur, remains underexplored compared to whole-trajectory analysis. Second, many existing methods depend on carefully tuned thresholds, limiting their adaptability in real-world applications. Moreover, the irregular sampling of trajectory data and the presence of noise in training sets further degrade model performance, making it difficult to learn reliable representations of normal routes. To address these challenges, we propose a contrastive reinforcement learning framework for online trajectory anomaly detection, CroTad. Our method is threshold-free and robust to noisy, irregularly sampled data. By incorporating contrastive learning, CroTad learns to extract diverse normal travel patterns for different itineraries and effectively distinguish anomalous behaviours at both sub-trajectory and point levels. The detection module leverages deep reinforcement learning to perform online, real-time anomaly scoring, enabling timely and fine-grained identification of abnormal segments. Extensive experiments on two real-world datasets demonstrate the effectiveness and robustness of our framework across various evaluation scenarios.", "date": "2025-11-21", "categories": []}
{"id": "2511.17094", "title": "Sparse Reasoning is Enough: Biological-Inspired Framework for Video Anomaly Detection with Large Pre-trained Models", "abstract": "Video anomaly detection (VAD) plays a vital role in real-world applications such as security surveillance, autonomous driving, and industrial monitoring. Recent advances in large pre-trained models have opened new opportunities for training-free VAD by leveraging rich prior knowledge and general reasoning capabilities. However, existing studies typically rely on dense frame-level inference, incurring high computational costs and latency. This raises a fundamental question: Is dense reasoning truly necessary when using powerful pre-trained models in VAD systems? To answer this, we propose ReCoVAD, a novel framework inspired by the dual reflex and conscious pathways of the human nervous system, enabling selective frame processing to reduce redundant computation. ReCoVAD consists of two core pathways: (i) a Reflex pathway that uses a lightweight CLIP-based module to fuse visual features with prototype prompts and produce decision vectors, which query a dynamic memory of past frames and anomaly scores for fast response; and (ii) a Conscious pathway that employs a medium-scale vision-language model to generate textual event descriptions and refined anomaly scores for novel frames. It continuously updates the memory and prototype prompts, while an integrated large language model periodically reviews accumulated descriptions to identify unseen anomalies, correct errors, and refine prototypes. Extensive experiments show that ReCoVAD achieves state-of-the-art training-free performance while processing only 28.55\\% and 16.04\\% of the frames used by previous methods on the UCF-Crime and XD-Violence datasets, demonstrating that sparse reasoning is sufficient for effective large-model-based VAD.", "date": "2025-11-21", "categories": []}
{"id": "2508.15633", "title": "GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)", "abstract": "Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recently, studies have shown that anomalies on graphs induce spectral shifts. Some supervised methods have improved the utilization of such spectral domain information. However, they remain limited by the scarcity of labeled data due to the nature of anomalies. On the other hand, existing unsupervised learning approaches predominantly rely on spatial information or only employ low-pass filters, thereby losing the capacity for multi-band analysis. In this paper, we propose Graph Autoencoder with Spectral Encoder and Spectral Decoder (GRASPED) for node anomaly detection. Our unsupervised learning model features an encoder based on Graph Wavelet Convolution, along with structural and attribute decoders. The Graph Wavelet Convolution-based encoder, combined with a Wiener Graph Deconvolution-based decoder, exhibits bandpass filter characteristics that capture global and local graph information at multiple scales. This design allows for a learning-based reconstruction of node attributes, effectively capturing anomaly information. Extensive experiments on several real-world graph anomaly detection datasets demonstrate that GRASPED outperforms current state-of-the-art models.", "date": "2025-08-21", "categories": []}
{"id": "2511.15870", "title": "AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture", "abstract": "Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.", "date": "2025-11-19", "categories": []}
{"id": "2511.16445", "title": "PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring", "abstract": "People living with dementia (PLwD) often show gradual shifts in how they communicate, becoming less expressive, more repetitive, or drifting off-topic in subtle ways. While caregivers may notice these changes informally, most computational tools are not designed to track such behavioral drift over time. This paper introduces PersonaDrift, a synthetic benchmark designed to evaluate machine learning and statistical methods for detecting progressive changes in daily communication, focusing on user responses to a digital reminder system. PersonaDrift simulates 60-day interaction logs for synthetic users modeled after real PLwD, based on interviews with caregivers. These caregiver-informed personas vary in tone, modality, and communication habits, enabling realistic diversity in behavior. The benchmark focuses on two forms of longitudinal change that caregivers highlighted as particularly salient: flattened sentiment (reduced emotional tone and verbosity) and off-topic replies (semantic drift). These changes are injected progressively at different rates to emulate naturalistic cognitive trajectories, and the framework is designed to be extensible to additional behaviors in future use cases. To explore this novel application space, we evaluate several anomaly detection approaches, unsupervised statistical methods (CUSUM, EWMA, One-Class SVM), sequence models using contextual embeddings (GRU + BERT), and supervised classifiers in both generalized and personalized settings. Preliminary results show that flattened sentiment can often be detected with simple statistical models in users with low baseline variability, while detecting semantic drift requires temporal modeling and personalized baselines. Across both tasks, personalized classifiers consistently outperform generalized ones, highlighting the importance of individual behavioral context.", "date": "2025-11-20", "categories": []}
{"id": "2408.07490", "title": "Not All Regions Are Equal: Attention-Guided Perturbation Network for Industrial Anomaly Detection", "abstract": "In unsupervised image anomaly detection, reconstruction methods aim to train models to capture normal patterns comprehensively for normal data reconstruction. Yet, these models sometimes retain unintended reconstruction capacity for anomalous regions during inference, leading to missed detections. To mitigate this issue, existing works perturb normal samples in a sample-agnostic manner, uniformly adding noise across spatial locations before reconstructing the original. Despite promising results, they disregard the fact that foreground locations are inherently more critical for robust reconstruction. Motivated by this, we present a novel reconstruction framework named Attention-Guided Perturbation Network (AGPNet) for industrial anomaly detection. Its core idea is to add perturbations guided by a sample-aware attention mask to improve the learning of invariant normal patterns at important locations. AGPNet consists of two branches, \\ie, a reconstruction branch and an auxiliary attention-based perturbation one. The reconstruction branch learns to reconstruct normal samples, while the auxiliary one aims to produce attention masks to guide the noise perturbation process for normal samples. By perturbing more aggressively at those important regions, we encourage the reconstruction branch to learn inherent normal patterns both comprehensively and robustly. Extensive experiments are conducted on several popular benchmarks covering MVTec-AD, VisA, and MVTec-3D, and show that AGPNet consistently obtains leading anomaly detection performance across a variety of setups, including few-shot, one-class, and multi-class ones.", "date": "2024-08-14", "categories": []}
{"id": "2509.09030", "title": "Contextual Learning for Anomaly Detection in Tabular Data", "abstract": "Anomaly detection is critical in domains such as cybersecurity and finance, especially when working with large-scale tabular data. Yet, unsupervised anomaly detection-where no labeled anomalies are available-remains challenging because traditional deep learning methods model a single global distribution, assuming all samples follow the same behavior. In contrast, real-world data often contain heterogeneous contexts (e.g., different users, accounts, or devices), where globally rare events may be normal within specific conditions. We introduce a contextual learning framework that explicitly models how normal behavior varies across contexts by learning conditional data distributions $P(\\mathbf{Y} \\mid \\mathbf{C})$ rather than a global joint distribution $P(\\mathbf{X})$. The framework encompasses (1) a probabilistic formulation for context-conditioned learning, (2) a principled bilevel optimization strategy for automatically selecting informative context features using early validation loss, and (3) theoretical grounding through variance decomposition and discriminative learning principles. We instantiate this framework using a novel conditional Wasserstein autoencoder as a simple yet effective model for tabular anomaly detection. Extensive experiments across eight benchmark datasets demonstrate that contextual learning consistently outperforms global approaches-even when the optimal context is not intuitively obvious-establishing a new foundation for anomaly detection in heterogeneous tabular data.", "date": "2025-09-10", "categories": []}
{"id": "2511.14062", "title": "LogPurge: Log Data Purification for Anomaly Detection via Rule-Enhanced Filtering", "abstract": "Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detection methods rely on training deep learning models on clean, anomaly-free log sequences. However, obtaining such clean log data requires costly and tedious human labeling, and existing automatic cleaning methods fail to fully integrate the specific characteristics and actual semantics of logs in their purification process. In this paper, we propose a cost-aware, rule-enhanced purification framework, LogPurge, that automatically selects a sufficient subset of normal log sequences from contamination log sequences to train a anomaly detection model. Our approach involves a two-stage filtering algorithm: In the first stage, we use a large language model (LLM) to remove clustered anomalous patterns and enhance system rules to improve LLM's understanding of system logs; in the second stage, we utilize a divide-and-conquer strategy that decomposes the remaining contaminated regions into smaller subproblems, allowing each to be effectively purified through the first stage procedure. Our experiments, conducted on two public datasets and one industrial dataset, show that our method significantly removes an average of 98.74% of anomalies while retaining 82.39% of normal samples. Compared to the latest unsupervised log sample selection algorithms, our method achieves F-1 score improvements of 35.7% and 84.11% on the public datasets, and an impressive 149.72% F-1 improvement on the private dataset, demonstrating the effectiveness of our approach.", "date": "2025-11-18", "categories": []}
{"id": "2511.05966", "title": "Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory", "abstract": "Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small number of training samples. In this paper, we propose a novel few-shot unsupervised multimodal industrial anomaly detection method based on structural commonality, CIF (Commonality In Few). To extract intra-class structural information, we employ hypergraphs, which are capable of modeling higher-order correlations, to capture the structural commonality within training samples, and use a memory bank to store this intra-class structural prior. Firstly, we design a semantic-aware hypergraph construction module tailored for single-semantic industrial images, from which we extract common structures to guide the construction of the memory bank. Secondly, we use a training-free hypergraph message passing module to update the visual features of test samples, reducing the distribution gap between test features and features in the memory bank. We further propose a hyperedge-guided memory search module, which utilizes structural information to assist the memory search process and reduce the false positive rate. Experimental results on the MVTec 3D-AD dataset and the Eyecandies dataset show that our method outperforms the state-of-the-art (SOTA) methods in few-shot settings. Code is available at https://github.com/Sunny5250/CIF.", "date": "2025-11-08", "categories": []}
{"id": "2511.11650", "title": "Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine", "abstract": "Water is a critical resource that must be managed efficiently. However, a substantial amount of water is lost each year due to leaks in Water Distribution Networks (WDNs). This underscores the need for reliable and effective leak detection and localization systems. In recent years, various solutions have been proposed, with data-driven approaches gaining increasing attention due to their superior performance. In this paper, we propose a new method for leak detection. The method is based on water pressure measurements acquired at a series of nodes of a WDN. Our technique is a fully data-driven solution that makes only use of the knowledge of the WDN topology, and a series of pressure data acquisitions obtained in absence of leaks. The proposed solution is based on an feature extractor and a one-class Support Vector Machines (SVM) trained on no-leak data, so that leaks are detected as anomalies. The results achieved on a simulate dataset using the Modena WDN demonstrate that the proposed solution outperforms recent methods for leak detection.", "date": "2025-11-10", "categories": []}
{"id": "2511.12122", "title": "Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks", "abstract": "This study addresses the problem of dynamic anomaly detection in accounting transactions and proposes a real-time detection method based on a Transformer to tackle the challenges of hidden abnormal behaviors and high timeliness requirements in complex trading environments. The approach first models accounting transaction data by representing multi-dimensional records as time-series matrices and uses embedding layers and positional encoding to achieve low-dimensional mapping of inputs. A sequence modeling structure with multi-head self-attention is then constructed to capture global dependencies and aggregate features from multiple perspectives, thereby enhancing the ability to detect abnormal patterns. The network further integrates feed-forward layers and regularization strategies to achieve deep feature representation and accurate anomaly probability estimation. To validate the effectiveness of the method, extensive experiments were conducted on a public dataset, including comparative analysis, hyperparameter sensitivity tests, environmental sensitivity tests, and data sensitivity tests. Results show that the proposed method outperforms baseline models in AUC, F1-Score, Precision, and Recall, and maintains stable performance under different environmental conditions and data perturbations. These findings confirm the applicability and advantages of the Transformer-based framework for dynamic anomaly detection in accounting transactions and provide methodological support for intelligent financial risk control and auditing.", "date": "2025-11-15", "categories": []}
{"id": "2511.12147", "title": "Finding Time Series Anomalies using Granular-ball Vector Data Description", "abstract": "Modeling normal behavior in dynamic, nonlinear time series data is challenging for effective anomaly detection. Traditional methods, such as nearest neighbor and clustering approaches, often depend on rigid assumptions, such as a predefined number of reliable neighbors or clusters, which frequently break down in complex temporal scenarios. To address these limitations, we introduce the Granular-ball One-Class Network (GBOC), a novel approach based on a data-adaptive representation called Granular-ball Vector Data Description (GVDD). GVDD partitions the latent space into compact, high-density regions represented by granular-balls, which are generated through a density-guided hierarchical splitting process and refined by removing noisy structures. Each granular-ball serves as a prototype for local normal behavior, naturally positioning itself between individual instances and clusters while preserving the local topological structure of the sample set. During training, GBOC improves the compactness of representations by aligning samples with their nearest granular-ball centers. During inference, anomaly scores are computed based on the distance to the nearest granular-ball. By focusing on dense, high-quality regions and significantly reducing the number of prototypes, GBOC delivers both robustness and efficiency in anomaly detection. Extensive experiments validate the effectiveness and superiority of the proposed method, highlighting its ability to handle the challenges of time series anomaly detection.", "date": "2025-11-15", "categories": []}
{"id": "2511.12351", "title": "Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach", "abstract": "Detecting anomalies in multivariate time series is essential for monitoring complex industrial systems, where high dimensionality, limited labeled data, and subtle dependencies between sensors cause significant challenges. This paper presents a deep reinforcement learning framework that combines a Variational Autoencoder (VAE), an LSTM-based Deep Q-Network (DQN), dynamic reward shaping, and an active learning module to address these issues in a unified learning framework. The main contribution is the implementation of Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection (DRSMT), which demonstrates how each component enhances the detection process. The VAE captures compact latent representations and reduces noise. The DQN enables adaptive, sequential anomaly classification, and the dynamic reward shaping balances exploration and exploitation during training by adjusting the importance of reconstruction and classification signals. In addition, active learning identifies the most uncertain samples for labeling, reducing the need for extensive manual supervision. Experiments on two multivariate benchmarks, namely Server Machine Dataset (SMD) and Water Distribution Testbed (WADI), show that the proposed method outperforms existing baselines in F1-score and AU-PR. These results highlight the effectiveness of combining generative modeling, reinforcement learning, and selective supervision for accurate and scalable anomaly detection in real-world multivariate systems.", "date": "2025-11-15", "categories": []}
{"id": "2511.12388", "title": "CEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection", "abstract": "Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond the training distribution due to decision boundaries that lack a clear definition of normality. Existing approaches typically address this by regularising the representation space during training, leading to separate optimisation in latent and label spaces. The learned normality is therefore not directly utilised at inference, and their anomaly scores often fall within arbitrary ranges that require explicit mapping or calibration for probabilistic interpretation. To achieve unified learning of geometric normality and label discrimination, we propose Centre-Enhanced Discriminative Learning (CEDL), a novel supervised anomaly detection framework that embeds geometric normality directly into the discriminative objective. CEDL reparameterises the conventional sigmoid-derived prediction logit through a centre-based radial distance function, unifying geometric and discriminative learning in a single end-to-end formulation. This design enables interpretable, geometry-aware anomaly scoring without post-hoc thresholding or reference calibration. Extensive experiments on tabular, time-series, and image data demonstrate that CEDL achieves competitive and balanced performance across diverse real-world anomaly detection tasks, validating its effectiveness and broad applicability.", "date": "2025-11-15", "categories": []}
{"id": "2511.12648", "title": "Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks", "abstract": "The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes are unable to provide sub-10 ms (milliseconds) anomaly detection and distributed coordination of large-scale networks of vehicles within an acceptable safety/privacy framework. This paper introduces a three-tier hybrid security architecture HAVEN (Hierarchical Autonomous Vehicle Enhanced Network), which decouples real-time local threat detection and distributed coordination operations. It incorporates a light ensemble anomaly detection model on the edge (first layer), Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale (middle layer), and selected blockchain mechanisms (top layer) to ensure critical security coordination. Extensive experimentation is done on a real-world autonomous driving dataset. Large-scale simulations with the number of vehicles ranging between 100 and 1000 and different attack types, such as sensor spoofing, jamming, and adversarial model poisoning, are conducted to test the scalability and resiliency of HAVEN. Experimental findings show sub-10 ms detection latency with an accuracy of 94% and F1-score of 92% across multimodal sensor data, Byzantine fault tolerance validated with 20\\% compromised nodes, and a reduced blockchain storage overhead, guaranteeing sufficient differential privacy. The proposed framework overcomes the important trade-off between real-time safety obligation and distributed security coordination with novel three-tiered processing. The scalable architecture of HAVEN is shown to provide great improvement in detection accuracy as well as network resilience over other methods.", "date": "2025-11-16", "categories": []}
{"id": "2511.12909", "title": "CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection", "abstract": "Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their generalizability to other 3D understanding tasks. In contrast, self-supervised point cloud models aim for general-purpose representation learning, yet our investigation reveals that these classical models are suboptimal at anomaly detection under the unified fine-tuning paradigm. This motivates us to develop a more generalizable 3D model that can effectively detect anomalies without relying on task-specific designs. Interestingly, we find that using only the curvature of each point as its anomaly score already outperforms several classical self-supervised and dedicated anomaly detection models, highlighting the critical role of curvature in 3D anomaly detection. In this paper, we propose a Curvature-Augmented Self-supervised Learning (CASL) framework based on a reconstruction paradigm. Built upon the classical U-Net architecture, our approach introduces multi-scale curvature prompts to guide the decoder in predicting the spatial coordinates of each point. Without relying on any dedicated anomaly detection mechanisms, it achieves leading detection performance through straightforward anomaly classification fine-tuning. Moreover, the learned representations generalize well to standard 3D understanding tasks such as point cloud classification. The code is available at https://github.com/zyh16143998882/CASL.", "date": "2025-11-17", "categories": []}
{"id": "2511.12938", "title": "ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios", "abstract": "Existing industrial anomaly detection methods mainly determine whether an anomaly is present. However, real-world applications also require discovering and classifying multiple anomaly types. Since industrial anomalies are semantically subtle and current methods do not sufficiently exploit image priors, direct clustering approaches often perform poorly. To address these challenges, we propose ProtoAnomalyNCD, a prototype-learning-based framework for discovering unseen anomaly classes of multiple types that can be integrated with various anomaly detection methods. First, to suppress background clutter, we leverage Grounded SAM with text prompts to localize object regions as priors for the anomaly classification network. Next, because anomalies usually appear as subtle and fine-grained patterns on the product, we introduce an Anomaly-Map-Guided Attention block. Within this block, we design a Region Guidance Factor that helps the attention module distinguish among background, object regions, and anomalous regions. By using both localized product regions and anomaly maps as priors, the module enhances anomalous features while suppressing background noise and preserving normal features for contrastive learning. Finally, under a unified prototype-learning framework, ProtoAnomalyNCD discovers and clusters unseen anomaly classes while simultaneously enabling multi-type anomaly classification. We further extend our method to detect unseen outliers, achieving task-level unification. Our method outperforms state-of-the-art approaches on the MVTec AD, MTD, and Real-IAD datasets.", "date": "2025-11-17", "categories": []}
{"id": "2511.12951", "title": "A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series", "abstract": "Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are therefore crucial for preventing systemic instability and supporting informed investment decisions. Traditional deep learning models, such as LSTM and GRU, often fail to capture long-term dependencies and complex periodic patterns in highly nonstationary financial data. To address this limitation, this study proposes a FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series, which integrates the Frequency Enhanced Decomposed Transformer (FEDformer) with a residual-based anomaly detector and a risk forecasting head. The FEDformer module models temporal dynamics in both time and frequency domains, decomposing signals into trend and seasonal components for improved interpretability. The residual-based detector identifies abnormal fluctuations by analyzing prediction errors, while the risk head predicts potential financial distress using learned latent embeddings. Experiments conducted on the S&P 500, NASDAQ Composite, and Brent Crude Oil datasets (2000-2024) demonstrate the superiority of the proposed model over benchmark methods, achieving a 15.7 percent reduction in RMSE and an 11.5 percent improvement in F1-score for anomaly detection. These results confirm the effectiveness of the model in capturing financial volatility, enabling reliable early-warning systems for market crash prediction and risk management.", "date": "2025-11-17", "categories": []}
{"id": "2511.13276", "title": "Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models", "abstract": "We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.", "date": "2025-11-17", "categories": []}
{"id": "2509.05796", "title": "Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance", "abstract": "Automated visual inspection in medical-device manufacturing faces unique challenges, including extremely low defect rates, limited annotated data, hardware restrictions on production lines, and the need for validated, explainable artificial-intelligence systems. This paper presents two attention-guided autoencoder architectures that address these constraints through complementary anomaly-detection strategies. The first employs a multi-scale structural-similarity (4-MS-SSIM) index for inline inspection, enabling interpretable, real-time defect detection on constrained hardware. The second applies a Mahalanobis-distance analysis of randomly reduced latent features for efficient feature-space monitoring and lifecycle verification. Both approaches share a lightweight backbone optimised for high-resolution imagery for typical manufacturing conditions. Evaluations on the Surface Seal Image (SSI) dataset-representing sterile-barrier packaging inspection-demonstrate that the proposed methods outperform reference baselines, including MOCCA, CPCAE, and RAG-PaDiM, under realistic industrial constraints. Cross-domain validation on the MVTec-Zipper benchmark confirms comparable accuracy to state-of-the-art anomaly-detection methods. The dual-mode framework integrates inline anomaly detection and supervisory monitoring, advancing explainable AI architectures toward greater reliability, observability, and lifecycle monitoring in safety-critical manufacturing environments. To facilitate reproducibility, the source code developed for the experiments has been released in the project repository, while the datasets were obtained from publicly available sources.", "date": "2025-09-06", "categories": []}
{"id": "2511.09917", "title": "Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection", "abstract": "Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically presuppose complete node attributes and structure information, a condition hardly satisfied in real-world scenarios owing to privacy, collection errors or dynamic node arrivals. Existing standard imputation schemes risk \"repairing\" rare anomalous nodes so that they appear normal, thereby introducing imputation bias into the detection process. In addition, when both node attributes and edges are missing simultaneously, estimation errors in one view can contaminate the other, causing cross-view interference that further undermines the detection performance. To overcome these challenges, we propose M$^2$V-UGAD, a multiple missing values-resistant unsupervised GAD framework on incomplete graphs. Specifically, a dual-pathway encoder is first proposed to independently reconstruct missing node attributes and graph structure, thereby preventing errors in one view from propagating to the other. The two pathways are then fused and regularized in a joint latent space so that normals occupy a compact inner manifold while anomalies reside on an outer shell. Lastly, to mitigate imputation bias, we sample latent codes just outside the normal region and decode them into realistic node features and subgraphs, providing hard negative examples that sharpen the decision boundary. Experiments on seven public benchmarks demonstrate that M$^2$V-UGAD consistently outperforms existing unsupervised GAD methods across varying missing rates.", "date": "2025-11-13", "categories": []}
{"id": "2511.10020", "title": "Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation", "abstract": "We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting-based generation pipeline. A subsequent contrastive refinement strategy enforces precise alignment between synthesized anomalies and their masks, thereby bolstering downstream anomaly detection accuracy. To facilitate training, we introduce AnomVerse, a collection of 12,987 anomaly-mask-caption triplets assembled from 13 publicly available datasets, where captions are automatically generated by multimodal large language models using structured visual prompts and template-based textual hints. Extensive experiments demonstrate that Anomagic trained on AnomVerse can synthesize more realistic and varied anomalies than prior methods, yielding superior improvements in downstream anomaly detection. Furthermore, Anomagic can generate anomalies for any normal-category image using user-defined prompts, establishing a versatile foundation model for anomaly generation.", "date": "2025-11-13", "categories": []}
{"id": "2511.10296", "title": "Fault Detection in Solar Thermal Systems using Probabilistic Reconstructions", "abstract": "Solar thermal systems (STS) present a promising avenue for low-carbon heat generation, with a well-running system providing heat at minimal cost and carbon emissions. However, STS can exhibit faults due to improper installation, maintenance, or operation, often resulting in a substantial reduction in efficiency or even damage to the system. As monitoring at the individual level is economically prohibitive for small-scale systems, automated monitoring and fault detection should be used to address such issues. Recent advances in data-driven anomaly detection, particularly in time series analysis, offer a cost-effective solution by leveraging existing sensors to identify abnormal system states.\n  Here, we propose a probabilistic reconstruction-based framework for anomaly detection. We evaluate our method on the publicly available PaSTS dataset of operational domestic STS, which features real-world complexities and diverse fault types. Our experiments show that reconstruction-based methods can detect faults in domestic STS both qualitatively and quantitatively, while generalizing to previously unseen systems. We also demonstrate that our model outperforms both simple and more complex deep learning baselines. Additionally, we show that heteroscedastic uncertainty estimation is essential to fault detection performance. Finally, we discuss the engineering overhead required to unlock these improvements and make a case for simple deep learning models.", "date": "2025-11-13", "categories": []}
{"id": "2511.10334", "title": "Learning to Tell Apart: Weakly Supervised Video Anomaly Detection via Disentangled Semantic Alignment", "abstract": "Recent advancements in weakly-supervised video anomaly detection have achieved remarkable performance by applying the multiple instance learning paradigm based on multimodal foundation models such as CLIP to highlight anomalous instances and classify categories. However, their objectives may tend to detect the most salient response segments, while neglecting to mine diverse normal patterns separated from anomalies, and are prone to category confusion due to similar appearance, leading to unsatisfactory fine-grained classification results. Therefore, we propose a novel Disentangled Semantic Alignment Network (DSANet) to explicitly separate abnormal and normal features from coarse-grained and fine-grained aspects, enhancing the distinguishability. Specifically, at the coarse-grained level, we introduce a self-guided normality modeling branch that reconstructs input video features under the guidance of learned normal prototypes, encouraging the model to exploit normality cues inherent in the video, thereby improving the temporal separation of normal patterns and anomalous events. At the fine-grained level, we present a decoupled contrastive semantic alignment mechanism, which first temporally decomposes each video into event-centric and background-centric components using frame-level anomaly scores and then applies visual-language contrastive learning to enhance class-discriminative representations. Comprehensive experiments on two standard benchmarks, namely XD-Violence and UCF-Crime, demonstrate that DSANet outperforms existing state-of-the-art methods.", "date": "2025-11-13", "categories": []}
{"id": "2407.06849", "title": "TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data", "abstract": "As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method.", "date": "2024-07-09", "categories": []}
{"id": "2411.13951", "title": "PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series", "abstract": "Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too small, not diverse and feature trivial anomalies, which hinders measurable progress in this research area. We propose a solution: a diverse, extensive, and non-trivial dataset generated via state-of-the-art simulation tools that reflects realistic behaviour of an automotive powertrain, including its multivariate, dynamic and variable-state properties. Additionally, our dataset represents a discrete-sequence problem, which remains unaddressed by previously-proposed solutions in literature. To cater for both unsupervised and semi-supervised anomaly detection settings, as well as time series generation and forecasting, we make different versions of the dataset available, where training and test subsets are offered in contaminated and clean versions, depending on the task. We also provide baseline results from a selection of approaches based on deterministic and variational autoencoders, as well as a non-parametric approach. As expected, the baseline experimentation shows that the approaches trained on the semi-supervised version of the dataset outperform their unsupervised counterparts, highlighting a need for approaches more robust to contaminated training data. Furthermore, results show that the threshold used can have a large influence on detection performance, hence more work needs to be invested in methods to find a suitable threshold without the need for labelled data.", "date": "2024-11-21", "categories": []}
{"id": "2511.08634", "title": "CADIC: Continual Anomaly Detection Based on Incremental Coreset", "abstract": "The primary objective of Continual Anomaly Detection (CAD) is to learn the normal patterns of new tasks under dynamic data distribution assumptions while mitigating catastrophic forgetting. Existing embedding-based CAD approaches continuously update a memory bank with new embeddings to adapt to sequential tasks. However, these methods require constructing class-specific sub-memory banks for each task, which restricts their flexibility and scalability. To address this limitation, we propose a novel CAD framework where all tasks share a unified memory bank. During training, the method incrementally updates embeddings within a fixed-size coreset, enabling continuous knowledge acquisition from sequential tasks without task-specific memory fragmentation. In the inference phase, anomaly scores are computed via a nearest-neighbor matching mechanism, achieving state-of-the-art detection accuracy. We validate the method through comprehensive experiments on MVTec AD and Visa datasets. Results show that our approach outperforms existing baselines, achieving average image-level AUROC scores of 0.972 (MVTec AD) and 0.891 (Visa). Notably, on a real-world electronic paper dataset, it demonstrates 100% accuracy in anomaly sample detection, confirming its robustness in practical scenarios. The implementation will be open-sourced on GitHub.", "date": "2025-11-10", "categories": []}
{"id": "2501.05530", "title": "Outlyingness Scores with Cluster Catch Digraphs", "abstract": "This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ graph-, density-, and distribution-based techniques, tailored to high-dimensional data with varying cluster shapes and intensities. OOS evaluates the outlyingness of a point relative to its nearest neighbors, while IOS assesses the total ``influence\" a point receives from others within its cluster. Both OSs effectively identify global and local outliers, invariant to data collinearity. Moreover, IOS is robust to the masking problems. With extensive Monte Carlo simulations, we compare the performance of both OSs with CCD-based, traditional, and state-of-the-art outlier detection methods. Both OSs exhibit substantial overall improvements over the CCD-based methods in both artificial and real-world data sets, particularly with IOS, which delivers the best overall performance among all the methods, especially in high-dimensional settings.\n  Keywords: Outlier detection, Outlyingness score, Graph-based clustering, Cluster catch digraphs, High-dimensional data.", "date": "2025-01-09", "categories": []}
{"id": "2504.20733", "title": "Unsupervised Surrogate Anomaly Detection", "abstract": "In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating from. Inspired by a similar concept in engineering, we refer to our methodology as surrogate anomaly detection. We formalize the concept of surrogate anomaly detection into a set of axioms required for optimal surrogate models and propose a new algorithm, named DEAN (Deep Ensemble ANomaly detection), designed to fulfill these criteria. We evaluate DEAN on 121 benchmark datasets, demonstrating its competitive performance against 19 existing methods, as well as the scalability and reliability of our method.", "date": "2025-04-29", "categories": []}
{"id": "2509.24378", "title": "AXIS: Explainable Time Series Anomaly Detection with Large Language Models", "abstract": "Time-series anomaly detection (TSAD) increasingly demands explanations that articulate not only if an anomaly occurred, but also what pattern it exhibits and why it is anomalous. Leveraging the impressive explanatory capabilities of Large Language Models (LLMs), recent works have attempted to treat time series as text for explainable TSAD. However, this approach faces a fundamental challenge: LLMs operate on discrete tokens and struggle to directly process long, continuous signals. Consequently, naive time-to-text serialization suffers from a lack of contextual grounding and representation alignment between the two modalities. To address this gap, we introduce AXIS, a framework that conditions a frozen LLM for nuanced time-series understanding. Instead of direct serialization, AXIS enriches the LLM's input with three complementary hints derived from the series: (i) a symbolic numeric hint for numerical grounding, (ii) a context-integrated, step-aligned hint distilled from a pretrained time-series encoder to capture fine-grained dynamics, and (iii) a task-prior hint that encodes global anomaly characteristics. Furthermore, to facilitate robust evaluation of explainability, we introduce a new benchmark featuring multi-format questions and rationales that supervise contextual grounding and pattern-level semantics. Extensive experiments, including both LLM-based and human evaluations, demonstrate that AXIS yields explanations of significantly higher quality and achieves competitive detection accuracy compared to general-purpose LLMs, specialized time-series LLMs, and time-series Vision Language Models.", "date": "2025-09-29", "categories": []}
{"id": "2510.13630", "title": "AVAR-Net: A Lightweight Audio-Visual Anomaly Recognition Framework with a Benchmark Dataset", "abstract": "Anomaly recognition plays a vital role in surveillance, transportation, healthcare, and public safety. However, most existing approaches rely solely on visual data, making them unreliable under challenging conditions such as occlusion, low illumination, and adverse weather. Moreover, the absence of large-scale synchronized audio-visual datasets has hindered progress in multimodal anomaly recognition. To address these limitations, this study presents AVAR-Net, a lightweight and efficient audio-visual anomaly recognition framework designed for real-world environments. AVAR-Net consists of four main modules: an audio feature extractor, a video feature extractor, fusion strategy, and a sequential pattern learning network that models cross-modal relationships for anomaly recognition. Specifically, the Wav2Vec2 model extracts robust temporal features from raw audio, while MobileViT captures both local and global visual representations from video frames. An early fusion mechanism combines these modalities, and a Multi-Stage Temporal Convolutional Network (MTCN) model that learns long-range temporal dependencies within the fused representation, enabling robust spatiotemporal reasoning. A novel Visual-Audio Anomaly Recognition (VAAR) dataset, is also introduced, serving as a medium-scale benchmark containing 3,000 real-world videos with synchronized audio across ten diverse anomaly classes. Experimental evaluations demonstrate that AVAR-Net achieves 89.29% accuracy on VAAR and 88.56% Average Precision on the XD-Violence dataset, improving Average Precision by 2.8% over existing state-of-the-art methods. These results highlight the effectiveness, efficiency, and generalization capability of the proposed framework, as well as the utility of VAAR as a benchmark for advancing multimodal anomaly recognition research.", "date": "2025-10-15", "categories": []}
{"id": "2511.03912", "title": "I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging", "abstract": "Unknown anomaly detection in medical imaging remains a fundamental challenge due to the scarcity of labeled anomalies and the high cost of expert supervision. We introduce an unsupervised, oracle-free framework that incrementally expands a trusted set of normal samples without any anomaly labels. Starting from a small, verified seed of normal images, our method alternates between lightweight adapter updates and uncertainty-gated sample admission. A frozen pretrained vision backbone is augmented with tiny convolutional adapters, ensuring rapid domain adaptation with negligible computational overhead. Extracted embeddings are stored in a compact coreset enabling efficient k-nearest neighbor anomaly (k-NN) scoring. Safety during incremental expansion is enforced by dual probabilistic gates, a sample is admitted into the normal memory only if its distance to the existing coreset lies within a calibrated z-score threshold, and its SWAG-based epistemic uncertainty remains below a seed-calibrated bound. This mechanism prevents drift and false inclusions without relying on generative reconstruction or replay buffers. Empirically, our system steadily refines the notion of normality as unlabeled data arrive, producing substantial gains over baselines. On COVID-CXR, ROC-AUC improves from 0.9489 to 0.9982 (F1: 0.8048 to 0.9746); on Pneumonia CXR, ROC-AUC rises from 0.6834 to 0.8968; and on Brain MRI ND-5, ROC-AUC increases from 0.6041 to 0.7269 and PR-AUC from 0.7539 to 0.8211. These results highlight the effectiveness and efficiency of the proposed framework for real-world, label-scarce medical imaging applications.", "date": "2025-11-05", "categories": []}
{"id": "2511.07429", "title": "Knowledge-Guided Textual Reasoning for Explainable Video Anomaly Detection via LLMs", "abstract": "We introduce Text-based Explainable Video Anomaly Detection (TbVAD), a language-driven framework for weakly supervised video anomaly detection that performs anomaly detection and explanation entirely within the textual domain. Unlike conventional WSVAD models that rely on explicit visual features, TbVAD represents video semantics through language, enabling interpretable and knowledge-grounded reasoning. The framework operates in three stages: (1) transforming video content into fine-grained captions using a vision-language model, (2) constructing structured knowledge by organizing the captions into four semantic slots (action, object, context, environment), and (3) generating slot-wise explanations that reveal which semantic factors contribute most to the anomaly decision. We evaluate TbVAD on two public benchmarks, UCF-Crime and XD-Violence, demonstrating that textual knowledge reasoning provides interpretable and reliable anomaly detection for real-world surveillance scenarios.", "date": "2025-10-30", "categories": []}
{"id": "2511.07995", "title": "Multivariate Time series Anomaly Detection:A Framework of Hidden Markov Models", "abstract": "In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statistical methods, is engaged here to detect anomalies in multivariate time series. We construct HMM-based anomaly detectors and in this context compare several transformation methods. A suite of experimental studies along with some comparative analysis is reported.", "date": "2025-11-11", "categories": []}
{"id": "2511.08072", "title": "Clustering-based Anomaly Detection in Multivariate Time Series Data", "abstract": "Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in science and engineering because anomaly scores come from the simultaneous consideration of the temporal and variable relationships. In this paper, we propose a clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series. First, we use a sliding window to generate a set of multivariate subsequences and thereafter apply an extended fuzzy clustering to reveal a structure present within the generated multivariate subsequences. Finally, a reconstruction criterion is employed to reconstruct the multivariate subsequences with the optimal cluster centers and the partition matrix. We construct a confidence index to quantify a level of anomaly detected in the series and apply Particle Swarm Optimization as an optimization vehicle for the problem of anomaly detection. Experimental studies completed on several synthetic and six real-world datasets suggest that the proposed methods can detect the anomalies in multivariate time series. With the help of available clusters revealed by the extended fuzzy clustering, the proposed framework can detect anomalies in the multivariate time series and is suitable for identifying anomalous amplitude and shape patterns in various application domains such as health care, weather data analysis, finance, and disease outbreak detection.", "date": "2025-11-11", "categories": []}
{"id": "2511.08173", "title": "VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion", "abstract": "Detecting visual anomalies in diverse, multi-class real-world images is a significant challenge. We introduce \\ours, a novel unsupervised multi-class visual anomaly detection framework. It integrates a Latent Diffusion Model (LDM) with a Vision-Language Model (VLM) for enhanced anomaly localization and detection. Specifically, a pre-trained VLM with a simple prompt extracts detailed image descriptions, serving as additional conditioning for LDM training. Current diffusion-based methods rely on synthetic noise generation, limiting their generalization and requiring per-class model training, which hinders scalability. \\ours, however, leverages VLMs to obtain normal captions without manual annotations or additional training. These descriptions condition the diffusion model, learning a robust normal image feature representation for multi-class anomaly detection. Our method achieves competitive performance, improving the pixel-level Per-Region-Overlap (PRO) metric by up to 25 points on the Real-IAD dataset and 8 points on the COCO-AD dataset, outperforming state-of-the-art diffusion-based approaches. Code is available at https://github.com/giddyyupp/VLMDiff.", "date": "2025-11-11", "categories": []}
{"id": "2411.11293", "title": "AnomalyAID: Reliable Interpretation for Semi-supervised Network Anomaly Detection", "abstract": "Semi-supervised Learning plays a crucial role in network anomaly detection applications, however, learning anomaly patterns with limited labeled samples is not easy. Additionally, the lack of interpretability creates key barriers to the adoption of semi-supervised frameworks in practice. Most existing interpretation methods are developed for supervised/unsupervised frameworks or non-security domains and fail to provide reliable interpretations. In this paper, we propose AnomalyAID, a general framework aiming to (1) make the anomaly detection process interpretable and improve the reliability of interpretation results, and (2) assign high-confidence pseudo labels to unlabeled samples for improving the performance of anomaly detection systems with limited supervised data. For (1), we propose a novel interpretation approach that leverages global and local interpreters to provide reliable explanations, while for (2), we design a new two-stage semi-supervised learning framework for network anomaly detection by aligning both stages' model predictions with special constraints. We apply AnomalyAID over two representative network anomaly detection tasks and extensively evaluate AnomalyAID with representative prior works. Experimental results demonstrate that AnomalyAID can provide accurate detection results with reliable interpretations for semi-supervised network anomaly detection systems.", "date": "2024-11-18", "categories": []}
{"id": "2511.05564", "title": "M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) is an essential task in the image processing community with prospects in video surveillance, which faces fundamental challenges in balancing detection accuracy with computational efficiency. As video content becomes increasingly complex with diverse behavioral patterns and contextual scenarios, traditional VAD approaches struggle to provide robust assessment for modern surveillance systems. Existing methods either lack comprehensive spatial-temporal modeling or require excessive computational resources for real-time applications. In this regard, we present a Mamba-based multi-scale spatial-temporal learning (M2S2L) framework in this paper. The proposed method employs hierarchical spatial encoders operating at multiple granularities and multi-temporal encoders capturing motion dynamics across different time scales. We also introduce a feature decomposition mechanism to enable task-specific optimization for appearance and motion reconstruction, facilitating more nuanced behavioral modeling and quality-aware anomaly assessment. Experiments on three benchmark datasets demonstrate that M2S2L framework achieves 98.5%, 92.1%, and 77.9% frame-level AUCs on UCSD Ped2, CUHK Avenue, and ShanghaiTech respectively, while maintaining efficiency with 20.1G FLOPs and 45 FPS inference speed, making it suitable for practical surveillance deployment.", "date": "2025-11-04", "categories": []}
{"id": "2511.05978", "title": "Kunlun Anomaly Troubleshooter: Enabling Kernel-Level Anomaly Detection and Causal Reasoning for Large Model Distributed Inference", "abstract": "Anomaly troubleshooting for large model distributed inference (LMDI) remains a critical challenge. Resolving anomalies such as inference performance degradation or latency jitter in distributed system demands significant manual efforts from domain experts, resulting in extremely time-consuming diagnosis processes with relatively low accuracy. In this paper, we introduce Kunlun Anomaly Troubleshooter (KAT), the first anomaly troubleshooting framework tailored for LMDI. KAT addresses this problem through two core innovations. First, KAT exploits the synchronicity and consistency of GPU workers, innovatively leverages function trace data to precisely detect kernel-level anomalies and associated hardware components at nanosecond resolution. Second, KAT integrates these detection results into a domain-adapted LLM, delivering systematic causal reasoning and natural language interpretation of complex anomaly symptoms. Evaluations conducted in Alibaba Cloud Service production environment indicate that KAT achieves over 0.884 precision and 0.936 recall in anomaly detection, providing detail anomaly insights that significantly narrow down the diagnostic scope and improve both the efficiency and success rate of troubleshooting.", "date": "2025-11-08", "categories": []}
{"id": "2511.06054", "title": "Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection", "abstract": "Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branching, significantly enhancing the flexibility of evaluation tree construction. Complementing this, the FuBIF Feature Importance (FuBIFFI) algorithm extends the interpretability in IF-based approaches by providing feature importance scores across possible FuBIF models. This paper details the operational framework of FuBIF, evaluates its performance against established methods, and explores its theoretical contributions. An open-source implementation is provided to encourage further research and ensure reproducibility.", "date": "2025-11-08", "categories": []}
{"id": "2511.06687", "title": "AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer", "abstract": "Anomaly generation has been widely explored to address the scarcity of anomaly images in real-world data. However, existing methods typically suffer from at least one of the following limitations, hindering their practical deployment: (1) lack of visual realism in generated anomalies; (2) dependence on large amounts of real images; and (3) use of memory-intensive, heavyweight model architectures. To overcome these limitations, we propose AnoStyler, a lightweight yet effective method that frames zero-shot anomaly generation as text-guided style transfer. Given a single normal image along with its category label and expected defect type, an anomaly mask indicating the localized anomaly regions and two-class text prompts representing the normal and anomaly states are generated using generalizable category-agnostic procedures. A lightweight U-Net model trained with CLIP-based loss functions is used to stylize the normal image into a visually realistic anomaly image, where anomalies are localized by the anomaly mask and semantically aligned with the text prompts. Extensive experiments on the MVTec-AD and VisA datasets show that AnoStyler outperforms existing anomaly generation methods in generating high-quality and diverse anomaly images. Furthermore, using these generated anomalies helps enhance anomaly detection performance.", "date": "2025-11-10", "categories": []}
{"id": "2511.07023", "title": "Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time", "abstract": "Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume identical training and testing distributions, which is rarely valid in practice. In real-world scenarios, unseen but normal samples may emerge during deployment, leading to a normality shift that degrades the performance of GAD models trained on the original data. Through empirical analysis, we reveal that the degradation arises from (1) semantic confusion, where unseen normal samples are misinterpreted as anomalies due to their novel patterns, and (2) aggregation contamination, where the representations of seen normal nodes are distorted by unseen normals through message aggregation. While retraining or fine-tuning GAD models could be a potential solution to the above challenges, the high cost of model retraining and the difficulty of obtaining labeled data often render this approach impractical in real-world applications. To bridge the gap, we proposed a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns (TUNE) in GAD. To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level. Moreover, we utilize the minimization of representation-level shift as a supervision signal to train the aligner, which leverages the estimated aggregation contamination as a key indicator of normality shift. Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.", "date": "2025-11-10", "categories": []}
{"id": "2511.07233", "title": "Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection", "abstract": "Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representative examples of all possible anomalies is infeasible, we tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs. To this end, we introduce a corruption model that injects artificial disruptions into training images to mimic structural defects. While reminiscent of denoising autoencoders, our approach differs in two key aspects. First, instead of unstructured i.i.d.\\ noise, we apply structured, spatially coherent perturbations that make the task a hybrid of segmentation and inpainting. Second, and counterintuitively, we add and preserve Gaussian noise on top of the occlusions, which acts as a Tikhonov regularizer anchoring the Jacobian of the reconstruction function toward identity. This identity-anchored regularization stabilizes reconstruction and further improves both detection and segmentation accuracy. On the MVTec AD benchmark, our method achieves state-of-the-art results (I/P-AUROC: 99.9/99.4), supporting our theoretical framework and demonstrating its practical relevance for automatic inspection.", "date": "2025-11-10", "categories": []}
{"id": "2511.07276", "title": "RobustA: Robust Anomaly Detection in Multimodal Data", "abstract": "In recent years, multimodal anomaly detection methods have demonstrated remarkable performance improvements over video-only models. However, real-world multimodal data is often corrupted due to unforeseen environmental distortions. In this paper, we present the first-of-its-kind work that comprehensively investigates the adverse effects of corrupted modalities on multimodal anomaly detection task. To streamline this work, we propose RobustA, a carefully curated evaluation dataset to systematically observe the impacts of audio and visual corruptions on the overall effectiveness of anomaly detection systems. Furthermore, we propose a multimodal anomaly detection method, which shows notable resilience against corrupted modalities. The proposed method learns a shared representation space for different modalities and employs a dynamic weighting scheme during inference based on the estimated level of corruption. Our work represents a significant step forward in enabling the real-world application of multimodal anomaly detection, addressing situations where the likely events of modality corruptions occur. The proposed evaluation dataset with corrupted modalities and respective extracted features will be made publicly available.", "date": "2025-11-10", "categories": []}
{"id": "2501.15365", "title": "A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data", "abstract": "In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service quality, preventing financial losses, and maintaining robust security standards. While machine learning algorithms have shown promise in achieving high accuracy for anomaly detection, their performance is often constrained by the specific conditions of their training data. A persistent challenge in this domain is the scarcity of labeled data for anomaly detection in time-series datasets. This limitation hampers the training efficacy of both traditional machine learning and advanced deep learning models. To address this, unsupervised transfer learning emerges as a viable solution, leveraging unlabeled data from a source domain to identify anomalies in an unlabeled target domain. However, many existing approaches still depend on a small amount of labeled data from the target domain. To overcome these constraints, we propose a transfer learning-based model for anomaly detection in multivariate time-series datasets. Unlike conventional methods, our approach does not require labeled data in either the source or target domains. Empirical evaluations on novel intrusion detection datasets demonstrate that our model outperforms existing techniques in accurately identifying anomalies within an entirely unlabeled target domain.", "date": "2025-01-26", "categories": []}
{"id": "2511.04729", "title": "Knowledge-based anomaly detection for identifying network-induced shape artifacts", "abstract": "Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artifacts, distortions, and unrealistic features that compromise model performance and clinical utility. This work introduces a novel knowledge-based anomaly detection method for detecting network-induced shape artifacts in synthetic images. The introduced method utilizes a two-stage framework comprising (i) a novel feature extractor that constructs a specialized feature space by analyzing the per-image distribution of angle gradients along anatomical boundaries, and (ii) an isolation forest-based anomaly detector. We demonstrate the effectiveness of the method for identifying network-induced shape artifacts in two synthetic mammography datasets from models trained on CSAW-M and VinDr-Mammo patient datasets respectively. Quantitative evaluation shows that the method successfully concentrates artifacts in the most anomalous partition (1st percentile), with AUC values of 0.97 (CSAW-syn) and 0.91 (VMLO-syn). In addition, a reader study involving three imaging scientists confirmed that images identified by the method as containing network-induced shape artifacts were also flagged by human readers with mean agreement rates of 66% (CSAW-syn) and 68% (VMLO-syn) for the most anomalous partition, approximately 1.5-2 times higher than the least anomalous partition. Kendall-Tau correlations between algorithmic and human rankings were 0.45 and 0.43 for the two datasets, indicating reasonable agreement despite the challenging nature of subtle artifact detection. This method is a step forward in the responsible use of synthetic data, as it allows developers to evaluate synthetic images for known anatomic constraints and pinpoint and address specific issues to improve the overall quality of a synthetic dataset.", "date": "2025-11-06", "categories": []}
{"id": "2511.05245", "title": "ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining", "abstract": "The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of anomaly detection (i.e., pretraining in natural images doesn't aim to distinguish between normal and abnormal). Moreover, natural images and industrial image data in AD scenarios typically have the distribution shift. The two issues can cause ImageNet-pretrained features to be suboptimal for AD tasks. To further promote the development of the AD field, pretrained representations specially for AD tasks are eager and very valuable. To this end, we propose a novel AD representation learning framework specially designed for learning robust and discriminative pretrained representations for industrial anomaly detection. Specifically, closely surrounding the goal of anomaly detection (i.e., focus on discrepancies between normals and anomalies), we propose angle- and norm-oriented contrastive losses to maximize the angle size and norm difference between normal and abnormal features simultaneously. To avoid the distribution shift from natural images to AD images, our pretraining is performed on a large-scale AD dataset, RealIAD. To further alleviate the potential shift between pretraining data and downstream AD datasets, we learn the pretrained AD representations based on the class-generalizable representation, residual features. For evaluation, based on five embedding-based AD methods, we simply replace their original features with our pretrained representations. Extensive experiments on five AD datasets and five backbones consistently show the superiority of our pretrained features. The code is available at https://github.com/xcyao00/ADPretrain.", "date": "2025-11-07", "categories": []}
{"id": "2511.03993", "title": "Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection", "abstract": "Network anomaly detection systems encounter several challenges with traditional detectors trained offline. They become susceptible to concept drift and new threats such as zero-day or polymorphic attacks. To address this limitation, we propose a Ca$^{2+}$-modulated learning framework that draws inspiration from astrocytic Ca$^{2+}$ signaling in the brain, where rapid, context-sensitive adaptation enables robust information processing. Our approach couples a multicellular astrocyte dynamics simulator with a deep neural network (DNN). The simulator models astrocytic Ca$^{2+}$ dynamics through three key mechanisms: IP$_3$-mediated Ca$^{2+}$ release, SERCA pump uptake, and conductance-aware diffusion through gap junctions between cells. Evaluation of our proposed network on CTU-13 (Neris) network traffic data demonstrates the effectiveness of our biologically plausible approach. The Ca$^{2+}$-gated model outperforms a matched baseline DNN, achieving up to $\\sim$98\\% accuracy with reduced false positives and negatives across multiple train/test splits. Importantly, this improved performance comes with negligible runtime overhead once Ca$^{2+}$ trajectories are precomputed. While demonstrated here for cybersecurity applications, this Ca$^{2+}$-modulated learning framework offers a generic solution for streaming detection tasks that require rapid, biologically grounded adaptation to evolving data patterns.", "date": "2025-11-06", "categories": []}
{"id": "2511.04086", "title": "DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection", "abstract": "With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graphs that deviate from normal behavioral patterns. However, most Graph Neural Network (GNN) approaches implicitly assume that the training set is clean, containing only normal graphs, which is rarely true in practice. Even modest contamination by anomalous graphs can distort learned representations and sharply degrade performance. To address this challenge, we propose DeNoise, a robust UGAD framework explicitly designed for contaminated training data. It jointly optimizes a graph-level encoder, an attribute decoder, and a structure decoder via an adversarial objective to learn noise-resistant embeddings. Further, DeNoise introduces an encoder anchor-alignment denoising mechanism that fuses high-information node embeddings from normal graphs into all graph embeddings, improving representation quality while suppressing anomaly interference. A contrastive learning component then compacts normal graph embeddings and repels anomalous ones in the latent space. Extensive experiments on eight real-world datasets demonstrate that DeNoise consistently learns reliable graph-level representations under varying noise intensities and significantly outperforms state-of-the-art UGAD baselines.", "date": "2025-11-06", "categories": []}
{"id": "2408.16612", "title": "Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection", "abstract": "The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL's potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects. Code: https://github.com/muleina/CMS\\_HCAL\\_ML\\_OnlineDQM .", "date": "2024-08-29", "categories": []}
{"id": "2506.11772", "title": "CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection", "abstract": "Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types (e.g., local and global defect), and the scarcity of training data. As such, it necessitates a comprehensive model capable of capturing both low-level and high-level features, even with limited data. To address this, we propose CLIPFUSION, a method that leverages both discriminative and generative foundation models. Specifically, the CLIP-based discriminative model excels at capturing global features, while the diffusion-based generative model effectively captures local details, creating a synergistic and complementary approach. Notably, we introduce a methodology for utilizing cross-attention maps and feature maps extracted from diffusion models specifically for anomaly detection. Experimental results on benchmark datasets (MVTec-AD, VisA) demonstrate that CLIPFUSION consistently outperforms baseline methods, achieving outstanding performance in both anomaly segmentation and classification. We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection, providing a scalable solution for real-world applications.", "date": "2025-06-13", "categories": []}
{"id": "2511.03095", "title": "Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies", "abstract": "Modern artificial intelligence has revolutionized our ability to extract rich and versatile data representations across scientific disciplines. Yet, the statistical properties of these representations remain poorly controlled, causing misspecified anomaly detection (AD) methods to falter. Weak or rare signals can remain hidden within the apparent regularity of normal data, creating a gap in our ability to detect and interpret anomalies. We examine this gap and identify a set of structural desiderata for detection methods operating under minimal prior information: sparsity, to enforce parsimony; locality, to preserve geometric sensitivity; and competition, to promote efficient allocation of model capacity. These principles define a class of self-organizing local kernels that adaptively partition the representation space around regions of statistical imbalance. As an instantiation of these principles, we introduce SparKer, a sparse ensemble of Gaussian kernels trained within a semi-supervised Neyman--Pearson framework to locally model the likelihood ratio between a sample that may contain anomalies and a nominal, anomaly-free reference. We provide theoretical insights into the mechanisms that drive detection and self-organization in the proposed model, and demonstrate the effectiveness of this approach on realistic high-dimensional problems of scientific discovery, open-world novelty detection, intrusion detection, and generative-model validation. Our applications span both the natural- and computer-science domains. We demonstrate that ensembles containing only a handful of kernels can identify statistically significant anomalous locations within representation spaces of thousands of dimensions, underscoring both the interpretability, efficiency and scalability of the proposed approach.", "date": "2025-11-05", "categories": []}
{"id": "2511.03149", "title": "Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction", "abstract": "Forecasting anomalies (anomaly prediction) in multivariate time series from different real-world, dynamic, and complex systems is vital for preempting critical failures, leading to a substantial minimization in operational costs and human labor. Yet, existing methods are limited to specific systems while failing to generalize to evolving anomaly patterns over time. In contrast, pretrained Time Series Foundation Models (TSFMs) have recently demonstrated strong generalization and zero-shot forecasting capabilities. However, their potential remains untapped for anomaly prediction, a task fundamentally different from forecasting normal behavior. Thus, we present Forecast2Anomaly (F2A), a novel framework that empowers TSFMs with anomaly prediction abilities through two key innovations. First, we propose a joint forecast-anomaly loss that fine-tunes TSFMs to accurately forecast future signals even at anomalous time points. Second, we introduce a Retrieval-Augmented Generation (RAG) module that retrieves historically relevant horizons and conditions predictions on them. This component dynamically adapts to distributional shifts at inference time, enabling F2A to track evolving anomalies without requiring model updates. By combining targeted fine-tuning with dynamic retrieval, F2A bridges the gap between robust TSFM zero-shot forecasting and zero-shot anomaly prediction. Extensive experiments across 16 diverse datasets and multiple TSFM backbones show that F2A consistently outperforms state-of-the-art methods, offering a scalable, zero-shot anomaly prediction solution for real-world applications.", "date": "2025-11-05", "categories": []}
{"id": "2511.03267", "title": "IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection", "abstract": "3D anomaly detection (3D-AD) plays a critical role in industrial manufacturing, particularly in ensuring the reliability and safety of core equipment components. Although existing 3D datasets like Real3D-AD and MVTec 3D-AD offer broad application support, they fall short in capturing the complexities and subtle defects found in real industrial environments. This limitation hampers precise anomaly detection research, especially for industrial equipment components (IEC) such as bearings, rings, and bolts. To address this challenge, we have developed a point cloud anomaly detection dataset (IEC3D-AD) specific to real industrial scenarios. This dataset is directly collected from actual production lines, ensuring high fidelity and relevance. Compared to existing datasets, IEC3D-AD features significantly improved point cloud resolution and defect annotation granularity, facilitating more demanding anomaly detection tasks. Furthermore, inspired by generative 2D-AD methods, we introduce a novel 3D-AD paradigm (GMANet) on IEC3D-AD. This paradigm generates synthetic point cloud samples based on geometric morphological analysis, then reduces the margin and increases the overlap between normal and abnormal point-level features through spatial discrepancy optimization. Extensive experiments demonstrate the effectiveness of our method on both IEC3D-AD and other datasets.", "date": "2025-11-05", "categories": []}
{"id": "2511.03285", "title": "Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices", "abstract": "This study addresses the problem of anomaly detection and root cause tracing in microservice architectures and proposes a unified framework that combines graph neural networks with temporal modeling. The microservice call chain is abstracted as a directed graph, where multidimensional features of nodes and edges are used to construct a service topology representation, and graph convolution is applied to aggregate features across nodes and model dependencies, capturing complex structural relationships among services. On this basis, gated recurrent units are introduced to model the temporal evolution of call chains, and multi-layer stacking and concatenation operations are used to jointly obtain structural and temporal representations, improving the ability to identify anomaly patterns. Furthermore, anomaly scoring functions at both the node and path levels are defined to achieve unified modeling from local anomaly detection to global call chain tracing, which enables the identification of abnormal service nodes and the reconstruction of potential anomaly propagation paths. Sensitivity experiments are then designed from multiple dimensions, including hyperparameters, environmental disturbances, and data distribution, to evaluate the framework, and results show that it outperforms baseline methods in key metrics such as AUC, ACC, Recall, and F1-Score, maintaining high accuracy and stability under dynamic topologies and complex environments. This research not only provides a new technical path for anomaly detection in microservices but also lays a methodological foundation for intelligent operations in distributed systems.", "date": "2025-11-05", "categories": []}
{"id": "2409.08020", "title": "Network Anomaly Traffic Detection via Multi-view Feature Fusion", "abstract": "Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In this regard, we propose a Multi-view Feature Fusion (MuFF) method for network anomaly traffic detection. MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively. It learns temporal and interactive features. These features are then fused from different perspectives for anomaly traffic detection. Extensive experiments on six real traffic datasets show that MuFF has excellent performance in network anomalous traffic detection, which makes up for the shortcomings of detection under a single perspective.", "date": "2024-09-12", "categories": []}
{"id": "2511.02301", "title": "Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series", "abstract": "The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and imbalanced anomaly distributions. To address this gap, we propose a Federated Quantum Kernel Learning (FQKL) framework that integrates quantum feature maps with federated aggregation to enable distributed, privacy-preserving anomaly detection across heterogeneous IoT networks. In our design, quantum edge nodes locally compute compressed kernel statistics using parameterized quantum circuits and share only these summaries with a central server, which constructs a global Gram matrix and trains a decision function (e.g., Fed-QSVM). Experimental results on synthetic IIoT benchmarks demonstrate that FQKL achieves superior generalization in capturing complex temporal correlations compared to classical federated baselines, while significantly reducing communication overhead. This work highlights the promise of quantum kernels in federated settings, advancing the path toward scalable, robust, and quantum-enhanced intelligence for next-generation IoT infrastructures.", "date": "2025-11-04", "categories": []}
{"id": "2508.06350", "title": "Aligning Effective Tokens with Video Anomaly in Large Language Models", "abstract": "Understanding abnormal events in videos is a vital and challenging task that has garnered significant attention in a wide range of applications. Although current video understanding Multi-modal Large Language Models (MLLMs) are capable of analyzing general videos, they often struggle to handle anomalies due to the spatial and temporal sparsity of abnormal events, where the redundant information always leads to suboptimal outcomes. To address these challenges, exploiting the representation and generalization capabilities of Vison Language Models (VLMs) and Large Language Models (LLMs), we propose VA-GPT, a novel MLLM designed for summarizing and localizing abnormal events in various videos. Our approach efficiently aligns effective tokens between visual encoders and LLMs through two key proposed modules: Spatial Effective Token Selection (SETS) and Temporal Effective Token Generation (TETG). These modules enable our model to effectively capture and analyze both spatial and temporal information associated with abnormal events, resulting in more accurate responses and interactions. Furthermore, we construct an instruction-following dataset specifically for fine-tuning video-anomaly-aware MLLMs, and introduce a cross-domain evaluation benchmark based on XD-Violence dataset. Our proposed method outperforms existing state-of-the-art methods on various benchmarks.", "date": "2025-08-08", "categories": []}
{"id": "2511.00462", "title": "Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders", "abstract": "An anomaly detection method based on deep autoencoders is proposed to address anomalies that often occur in enterprise-level ETL data streams. The study first analyzes multiple types of anomalies in ETL processes, including delays, missing values, duplicate loading, and sudden abnormal changes, and applies data standardization and feature modeling to ensure stable and usable inputs. In the method design, the encoder-decoder structure compresses high-dimensional inputs into latent representations and reconstructs them, while reconstruction error is used to measure anomaly levels. Regularization constraints are introduced in the latent space to enhance feature sparsity and distribution learning, thereby improving robustness in complex data streams. Systematic analyses under different hyperparameter settings, environmental changes, and data characteristics show that the proposed method achieves superior performance in AUC, ACC, Precision, and Recall. The results demonstrate that the deep autoencoder-based detection mechanism can effectively capture latent distribution patterns in enterprise-level ETL data streams and accurately identify diverse anomalies, providing reliable support for enterprise data processing and intelligent analysis.", "date": "2025-11-01", "categories": []}
{"id": "2511.00580", "title": "TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection", "abstract": "Video anomalies often depend on contextual information available and temporal evolution. Non-anomalous action in one context can be anomalous in some other context. Most anomaly detectors, however, do not notice this type of context, which seriously limits their capability to generalize to new, real-life situations. Our work addresses the context-aware zero-shot anomaly detection challenge, in which systems need to learn adaptively to detect new events by correlating temporal and appearance features with textual traces of memory in real time. Our approach defines a memory-augmented pipeline, correlating temporal signals with visual embeddings using cross-attention, and real-time zero-shot anomaly classification by contextual similarity scoring. We achieve 90.4\\% AUC on UCF-Crime and 83.67\\% AP on XD-Violence, a new state-of-the-art among zero-shot models. Our model achieves real-time inference with high precision and explainability for deployment. We show that, by fusing cross-attention temporal fusion and contextual memory, we achieve high fidelity anomaly detection, a step towards the applicability of zero-shot models in real-world surveillance and infrastructure monitoring.", "date": "2025-11-01", "categories": []}
{"id": "2511.00613", "title": "CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World", "abstract": "How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize on detecting unexpected occurrences deviated from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle context that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. This also serves as a rigorous and fair probing evaluation suite for generative-discriminative as well as generalized-specialized vision-language models (VLMs). To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.", "date": "2025-11-01", "categories": []}
{"id": "2511.00962", "title": "A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis", "abstract": "Most video-anomaly research stops at frame-wise detection, offering little insight into why an event is abnormal, typically outputting only frame-wise anomaly scores without spatial or semantic context. Recent video anomaly localization and video anomaly understanding methods improve explainability but remain data-dependent and task-specific. We propose a unified reasoning framework that bridges the gap between temporal detection, spatial localization, and textual explanation. Our approach is built upon a chained test-time reasoning process that sequentially connects these tasks, enabling holistic zero-shot anomaly analysis without any additional training. Specifically, our approach leverages intra-task reasoning to refine temporal detections and inter-task chaining for spatial and semantic understanding, yielding improved interpretability and generalization in a fully zero-shot manner. Without any additional data or gradients, our method achieves state-of-the-art zero-shot performance across multiple video anomaly detection, localization, and explanation benchmarks. The results demonstrate that careful prompt design with task-wise chaining can unlock the reasoning power of foundation models, enabling practical, interpretable video anomaly analysis in a fully zero-shot manner. Project Page: https://rathgrith.github.io/Unified_Frame_VAA/.", "date": "2025-11-02", "categories": []}
{"id": "2511.01745", "title": "An Open-Access Benchmark of Statistical and Machine-Learning Anomaly Detection Methods for Battery Applications", "abstract": "Battery safety is critical in applications ranging from consumer electronics to electric vehicles and aircraft, where undetected anomalies could trigger safety hazards or costly downtime. In this study, we present OSBAD as an open-source benchmark for anomaly detection frameworks in battery applications. By benchmarking 15 diverse algorithms encompassing statistical, distance-based, and unsupervised machine-learning methods, OSBAD enables a systematic comparison of anomaly detection methods across heterogeneous datasets. In addition, we demonstrate how a physics- and statistics-informed feature transformation workflow enhances anomaly separability by decomposing collective anomalies into point anomalies. To address a major bottleneck in unsupervised anomaly detection due to incomplete labels, we propose a Bayesian optimization pipeline that facilitates automated hyperparameter tuning based on transfer-learning and regression proxies. Through validation on datasets covering both liquid and solid-state chemistries, we further demonstrate the cross-chemistry generalization capability of OSBAD to identify irregularities across different electrochemical systems. By making benchmarking database with open-source reproducible anomaly detection workflows available to the community, OSBAD establishes a unified foundation for developing safe, scalable, and transferable anomaly detection tools in battery analytics. This research underscores the significance of physics- and statistics-informed feature engineering as well as model selection with probabilistic hyperparameter tuning, in advancing trustworthy, data-driven diagnostics for safety-critical energy systems.", "date": "2025-11-03", "categories": []}
{"id": "2505.19022", "title": "Rethinking Metrics and Benchmarks of Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD), which aims to detect anomalies that deviate from expectation, has attracted increasing attention in recent years. Existing advancements in VAD primarily focus on model architectures and training strategies, while devoting insufficient attention to evaluation metrics and benchmarks. In this paper, we rethink VAD evaluation methods through comprehensive analyses, revealing three critical limitations in current practices: 1) existing metrics are significantly influenced by single annotation bias; 2) current metrics fail to reward early detection of anomalies; 3) available benchmarks lack the capability to evaluate scene overfitting of fully/weakly-supervised algorithms. To address these limitations, we propose three novel evaluation methods: first, we establish probabilistic AUC/AP (Prob-AUC/AP) metrics utlizing multi-round annotations to mitigate single annotation bias; second, we develop a Latency-aware Average Precision (LaAP) metric that rewards early and accurate anomaly detection; and finally, we introduce two hard normal benchmarks (UCF-HN, MSAD-HN) with videos specifically designed to evaluate scene overfitting. We report performance comparisons of ten state-of-the-art VAD approaches using our proposed evaluation methods, providing novel perspectives for future VAD model development. We release our data and code in https://github.com/Kamino666/RethinkingVAD.", "date": "2025-05-25", "categories": []}
{"id": "2510.26008", "title": "Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry", "abstract": "Modern machine learning (ML) has grown into a tightly coupled, full-stack ecosystem that combines hardware, software, network, and applications. Many users rely on cloud providers for elastic, isolated, and cost-efficient resources. Unfortunately, these platforms as a service use virtualization, which means operators have little insight into the users' workloads. This hinders resource optimizations by the operator, which is essential to ensure cost efficiency and minimize execution time. In this paper, we argue that workload knowledge is unnecessary for system-level optimization. We propose Reveal, which takes a hardware-centric approach, relying only on hardware signals - fully accessible by operators. Using low-level signals collected from the system, Reveal detects anomalies through an unsupervised learning pipeline. The pipeline is developed by analyzing over 30 popular ML models on various hardware platforms, ensuring adaptability to emerging workloads and unknown deployment patterns. Using Reveal, we successfully identified both network and system configuration issues, accelerating the DeepSeek model by 5.97%.", "date": "2025-10-29", "categories": []}
{"id": "2510.27304", "title": "Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift", "abstract": "With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability to rapid anomaly changes, known as concept drift. In contrast, streaming learning integrates online and incremental learning, enabling seamless updates and concept drift detection to improve robustness. This study investigates anomaly detection in streaming IoT traffic as binary classification, comparing batch and streaming learning approaches while assessing the limitations of current IoT traffic datasets. We simulated heterogeneous network data streams by carefully mixing existing datasets and streaming the samples one by one. Our results highlight the failure of batch models to handle concept drift, but also reveal persisting limitations of current datasets to expose model limitations due to low traffic heterogeneity. We also investigated the competitiveness of tree-based ML algorithms, well-known in batch anomaly detection, and compared it to non-tree-based ones, confirming the advantages of the former. Adaptive Random Forest achieved F1-score of 0.990 $\\pm$ 0.006 at one-third the computational cost of its batch counterpart. Hoeffding Adaptive Tree reached F1-score of 0.910 $\\pm$ 0.007, reducing computational cost by four times, making it a viable choice for online applications despite a slight trade-off in stability.", "date": "2025-10-31", "categories": []}
{"id": "2406.02927", "title": "Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs", "abstract": "Despite the relentless progress of deep learning models in analyzing the system conditions under cyber-physical events, their abilities are limited in the power system domain due to data availability issues, cost of data acquisition, and lack of interpretation and extrapolation for the data beyond the training windows. In addition, the integration of distributed energy resources (DERs) such as wind and solar generations increases the complexities and nonlinear nature of power systems. Therefore, an interpretable and reliable methodology is of utmost need to increase the confidence of power system operators and their situational awareness for making reliable decisions. This has led to the development of physics-informed neural network (PINN) models as more interpretable, trustworthy, and robust models where the underlying principled laws are integrated into the training process of neural network models to achieve improved performance. This paper proposes a multivariate physics-informed convolutional autoencoder (PIConvAE) model to detect cyber anomalies in power distribution systems with unbalanced configurations and high penetration of DERs. The physical laws are integrated through a customized loss function that embeds the underlying Kirchhoff's circuit laws into the training process of the autoencoder. The performance of the multivariate PIConvAE model is evaluated on two unbalanced power distribution grids, IEEE 123-bus system and a real-world feeder in Riverside, CA. The results show the exceptional performance of the proposed method in detecting various cyber anomalies in both systems. In addition, the model's effectiveness is evaluated in data scarcity scenarios with different training data ratios. Finally, the model's performance is compared with existing machine learning models where the PIConvAE model surpasses other models with considerably higher detection metrics.", "date": "2024-06-05", "categories": []}
{"id": "2407.11873", "title": "Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection", "abstract": "The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in $\\bbR^d$. In this work, we extend the concept of Mahalanobis distance to separable Banach spaces by reinterpreting it as a Cameron-Martin norm associated with a probability measure. This approach leads to a basis-free, data-driven notion of anomaly distance through the so-called variance norm, which can naturally be estimated using empirical measures of a sample. Our framework generalizes the classical $\\bbR^d$, functional $(L^2[0,1])^d$, and kernelized settings; importantly, it incorporates non-injective covariance operators. We prove that the variance norm is invariant under invertible bounded linear transformations of the data, extending previous results which are limited to unitary operators. In the Hilbert space setting, we connect the variance norm to the RKHS of the covariance operator, and establish consistency and convergence results for estimation using empirical measures with Tikhonov regularization. Using the variance norm, we introduce the notion of a kernelized nearest-neighbour Mahalanobis distance, and study some of its finite-sample concentration properties. In an empirical study on 12 real-world data sets, we demonstrate that the kernelized nearest-neighbour Mahalanobis distance outperforms the traditional kernelized Mahalanobis distance for multivariate time series novelty detection, using state-of-the-art time series kernels such as the signature, global alignment, and Volterra reservoir kernels.", "date": "2024-07-16", "categories": []}
{"id": "2510.25809", "title": "Flex-GAD : Flexible Graph Anomaly Detection", "abstract": "Detecting anomalous nodes in attributed networks, where each node is associated with both structural connections and descriptive attributes, is essential for identifying fraud, misinformation, and suspicious behavior in domains such as social networks, academic citation graphs, and e-commerce platforms. We propose Flex-GAD, a novel unsupervised framework for graph anomaly detection at the node level. Flex-GAD integrates two encoders to capture complementary aspects of graph data. The framework incorporates a novel community-based GCN encoder to model intra-community and inter-community information into node embeddings, thereby ensuring structural consistency, along with a standard attribute encoder. These diverse representations are fused using a self-attention-based representation fusion module, which enables adaptive weighting and effective integration of the encoded information. This fusion mechanism allows automatic emphasis of the most relevant node representation across different encoders. We evaluate Flex-GAD on seven real-world attributed graphs with varying sizes, node degrees, and attribute homogeneity. Flex-GAD achieves an average AUC improvement of 7.98% over the previously best-performing method, GAD-NR, demonstrating its effectiveness and flexibility across diverse graph structures. Moreover, it significantly reduces training time, running 102x faster per epoch than Anomaly DAE and 3x faster per epoch than GAD-NR on average across seven benchmark datasets.", "date": "2025-10-29", "categories": []}
{"id": "2510.26159", "title": "Segmentation over Complexity: Evaluating Ensemble and Hybrid Approaches for Anomaly Detection in Industrial Time Series", "abstract": "In this study, we investigate the effectiveness of advanced feature engineering and hybrid model architectures for anomaly detection in a multivariate industrial time series, focusing on a steam turbine system. We evaluate the impact of change point-derived statistical features, clustering-based substructure representations, and hybrid learning strategies on detection performance. Despite their theoretical appeal, these complex approaches consistently underperformed compared to a simple Random Forest + XGBoost ensemble trained on segmented data. The ensemble achieved an AUC-ROC of 0.976, F1-score of 0.41, and 100% early detection within the defined time window. Our findings highlight that, in scenarios with highly imbalanced and temporally uncertain data, model simplicity combined with optimized segmentation can outperform more sophisticated architectures, offering greater robustness, interpretability, and operational utility.", "date": "2025-10-30", "categories": []}
{"id": "2510.26464", "title": "Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection", "abstract": "Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-language models (VLMs) to recognize potentially anomalous regions through feature similarity between text descriptions and images. However, due to the lack of detailed textual descriptions, these methods can only pre-define image-level descriptions to match each visual patch token to identify potential anomalous regions, which leads to the semantic misalignment between image descriptions and patch-level visual anomalies, achieving sub-optimal localization performance. To address the above issues, we propose the Multi-Level Fine-Grained Semantic Caption (MFSC) to provide multi-level and fine-grained textual descriptions for existing anomaly detection datasets with automatic construction pipeline. Based on the MFSC, we propose a novel framework named FineGrainedAD to improve anomaly localization performance, which consists of two components: Multi-Level Learnable Prompt (MLLP) and Multi-Level Semantic Alignment (MLSA). MLLP introduces fine-grained semantics into multi-level learnable prompts through automatic replacement and concatenation mechanism, while MLSA designs region aggregation strategy and multi-level alignment training to facilitate learnable prompts better align with corresponding visual regions. Experiments demonstrate that the proposed FineGrainedAD achieves superior overall performance in few-shot settings on MVTec-AD and VisA datasets.", "date": "2025-10-30", "categories": []}
{"id": "2510.26684", "title": "Process Integrated Computer Vision for Real-Time Failure Prediction in Steel Rolling Mill", "abstract": "We present a long-term deployment study of a machine vision-based anomaly detection system for failure prediction in a steel rolling mill. The system integrates industrial cameras to monitor equipment operation, alignment, and hot bar motion in real time along the process line. Live video streams are processed on a centralized video server using deep learning models, enabling early prediction of equipment failures and process interruptions, thereby reducing unplanned breakdown costs. Server-based inference minimizes the computational load on industrial process control systems (PLCs), supporting scalable deployment across production lines with minimal additional resources. By jointly analyzing sensor data from data acquisition systems and visual inputs, the system identifies the location and probable root causes of failures, providing actionable insights for proactive maintenance. This integrated approach enhances operational reliability, productivity, and profitability in industrial manufacturing environments.", "date": "2025-10-30", "categories": []}
{"id": "2409.11529", "title": "Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling", "abstract": "Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. While deep learning has shown state-of-the-art AD performance, its application in critical systems is hindered by concerns regarding training data efficiency, domain adaptation and interpretability. This work considers AD in network flows using incomplete measurements, leveraging a robust tensor decomposition approach and deep unrolling techniques to address these challenges. We first propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective where the normal flows are modeled as low-rank tensors and anomalies as sparse. An augmentation of the objective is introduced to decrease the computational cost. We apply deep unrolling to derive a novel deep network architecture based on our proposed algorithm, treating the regularization parameters as learnable weights. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics, improving AD performance while maintaining a low parameter count and preserving the problem's permutation equivariances. To optimize the deep network weights for detection performance, we employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. Extensive experiments on synthetic and real-world data demonstrate that our proposed deep network architecture exhibits a high training data efficiency, outperforms reference methods, and adapts seamlessly to varying network topologies.", "date": "2024-09-17", "categories": []}
{"id": "2410.05900", "title": "MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos", "abstract": "Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end, we propose a Multi-Timescale Feature Learning (MTFL) method to enhance the representation of anomaly features. Short, medium, and long temporal tubelets are employed to extract spatio-temporal video features using a Video Swin Transformer. Experimental results demonstrate that MTFL outperforms state-of-the-art methods on the UCF-Crime dataset, achieving an anomaly detection performance 89.78% AUC. Moreover, it performs complementary to SotA with 95.32% AUC on the ShanghaiTech and 84.57% AP on the XD-Violence dataset. Furthermore, we generate an extended dataset of the UCF-Crime for development and evaluation on a wider range of anomalies, namely Video Anomaly Detection Dataset (VADD), involving 2,591 videos in 18 classes with extensive coverage of realistic anomalies.", "date": "2024-10-08", "categories": []}
{"id": "2503.23981", "title": "Federated Structured Sparse PCA for Anomaly Detection in IoT Networks", "abstract": "Although federated learning has gained prominence as a privacy-preserving framework tailored for distributed Internet of Things (IoT) environments, current federated principal component analysis (PCA) methods lack integration of sparsity, a critical feature for robust anomaly detection. To address this limitation, we propose a novel federated structured sparse PCA (FedSSP) approach for anomaly detection in IoT networks. The proposed model uniquely integrates double sparsity regularization: (1) row-wise sparsity governed by $\\ell_{2,p}$-norm with $p\\in [0,1)$ to eliminate redundant feature dimensions, and (2) element-wise sparsity via $\\ell_{q}$-norm with $q\\in [0,1)$ to suppress noise-sensitive components. To solve this nonconvex problem in a distributed setting, we devise an efficient optimization algorithm based on the proximal alternating minimization (PAM). Numerical experiments validate that incorporating structured sparsity enhances both model interpretability and detection accuracy. Our code is available at https://github.com/xianchaoxiu/FedSSP.", "date": "2025-03-31", "categories": []}
{"id": "2508.02293", "title": "Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning", "abstract": "So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manual data curation, introducing bias and limiting adaptability. We propose Confident Meta-learning (CoMet), a novel training strategy that enables deep anomaly detection models to learn from uncurated datasets where nominal and anomalous samples coexist, eliminating the need for explicit filtering. Our approach integrates Soft Confident Learning, which assigns lower weights to low-confidence samples, and Meta-Learning, which stabilizes training by regularizing updates based on training validation loss covariance. This prevents overfitting and enhances robustness to noisy data. CoMet is model-agnostic and can be applied to any anomaly detection method trainable via gradient descent. Experiments on MVTec-AD, VIADUCT, and KSDD2 with two state-of-the-art models demonstrate the effectiveness of our approach, consistently improving over the baseline methods, remaining insensitive to anomalies in the training set, and setting a new state-of-the-art across all datasets. Code is available at https://github.com/aqeeelmirza/CoMet", "date": "2025-08-04", "categories": []}
{"id": "2508.15256", "title": "Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images", "abstract": "Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods, primarily designed for industrial settings, face limitations in pathology due to computational constraints, diverse tissue structures, and lack of interpretability. To address these challenges, we propose Ano-NAViLa, a Normal and Abnormal pathology knowledge-augmented Vision-Language model for Anomaly detection in pathology images. Ano-NAViLa is built on a pre-trained vision-language model with a lightweight trainable MLP. By incorporating both normal and abnormal pathology knowledge, Ano-NAViLa enhances accuracy and robustness to variability in pathology images and provides interpretability through image-text associations. Evaluated on two lymph node datasets from different organs, Ano-NAViLa achieves the state-of-the-art performance in anomaly detection and localization, outperforming competing models.", "date": "2025-08-21", "categories": []}
{"id": "2509.26386", "title": "PANDA: Towards Generalist Video Anomaly Detection via Agentic AI Engineer", "abstract": "Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios. Previous methods typically rely on domain-specific training data and manual adjustments when applying to new scenarios and unseen anomaly types, suffering from high labor costs and limited generalization. Therefore, we aim to achieve generalist VAD, \\ie, automatically handle any scene and any anomaly types without training data or human involvement. In this work, we propose PANDA, an agentic AI engineer based on MLLMs. Specifically, we achieve PANDA by comprehensively devising four key capabilities: (1) self-adaptive scene-aware strategy planning, (2) goal-driven heuristic reasoning, (3) tool-augmented self-reflection, and (4) self-improving chain-of-memory. Concretely, we develop a self-adaptive scene-aware RAG mechanism, enabling PANDA to retrieve anomaly-specific knowledge for anomaly detection strategy planning. Next, we introduce a latent anomaly-guided heuristic prompt strategy to enhance reasoning precision. Furthermore, PANDA employs a progressive reflection mechanism alongside a suite of context-aware tools to iteratively refine decision-making in complex scenarios. Finally, a chain-of-memory mechanism enables PANDA to leverage historical experiences for continual performance improvement. Extensive experiments demonstrate that PANDA achieves state-of-the-art performance in multi-scenario, open-set, and complex scenario settings without training and manual involvement, validating its generalizable and robust anomaly detection capability. Code is released at https://github.com/showlab/PANDA.", "date": "2025-09-30", "categories": []}
{"id": "2510.23619", "title": "Short Ticketing Detection Framework Analysis Report", "abstract": "This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduces an A/B/C/D station classification system that successfully identifies suspicious patterns across 30 high-risk stations. The framework employs four complementary algorithms: Isolation Forest, Local Outlier Factor, One-Class SVM, and Mahalanobis Distance. Key findings include the identification of five distinct short ticketing patterns and potential for short ticketing recovery in transportation systems.", "date": "2025-10-21", "categories": []}
{"id": "2510.23702", "title": "In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys", "abstract": "Distance-based methods involve the computation of distance values between features and are a well-established paradigm in machine learning. In anomaly detection, anomalies are identified by their large distance from normal data points. However, the performance of these methods often hinges on a single, user-selected distance metric (e.g., Euclidean), which may not be optimal for the complex, high-dimensional feature spaces common in astronomy. Here, we introduce a novel anomaly detection method, Distance Multi-Metric Anomaly Detection (DiMMAD), which uses an ensemble of distance metrics to find novelties.\n  Using multiple distance metrics is effectively equivalent to using different geometries in the feature space. By using a robust ensemble of diverse distance metrics, we overcome the metric-selection problem, creating an anomaly score that is not reliant on any single definition of distance. We demonstrate this multi-metric approach as a tool for simple, interpretable scientific discovery on astronomical time series -- (1) with simulated data for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, and (2) real data from the Zwicky Transient Facility.\n  We find that DiMMAD excels at out-of-distribution anomaly detection -- anomalies in the data that might be new classes -- and beats other state-of-the-art methods in the goal of maximizing the diversity of new classes discovered. For rare in-distribution anomaly detection, DiMMAD performs similarly to other methods, but may allow for improved interpretability. All our code is open source: DiMMAD is implemented within DistClassiPy: https://github.com/sidchaini/distclassipy/, while all code to reproduce the results of this paper is available here: https://github.com/sidchaini/dimmad/.", "date": "2025-10-27", "categories": []}
{"id": "2510.24398", "title": "Unsupervised Detection of Post-Stroke Brain Abnormalities", "abstract": "Post-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly recognised as imaging biomarkers of recovery and outcome, remain poorly captured by supervised segmentation methods. We evaluate REFLECT, a flow-based generative model, for unsupervised detection of both focal and non-lesional abnormalities in post-stroke patients. Using dual-expert central-slice annotations on ATLAS data, performance was assessed at the object level with Free-Response ROC analysis for anomaly maps. Two models were trained on lesion-free slices from stroke patients (ATLAS) and on healthy controls (IXI) to test the effect of training data. On ATLAS test subjects, the IXI-trained model achieved higher lesion segmentation (Dice = 0.37 vs 0.27) and improved sensitivity to non-lesional abnormalities (FROC = 0.62 vs 0.43). Training on fully healthy anatomy improves the modelling of normal variability, enabling broader and more reliable detection of structural abnormalities.", "date": "2025-10-28", "categories": []}
{"id": "2410.01737", "title": "Robust Modality-incomplete Anomaly Detection: A Modality-instructive Framework with Benchmark", "abstract": "Multimodal Industrial Anomaly Detection (MIAD), which utilizes 3D point clouds and 2D RGB images to identify abnormal regions in products, plays a crucial role in industrial quality inspection. However, traditional MIAD settings assume that all 2D and 3D modalities are paired, ignoring the fact that multimodal data collected from the real world is often imperfect due to missing modalities. Additionally, models trained on modality-incomplete data are prone to overfitting. Therefore, MIAD models that demonstrate robustness against modality-incomplete data are highly desirable in practice. To address this, we introduce a pioneering study that comprehensively investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD), and under the guidance of experts, we construct the MIIAD Bench with rich modality-missing settings to account for imperfect learning environments with incomplete multimodal information. As expected, we find that most existing MIAD methods perform poorly on the MIIAD Bench, leading to significant performance degradation. To tackle this challenge, we propose a novel two-stage Robust modAlity-aware fusing and Detecting framewoRk, abbreviated as RADAR. Specifically: i) We propose Modality-incomplete Instruction to guide the multimodal Transformer to robustly adapt to various modality-incomplete scenarios, and implement adaptive parameter learning based on HyperNetwork. ii) Then, we construct a Double-Pseudo Hybrid Module to highlight the uniqueness of modality combinations, mitigating overfitting issues and further enhancing the robustness of the MIIAD model. Our experimental results demonstrate that the proposed RADAR significantly outperforms traditional MIAD methods on our newly created MIIAD dataset, proving its practical application value.", "date": "2024-10-02", "categories": []}
{"id": "2507.17394", "title": "HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMs", "abstract": "Video Anomaly Detection (VAD) aims to identify and locate deviations from normal patterns in video sequences. Traditional methods often struggle with substantial computational demands and a reliance on extensive labeled datasets, thereby restricting their practical applicability. To address these constraints, we propose HiProbe-VAD, a novel framework that leverages pre-trained Multimodal Large Language Models (MLLMs) for VAD without requiring fine-tuning. In this paper, we discover that the intermediate hidden states of MLLMs contain information-rich representations, exhibiting higher sensitivity and linear separability for anomalies compared to the output layer. To capitalize on this, we propose a Dynamic Layer Saliency Probing (DLSP) mechanism that intelligently identifies and extracts the most informative hidden states from the optimal intermediate layer during the MLLMs reasoning. Then a lightweight anomaly scorer and temporal localization module efficiently detects anomalies using these extracted hidden states and finally generate explanations. Experiments on the UCF-Crime and XD-Violence datasets demonstrate that HiProbe-VAD outperforms existing training-free and most traditional approaches. Furthermore, our framework exhibits remarkable cross-model generalization capabilities in different MLLMs without any tuning, unlocking the potential of pre-trained MLLMs for video anomaly detection and paving the way for more practical and scalable solutions.", "date": "2025-07-23", "categories": []}
{"id": "2509.05663", "title": "DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches", "abstract": "Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance, while others, despite claiming to be unsupervised, need to be calibrated using a labelled data subset, which is often not available in the real world. This work integrates active learning with an existing unsupervised anomaly detection method by selectively querying the labels of multivariate time series, which are then used to refine the threshold selection process. To achieve this, we introduce a novel query strategy called the dissimilarity-based query strategy (DQS). DQS aims to maximise the diversity of queried samples by evaluating the similarity between anomaly scores using dynamic time warping. We assess the detection performance of DQS in comparison to other query strategies and explore the impact of mislabelling, a topic that is underexplored in the literature. Our findings indicate that DQS performs best in small-budget scenarios, though the others appear to be more robust when faced with mislabelling. Therefore, in the real world, the choice of query strategy depends on the expertise of the oracle and the number of samples they are willing to label. Regardless, all query strategies outperform the unsupervised threshold even in the presence of mislabelling. Thus, whenever it is feasible to query an oracle, employing an active learning-based threshold is recommended.", "date": "2025-09-06", "categories": []}
{"id": "2510.21710", "title": "A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems", "abstract": "Instant payment infrastructures have stringent performance requirements, processing millions of transactions daily with zero-downtime expectations. Traditional monitoring approaches fail to bridge the gap between technical infrastructure metrics and business process visibility. We introduce a novel feature engineering approach based on processing times computed between consecutive ISO 20022 message exchanges, creating a compact representation of system state. By applying anomaly detection to these features, we enable early failure detection and localization, allowing incident classification. Experimental evaluation on the TARGET Instant Payment Settlement (TIPS) system, using both real-world incidents and controlled simulations, demonstrates the approach's effectiveness in detecting diverse anomaly patterns and provides inherently interpretable explanations that enable operators to understand the business impact. By mapping features to distinct processing phases, the resulting framework differentiates between internal and external payment system issues, significantly reduces investigation time, and bridges observability gaps in distributed systems where transaction state is fragmented across multiple entities.", "date": "2025-08-07", "categories": []}
{"id": "2510.21793", "title": "2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection", "abstract": "Industrial anomaly detection (IAD) increasingly benefits from integrating 2D and 3D data, but robust cross-modal fusion remains challenging. We propose a novel unsupervised framework, Multi-Modal Attention-Driven Fusion Restoration (MAFR), which synthesises a unified latent space from RGB images and point clouds using a shared fusion encoder, followed by attention-guided, modality-specific decoders. Anomalies are localised by measuring reconstruction errors between input features and their restored counterparts. Evaluations on the MVTec 3D-AD and Eyecandies benchmarks demonstrate that MAFR achieves state-of-the-art results, with a mean I-AUROC of 0.972 and 0.901, respectively. The framework also exhibits strong performance in few-shot learning settings, and ablation studies confirm the critical roles of the fusion architecture and composite loss. MAFR offers a principled approach for fusing visual and geometric information, advancing the robustness and accuracy of industrial anomaly detection. Code is available at https://github.com/adabrh/MAFR", "date": "2025-10-20", "categories": []}
{"id": "2510.22056", "title": "Human-Centric Anomaly Detection in Surveillance Videos Using YOLO-World and Spatio-Temporal Deep Learning", "abstract": "Anomaly detection in surveillance videos remains a challenging task due to the diversity of abnormal events, class imbalance, and scene-dependent visual clutter. To address these issues, we propose a robust deep learning framework that integrates human-centric preprocessing with spatio-temporal modeling for multi-class anomaly classification. Our pipeline begins by applying YOLO-World - an open-vocabulary vision-language detector - to identify human instances in raw video clips, followed by ByteTrack for consistent identity-aware tracking. Background regions outside detected bounding boxes are suppressed via Gaussian blurring, effectively reducing scene-specific distractions and focusing the model on behaviorally relevant foreground content. The refined frames are then processed by an ImageNet-pretrained InceptionV3 network for spatial feature extraction, and temporal dynamics are captured using a bidirectional LSTM (BiLSTM) for sequence-level classification. Evaluated on a five-class subset of the UCF-Crime dataset (Normal, Burglary, Fighting, Arson, Explosion), our method achieves a mean test accuracy of 92.41% across three independent trials, with per-class F1-scores consistently exceeding 0.85. Comprehensive evaluation metrics - including confusion matrices, ROC curves, and macro/weighted averages - demonstrate strong generalization and resilience to class imbalance. The results confirm that foreground-focused preprocessing significantly enhances anomaly discrimination in real-world surveillance scenarios.", "date": "2025-10-24", "categories": []}
{"id": "2510.22619", "title": "CLEANet: Robust and Efficient Anomaly Detection in Contaminated Multivariate Time Series", "abstract": "Multivariate time series (MTS) anomaly detection is essential for maintaining the reliability of industrial systems, yet real-world deployment is hindered by two critical challenges: training data contamination (noises and hidden anomalies) and inefficient model inference. Existing unsupervised methods assume clean training data, but contamination distorts learned patterns and degrades detection accuracy. Meanwhile, complex deep models often overfit to contamination and suffer from high latency, limiting practical use. To address these challenges, we propose CLEANet, a robust and efficient anomaly detection framework in contaminated multivariate time series. CLEANet introduces a Contamination-Resilient Training Framework (CRTF) that mitigates the impact of corrupted samples through an adaptive reconstruction weighting strategy combined with clustering-guided contrastive learning, thereby enhancing robustness. To further avoid overfitting on contaminated data and improve computational efficiency, we design a lightweight conjugate MLP that disentangles temporal and cross-feature dependencies. Across five public datasets, CLEANet achieves up to 73.04% higher F1 and 81.28% lower runtime compared with ten state-of-the-art baselines. Furthermore, integrating CRTF into three advanced models yields an average 5.35% F1 gain, confirming its strong generalizability.", "date": "2025-10-26", "categories": []}
{"id": "2510.22928", "title": "Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond", "abstract": "Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety but challenging for existing methods due to their limited sensitivity, scalability, and inability to capture intricate dependencies. We propose the Diffuse to Detect (DTD) framework, a novel approach that innovatively adapts diffusion models for anomaly detection, diverging from their conventional use in generative tasks with high inference time. By comparison, DTD employs a single-step diffusion process to predict noise patterns, enabling rapid and precise identification of anomalies without reconstruction errors. This approach is grounded in robust theoretical foundations that link noise prediction to the data distribution's score function, ensuring reliable deviation detection. By integrating Graph Neural Networks to model sensor relationships as dynamic graphs, DTD effectively captures spatial (inter-sensor) and temporal anomalies. Its two-branch architecture, with parametric neural network-based energy scoring for scalability and nonparametric statistical methods for interpretability, provides flexible trade-offs between computational efficiency and transparency. Extensive evaluations on UAV sensor data, multivariate time series, and images demonstrate DTD's superior performance over existing methods, underscoring its generality across diverse data modalities. This versatility, combined with its adaptability, positions DTD as a transformative solution for safety-critical applications, including industrial monitoring and beyond.", "date": "2025-10-27", "categories": []}
{"id": "2510.23136", "title": "A method for outlier detection based on cluster analysis and visual expert criteria", "abstract": "Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering-based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance-related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.", "date": "2025-10-27", "categories": []}
{"id": "2510.23190", "title": "Evaluation of Vision-LLMs in Surveillance Video", "abstract": "The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective response and prevention. The ability for an embodied agent to recognize unexpected events is fundamentally tied to its capacity for spatial reasoning. This paper investigates the spatial reasoning of vision-language models (VLMs) by framing anomalous action recognition as a zero-shot, language-grounded task, addressing the embodied perception challenge of interpreting dynamic 3D scenes from sparse 2D video. Specifically, we investigate whether small, pre-trained vision--LLMs can act as spatially-grounded, zero-shot anomaly detectors by converting video into text descriptions and scoring labels via textual entailment. We evaluate four open models on UCF-Crime and RWF-2000 under prompting and privacy-preserving conditions. Few-shot exemplars can improve accuracy for some models, but may increase false positives, and privacy filters -- especially full-body GAN transforms -- introduce inconsistencies that degrade accuracy. These results chart where current vision--LLMs succeed (simple, spatially salient events) and where they falter (noisy spatial cues, identity obfuscation). Looking forward, we outline concrete paths to strengthen spatial grounding without task-specific training: structure-aware prompts, lightweight spatial memory across clips, scene-graph or 3D-pose priors during description, and privacy methods that preserve action-relevant geometry. This positions zero-shot, language-grounded pipelines as adaptable building blocks for embodied, real-world video understanding. Our implementation for evaluating VLMs is publicly available at: https://github.com/pascalbenschopTU/VLLM_AnomalyRecognition", "date": "2025-10-27", "categories": []}
{"id": "2510.23327", "title": "GRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques", "abstract": "This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors that integrates statistical analysis and deep learning to ensure the reliability of sensor data. The proposed approach combines the Reinforced Exponential Moving Average (REMA), which adapts smoothing factors and thresholding for outlier detection, with the Multi-Stage Sliding Window (MS-SW) technique for capturing both short- and long-term patterns. These features are processed using a lightweight Gated Recurrent Unit (GRU) model, which detects and classifies anomalies based on bias types, while a recovery module restores damaged sensor data to ensure continuous system operation. GRAD has a lightweight architecture consisting of two layers of GRU with a limited number of neurons that make it appropriate for real-time applications while maintaining high detection accuracy. The GRAD framework achieved remarkable performance in anomaly detection and classification. The model demonstrated an overall F1-score of 97.6% for abnormal data and 99.4% for normal data, signifying its high accuracy in distinguishing between normal and anomalous sensor data. Regarding the anomaly classification, GRAD successfully categorized different anomaly types with high precision, enabling the recovery module to accurately restore damaged sensor data. Relative to analogous studies, GRAD surpasses current models by attaining a balance between elevated detection accuracy and diminished computational expense. These results demonstrate GRAD's potential as a reliable and efficient solution for real-time anomaly detection in autonomous vehicle systems, guaranteeing safe vehicle operation with minimal computational overhead.", "date": "2025-10-27", "categories": []}
{"id": "2506.09237", "title": "PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies", "abstract": "Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to adversarial attacks due to limitations in training data, which typically include only normal, unlabeled samples. This study introduces PatchGuard, an adversarially robust AD and AL method that incorporates pseudo anomalies with localization masks within a Vision Transformer (ViT)-based architecture to address these vulnerabilities. We begin by examining the essential properties of pseudo anomalies, and follow it by providing theoretical insights into the attention mechanisms required to enhance the adversarial robustness of AD and AL systems. We then present our approach, which leverages Foreground-Aware Pseudo-Anomalies to overcome the deficiencies of previous anomaly-aware methods. Our method incorporates these crafted pseudo-anomaly samples into a ViT-based framework, with adversarial training guided by a novel loss function designed to improve model robustness, as supported by our theoretical analysis. Experimental results on well-established industrial and medical datasets demonstrate that PatchGuard significantly outperforms previous methods in adversarial settings, achieving performance gains of $53.2\\%$ in AD and $68.5\\%$ in AL, while also maintaining competitive accuracy in non-adversarial settings. The code repository is available at https://github.com/rohban-lab/PatchGuard .", "date": "2025-06-10", "categories": []}
{"id": "2510.17611", "title": "One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection", "abstract": "Unsupervised anomaly detection (UAD) has evolved from building specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the most advanced one-for-one counterparts. Moreover, the field has fragmented into specialized methods tailored to specific scenarios (multi-class, 3D, few-shot, etc.), creating deployment barriers and highlighting the need for a unified solution. In this paper, we present Dinomaly2, the first unified framework for full-spectrum image UAD, which bridges the performance gap in multi-class models while seamlessly extending across diverse data modalities and task settings. Guided by the \"less is more\" philosophy, we demonstrate that the orchestration of five simple element achieves superior performance in a standard reconstruction-based framework. This methodological minimalism enables natural extension across diverse tasks without modification, establishing that simplicity is the foundation of true universality. Extensive experiments on 12 UAD benchmarks demonstrate Dinomaly2's full-spectrum superiority across multiple modalities (2D, multi-view, RGB-3D, RGB-IR), task settings (single-class, multi-class, inference-unified multi-class, few-shot) and application domains (industrial, biological, outdoor). For example, our multi-class model achieves unprecedented 99.9% and 99.3% image-level (I-) AUROC on MVTec-AD and VisA respectively. For multi-view and multi-modal inspection, Dinomaly2 demonstrates state-of-the-art performance with minimum adaptations. Moreover, using only 8 normal examples per class, our method surpasses previous full-shot models, achieving 98.7% and 97.4% I-AUROC on MVTec-AD and VisA. The combination of minimalistic design, computational scalability, and universal applicability positions Dinomaly2 as a unified solution for the full spectrum of real-world anomaly detection applications.", "date": "2025-10-20", "categories": []}
{"id": "2510.21449", "title": "MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for a more nuanced understanding of anomalies. However, online VAD has seldom received attention due to real-time constraints and computational intensity. In this paper, we introduce a novel Memory-based online scoring queue scheme for Training-free VAD (MoniTor), to address the inherent complexities in online VAD. Specifically, MoniTor applies a streaming input to VLMs, leveraging the capabilities of pre-trained large-scale models. To capture temporal dependencies more effectively, we incorporate a novel prediction mechanism inspired by Long Short-Term Memory (LSTM) networks. This ensures the model can effectively model past states and leverage previous predictions to identify anomalous behaviors. Thereby, it better understands the current frame. Moreover, we design a scoring queue and an anomaly prior to dynamically store recent scores and cover all anomalies in the monitoring scenario, providing guidance for LLMs to distinguish between normal and abnormal behaviors over time. We evaluate MoniTor on two large datasets (i.e., UCF-Crime and XD-Violence) containing various surveillance and real-world scenarios. The results demonstrate that MoniTor outperforms state-of-the-art methods and is competitive with weakly supervised methods without training. Code is available at https://github.com/YsTvT/MoniTor.", "date": "2025-10-24", "categories": []}
{"id": "2510.21532", "title": "FrameShield: Adversarially Robust Video Anomaly Detection", "abstract": "Weakly Supervised Video Anomaly Detection (WSVAD) has achieved notable advancements, yet existing models remain vulnerable to adversarial attacks, limiting their reliability. Due to the inherent constraints of weak supervision, where only video-level labels are provided despite the need for frame-level predictions, traditional adversarial defense mechanisms, such as adversarial training, are not effective since video-level adversarial perturbations are typically weak and inadequate. To address this limitation, pseudo-labels generated directly from the model can enable frame-level adversarial training; however, these pseudo-labels are inherently noisy, significantly degrading performance. We therefore introduce a novel Pseudo-Anomaly Generation method called Spatiotemporal Region Distortion (SRD), which creates synthetic anomalies by applying severe augmentations to localized regions in normal videos while preserving temporal consistency. Integrating these precisely annotated synthetic anomalies with the noisy pseudo-labels substantially reduces label noise, enabling effective adversarial training. Extensive experiments demonstrate that our method significantly enhances the robustness of WSVAD models against adversarial attacks, outperforming state-of-the-art methods by an average of 71.0\\% in overall AUROC performance across multiple benchmarks. The implementation and code are publicly available at https://github.com/rohban-lab/FrameShield.", "date": "2025-10-24", "categories": []}
{"id": "2504.15846", "title": "Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions", "abstract": "Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints necessitate robust methods for identifying regions of interest in real time. This work presents an adaptive outlier detection algorithm based on the reconstruction error of Principal Component Analysis (PCA) for feature reduction, designed explicitly for space mission applications. The algorithm adapts dynamically to evolving data distributions by using Incremental PCA, enabling deployment without a predefined model for all possible conditions. A pre-scaling process normalizes each feature's magnitude while preserving relative variance within feature types. We demonstrate the algorithm's effectiveness in detecting space plasma events, such as distinct space environments, dayside and nightside transients phenomena, and transition layers through NASA's MMS mission observations. Additionally, we apply the method to NASA's THEMIS data, successfully identifying a dayside transient using onboard-available measurements.", "date": "2025-04-22", "categories": []}
{"id": "2510.10750", "title": "Uncovering Anomalous Events for Marine Environmental Monitoring via Visual Anomaly Detection", "abstract": "Underwater video monitoring is a promising strategy for assessing marine biodiversity, but the vast volume of uneventful footage makes manual inspection highly impractical. In this work, we explore the use of visual anomaly detection (VAD) based on deep neural networks to automatically identify interesting or anomalous events. We introduce AURA, the first multi-annotator benchmark dataset for underwater VAD, and evaluate four VAD models across two marine scenes. We demonstrate the importance of robust frame selection strategies to extract meaningful video segments. Our comparison against multiple annotators reveals that VAD performance of current models varies dramatically and is highly sensitive to both the amount of training data and the variability in visual content that defines \"normal\" scenes. Our results highlight the value of soft and consensus labels and offer a practical approach for supporting scientific exploration and scalable biodiversity monitoring.", "date": "2025-10-12", "categories": []}
{"id": "2510.20334", "title": "Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment", "abstract": "The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.", "date": "2025-10-23", "categories": []}
{"id": "2510.20718", "title": "Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series", "abstract": "Semiconductor manufacturing is an extremely complex and precision-driven process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate time-series analysis has emerged as a critical field for real-time monitoring and fault detection in such environments. However, anomaly prediction in semiconductor fabrication presents several critical challenges, including high dimensionality of sensor data and severe class imbalance due to the rarity of true faults. Furthermore, the complex interdependencies between variables complicate both anomaly prediction and root-cause-analysis. This paper proposes two novel approaches to advance the field from anomaly detection to anomaly prediction, an essential step toward enabling real-time process correction and proactive fault prevention. The proposed anomaly prediction framework contains two main stages: (a) training a forecasting model on a dataset assumed to contain no anomalies, and (b) performing forecast on unseen time series data. The forecast is compared with the forecast of the trained signal. Deviations beyond a predefined threshold are flagged as anomalies. The two approaches differ in the forecasting model employed. The first assumes independence between variables by utilizing the N-BEATS model for univariate time series forecasting. The second lifts this assumption by utilizing a Graph Neural Network (GNN) to capture inter-variable relationships. Both models demonstrate strong forecasting performance up to a horizon of 20 time points and maintain stable anomaly prediction up to 50 time points. The GNN consistently outperforms the N-BEATS model while requiring significantly fewer trainable parameters and lower computational cost. These results position the GNN as promising solution for online anomaly forecasting to be deployed in manufacturing environments.", "date": "2025-10-23", "categories": []}
{"id": "2510.18998", "title": "An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version", "abstract": "Time series anomaly detection is important in modern large-scale systems and is applied in a variety of domains to analyze and monitor the operation of diverse systems. Unsupervised approaches have received widespread interest, as they do not require anomaly labels during training, thus avoiding potentially high costs and having wider applications. Among these, autoencoders have received extensive attention. They use reconstruction errors from compressed representations to define anomaly scores. However, representations learned by autoencoders are sensitive to anomalies in training time series, causing reduced accuracy. We propose a novel encode-then-decompose paradigm, where we decompose the encoded representation into stable and auxiliary representations, thereby enhancing the robustness when training with contaminated time series. In addition, we propose a novel mutual information based metric to replace the reconstruction errors for identifying anomalies. Our proposal demonstrates competitive or state-of-the-art performance on eight commonly used multi- and univariate time series benchmarks and exhibits robustness to time series with different contamination ratios.", "date": "2025-10-21", "categories": []}
{"id": "2410.04039", "title": "BlockScan: Detecting Anomalies in Blockchain Transactions", "abstract": "We propose BlockScan, a customized Transformer for anomaly detection in blockchain transactions. Unlike existing methods that rely on rule-based systems or directly apply off-the-shelf large language models (LLMs), BlockScan introduces a series of customized designs to effectively model the unique data structure of blockchain transactions. First, a blockchain transaction is multi-modal, containing blockchain-specific tokens, texts, and numbers. We design a novel modularized tokenizer to handle these multi-modal inputs, balancing the information across different modalities. Second, we design a customized masked language modeling mechanism for pretraining the Transformer architecture, incorporating RoPE embedding and FlashAttention for handling longer sequences. Finally, we design a novel anomaly detection method based on the model outputs. We further provide theoretical analysis for the detection method of our system. Extensive evaluations on Ethereum and Solana transactions demonstrate BlockScan's exceptional capability in anomaly detection while maintaining a low false positive rate. Remarkably, BlockScan is the only method that successfully detects anomalous transactions on Solana with high accuracy, whereas all other approaches achieved very low or zero detection recall scores. This work sets a new benchmark for applying Transformer-based approaches in blockchain data analysis.", "date": "2024-10-05", "categories": []}
{"id": "2510.18187", "title": "VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis", "abstract": "Detecting anomalies in crowded scenes is challenging due to severe inter-person occlusions and highly dynamic, context-dependent motion patterns. Existing approaches often struggle to adapt to varying crowd densities and lack interpretable anomaly indicators. To address these limitations, we introduce VelocityNet, a dual-pipeline framework that combines head detection and dense optical flow to extract person-specific velocities. Hierarchical clustering categorizes these velocities into semantic motion classes (halt, slow, normal, and fast), and a percentile-based anomaly scoring system measures deviations from learned normal patterns. Experiments demonstrate the effectiveness of our framework in real-time detection of diverse anomalous motion patterns within densely crowded environments.", "date": "2025-10-21", "categories": []}
{"id": "2510.18328", "title": "Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching", "abstract": "We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generative modeling framework that learns velocity fields between probability distributions and has shown strong performance compared to diffusion models and generative adversarial networks. Instead of directly applying flow matching as originally formulated, TCCM builds on its core idea -- learning velocity fields between distributions -- but simplifies the framework by predicting a time-conditioned contraction vector toward a fixed target (the origin) at each sampled time step. This design offers three key advantages: (1) a lightweight and scalable training objective that removes the need for solving ordinary differential equations during training and inference; (2) an efficient scoring strategy called one time-step deviation, which quantifies deviation from expected contraction behavior in a single forward pass, addressing the inference bottleneck of existing continuous-time models such as DTE (a diffusion-based model with leading anomaly detection accuracy but heavy inference cost); and (3) explainability and provable robustness, as the learned velocity field operates directly in input space, making the anomaly score inherently feature-wise attributable; moreover, the score function is Lipschitz-continuous with respect to the input, providing theoretical guarantees under small perturbations. Extensive experiments on the ADBench benchmark show that TCCM strikes a favorable balance between detection accuracy and inference cost, outperforming state-of-the-art methods -- especially on high-dimensional and large-scale datasets. The source code is available at our GitHub repository.", "date": "2025-10-21", "categories": []}
{"id": "2310.04055", "title": "Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification", "abstract": "Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in real-world deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propose RedJasper, a two-staged anomaly detection method specifically designed for real-world FL deployments. It identifies suspicious activities in the first stage, then activates the second stage conditionally to further scrutinize the suspicious local models, employing the 3{\\sigma} rule to identify real malicious local models and filtering them out from FL training. To ensure integrity and transparency within the FL system, RedJasper integrates zero-knowledge proofs, enabling clients to cryptographically verify the server's detection process without relying on the server's goodwill. RedJasper operates without unrealistic assumptions and avoids interfering with FL training in attack-free scenarios. It bridges the gap between theoretical advances in FL security and the practical demands of real-world deployment. Experimental results demonstrate that RedJasper consistently delivers performance comparable to benign cases, highlighting its effectiveness in identifying potential attacks and eliminating malicious models with high accuracy.", "date": "2023-10-06", "categories": []}
{"id": "2502.14293", "title": "Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision", "abstract": "Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present GADT3, a novel test-time training framework for cross-domain GAD. GADT3 combines supervised and self-supervised learning during training while adapting to a new domain during test time using only self-supervised learning by leveraging a homophily-based affinity score that captures domain-invariant properties of anomalies. Our framework introduces four key innovations to cross-domain GAD: an effective self-supervision scheme, an attention-based mechanism that dynamically learns edge importance weights during message passing, domain-specific encoders for handling heterogeneous features, and class-aware regularization to address imbalance. Experiments across multiple cross-domain settings demonstrate that GADT3 significantly outperforms existing approaches, achieving average improvements of over 8.2\\% in AUROC and AUPRC compared to the best competing model.", "date": "2025-02-20", "categories": []}
{"id": "2505.17881", "title": "Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization", "abstract": "In recent years, tensor decomposition-based approaches for hyperspectral anomaly detection (HAD) have gained significant attention in the field of remote sensing. However, existing methods often fail to fully leverage both the global correlations and local smoothness of the background components in hyperspectral images (HSIs), which exist in both the spectral and spatial domains. This limitation results in suboptimal detection performance. To mitigate this critical issue, we put forward a novel HAD method named HAD-EUNTRFR, which incorporates an enhanced unified nonconvex tensor ring (TR) factors regularization. In the HAD-EUNTRFR framework, the raw HSIs are first decomposed into background and anomaly components. The TR decomposition is then employed to capture the spatial-spectral correlations within the background component. Additionally, we introduce a unified and efficient nonconvex regularizer, induced by tensor singular value decomposition (TSVD), to simultaneously encode the low-rankness and sparsity of the 3-D gradient TR factors into a unique concise form. The above characterization scheme enables the interpretable gradient TR factors to inherit the low-rankness and smoothness of the original background. To further enhance anomaly detection, we design a generalized nonconvex regularization term to exploit the group sparsity of the anomaly component. To solve the resulting doubly nonconvex model, we develop a highly efficient optimization algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results on several benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) approaches in terms of detection accuracy.", "date": "2025-05-23", "categories": []}
{"id": "2507.17486", "title": "Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease", "abstract": "Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological disorders. In this work, we focus on Bayesian flow networks (BFNs), a novel class of generative models, which have not yet been applied to medical imaging or anomaly detection. BFNs combine the strength of diffusion frameworks and Bayesian inference. We introduce AnoBFN, an extension of BFNs for UAD, designed to: i) perform conditional image generation under high levels of spatially correlated noise, and ii) preserve subject specificity by incorporating a recursive feedback from the input image throughout the generative process. We evaluate AnoBFN on the challenging task of Alzheimer's disease-related anomaly detection in FDG PET images. Our approach outperforms other state-of-the-art methods based on VAEs (beta-VAE), GANs (f-AnoGAN), and diffusion models (AnoDDPM), demonstrating its effectiveness at detecting anomalies while reducing false positive rates.", "date": "2025-07-23", "categories": []}
{"id": "2509.01719", "title": "Robust Anomaly Detection through Multi-Modal Autoencoder Fusion for Small Vehicle Damage Detection", "abstract": "Wear and tear detection in fleet and shared vehicle systems is a critical challenge, particularly in rental and car-sharing services, where minor damage, such as dents, scratches, and underbody impacts, often goes unnoticed or is detected too late. Currently, manual inspection methods are the default approach, but are labour-intensive and prone to human error. In contrast, state-of-the-art image-based methods are less reliable when the vehicle is moving, and they cannot effectively capture underbody damage due to limited visual access and spatial coverage. This work introduces a novel multi-modal architecture based on anomaly detection to address these issues. Sensors such as Inertial Measurement Units (IMUs) and microphones are integrated into a compact device mounted on the vehicle's windshield. This approach supports real-time damage detection while avoiding the need for highly resource-intensive sensors. We developed multiple variants of multi-modal autoencoder-based architectures and evaluated them against unimodal and state-of-the-art methods. Our multi-modal ensemble model with pooling achieved the highest performance, with a Receiver Operating Characteristic-Area Under Curve (ROC-AUC) of 92%, demonstrating its effectiveness in real-world applications. This approach can also be extended to other applications, such as improving automotive safety. It can integrate with airbag systems for efficient deployment and help autonomous vehicles by complementing other sensors in collision detection.", "date": "2025-09-01", "categories": []}
{"id": "2510.15188", "title": "OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs", "abstract": "Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these logs as connected entities and events, revealing relationships that are missed by linear log representations. Existing systems apply anomaly detection to these graphs but often suffer from high false positive rates and coarse-grained alerts. Their reliance on node attributes like file paths or IPs leads to spurious correlations, reducing detection robustness and reliability. To fully understand an attack's progression and impact, security analysts need systems that can generate accurate, human-like narratives of the entire attack. To address these challenges, we introduce OCR-APT, a system for APT detection and reconstruction of human-like attack stories. OCR-APT uses Graph Neural Networks (GNNs) for subgraph anomaly detection, learning behavior patterns around nodes rather than fragile attributes such as file paths or IPs. This approach leads to a more robust anomaly detection. It then iterates over detected subgraphs using Large Language Models (LLMs) to reconstruct multi-stage attack stories. Each stage is validated before proceeding, reducing hallucinations and ensuring an interpretable final report. Our evaluations on the DARPA TC3, OpTC, and NODLINK datasets show that OCR-APT outperforms state-of-the-art systems in both detection accuracy and alert interpretability. Moreover, OCR-APT reconstructs human-like reports that comprehensively capture the attack story.", "date": "2025-10-16", "categories": []}
{"id": "2510.16014", "title": "STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter", "abstract": "While Time Series Foundation Models (TSFMs) have demonstrated remarkable success in Multivariate Time Series Anomaly Detection (MTSAD), however, in real-world industrial scenarios, many time series comprise not only numerical variables such as temperature and flow, but also numerous discrete state variables that describe the system status, such as valve on/off or day of the week. Existing TSFMs often overlook the distinct categorical nature of state variables and their critical role as conditions, typically treating them uniformly with numerical variables. This inappropriate modeling approach prevents the model from fully leveraging state information and even leads to a significant degradation in detection performance after state variables are integrated. To address this critical limitation, this paper proposes a novel STate-aware AdapteR (STAR). STAR is a plug-and-play module designed to enhance the capability of TSFMs in modeling and leveraging state variables during the fine-tuning stage. Specifically, STAR comprisesthree core components: (1) We design an Identity-guided State Encoder, whicheffectively captures the complex categorical semantics of state variables through a learnable State Memory. (2) We propose a Conditional Bottleneck Adapter, which dynamically generates low-rank adaptation parameters conditioned on the current state, thereby flexibly injecting the influence of state variables into the backbone model. (3) We also introduce a Numeral-State Matching module to more effectively detect anomalies inherent to the state variables themselves. Extensive experiments conducted on real-world datasets demonstrate that STAR can improve the performance of existing TSFMs on MTSAD.", "date": "2025-10-15", "categories": []}
{"id": "2510.16036", "title": "IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection", "abstract": "The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most traditional IAD methods lack the ability to provide multi-turn human-machine dialogues and detailed descriptions, such as the color of objects, the shape of an anomaly, or specific types of anomalies. At the same time, methods based on large pre-trained models have not fully stimulated the ability of large models in anomaly detection tasks. In this paper, we explore the combination of rich text semantics with both image-level and pixel-level information from images and propose IAD-GPT, a novel paradigm based on MLLMs for IAD. We employ Abnormal Prompt Generator (APG) to generate detailed anomaly prompts for specific objects. These specific prompts from the large language model (LLM) are used to activate the detection and segmentation functions of the pre-trained visual-language model (i.e., CLIP). To enhance the visual grounding ability of MLLMs, we propose Text-Guided Enhancer, wherein image features interact with normal and abnormal text prompts to dynamically select enhancement pathways, which enables language models to focus on specific aspects of visual data, enhancing their ability to accurately interpret and respond to anomalies within images. Moreover, we design a Multi-Mask Fusion module to incorporate mask as expert knowledge, which enhances the LLM's perception of pixel-level anomalies. Extensive experiments on MVTec-AD and VisA datasets demonstrate our state-of-the-art performance on self-supervised and few-shot anomaly detection and segmentation tasks, such as MVTec-AD and VisA datasets. The codes are available at \\href{https://github.com/LiZeWen1225/IAD-GPT}{https://github.com/LiZeWen1225/IAD-GPT}.", "date": "2025-10-16", "categories": []}
{"id": "2510.16044", "title": "A Novel GPT-Based Framework for Anomaly Detection in System Logs", "abstract": "Identification of anomalous events within system logs constitutes a pivotal element within the frame- work of cybersecurity defense strategies. However, this process faces numerous challenges, including the management of substantial data volumes, the distribution of anomalies, and the precision of con- ventional methods. To address this issue, the present paper puts forward a proposal for an intelligent detection method for system logs based on Genera- tive Pre-trained Transformers (GPT). The efficacy of this approach is attributable to a combination of structured input design and a Focal Loss op- timization strategy, which collectively result in a substantial enhancement of the performance of log anomaly detection. The initial approach involves the conversion of raw logs into event ID sequences through the use of the Drain parser. Subsequently, the Focal Loss loss function is employed to address the issue of class imbalance. The experimental re- sults demonstrate that the optimized GPT-2 model significantly outperforms the unoptimized model in a range of key metrics, including precision, recall, and F1 score. In specific tasks, comparable or superior performance has been demonstrated to that of the GPT-3.5 API.", "date": "2025-10-16", "categories": []}
{"id": "2510.16290", "title": "Cerberus: Real-Time Video Anomaly Detection via Cascaded Vision-Language Models", "abstract": "Video anomaly detection (VAD) has rapidly advanced by recent development of Vision-Language Models (VLMs). While these models offer superior zero-shot detection capabilities, their immense computational cost and unstable visual grounding performance hinder real-time deployment. To overcome these challenges, we introduce Cerberus, a two-stage cascaded system designed for efficient yet accurate real-time VAD. Cerberus learns normal behavioral rules offline, and combines lightweight filtering with fine-grained VLM reasoning during online inference. The performance gains of Cerberus come from two key innovations: motion mask prompting and rule-based deviation detection. The former directs the VLM's attention to regions relevant to motion, while the latter identifies anomalies as deviations from learned norms rather than enumerating possible anomalies. Extensive evaluations on four datasets show that Cerberus on average achieves 57.68 fps on an NVIDIA L40S GPU, a 151.79$\\times$ speedup, and 97.2\\% accuracy comparable to the state-of-the-art VLM-based VAD methods, establishing it as a practical solution for real-time video analytics.", "date": "2025-10-18", "categories": []}
{"id": "2510.16370", "title": "MIRAD - A comprehensive real-world robust anomaly detection dataset for Mass Individualization", "abstract": "Social manufacturing leverages community collaboration and scattered resources to realize mass individualization in modern industry. However, this paradigm shift also introduces substantial challenges in quality control, particularly in defect detection. The main difficulties stem from three aspects. First, products often have highly customized configurations. Second, production typically involves fragmented, small-batch orders. Third, imaging environments vary considerably across distributed sites. To overcome the scarcity of real-world datasets and tailored algorithms, we introduce the Mass Individualization Robust Anomaly Detection (MIRAD) dataset. As the first benchmark explicitly designed for anomaly detection in social manufacturing, MIRAD captures three critical dimensions of this domain: (1) diverse individualized products with large intra-class variation, (2) data collected from six geographically dispersed manufacturing nodes, and (3) substantial imaging heterogeneity, including variations in lighting, background, and motion conditions. We then conduct extensive evaluations of state-of-the-art (SOTA) anomaly detection methods on MIRAD, covering one-class, multi-class, and zero-shot approaches. Results show a significant performance drop across all models compared with conventional benchmarks, highlighting the unresolved complexities of defect detection in real-world individualized production. By bridging industrial requirements and academic research, MIRAD provides a realistic foundation for developing robust quality control solutions essential for Industry 5.0. The dataset is publicly available at https://github.com/wu33learn/MIRAD.", "date": "2025-10-18", "categories": []}
{"id": "2510.16511", "title": "Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection", "abstract": "Real-world multivariate time series anomalies are rare and often unlabeled. Additionally, prevailing methods rely on increasingly complex architectures tuned to benchmarks, detecting only fragments of anomalous segments and overstating performance. In this paper, we introduce OracleAD, a simple and interpretable unsupervised framework for multivariate time series anomaly detection. OracleAD encodes each variable's past sequence into a single causal embedding to jointly predict the present time point and reconstruct the input window, effectively modeling temporal dynamics. These embeddings then undergo a self-attention mechanism to project them into a shared latent space and capture spatial relationships. These relationships are not static, since they are modeled by a property that emerges from each variable's temporal dynamics. The projected embeddings are aligned to a Stable Latent Structure (SLS) representing normal-state relationships. Anomalies are identified using a dual scoring mechanism based on prediction error and deviation from the SLS, enabling fine-grained anomaly diagnosis at each time point and across individual variables. Since any noticeable SLS deviation originates from embeddings that violate the learned temporal causality of normal data, OracleAD directly pinpoints the root-cause variables at the embedding level. OracleAD achieves state-of-the-art results across multiple real-world datasets and evaluation protocols, while remaining interpretable through SLS.", "date": "2025-10-18", "categories": []}
{"id": "2510.16865", "title": "Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection", "abstract": "3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invariance. These limitations become more pronounced when registration fails, leading to unreliable detection results. We argue that point-cloud registration plays an essential role not only in aligning geometric structures but also in guiding feature extraction toward rotation-invariant and locally discriminative representations. To this end, we propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives of point-cloud registration and memory-based anomaly detection. Our key insight is that both tasks rely on modeling local geometric structures and leveraging feature similarity across samples. By embedding feature extraction into the registration learning process, our framework jointly optimizes alignment and representation learning. This integration enables the network to acquire features that are both robust to rotations and highly effective for anomaly detection. Extensive experiments on the Anomaly-ShapeNet and Real3D-AD datasets demonstrate that our method consistently outperforms existing approaches in effectiveness and generalizability.", "date": "2025-10-19", "categories": []}
{"id": "2510.17562", "title": "Formally Exploring Time-Series Anomaly Detection Evaluation Metrics", "abstract": "Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection methods have been proposed, their performance remains unclear because current metrics capture only narrow aspects of the task and often yield misleading results. We address this issue by introducing verifiable properties that formalize essential requirements for evaluating time-series anomaly detection. These properties enable a theoretical framework that supports principled evaluations and reliable comparisons. Analyzing 37 widely used metrics, we show that most satisfy only a few properties, and none satisfy all, explaining persistent inconsistencies in prior results. To close this gap, we propose LARM, a flexible metric that provably satisfies all properties, and extend it to ALARM, an advanced variant meeting stricter requirements.", "date": "2025-10-20", "categories": []}
{"id": "2508.07441", "title": "Leveraging Learning Bias for Noisy Anomaly Detection", "abstract": "This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-free training data, but real-world contamination leads models to absorb anomalies as normal, degrading detection performance. To mitigate this, we propose a two-stage framework that systematically exploits inherent learning bias in models. The learning bias stems from: (1) the statistical dominance of normal samples, driving models to prioritize learning stable normal patterns over sparse anomalies, and (2) feature-space divergence, where normal data exhibit high intra-class consistency while anomalies display high diversity, leading to unstable model responses. Leveraging the learning bias, stage 1 partitions the training set into subsets, trains sub-models, and aggregates cross-model anomaly scores to filter a purified dataset. Stage 2 trains the final detector on this dataset. Experiments on the Real-IAD benchmark demonstrate superior anomaly detection and localization performance under different noise conditions. Ablation studies further validate the framework's contamination resilience, emphasizing the critical role of learning bias exploitation. The model-agnostic design ensures compatibility with diverse unsupervised backbones, offering a practical solution for real-world scenarios with imperfect training data. Code is available at https://github.com/hustzhangyuxin/LLBNAD.", "date": "2025-08-10", "categories": []}
{"id": "2508.19060", "title": "No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimes", "abstract": "Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such as unsupervised, weakly supervised, mixed supervision, and fully supervised settings. To address these challenges, we propose SuperSimpleNet, a highly efficient and adaptable discriminative model built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel synthetic anomaly generation process, an enhanced classification head, and an improved learning procedure, enabling efficient training in all four supervision scenarios, making it the first model capable of fully leveraging all available data annotations. SuperSimpleNet sets a new standard for performance across all scenarios, as demonstrated by its results on four challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an inference time below 10 ms. With its ability to unify diverse supervision paradigms while maintaining outstanding speed and reliability, SuperSimpleNet represents a promising step forward in addressing real-world manufacturing challenges and bridging the gap between academic research and industrial applications. Code: https://github.com/blaz-r/SuperSimpleNet", "date": "2025-08-26", "categories": []}
{"id": "2510.00495", "title": "Normal-Abnormal Guided Generalist Anomaly Detection", "abstract": "Generalist Anomaly Detection (GAD) aims to train a unified model on an original domain that can detect anomalies in new target domains. Previous GAD methods primarily use only normal samples as references, overlooking the valuable information contained in anomalous samples that are often available in real-world scenarios. To address this limitation, we propose a more practical approach: normal-abnormal-guided generalist anomaly detection, which leverages both normal and anomalous samples as references to guide anomaly detection across diverse domains. We introduce the Normal-Abnormal Generalist Learning (NAGL) framework, consisting of two key components: Residual Mining (RM) and Anomaly Feature Learning (AFL). RM extracts abnormal patterns from normal-abnormal reference residuals to establish transferable anomaly representations, while AFL adaptively learns anomaly features in query images through residual mapping to identify instance-aware anomalies. Our approach effectively utilizes both normal and anomalous references for more accurate and efficient cross-domain anomaly detection. Extensive experiments across multiple benchmarks demonstrate that our method significantly outperforms existing GAD approaches. This work represents the first to adopt a mixture of normal and abnormal samples as references in generalist anomaly detection. The code and datasets are available at https://github.com/JasonKyng/NAGL.", "date": "2025-10-01", "categories": []}
{"id": "2510.15010", "title": "Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines", "abstract": "Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in wind turbines. The method integrates Variational Autoencoders (VAE), LSTM Autoencoders, and Transformer architectures, each capturing different temporal and contextual patterns from high-dimensional SCADA data. A unique feature engineering pipeline extracts temporal, statistical, and frequency-domain indicators, which are then processed by the deep models. Ensemble scoring combines model predictions, followed by adaptive thresholding to detect operational anomalies without requiring labeled fault data. Evaluated on the CARE dataset containing 89 years of real-world turbine data across three wind farms, the proposed method achieves an AUC-ROC of 0.947 and early fault detection up to 48 hours prior to failure. This approach offers significant societal value by enabling predictive maintenance, reducing turbine failures, and enhancing operational efficiency in large-scale wind energy deployments.", "date": "2025-10-16", "categories": []}
{"id": "2510.15602", "title": "Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection", "abstract": "Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10x speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: https://reality.tf.fau.de/pub/ardelean2025quantized.html", "date": "2025-10-17", "categories": []}
{"id": "2405.13571", "title": "Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation", "abstract": "Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.", "date": "2024-05-22", "categories": []}
{"id": "2510.14287", "title": "Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing", "abstract": "Reservoir computing (RC) establishes the basis for the processing of time-series data by exploiting the high-dimensional spatiotemporal response of a recurrent neural network to an input signal. In particular, RC trains only the output layer weights. This simplicity has drawn attention especially in Edge Artificial Intelligence (AI) applications. Edge AI enables time-series anomaly detection in real time, which is important because detection delays can lead to serious incidents. However, achieving adequate anomaly-detection performance with RC alone may require an unacceptably large reservoir on resource-constrained edge devices. Without enlarging the reservoir, attention mechanisms can improve accuracy, although they may require substantial computation and undermine the learning efficiency of RC. In this study, to improve the anomaly detection performance of RC without sacrificing learning efficiency, we propose a spectral residual RC (SR-RC) that integrates the spectral residual (SR) method - a learning-free, bottom-up attention mechanism - with RC. We demonstrated that SR-RC outperformed conventional RC and logistic-regression models based on values extracted by the SR method across benchmark tasks and real-world time-series datasets. Moreover, because the SR method, similarly to RC, is well suited for hardware implementation, SR-RC suggests a practical direction for deploying RC as Edge AI for time-series anomaly detection.", "date": "2025-10-16", "categories": []}
{"id": "2510.14688", "title": "Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications", "abstract": "This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks, encompassing possible use cases such as brain-machine interfaces and remote environmental monitoring. In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame. The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system. The queried neuro-SNs respond to the reader with impulse radio (IR) transmissions that directly encode the sensed local events. The reader processes these event-driven signals to determine whether the monitored environment is in a normal or anomalous state, while rigorously controlling the false discovery rate (FDR) of detections below a predefined threshold. The proposed approach employs an online hypothesis testing method with e-values to maintain FDR control without requiring knowledge of the anomaly rate, and it dynamically optimizes the sensor querying strategy by casting it as a best-arm identification problem in a multi-armed bandit framework. Extensive performance evaluation demonstrates that the proposed method can reliably detect anomalies under stringent FDR requirements, while efficiently scheduling sensor communications and achieving low detection latency.", "date": "2025-10-16", "categories": []}
{"id": "2504.04911", "title": "IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR", "abstract": "Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as `normal'. In the testing phase, they identify patterns that deviate from this normal distribution as `anomalies'. To learn the `normal' distribution, prevailing methods\n  corrupt the images and train a model to reconstruct them. During testing, the model attempts to reconstruct corrupted inputs based on the learned `normal' distribution. Deviations from this distribution lead to high reconstruction errors, which indicate potential anomalies. However, corrupting an input image inevitably causes information loss even in normal regions, leading to suboptimal reconstruction and an increased risk of false positives. To alleviate this, we propose $\\rm{IterMask3D}$, an iterative spatial mask-refining strategy designed for 3D brain MRI. We iteratively spatially mask areas of the image as corruption and reconstruct them, then shrink the mask based on reconstruction error. This process iteratively unmasks `normal' areas to the model, whose information further guides reconstruction of `normal' patterns under the mask to be reconstructed accurately, reducing false positives. In addition, to achieve better reconstruction performance, we also propose using high-frequency image content as additional structural information to guide the reconstruction of the masked area. Extensive experiments on the detection of both synthetic and real-world imaging artifacts, as well as segmentation of various pathological lesions across multiple MRI sequences, consistently demonstrate the effectiveness of our proposed method. Code is available at https://github.com/ZiyunLiang/IterMask3D.", "date": "2025-04-07", "categories": []}
{"id": "2510.13311", "title": "Isolation-based Spherical Ensemble Representations for Anomaly Detection", "abstract": "Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring. Despite extensive research, existing unsupervised anomaly detection methods still face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types. To address these problems, we propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity. ISER constructs ensemble representations where hypersphere radii encode density information: smaller radii indicate dense regions while larger radii correspond to sparse areas. We introduce a novel similarity-based scoring method that measures pattern consistency by comparing ensemble representations against a theoretical anomaly reference pattern. Additionally, we enhance the performance of Isolation Forest by using ISER and adapting the scoring function to address axis-parallel bias and local anomaly detection limitations. Comprehensive experiments on 22 real-world datasets demonstrate ISER's superior performance over 11 baseline methods.", "date": "2025-10-15", "categories": []}
{"id": "2510.13368", "title": "Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services", "abstract": "This paper addresses the challenges of complex dependencies and diverse anomaly patterns in cloud service environments by proposing a dependency modeling and anomaly detection method that integrates contrastive learning. The method abstracts service interactions into a dependency graph, extracts temporal and structural features through embedding functions, and employs a graph convolution mechanism to aggregate neighborhood information for context-aware service representations. A contrastive learning framework is then introduced, constructing positive and negative sample pairs to enhance the separability of normal and abnormal patterns in the representation space. Furthermore, a temporal consistency constraint is designed to maintain representation stability across time steps and reduce the impact of short-term fluctuations and noise. The overall optimization combines contrastive loss and temporal consistency loss to ensure stable and reliable detection across multi-dimensional features. Experiments on public datasets systematically evaluate the method from hyperparameter, environmental, and data sensitivity perspectives. Results show that the proposed approach significantly outperforms existing methods on key metrics such as Precision, Recall, F1-Score, and AUC, while maintaining robustness under conditions of sparse labeling, monitoring noise, and traffic fluctuations. This study verifies the effectiveness of integrating dependency modeling with contrastive learning, provides a complete technical solution for cloud service anomaly detection, and demonstrates strong adaptability and stability in complex environments.", "date": "2025-10-15", "categories": []}
{"id": "2510.13643", "title": "Towards Adversarial Robustness and Uncertainty Quantification in DINOv2-based Few-Shot Anomaly Detection", "abstract": "Foundation models such as DINOv2 have shown strong performance in few-shot anomaly detection, yet two key questions remain unexamined: (i) how susceptible are these detectors to adversarial perturbations; and (ii) how well do their anomaly scores reflect calibrated uncertainty? Building on AnomalyDINO, a training-free deep nearest-neighbor detector over DINOv2 features, we present one of the first systematic studies of adversarial attacks and uncertainty estimation in this setting. To enable white-box gradient attacks while preserving test-time behavior, we attach a lightweight linear head to frozen DINOv2 features only for crafting perturbations. Using this heuristic, we evaluate the impact of FGSM across the MVTec-AD and VisA datasets and observe consistent drops in F1, AUROC, AP, and G-mean, indicating that imperceptible perturbations can flip nearest-neighbor relations in feature space to induce confident misclassification. Complementing robustness, we probe reliability and find that raw anomaly scores are poorly calibrated, revealing a gap between confidence and correctness that limits safety-critical use. As a simple, strong baseline toward trustworthiness, we apply post-hoc Platt scaling to the anomaly scores for uncertainty estimation. The resulting calibrated posteriors yield significantly higher predictive entropy on adversarially perturbed inputs than on clean ones, enabling a practical flagging mechanism for attack detection while reducing calibration error (ECE). Our findings surface concrete vulnerabilities in DINOv2-based few-shot anomaly detectors and establish an evaluation protocol and baseline for robust, uncertainty-aware anomaly detection. We argue that adversarial robustness and principled uncertainty quantification are not optional add-ons but essential capabilities if anomaly detection systems are to be trustworthy and ready for real-world deployment.", "date": "2025-10-15", "categories": []}
{"id": "2510.11827", "title": "Combining Euclidean and Hyperbolic Representations for Node-level Anomaly Detection", "abstract": "Node-level anomaly detection (NAD) is challenging due to diverse structural patterns and feature distributions. As such, NAD is a critical task with several applications which range from fraud detection, cybersecurity, to recommendation systems. We introduce Janus, a framework that jointly leverages Euclidean and Hyperbolic Graph Neural Networks to capture complementary aspects of node representations. Each node is described by two views, composed by the original features and structural features derived from random walks and degrees, then embedded into Euclidean and Hyperbolic spaces. A multi Graph-Autoencoder framework, equipped with a contrastive learning objective as regularization term, aligns the embeddings across the Euclidean and Hyperbolic spaces, highlighting nodes whose views are difficult to reconcile and are thus likely anomalous. Experiments on four real-world datasets show that Janus consistently outperforms shallow and deep baselines, empirically demonstrating that combining multiple geometric representations provides a robust and effective approach for identifying subtle and complex anomalies in graphs.", "date": "2025-10-13", "categories": []}
{"id": "2510.12076", "title": "BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data", "abstract": "Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anomaly Detection for Human Mobility Data), an unsupervised framework that captures individualized behavioral signatures across large populations and uncovers fine-grained anomalies by jointly modeling spatial context and temporal dynamics. BeSTAD learns semantically enriched mobility representations that integrate location meaning and temporal patterns, enabling the detection of subtle deviations in individual movement behavior. BeSTAD further employs a behavior-cluster-aware modeling mechanism that builds personalized behavioral profiles from normal activity and identifies anomalies through cross-period behavioral comparison with consistent semantic alignment. Building on prior work in mobility behavior clustering, this approach enables not only the detection of behavioral shifts and deviations from established routines but also the identification of individuals exhibiting such changes within large-scale mobility datasets. By learning individual behaviors directly from unlabeled data, BeSTAD advances anomaly detection toward personalized and interpretable mobility analysis.", "date": "2025-10-14", "categories": []}
{"id": "2510.12489", "title": "CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling", "abstract": "Time series anomaly detection plays a crucial role in a wide range of real-world applications. Given that time series data can exhibit different patterns at different sampling granularities, multi-scale modeling has proven beneficial for uncovering latent anomaly patterns that may not be apparent at a single scale. However, existing methods often model multi-scale information independently or rely on simple feature fusion strategies, neglecting the dynamic changes in cross-scale associations that occur during anomalies. Moreover, most approaches perform multi-scale modeling based on fixed sliding windows, which limits their ability to capture comprehensive contextual information. In this work, we propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window modeling into account. We propose a cross-scale reconstruction that reconstructs fine-grained series from coarser series, explicitly capturing cross-scale associations. Furthermore, we design a query library and incorporate global multi-scale context to overcome the limitations imposed by fixed window sizes. Extensive experiments conducted on multiple real-world datasets using nine evaluation metrics validate the effectiveness of CrossAD, demonstrating state-of-the-art performance in anomaly detection.", "date": "2025-10-14", "categories": []}
{"id": "2510.12686", "title": "Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories", "abstract": "Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aware Segmentation (SAS) method that adaptively defines segment boundaries based on local spatial-temporal density. Building upon these segments, we introduce three domain-specific indicators to capture abnormal stop behaviors. To further mitigate the impact of sparsity, we develop Locally Temporal-Indicator Guided Adjustment (LTIGA), which smooths these indicators via local similarity graphs. To overcome label scarcity, we construct a spatial-temporal graph where each segment is a node with LTIGA-refined features. We apply label propagation to expand weak supervision across the graph, followed by a GCN to learn relational patterns. A final self-training module incorporates high-confidence pseudo-labels to iteratively improve predictions. Experiments on real-world coach data show an AUC of 0.854 and AP of 0.866 using only 10 labeled instances, outperforming prior methods. The code and dataset are publicly available at \\href{https://github.com/pangjunbiao/Abnormal-Stop-Detection-SSL.git}", "date": "2025-10-14", "categories": []}
{"id": "2408.08228", "title": "Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective", "abstract": "Reconstruction-based methods, particularly those leveraging autoencoders, have been widely adopted for anomaly detection task in brain MRI. Unlike most existing works try to improve the task accuracy through architectural or algorithmic innovations, we tackle this task from image quality assessment (IQA) perspective, an under-explored direction in the field. Due to the limitations of conventional metrics such as l1 in capturing the nuanced differences in reconstructed images for medical anomaly detection, we propose fusion quality, a novel metric that wisely integrates the structure-level sensitivity of Structural Similarity Index Measure (SSIM) with the pixel-level precision of l1. The metric offers a more comprehensive assessment of reconstruction quality, considering intensity (subtractive property of l1 and divisive property of SSIM), contrast, and structural similarity. Furthermore, the proposed metric makes subtle regional variations more impactful in the final assessment. Thus, considering the inherent divisive properties of SSIM, we design an average intensity ratio (AIR)-based data transformation that amplifies the divisive discrepancies between normal and abnormal regions, thereby enhancing anomaly detection. By fusing the aforementioned two components, we devise the IQA approach. Experimental results on two distinct brain MRI datasets show that our IQA approach significantly enhances medical anomaly detection performance when integrated with state-of-the-art baselines.", "date": "2024-08-15", "categories": []}
{"id": "2510.09977", "title": "An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing", "abstract": "Online sensing plays an important role in advancing modern manufacturing. The real-time sensor signals, which can be stored as high-resolution time series data, contain rich information about the operation status. One of its popular usages is online process monitoring, which can be achieved by effective anomaly detection from the sensor signals. However, most existing approaches either heavily rely on labeled data for training supervised models, or are designed to detect only extreme outliers, thus are ineffective at identifying subtle semantic off-track anomalies to capture where new regimes or unexpected routines start. To address this challenge, we propose an matrix profile-based unsupervised anomaly detection algorithm that captures fabrication cycle similarity and performs semantic segmentation to precisely identify the onset of defect anomalies in additive manufacturing. The effectiveness of the proposed method is demonstrated by the experiments on real-world sensor data.", "date": "2025-10-11", "categories": []}
{"id": "2510.10456", "title": "On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection", "abstract": "Zero-shot image anomaly classification (AC) and segmentation (AS) are vital for industrial quality control, detecting defects without prior training data. Existing representation-based methods compare patch features with nearest neighbors in unlabeled test images but struggle with consistent anomalies -- similar defects recurring across multiple images -- resulting in poor AC/AS performance. We introduce Consistent-Anomaly Detection Graph (CoDeGraph), a novel algorithm that identifies and filters consistent anomalies from similarity computations. Our key insight is that normal patches in industrial images show stable, gradually increasing similarity to other test images, while consistent-anomaly patches exhibit abrupt similarity spikes after exhausting a limited set of similar matches, a phenomenon we term ``neighbor-burnout.'' CoDeGraph constructs an image-level graph, with images as nodes and edges connecting those with shared consistent-anomaly patterns, using community detection to filter these anomalies. We provide a theoretical foundation using Extreme Value Theory to explain the effectiveness of our approach. Experiments on MVTec AD with the ViT-L-14-336 backbone achieve 98.3% AUROC for AC and AS performance of 66.8% (+4.2%) F1 and 68.1% (+5.4%) AP over state-of-the-art zero-shot methods. Using the DINOv2 backbone further improves segmentation, yielding 69.1% (+6.5%) F1 and 71.9% (+9.2%) AP, demonstrating robustness across architectures.", "date": "2025-10-12", "categories": []}
{"id": "2510.11028", "title": "Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts", "abstract": "Recently, the powerful generalization ability exhibited by foundation models has brought forth new solutions for zero-shot anomaly segmentation tasks. However, guiding these foundation models correctly to address downstream tasks remains a challenge. This paper proposes a novel two-stage framework, for zero-shot anomaly segmentation tasks in industrial anomaly detection. This framework excellently leverages the powerful anomaly localization capability of CLIP and the boundary perception ability of SAM.(1) To mitigate SAM's inclination towards object segmentation, we propose the Co-Feature Point Prompt Generation (PPG) module. This module collaboratively utilizes CLIP and SAM to generate positive and negative point prompts, guiding SAM to focus on segmenting anomalous regions rather than the entire object. (2) To further optimize SAM's segmentation results and mitigate rough boundaries and isolated noise, we introduce the Cascaded Prompts for SAM (CPS) module. This module employs hybrid prompts cascaded with a lightweight decoder of SAM, achieving precise segmentation of anomalous regions. Across multiple datasets, consistent experimental validation demonstrates that our approach achieves state-of-the-art zero-shot anomaly segmentation results. Particularly noteworthy is our performance on the Visa dataset, where we outperform the state-of-the-art methods by 10.3\\% and 7.7\\% in terms of {$F_1$-max} and AP metrics, respectively.", "date": "2025-10-13", "categories": []}
{"id": "2510.11084", "title": "Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series", "abstract": "Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of causal relationships. Traditional approaches assume statistical independence between variables in unsupervised settings, whereas recent methods capture feature correlations through graph representation learning. However, their representations fail to explicitly infer the causal relationships over different time periods. To solve the problem, we propose Causally Disentangled Representation Learning for Anomaly Detection (CDRL4AD) to detect anomalies and identify their causal relationships in multivariate time series. First, we design the causal process as model input, the temporal heterogeneous graph, and causal relationships. Second, our representation identifies causal relationships over different time periods and disentangles latent variables to infer the corresponding causal factors. Third, our experiments on real-world datasets demonstrate that CDRL4AD outperforms state-of-the-art methods in terms of accuracy and root cause analysis. Fourth, our model analysis validates hyperparameter sensitivity and the time complexity of CDRL4AD. Last, we conduct a case study to show how our approach assists human experts in diagnosing the root causes of anomalies.", "date": "2025-10-13", "categories": []}
{"id": "2510.11141", "title": "A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)", "abstract": "Time series anomaly detection is critical for modern digital infrastructures, yet existing methods lack systematic cross-domain evaluation. We present a comprehensive forecasting-based framework unifying classical methods (Holt-Winters, SARIMA) with deep learning architectures (LSTM, Informer) under a common residual-based detection interface. Our modular pipeline integrates preprocessing (normalization, STL decomposition), four forecasting models, four detection methods, and dual evaluation through forecasting metrics (MAE, RMSE, PCC) and detection metrics (Precision, Recall, F1, AUC). We conduct the first complete evaluation on the Numenta Anomaly Benchmark (58 datasets, 7 categories) with 232 model training runs and 464 detection evaluations achieving 100\\% success rate. LSTM achieves best performance (F1: 0.688, ranking first or second on 81\\% of datasets) with exceptional correlation on complex patterns (PCC: 0.999). Informer provides competitive accuracy (F1: 0.683) with 30\\% faster training. Classical methods achieve perfect predictions on simple synthetic data with 60 lower cost but show 2-3 worse F1-scores on real-world datasets. Forecasting quality dominates detection performance: differences between detection methods (F1: 0.621-0.688) are smaller than between forecasting models (F1: 0.344-0.688). Our findings provide evidence-based guidance: use LSTM for complex patterns, Informer for efficiency-critical deployments, and classical methods for simple periodic data with resource constraints. The complete implementation and results establish baselines for future forecasting-based anomaly detection research.", "date": "2025-10-13", "categories": []}
{"id": "2412.04784", "title": "NLP-ADBench: NLP Anomaly Detection Benchmark", "abstract": "Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing attempts, and spam reviews. We introduce NLP-ADBench, the most comprehensive NLP anomaly detection (NLP-AD) benchmark to date, which includes eight curated datasets and 19 state-of-the-art algorithms. These span 3 end-to-end methods and 16 two-step approaches that adapt classical, non-AD methods to language embeddings from BERT and OpenAI. Our empirical results show that no single model dominates across all datasets, indicating a need for automated model selection. Moreover, two-step methods with transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings outperforming those of BERT. We release NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, providing a unified framework for NLP-AD and supporting future investigations.", "date": "2024-12-06", "categories": []}
{"id": "2508.00047", "title": "TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection", "abstract": "Time-series anomaly detection plays a central role across a wide range of application domains. With the increasing proliferation of the Internet of Things (IoT) and smart manufacturing, time-series data has dramatically increased in both scale and dimensionality. This growth has exposed the limitations of traditional statistical methods in handling the high heterogeneity and complexity of such data. Inspired by the recent success of large language models (LLMs) in multimodal tasks across language and vision domains, we propose a novel unsupervised anomaly detection framework: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection (TriP-LLM). TriP-LLM integrates local and global temporal features through a triple-branch design comprising Patching, Selecting, and Global modules, to encode the input time-series into patch-wise representations, which are then processed by a frozen, pretrained LLM. A lightweight patch-wise decoder reconstructs the input, from which anomaly scores are derived. We evaluate TriP-LLM on several public benchmark datasets using PATE, a recently proposed threshold-free evaluation metric, and conduct all comparisons within a unified open-source framework to ensure fairness. Experimental results show that TriP-LLM consistently outperforms recent state-of-the-art (SOTA) methods across all datasets, demonstrating strong detection capabilities. Furthermore, through extensive ablation studies, we verify the substantial contribution of the LLM to the overall architecture. Compared to LLM-based approaches using Channel Independence (CI) patch processing, TriP-LLM achieves significantly lower memory consumption, making it more suitable for GPU memory-constrained environments. All code and model checkpoints of TriP-LLM are publicly available on https://github.com/YYZStart/TriP-LLM.git", "date": "2025-07-31", "categories": []}
{"id": "2508.17086", "title": "Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning", "abstract": "Trade-based manipulation (TBM) undermines the fairness and stability of financial markets drastically. Spoofing, one of the most covert and deceptive TBM strategies, exhibits complex anomaly patterns across multilevel prices, while often being simplified as a single-level manipulation. These patterns are usually concealed within the rich, hierarchical information of the Limit Order Book (LOB), which is challenging to leverage due to high dimensionality and noise. To address this, we propose a representation learning framework combining a cascaded LOB representation architecture with supervised contrastive learning. Extensive experiments demonstrate that our framework consistently improves detection performance across diverse models, with Transformer-based architectures achieving state-of-the-art results. In addition, we conduct systematic analyses and ablation studies to investigate multilevel manipulation and the contributions of key components for detection, offering broader insights into representation learning and anomaly detection for complex time series data.", "date": "2025-08-23", "categories": []}
{"id": "2510.08747", "title": "RFOD: Random Forest-based Outlier Detection for Tabular Data", "abstract": "Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalies can cause serious operational and economic impacts. Despite advances in both data mining and deep learning, many existing methods struggle with mixed-type tabular data, often relying on encoding schemes that lose important semantic information. Moreover, they frequently lack interpretability, offering little insight into which specific values cause anomalies. To overcome these challenges, we introduce \\textsf{\\textbf{RFOD}}, a novel \\textsf{\\textbf{R}}andom \\textsf{\\textbf{F}}orest-based \\textsf{\\textbf{O}}utlier \\textsf{\\textbf{D}}etection framework tailored for tabular data. Rather than modeling a global joint distribution, \\textsf{RFOD} reframes anomaly detection as a feature-wise conditional reconstruction problem, training dedicated random forests for each feature conditioned on the others. This design robustly handles heterogeneous data types while preserving the semantic integrity of categorical features. To further enable precise and interpretable detection, \\textsf{RFOD} combines Adjusted Gower's Distance (AGD) for cell-level scoring, which adapts to skewed numerical data and accounts for categorical confidence, with Uncertainty-Weighted Averaging (UWA) to aggregate cell-level scores into robust row-level anomaly scores. Extensive experiments on 15 real-world datasets demonstrate that \\textsf{RFOD} consistently outperforms state-of-the-art baselines in detection accuracy while offering superior robustness, scalability, and interpretability for mixed-type tabular data.", "date": "2025-10-09", "categories": []}
{"id": "2510.09071", "title": "Visual Anomaly Detection for Reliable Robotic Implantation of Flexible Microelectrode Array", "abstract": "Flexible microelectrode (FME) implantation into brain cortex is challenging due to the deformable fiber-like structure of FME probe and the interaction with critical bio-tissue. To ensure reliability and safety, the implantation process should be monitored carefully. This paper develops an image-based anomaly detection framework based on the microscopic cameras of the robotic FME implantation system. The unified framework is utilized at four checkpoints to check the micro-needle, FME probe, hooking result, and implantation point, respectively. Exploiting the existing object localization results, the aligned regions of interest (ROIs) are extracted from raw image and input to a pretrained vision transformer (ViT). Considering the task specifications, we propose a progressive granularity patch feature sampling method to address the sensitivity-tolerance trade-off issue at different locations. Moreover, we select a part of feature channels with higher signal-to-noise ratios from the raw general ViT features, to provide better descriptors for each specific scene. The effectiveness of the proposed methods is validated with the image datasets collected from our implantation system.", "date": "2025-10-10", "categories": []}
{"id": "2510.09079", "title": "Improving Anomaly Detection in Industrial Time Series: The Role of Segmentation and Heterogeneous Ensemble", "abstract": "Concerning machine learning, segmentation models can identify state changes within time series, facilitating the detection of transitions between normal and anomalous conditions. Specific techniques such as Change Point Detection (CPD), particularly algorithms like ChangeFinder, have been successfully applied to segment time series and improve anomaly detection by reducing temporal uncertainty, especially in multivariate environments. In this work, we explored how the integration of segmentation techniques, combined with a heterogeneous ensemble, can enhance anomaly detection in an industrial production context. The results show that applying segmentation as a pre-processing step before selecting heterogeneous ensemble algorithms provided a significant advantage in our case study, improving the AUC-ROC metric from 0.8599 (achieved with a PCA and LSTM ensemble) to 0.9760 (achieved with Random Forest and XGBoost). This improvement is imputable to the ability of segmentation to reduce temporal ambiguity and facilitate the learning process of supervised algorithms. In our future work, we intend to assess the benefit of introducing weighted features derived from the study of change points, combined with segmentation and the use of heterogeneous ensembles, to further optimize model performance in early anomaly detection.", "date": "2025-10-10", "categories": []}
{"id": "2510.09452", "title": "On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification", "abstract": "Unsupervised anomaly detection is often framed around two widely studied paradigms. Deep one-class classification, exemplified by Deep SVDD, learns compact latent representations of normality, while density estimators realized by normalizing flows directly model the likelihood of nominal data. In this work, we show that uniformly scaling flows (USFs), normalizing flows with a constant Jacobian determinant, precisely connect these approaches. Specifically, we prove how training a USF via maximum-likelihood reduces to a Deep SVDD objective with a unique regularization that inherently prevents representational collapse. This theoretical bridge implies that USFs inherit both the density faithfulness of flows and the distance-based reasoning of one-class methods. We further demonstrate that USFs induce a tighter alignment between negative log-likelihood and latent norm than either Deep SVDD or non-USFs, and how recent hybrid approaches combining one-class objectives with VAEs can be naturally extended to USFs. Consequently, we advocate using USFs as a drop-in replacement for non-USFs in modern anomaly detection architectures. Empirically, this substitution yields consistent performance gains and substantially improved training stability across multiple benchmarks and model backbones for both image-level and pixel-level detection. These results unify two major anomaly detection paradigms, advancing both theoretical understanding and practical performance.", "date": "2025-10-10", "categories": []}
{"id": "2510.03288", "title": "LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptation", "abstract": "Log-based anomaly detection is a essential task for ensuring the reliability and performance of software systems. However, the performance of existing anomaly detection methods heavily relies on labeling, while labeling a large volume of logs is highly challenging. To address this issue, many approaches based on transfer learning and active learning have been proposed. Nevertheless, their effectiveness is hindered by issues such as the gap between source and target system data distributions and cold-start problems. In this paper, we propose LogAction, a novel log-based anomaly detection model based on active domain adaptation. LogAction integrates transfer learning and active learning techniques. On one hand, it uses labeled data from a mature system to train a base model, mitigating the cold-start issue in active learning. On the other hand, LogAction utilize free energy-based sampling and uncertainty-based sampling to select logs located at the distribution boundaries for manual labeling, thus addresses the data distribution gap in transfer learning with minimal human labeling efforts. Experimental results on six different combinations of datasets demonstrate that LogAction achieves an average 93.01% F1 score with only 2% of manual labels, outperforming some state-of-the-art methods by 26.28%. Website: https://logaction.github.io", "date": "2025-09-29", "categories": []}
{"id": "2510.07606", "title": "Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects", "abstract": "Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting small, transient anomalies (2-10 cm) remains a significant challenge due to complex vehicle dynamics, signal noise, and the scarcity of labeled data limiting supervised approaches. This study addresses these issues through unsupervised deep learning. We introduce an incremental synthetic data benchmark designed to systematically evaluate model robustness against progressively complex challenges like speed variations, multi-channel inputs, and realistic noise patterns encountered in iSHM. Using this benchmark, we evaluate several established unsupervised models alongside our proposed Attention-Focused Transformer. Our model employs a self-attention mechanism, trained via reconstruction but innovatively deriving anomaly scores primarily from deviations in learned attention weights, aiming for both effectiveness and computational efficiency. Benchmarking results reveal that while transformer-based models generally outperform others, all tested models exhibit significant vulnerability to high-frequency localized noise, identifying this as a critical bottleneck for practical deployment. Notably, our proposed model achieves accuracy comparable to the state-of-the-art solution while demonstrating better inference speed. This highlights the crucial need for enhanced noise robustness in future iSHM models and positions our more efficient attention-based approach as a promising foundation for developing practical onboard anomaly detection systems.", "date": "2025-10-08", "categories": []}
{"id": "2510.07847", "title": "Meta-Learning Based Few-Shot Graph-Level Anomaly Detection", "abstract": "Graph-level anomaly detection aims to identify anomalous graphs or subgraphs within graph datasets, playing a vital role in various fields such as fraud detection, review classification, and biochemistry. While Graph Neural Networks (GNNs) have made significant progress in this domain, existing methods rely heavily on large amounts of labeled data, which is often unavailable in real-world scenarios. Additionally, few-shot anomaly detection methods based on GNNs are prone to noise interference, resulting in poor embedding quality and reduced model robustness. To address these challenges, we propose a novel meta-learning-based graph-level anomaly detection framework (MA-GAD), incorporating a graph compression module that reduces the graph size, mitigating noise interference while retaining essential node information. We also leverage meta-learning to extract meta-anomaly information from similar networks, enabling the learning of an initialization model that can rapidly adapt to new tasks with limited samples. This improves the anomaly detection performance on target graphs, and a bias network is used to enhance the distinction between anomalous and normal nodes. Our experimental results, based on four real-world biochemical datasets, demonstrate that MA-GAD outperforms existing state-of-the-art methods in graph-level anomaly detection under few-shot conditions. Experiments on both graph anomaly and subgraph anomaly detection tasks validate the framework's effectiveness on real-world datasets.", "date": "2025-10-09", "categories": []}
{"id": "2510.03363", "title": "Unified Unsupervised Anomaly Detection via Matching Cost Filtering", "abstract": "Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and medical analysis, where anomalies are scarce due to privacy concerns and cold-start constraints. Existing methods, whether reconstruction-based (restoring normal counterparts) or embedding-based (pretrained representations), fundamentally conduct image- or feature-level matching to generate anomaly maps. Nonetheless, matching noise has been largely overlooked, limiting their detection ability. Beyond earlier focus on unimodal RGB-based UAD, recent advances expand to multimodal scenarios, e.g., RGB-3D and RGB-Text, enabled by point cloud sensing and vision-language models. Despite shared challenges, these lines remain largely isolated, hindering a comprehensive understanding and knowledge transfer. In this paper, we advocate unified UAD for both unimodal and multimodal settings in the matching perspective. Under this insight, we present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model. The cost volume is constructed by matching a test sample against normal samples from the same or different modalities, followed by a learnable filtering module with multi-layer attention guidance from the test sample, mitigating matching noise and highlighting subtle anomalies. Comprehensive experiments on 22 diverse benchmarks demonstrate the efficacy of UCF in enhancing a variety of UAD methods, consistently achieving new state-of-the-art results in both unimodal (RGB) and multimodal (RGB-3D, RGB-Text) UAD scenarios. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.", "date": "2025-10-03", "categories": []}
{"id": "2510.06530", "title": "From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond", "abstract": "The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to continue beyond the 5G era. However, vulnerabilities in control-plane protocols, such as Radio Resource Control (RRC) and Non-Access Stratum (NAS), pose significant security threats, such as Blind Denial of Service (DoS) attacks. Despite the availability of existing anomaly detection methods that leverage rule-based systems or traditional machine learning methods, these methods have several limitations, including the need for extensive training data, predefined rules, and limited explainability. Addressing these challenges, we propose a novel anomaly detection framework that leverages the capabilities of Large Language Models (LLMs) in zero-shot mode with unordered data and short natural language attack descriptions within the Open Radio Access Network (O-RAN) architecture. We analyse robustness to prompt variation, demonstrate the practicality of automating the attack descriptions and show that detection quality relies on the semantic completeness of the description rather than its phrasing or length. We utilise an RRC/NAS dataset to evaluate the solution and provide an extensive comparison of open-source and proprietary LLM implementations to demonstrate superior performance in attack detection. We further validate the practicality of our framework within O-RAN's real-time constraints, illustrating its potential for detecting other Layer-3 attacks.", "date": "2025-10-08", "categories": []}
{"id": "2510.06910", "title": "Vacuum Spiker: A Spiking Neural Network-Based Model for Efficient Anomaly Detection in Time Series", "abstract": "Anomaly detection is a key task across domains such as industry, healthcare, and cybersecurity. Many real-world anomaly detection problems involve analyzing multiple features over time, making time series analysis a natural approach for such problems. While deep learning models have achieved strong performance in this field, their trend to exhibit high energy consumption limits their deployment in resource-constrained environments such as IoT devices, edge computing platforms, and wearables. To address this challenge, this paper introduces the \\textit{Vacuum Spiker algorithm}, a novel Spiking Neural Network-based method for anomaly detection in time series. It incorporates a new detection criterion that relies on global changes in neural activity rather than reconstruction or prediction error. It is trained using Spike Time-Dependent Plasticity in a novel way, intended to induce changes in neural activity when anomalies occur. A new efficient encoding scheme is also proposed, which discretizes the input space into non-overlapping intervals, assigning each to a single neuron. This strategy encodes information with a single spike per time step, improving energy efficiency compared to conventional encoding methods. Experimental results on publicly available datasets show that the proposed algorithm achieves competitive performance while significantly reducing energy consumption, compared to a wide set of deep learning and machine learning baselines. Furthermore, its practical utility is validated in a real-world case study, where the model successfully identifies power curtailment events in a solar inverter. These results highlight its potential for sustainable and efficient anomaly detection.", "date": "2025-10-08", "categories": []}
{"id": "2510.05903", "title": "Kaputt: A Large-Scale Dataset for Visual Defect Detection", "abstract": "We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios with highly controlled poses and a limited number of object categories. Existing benchmarks like MVTec-AD [6] and VisA [33] have reached saturation, with state-of-the-art methods achieving up to 99.9% AUROC scores. In contrast to manufacturing, anomaly detection in retail logistics faces new challenges, particularly in the diversity and variability of object pose and appearance. Leading anomaly detection methods fall short when applied to this new setting. To bridge this gap, we introduce a new benchmark that overcomes the current limitations of existing datasets. With over 230,000 images (and more than 29,000 defective instances), it is 40 times larger than MVTec-AD and contains more than 48,000 distinct objects. To validate the difficulty of the problem, we conduct an extensive evaluation of multiple state-of-the-art anomaly detection methods, demonstrating that they do not surpass 56.96% AUROC on our dataset. Further qualitative analysis confirms that existing methods struggle to leverage normal samples under heavy pose and appearance variation. With our large-scale dataset, we set a new benchmark and encourage future research towards solving this challenging problem in retail logistics anomaly detection. The dataset is available for download under https://www.kaputt-dataset.com.", "date": "2025-10-07", "categories": []}
{"id": "2510.05919", "title": "An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals", "abstract": "Anomaly detection in 12-lead electrocardiograms (ECGs) is critical for identifying deviations associated with cardiovascular disease. This work presents a comparative analysis of three autoencoder-based architectures: convolutional autoencoder (CAE), variational autoencoder with bidirectional long short-term memory (VAE-BiLSTM), and VAE-BiLSTM with multi-head attention (VAE-BiLSTM-MHA), for unsupervised anomaly detection in ECGs. To the best of our knowledge, this study reports the first application of a VAE-BiLSTM-MHA architecture to ECG anomaly detection. All models are trained on normal ECG samples to reconstruct non-anomalous cardiac morphology and detect deviations indicative of disease. Using a unified preprocessing and evaluation pipeline on the public China Physiological Signal Challenge (CPSC) dataset, the attention-augmented VAE achieves the best performance, with an AUPRC of 0.81 and a recall of 0.85 on the held-out test set, outperforming the other architectures. To support clinical triage, this model is further integrated into an interactive dashboard that visualizes anomaly localization. In addition, a performance comparison with baseline models from the literature is provided.", "date": "2025-10-07", "categories": []}
{"id": "2505.17692", "title": "ViP$^2$-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detection", "abstract": "Zero-shot anomaly detection (ZSAD) aims to detect anomalies without any target domain training samples, relying solely on external auxiliary data. Existing CLIP-based methods attempt to activate the model's ZSAD potential via handcrafted or static learnable prompts. The former incur high engineering costs and limited semantic coverage, whereas the latter apply identical descriptions across diverse anomaly types, thus fail to adapt to complex variations. Furthermore, since CLIP is originally pretrained on large-scale classification tasks, its anomaly segmentation quality is highly sensitive to the exact wording of class names, severely constraining prompting strategies that depend on class labels. To address these challenges, we introduce ViP$^{2}$-CLIP. The key insight of ViP$^{2}$-CLIP is a Visual-Perception Prompting (ViP-Prompt) mechanism, which fuses global and multi-scale local visual context to adaptively generate fine-grained textual prompts, eliminating manual templates and class-name priors. This design enables our model to focus on precise abnormal regions, making it particularly valuable when category labels are ambiguous or privacy-constrained. Extensive experiments on 15 industrial and medical benchmarks demonstrate that ViP$^{2}$-CLIP achieves state-of-the-art performance and robust cross-domain generalization.", "date": "2025-05-23", "categories": []}
{"id": "2508.01844", "title": "Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments", "abstract": "Anomaly detection in cloud environments remains both critical and challenging. Existing context-level benchmarks typically focus on either metrics or logs and often lack reliable annotation, while most detection methods emphasize point anomalies within a single modality, overlooking contextual signals and limiting real-world applicability. Constructing a benchmark for context anomalies that combines metrics and logs is inherently difficult: reproducing anomalous scenarios on real servers is often infeasible or potentially harmful, while generating synthetic data introduces the additional challenge of maintaining cross-modal consistency. We introduce CloudAnoBench, a large-scale benchmark for context anomalies in cloud environments, comprising 28 anomalous scenarios and 16 deceptive normal scenarios, with 1,252 labeled cases and roughly 200,000 log and metric entries. Compared with prior benchmarks, CloudAnoBench exhibits higher ambiguity and greater difficulty, on which both prior machine learning methods and vanilla LLM prompting perform poorly. To demonstrate its utility, we further propose CloudAnoAgent, an LLM-based agent enhanced by symbolic verification that integrates metrics and logs. This agent system achieves substantial improvements in both anomaly detection and scenario identification on CloudAnoBench, and shows strong generalization to existing datasets. Together, CloudAnoBench and CloudAnoAgent lay the groundwork for advancing context-aware anomaly detection in cloud systems. Project Page: https://jayzou3773.github.io/cloudanobench-agent/", "date": "2025-08-03", "categories": []}
{"id": "2509.15493", "title": "FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data", "abstract": "Given a set of financial transactions (who buys from whom, when, and for how much), as well as prior information from buyers and sellers, how can we find fraudulent transactions? If we have labels for some transactions for known types of fraud, we can build a classifier. However, we also want to find new types of fraud, still unknown to the domain experts ('Detection'). Moreover, we also want to provide evidence to experts that supports our opinion ('Justification'). In this paper, we propose FRAUDGUESS, to achieve two goals: (a) for 'Detection', it spots new types of fraud as micro-clusters in a carefully designed feature space; (b) for 'Justification', it uses visualization and heatmaps for evidence, as well as an interactive dashboard for deep dives. FRAUDGUESS is used in real life and is currently considered for deployment in an Anonymous Financial Institution (AFI). Thus, we also present the three new behaviors that FRAUDGUESS discovered in a real, million-scale financial dataset. Two of these behaviors are deemed fraudulent or suspicious by domain experts, catching hundreds of fraudulent transactions that would otherwise go un-noticed.", "date": "2025-09-19", "categories": []}
{"id": "2510.03266", "title": "Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model", "abstract": "Climate anomalies significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under the SSP585 scenario. The VAE architecture employs three dense layers and a latent space with an input sequence length of 12 months, trained on a normalized GPP time series to reconstruct the GPP and identifying anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques, while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. Unlike SSA, the VAE method does not require one to define the periodicity of the signals in the data; it discovers them from the data.", "date": "2025-09-26", "categories": []}
{"id": "2510.03486", "title": "Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domains", "abstract": "Detecting anomalies in large, distributed systems presents several challenges. The first challenge arises from the sheer volume of data that needs to be processed. Flagging anomalies in a high-throughput environment calls for a careful consideration of both algorithm and system design. The second challenge comes from the heterogeneity of time-series datasets that leverage such a system in production. In practice, anomaly detection systems are rarely deployed for a single use case. Typically, there are several metrics to monitor, often across several domains (e.g. engineering, business and operations). A one-size-fits-all approach rarely works, so these systems need to be fine-tuned for every application - this is often done manually. The third challenge comes from the fact that determining the root-cause of anomalies in such settings is akin to finding a needle in a haystack. Identifying (in real time) a time-series dataset that is associated causally with the anomalous time-series data is a very difficult problem. In this paper, we describe a unified framework that addresses these challenges. Reasoning based Anomaly Detection Framework (RADF) is designed to perform real time anomaly detection on very large datasets. This framework employs a novel technique (mSelect) that automates the process of algorithm selection and hyper-parameter tuning for each use case. Finally, it incorporates a post-detection capability that allows for faster triaging and root-cause determination. Our extensive experiments demonstrate that RADF, powered by mSelect, surpasses state-of-the-art anomaly detection models in AUC performance for 5 out of 9 public benchmarking datasets. RADF achieved an AUC of over 0.85 for 7 out of 9 datasets, a distinction unmatched by any other state-of-the-art model.", "date": "2025-10-03", "categories": []}
{"id": "2510.03859", "title": "Adaptive and Explainable AI Agents for Anomaly Detection in Critical IoT Infrastructure using LLM-Enhanced Contextual Reasoning", "abstract": "Ensuring that critical IoT systems function safely and smoothly depends a lot on finding anomalies quickly. As more complex systems, like smart healthcare, energy grids and industrial automation, appear, it is easier to see the shortcomings of older methods of detection. Monitoring failures usually happen in dynamic, high dimensional situations, especially when data is incomplete, messy or always evolving. Such limits point out the requirement for adaptive, intelligent systems that always improve and think. LLMs are now capable of significantly changing how context is understood and semantic inference is done across all types of data. This proposal suggests using an LLM supported contextual reasoning method along with XAI agents to improve how anomalies are found in significant IoT environments. To discover hidden patterns and notice inconsistencies in data streams, it uses attention methods, avoids dealing with details from every time step and uses memory buffers with meaning. Because no code AI stresses transparency and interpretability, people can check and accept the AI's decisions, helping ensure AI follows company policies. The two architectures are put together in a test that compares the results of the traditional model with those of the suggested LLM enhanced model. Important measures to check are the accuracy of detection, how much inaccurate information is included in the results, how clearly the findings can be read and how fast the system responds under different test situations. The metaheuristic is tested in simulations of real world smart grid and healthcare contexts to check its adaptability and reliability. From the study, we see that the new approach performs much better than most existing models in both accuracy and interpretation, so it could be a good fit for future anomaly detection tasks in IoT", "date": "2025-10-04", "categories": []}
{"id": "2510.03911", "title": "THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series", "abstract": "Time series anomaly detection forms a very crucial area in several domains but poses substantial challenges. Due to time series data possessing seasonality, trends, noise, and evolving patterns (concept drift), it becomes very difficult to set a general notion of what constitutes normal behavior. Anomalies themselves could be varied, ranging from a single outlier to contextual or collective anomalies, and are normally very rare; hence, the dataset is largely imbalanced. Additional layers of complexities arise due to the problems of increased dimensionality of modern time series, real-time detection criteria, setting up appropriate detection thresholds, and arriving at results that are interpretable. To embrace these multifaceted challenges, very strong, flexible, and interpretable approaches are required. This paper presents THEMIS, a new framework for time series anomaly detection that exploits pretrained knowledge from foundation models. THEMIS extracts embeddings from the encoder of the Chronos time series foundation model and applies outlier detection techniques like Local Outlier Factor and Spectral Decomposition on the self-similarity matrix, to spot anomalies in the data. Our experiments show that this modular method achieves SOTA results on the MSL dataset and performs quite competitively on the SMAP and SWAT$^*$ datasets. Notably, THEMIS exceeds models trained specifically for anomaly detection, presenting hyperparameter robustness and interpretability by default. This paper advocates for pretrained representations from foundation models for performing efficient and adaptable anomaly detection for time series data.", "date": "2025-10-04", "categories": []}
{"id": "2510.03962", "title": "SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data", "abstract": "Time series anomaly detection plays a crucial role in a wide range of fields, such as healthcare and internet traffic monitoring. The emergence of large language models (LLMs) offers new opportunities for detecting anomalies in the ubiquitous time series data. Traditional approaches struggle with variable-length time series sequences and context-based anomalies. We propose Soft Prompt Enhanced Anomaly Recognition (SPEAR), a novel approach to leverage LLMs for anomaly detection with soft prompts and quantization. Our methodology involves quantizing and transforming the time series data into input embeddings and combining them with learnable soft prompt embeddings. These combined embeddings are then fed into a frozen LLM. The soft prompts are updated iteratively based on a cross-entropy loss, allowing the model to adapt to time series anomaly detection. The use of soft prompts helps adapt LLMs effectively to time series tasks, while quantization ensures optimal handling of sequences, as LLMs are designed to handle discrete sequences. Our experimental results demonstrate that soft prompts effectively increase LLMs' performance in downstream tasks regarding time series anomaly detection.", "date": "2025-10-04", "categories": []}
{"id": "2510.04710", "title": "ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts", "abstract": "Web service administrators must ensure the stability of multiple systems by promptly detecting anomalies in Key Performance Indicators (KPIs). Achieving the goal of \"train once, infer across scenarios\" remains a fundamental challenge for time series anomaly detection models. Beyond improving zero-shot generalization, such models must also flexibly handle sequences of varying lengths during inference, ranging from one hour to one week, without retraining. Conventional approaches rely on sliding-window encoding and self-supervised learning, which restrict inference to fixed-length inputs. Large Language Models (LLMs) have demonstrated remarkable zero-shot capabilities across general domains. However, when applied to time series data, they face inherent limitations due to context length. To address this issue, we propose ViTs, a Vision-Language Model (VLM)-based framework that converts time series curves into visual representations. By rescaling time series images, temporal dependencies are preserved while maintaining a consistent input size, thereby enabling efficient processing of arbitrarily long sequences without context constraints. Training VLMs for this purpose introduces unique challenges, primarily due to the scarcity of aligned time series image-text data. To overcome this, we employ an evolutionary algorithm to automatically generate thousands of high-quality image-text pairs and design a three-stage training pipeline consisting of: (1) time series knowledge injection, (2) anomaly detection enhancement, and (3) anomaly reasoning refinement. Extensive experiments demonstrate that ViTs substantially enhance the ability of VLMs to understand and detect anomalies in time series data. All datasets and code will be publicly released at: https://anonymous.4open.science/r/ViTs-C484/.", "date": "2025-10-06", "categories": []}
{"id": "2410.10289", "title": "Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection", "abstract": "Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like \"damaged\", \"imperfect\", or \"defective\" objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt learning (CAP) module is introduced in FAPrompt to learn a set of complementary, decomposed abnormality prompts, where abnormality prompts are enforced to model diverse abnormal patterns derived from the same normality semantic. On the other hand, the fine-grained abnormality patterns can be different from one dataset to another. To enhance the cross-dataset generalization, another novel module, namely Data-dependent Abnormality Prior learning (DAP), is introduced in FAPrompt to learn a sample-wise abnormality prior from abnormal features of each test image to dynamically adapt the abnormality prompts to individual test images. Comprehensive experiments on 19 real-world datasets, covering both industrial defects and medical anomalies, demonstrate that FAPrompt substantially outperforms state-of-the-art methods in both image- and pixel-level ZSAD tasks. Code is available at https://github.com/mala-lab/FAPrompt.", "date": "2024-10-14", "categories": []}
{"id": "2505.01476", "title": "CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering", "abstract": "Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstruction-based) or by learning an image feature embedding space (embedding-based), existing approaches fundamentally rely on image-level or feature-level matching to derive anomaly scores. Often, such a matching process is inaccurate yet overlooked, leading to sub-optimal detection. To address this issue, we introduce the concept of cost filtering, borrowed from classical matching tasks, such as depth and flow estimation, into the UAD problem. We call this approach {\\em CostFilter-AD}. Specifically, we first construct a matching cost volume between the input and normal samples, comprising two spatial dimensions and one matching dimension that encodes potential matches. To refine this, we propose a cost volume filtering network, guided by the input observation as an attention query across multiple feature layers, which effectively suppresses matching noise while preserving edge structures and capturing subtle anomalies. Designed as a generic post-processing plug-in, CostFilter-AD can be integrated with either reconstruction-based or embedding-based methods. Extensive experiments on MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for both single- and multi-class UAD tasks. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.", "date": "2025-05-02", "categories": []}
{"id": "2510.01934", "title": "Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors", "abstract": "Few-shot anomaly detection streamlines and simplifies industrial safety inspection. However, limited samples make accurate differentiation between normal and abnormal features challenging, and even more so under category-agnostic conditions. Large-scale pre-training of foundation visual encoders has advanced many fields, as the enormous quantity of data helps to learn the general distribution of normal images. We observe that the anomaly amount in an image directly correlates with the difference in the learnt embeddings and utilize this to design a few-shot anomaly detector termed FoundAD. This is done by learning a nonlinear projection operator onto the natural image manifold. The simple operator acts as an effective tool for anomaly detection to characterize and identify out-of-distribution regions in an image. Extensive experiments show that our approach supports multi-class detection and achieves competitive performance while using substantially fewer parameters than prior methods. Backed up by evaluations with multiple foundation encoders, including fresh DINOv3, we believe this idea broadens the perspective on foundation features and advances the field of few-shot anomaly detection.", "date": "2025-10-02", "categories": []}
{"id": "2510.01970", "title": "Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection", "abstract": "Multivariate time series (MTS) anomaly detection identifies abnormal patterns where each timestamp contains multiple variables. Existing MTS anomaly detection methods fall into three categories: reconstruction-based, prediction-based, and classifier-based methods. However, these methods face two key challenges: (1) Unsupervised learning methods, such as reconstruction-based and prediction-based methods, rely on error thresholds, which can lead to inaccuracies; (2) Semi-supervised methods mainly model normal data and often underuse anomaly labels, limiting detection of subtle anomalies;(3) Supervised learning methods, such as classifier-based approaches, often fail to capture local relationships, incur high computational costs, and are constrained by the scarcity of labeled data. To address these limitations, we propose Moon, a supervised modality conversion-based multivariate time series anomaly detection framework. Moon enhances the efficiency and accuracy of anomaly detection while providing detailed anomaly analysis reports. First, Moon introduces a novel multivariate Markov Transition Field (MV-MTF) technique to convert numeric time series data into image representations, capturing relationships across variables and timestamps. Since numeric data retains unique patterns that cannot be fully captured by image conversion alone, Moon employs a Multimodal-CNN to integrate numeric and image data through a feature fusion model with parameter sharing, enhancing training efficiency. Finally, a SHAP-based anomaly explainer identifies key variables contributing to anomalies, improving interpretability. Extensive experiments on six real-world MTS datasets demonstrate that Moon outperforms six state-of-the-art methods by up to 93% in efficiency, 4% in accuracy and, 10.8% in interpretation performance.", "date": "2025-10-02", "categories": []}
{"id": "2510.02155", "title": "Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Prompting", "abstract": "Prompting has emerged as a practical way to adapt frozen vision-language models (VLMs) for video anomaly detection (VAD). Yet, existing prompts are often overly abstract, overlooking the fine-grained human-object interactions or action semantics that define complex anomalies in surveillance videos. We propose ASK-Hint, a structured prompting framework that leverages action-centric knowledge to elicit more accurate and interpretable reasoning from frozen VLMs. Our approach organizes prompts into semantically coherent groups (e.g. violence, property crimes, public safety) and formulates fine-grained guiding questions that align model predictions with discriminative visual cues. Extensive experiments on UCF-Crime and XD-Violence show that ASK-Hint consistently improves AUC over prior baselines, achieving state-of-the-art performance compared to both fine-tuned and training-free methods. Beyond accuracy, our framework provides interpretable reasoning traces towards anomaly and demonstrates strong generalization across datasets and VLM backbones. These results highlight the critical role of prompt granularity and establish ASK-Hint as a new training-free and generalizable solution for explainable video anomaly detection.", "date": "2025-10-02", "categories": []}
{"id": "2510.02236", "title": "PUL-Inter-slice Defender: An Anomaly Detection Solution for Distributed Slice Mobility Attacks", "abstract": "Network Slices (NSs) are virtual networks operating over a shared physical infrastructure, each designed to meet specific application requirements while maintaining consistent Quality of Service (QoS). In Fifth Generation (5G) networks, User Equipment (UE) can connect to and seamlessly switch between multiple NSs to access diverse services. However, this flexibility, known as Inter-Slice Switching (ISS), introduces a potential vulnerability that can be exploited to launch Distributed Slice Mobility (DSM) attacks, a form of Distributed Denial of Service (DDoS) attack. To secure 5G networks and their NSs against DSM attacks, we present in this work, PUL-Inter-Slice Defender; an anomaly detection solution that leverages Positive Unlabeled Learning (PUL) and incorporates a combination of Long Short-Term Memory Autoencoders and K-Means clustering. PUL-Inter-Slice Defender leverages the Third Generation Partnership Project (3GPP) key performance indicators and performance measurement counters as features for its machine learning models to detect DSM attack variants while maintaining robustness in the presence of contaminated training data. When evaluated on data collected from our 5G testbed based on the open-source free5GC and UERANSIM, a UE/ Radio Access Network (RAN) simulator; PUL-Inter-Slice Defender achieved F1-scores exceeding 98.50% on training datasets with 10% to 40% attack contamination, consistently outperforming its counterpart Inter-Slice Defender and other PUL based solutions combining One-Class Support Vector Machine (OCSVM) with Random Forest and XGBoost.", "date": "2025-10-02", "categories": []}
{"id": "2510.00087", "title": "Revealing the temporal dynamics of antibiotic anomalies in the infant gut microbiome with neural jump ODEs", "abstract": "Detecting anomalies in irregularly sampled multi-variate time-series is challenging, especially in data-scarce settings. Here we introduce an anomaly detection framework for irregularly sampled time-series that leverages neural jump ordinary differential equations (NJODEs). The method infers conditional mean and variance trajectories in a fully path dependent way and computes anomaly scores. On synthetic data containing jump, drift, diffusion, and noise anomalies, the framework accurately identifies diverse deviations. Applied to infant gut microbiome trajectories, it delineates the magnitude and persistence of antibiotic-induced disruptions: revealing prolonged anomalies after second antibiotic courses, extended duration treatments, and exposures during the second year of life. We further demonstrate the predictive capabilities of the inferred anomaly scores in accurately predicting antibiotic events and outperforming diversity-based baselines. Our approach accommodates unevenly spaced longitudinal observations, adjusts for static and dynamic covariates, and provides a foundation for inferring microbial anomalies induced by perturbations, offering a translational opportunity to optimize intervention regimens by minimizing microbial disruptions.", "date": "2025-09-30", "categories": []}
{"id": "2510.00148", "title": "Improved Hyperspectral Anomaly Detection via Unsupervised Subspace Modeling in the Signed Cumulative Distribution Transform Domain", "abstract": "Hyperspectral anomaly detection (HAD), a crucial approach for many civilian and military applications, seeks to identify pixels with spectral signatures that are anomalous relative to a preponderance of background signatures. Significant effort has been made to improve HAD techniques, but challenges arise due to complex real-world environments and, by definition, limited prior knowledge of potential signatures of interest. This paper introduces a novel HAD method by proposing a transport-based mathematical model to describe the pixels comprising a given hyperspectral image. In this approach, hyperspectral pixels are viewed as observations of a template pattern undergoing unknown deformations that enables their representation in the signed cumulative distribution transform (SCDT) domain. An unsupervised subspace modeling technique is then used to construct a model of abundant background signals in this domain, whereupon anomalous signals are detected as deviations from the learned model. Comprehensive evaluations across five distinct datasets illustrate the superiority of our approach compared to state-of-the-art methods.", "date": "2025-09-30", "categories": []}
{"id": "2510.00460", "title": "Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition", "abstract": "Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications, including video surveillance, medical imaging data, and urban traffic monitoring. Existing anomaly detection methods focus mainly on point anomalies and cannot deal with temporal and spatial dependencies that arise in spatio-temporal data. Tensor-based anomaly detection methods have been proposed to address this problem. Although existing methods can capture dependencies across different modes, they are primarily supervised and do not account for the specific structure of anomalies. Moreover, these methods focus mainly on extracting anomalous features without providing any statistical confidence. In this paper, we introduce an unsupervised tensor-based anomaly detection method that simultaneously considers the sparse and spatiotemporally smooth nature of anomalies. The anomaly detection problem is formulated as a regularized robust low-rank + sparse tensor decomposition where the total variation of the tensor with respect to the underlying spatial and temporal graphs quantifies the spatiotemporal smoothness of the anomalies. Once the anomalous features are extracted, we introduce a statistical anomaly scoring framework that accounts for local spatio-temporal dependencies. The proposed framework is evaluated on both synthetic and real data.", "date": "2025-10-01", "categories": []}
{"id": "2410.23774", "title": "Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions", "abstract": "An unsolved issue in widely used methods such as Support Vector Data Description (SVDD) and Small Sphere and Large Margin SVM (SSLM) for anomaly detection is their nonconvexity, which hampers the analysis of optimal solutions in a manner similar to SVMs and limits their applicability in large-scale scenarios. In this paper, we introduce a novel convex SSLM formulation which has been demonstrated to revert to a convex quadratic programming problem for hyperparameter values of interest. Leveraging the convexity of our method, we derive numerous results that are unattainable with traditional nonconvex approaches. We conduct a thorough analysis of how hyperparameters influence the optimal solution, pointing out scenarios where optimal solutions can be trivially found and identifying instances of ill-posedness. Most notably, we establish connections between our method and traditional approaches, providing a clear determination of when the optimal solution is unique--a task unachievable with traditional nonconvex methods. We also derive the nu-property to elucidate the interactions between hyperparameters and the fractions of support vectors and margin errors in both positive and negative classes.", "date": "2024-10-31", "categories": []}
{"id": "2509.25215", "title": "Anomaly detection by partitioning of multi-variate time series", "abstract": "In this article, we suggest a novel non-supervised partition based anomaly detection method for anomaly detection in multivariate time series called PARADISE. This methodology creates a partition of the variables of the time series while ensuring that the inter-variable relations remain untouched. This partitioning relies on the clustering of multiple correlation coefficients between variables to identify subsets of variables before executing anomaly detection algorithms locally for each of those subsets. Through multiple experimentations done on both synthetic and real datasets coming from the literature, we show the relevance of our approach with a significant improvement in anomaly detection performance.", "date": "2025-09-22", "categories": []}
{"id": "2406.03614", "title": "Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs", "abstract": "Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in feature dimensions adds significant complexity to data analysis. In this paper, we introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. To encode non-semantic categorical data from real-world financial records, we tested 3 pre-trained general purpose sentence-transformer models. For the downstream classification task, we implemented and evaluated 5 optimized ML models including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines, in selected settings even by a large margin. The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity. We discuss a promising perspective on using LLM embeddings for non-semantic data in the financial context and beyond.", "date": "2024-06-05", "categories": []}
{"id": "2507.17796", "title": "CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series", "abstract": "We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series analysis: delivering accurate predictions and enabling robust anomaly detection. Our method, Copula-based Conformal Anomaly Identification for Multivariate Time-Series (CoCAI), leverages a diffusion-based model to capture complex dependencies within the data, enabling high quality forecasting. The model's outputs are further calibrated using a conformal prediction technique, yielding predictive regions which are statistically valid, i.e., cover the true target values with a desired confidence level. Starting from these calibrated forecasts, robust outlier detection is performed by combining dimensionality reduction techniques with copula-based modeling, providing a statistically grounded anomaly score. CoCAI benefits from an offline calibration phase that allows for minimal overhead during deployment and delivers actionable results rooted in established theoretical foundations. Empirical tests conducted on real operational data derived from water distribution and sewerage systems confirm CoCAI's effectiveness in accurately forecasting target sequences of data and in identifying anomalous segments within them.", "date": "2025-07-23", "categories": []}
{"id": "2509.21943", "title": "Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning", "abstract": "Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust outlier detection remains unclear. This study compares an SPM approach with an explainable machine learning (ML) approach to establish transparent quality-control pipelines for plantar pressure datasets. Data from multiple centers were annotated by expert consensus and enriched with synthetic anomalies resulting in 798 valid samples and 2000 outliers. We evaluated (i) a non-parametric, registration-dependent SPM approach and (ii) a convolutional neural network (CNN), explained using SHapley Additive exPlanations (SHAP). Performance was assessed via nested cross-validation; explanation quality via a semantic differential survey with domain experts. The ML model reached high accuracy and outperformed SPM, which misclassified clinically meaningful variations and missed true outliers. Experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy, though SPM was assessed less complex. These findings highlight the complementary potential of SPM and explainable ML as approaches for automated outlier detection in plantar pressure data, and underscore the importance of explainability in translating complex model outputs into interpretable insights that can effectively inform decision-making.", "date": "2025-09-26", "categories": []}
{"id": "2509.22881", "title": "From Noise to Knowledge: A Comparative Study of Acoustic Anomaly Detection Models in Pumped-storage Hydropower Plants", "abstract": "In the context of industrial factories and energy producers, unplanned outages are highly costly and difficult to service. However, existing acoustic-anomaly detection studies largely rely on generic industrial or synthetic datasets, with few focused on hydropower plants due to limited access. This paper presents a comparative analysis of acoustic-based anomaly detection methods, as a way to improve predictive maintenance in hydropower plants. We address key challenges in the acoustic preprocessing under highly noisy conditions before extracting time- and frequency-domain features. Then, we benchmark three machine learning models: LSTM AE, K-Means, and OC-SVM, which are tested on two real-world datasets from the Rodundwerk II pumped-storage plant in Austria, one with induced anomalies and one with real-world conditions. The One-Class SVM achieved the best trade-off of accuracy (ROC AUC 0.966-0.998) and minimal training time, while the LSTM autoencoder delivered strong detection (ROC AUC 0.889-0.997) at the expense of higher computational cost.", "date": "2025-09-26", "categories": []}
{"id": "2509.23741", "title": "ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning", "abstract": "This paper explores the problem of class-agnostic anomaly detection (AD), where the objective is to train one class-agnostic AD model that can generalize to detect anomalies in diverse new classes from different domains without any retraining or fine-tuning on the target data. When applied for new classes, the performance of current single- and multi-class AD methods is still unsatisfactory. One fundamental reason is that representation learning in existing methods is still class-related, namely, feature correlation. To address this issue, we propose residual features and construct a simple but effective framework, termed ResAD. Our core insight is to learn the residual feature distribution rather than the initial feature distribution. Residual features are formed by matching and then subtracting normal reference features. In this way, we can effectively realize feature decorrelation. Even in new classes, the distribution of normal residual features would not remarkably shift from the learned distribution. In addition, we think that residual features still have one issue: scale correlation. To this end, we propose a feature hypersphere constraining approach, which learns to constrain initial normal residual features into a spatial hypersphere for enabling the feature scales of different classes as consistent as possible. Furthermore, we propose a novel logbarrier bidirectional contraction OCC loss and vector quantization based feature distribution matching module to enhance ResAD, leading to the improved version of ResAD (ResAD++). Comprehensive experiments on eight real-world AD datasets demonstrate that our ResAD++ can achieve remarkable AD results when directly used in new classes, outperforming state-of-the-art competing methods and also surpassing ResAD. The code is available at https://github.com/xcyao00/ResAD.", "date": "2025-09-28", "categories": []}
{"id": "2509.24414", "title": "ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection", "abstract": "One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation learning and limited sensitivity to anomalous dispersion in high-dimensional spaces. In this work, we conduct an empirical analysis showing that both normal and anomalous samples tend to scatter in high-dimensional space, especially anomalous samples are markedly more dispersed. We formalize this dispersion phenomenon as scattering, quantified by the mean pairwise distance among sample representations, and leverage it as an inductive signal to enhance spatio-temporal anomaly detection. Technically, we propose ScatterAD to model representation scattering across temporal and topological dimensions. ScatterAD incorporates a topological encoder for capturing graph-structured scattering and a temporal encoder for constraining over-scattering through mean squared error minimization between neighboring time steps. We introduce a contrastive fusion mechanism to ensure the complementarity of the learned temporal and topological representations. Additionally, we theoretically show that maximizing the conditional mutual information between temporal and topological views improves cross-view consistency and enhances more discriminative representations. Extensive experiments on multiple public benchmarks show that ScatterAD achieves state-of-the-art performance on multivariate time series anomaly detection. Code is available at this repository: https://github.com/jk-sounds/ScatterAD.", "date": "2025-09-29", "categories": []}
{"id": "2509.24448", "title": "Generalist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networks", "abstract": "Anomaly detection (AD) plays an important role in various real-world applications. Recent advancements in AD, however, are often biased towards industrial inspection, struggle to generalize to broader tasks like semantic anomaly detection and vice versa. Although recent methods have attempted to address general anomaly detection, their performance remains sensitive to dataset-specific settings and single-class tasks. In this paper, we propose a novel dual-model ensemble approach based on knowledge distillation (KD) to bridge this gap. Our framework consists of a teacher and two student models: an Encoder-Decoder model, specialized in detecting patch-level minor defects for industrial AD and an Encoder-Encoder model, optimized for semantic AD. Both models leverage a shared pre-trained encoder (DINOv2) to extract high-quality feature representations. The dual models are jointly learned using the Noisy-OR objective, and the final anomaly score is obtained using the joint probability via local and semantic anomaly scores derived from the respective models. We evaluate our method on eight public benchmarks under both single-class and multi-class settings: MVTec-AD, MVTec-LOCO, VisA and Real-IAD for industrial inspection and CIFAR-10/100, FMNIST and View for semantic anomaly detection. The proposed method achieved state-of-the-art accuracies in both domains, in multi-class as well as single-class settings, demonstrating generalization across multiple domains of anomaly detection. Our model achieved an image-level AUROC of 99.7% on MVTec-AD and 97.8% on CIFAR-10, which is significantly better than the prior general AD models in multi-class settings and even higher than the best specialist models on individual benchmarks.", "date": "2025-09-29", "categories": []}
{"id": "2503.13562", "title": "Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learning", "abstract": "In real-world applications, it is highly challenging to detect anomalous samples with extremely sparse anomalies, as they are highly similar to and thus easily confused with normal samples. Moreover, the number of anomalous samples is inherently scarce. This results in a dual imbalance Multi-Instance Learning (MIL) problem, manifesting at both the macro and micro levels. To address this \"needle-in-a-haystack problem\", we find that MIL problem can be reformulated as a fine-grained PU learning problem. This allows us to address the imbalance issue in an unbiased manner using micro-level balancing mechanisms. To this end, we propose a novel framework, Balanced Fine-Grained Positive-Unlabeled (BFGPU)-based on rigorous theoretical foundations. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of BFGPU.", "date": "2025-03-17", "categories": []}
{"id": "2409.05672", "title": "FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection", "abstract": "Outlier detection (OD) has a vast literature as it finds numerous real-world applications. Being an unsupervised task, model selection is a key bottleneck for OD without label supervision. Despite a long list of available OD algorithms with tunable hyperparameters, the lack of systematic approaches for unsupervised algorithm and hyperparameter selection limits their effective use in practice. In this paper, we present FoMo-0D, a pre-trained Foundation Model for zero/0-shot OD on tabular data, which bypasses the hurdle of model selection altogether. Having been pre-trained on synthetic data, FoMo-0D can directly predict the (outlier/inlier) label of test samples without parameter fine-tuning -- requiring no labeled data, and no additional training or hyperparameter tuning when given a new task. Extensive experiments on 57 real-world datasets against 26 baselines show that FoMo-0D is highly competitive; outperforming the majority of the baselines with no statistically significant difference from the 2nd best method. Further, FoMo-0D is efficient in inference time requiring only 7.7 ms per sample on average, with at least 7x speed-up compared to previous methods. To facilitate future research, our implementations for data synthesis and pre-training as well as model checkpoints are openly available at https://github.com/A-Chicharito-S/FoMo-0D.", "date": "2024-09-09", "categories": []}
{"id": "2504.00881", "title": "Detection of Anomalous Vehicular Traffic and Sensor Failures Using Data Clustering Techniques", "abstract": "The increasing availability of traffic data from sensor networks has created new opportunities for understanding vehicular dynamics and identifying anomalies. In this study, we employ clustering techniques to analyse traffic flow data with the dual objective of uncovering meaningful traffic patterns and detecting anomalies, including sensor failures and irregular congestion events.\n  We explore multiple clustering approaches, i.e partitioning and hierarchical methods, combined with various time-series representations and similarity measures. Our methodology is applied to real-world data from highway sensors, enabling us to assess the impact of different clustering frameworks on traffic pattern recognition. We also introduce a clustering-driven anomaly detection methodology that identifies deviations from expected traffic behaviour based on distance-based anomaly scores.\n  Results indicate that hierarchical clustering with symbolic representations provides robust segmentation of traffic patterns, while partitioning methods such as k-means and fuzzy c-means yield meaningful results when paired with Dynamic Time Warping. The proposed anomaly detection strategy successfully identifies sensor malfunctions and abnormal traffic conditions with minimal false positives, demonstrating its practical utility for real-time monitoring.\n  Real-world vehicular traffic data are provided by Autostrade Alto Adriatico S.p.A.", "date": "2025-04-01", "categories": []}
{"id": "2509.20946", "title": "A Real-Time On-Device Defect Detection Framework for Laser Power-Meter Sensors via Unsupervised Learning", "abstract": "We present an automated vision-based system for defect detection and classification of laser power meter sensor coatings. Our approach addresses the critical challenge of identifying coating defects such as thermal damage and scratches that can compromise laser energy measurement accuracy in medical and industrial applications. The system employs an unsupervised anomaly detection framework that trains exclusively on ``good'' sensor images to learn normal coating distribution patterns, enabling detection of both known and novel defect types without requiring extensive labeled defect datasets. Our methodology consists of three key components: (1) a robust preprocessing pipeline using Laplacian edge detection and K-means clustering to segment the area of interest, (2) synthetic data augmentation via StyleGAN2, and (3) a UFlow-based neural network architecture for multi-scale feature extraction and anomaly map generation. Experimental evaluation on 366 real sensor images demonstrates $93.8\\%$ accuracy on defective samples and $89.3\\%$ accuracy on good samples, with image-level AUROC of 0.957 and pixel-level AUROC of 0.961. The system provides potential annual cost savings through automated quality control and processing times of 0.5 seconds per image in on-device implementation.", "date": "2025-09-25", "categories": []}
{"id": "2509.20978", "title": "FracAug: Fractional Augmentation boost Graph-level Anomaly Detection under Limited Supervision", "abstract": "Graph-level anomaly detection (GAD) is critical in diverse domains such as drug discovery, yet high labeling costs and dataset imbalance hamper the performance of Graph Neural Networks (GNNs). To address these issues, we propose FracAug, an innovative plug-in augmentation framework that enhances GNNs by generating semantically consistent graph variants and pseudo-labeling with mutual verification. Unlike previous heuristic methods, FracAug learns semantics within given graphs and synthesizes fractional variants, guided by a novel weighted distance-aware margin loss. This captures multi-scale topology to generate diverse, semantic-preserving graphs unaffected by data imbalance. Then, FracAug utilizes predictions from both original and augmented graphs to pseudo-label unlabeled data, iteratively expanding the training set. As a model-agnostic module compatible with various GNNs, FracAug demonstrates remarkable universality and efficacy: experiments across 14 GNNs on 12 real-world datasets show consistent gains, boosting average AUROC, AUPRC, and F1-score by up to 5.72%, 7.23%, and 4.18%, respectively.", "date": "2025-09-25", "categories": []}
{"id": "2502.19307", "title": "Anomaly Detection in Complex Dynamical Systems: A Systematic Framework Using Embedding Theory and Physics-Inspired Consistency", "abstract": "Anomaly detection in complex dynamical systems is essential for ensuring reliability, safety, and efficiency in industrial and cyber-physical infrastructures. Predictive maintenance helps prevent costly failures, while cybersecurity monitoring has become critical as digitized systems face growing threats. Many of these systems exhibit oscillatory behaviors and bounded motion, requiring anomaly detection methods that capture structured temporal dependencies while adhering to physical consistency principles. In this work, we propose a system-theoretic approach to anomaly detection, grounded in classical embedding theory and physics-inspired consistency principles. We build upon the Fractal Whitney Embedding Prevalence Theorem that extends traditional embedding techniques to complex system dynamics. Additionally, we introduce state-derivative pairs as an embedding strategy to capture system evolution. To enforce temporal coherence, we develop a Temporal Differential Consistency Autoencoder (TDC-AE), incorporating a TDC-Loss that aligns the approximated derivatives of latent variables with their dynamic representations. We evaluate our method on two subsets (FD001, FD003) of the C-MAPSS dataset, a benchmark for turbofan engine degradation. TDC-AE machtes LSTMs and outperforms Transformers while achieving a nearly 100x reduction in MAC operations, making it particularly suited for lightweight edge computing. Our findings support the hypothesis that anomalies disrupt stable system dynamics, providing a robust signal for anomaly detection.", "date": "2025-02-26", "categories": []}
{"id": "2509.19366", "title": "Unsupervised Outlier Detection in Audit Analytics: A Case Study Using USA Spending Data", "abstract": "This study investigates the effectiveness of unsupervised outlier detection methods in audit analytics, utilizing USA spending data from the U.S. Department of Health and Human Services (DHHS) as a case example. We employ and compare multiple outlier detection algorithms, including Histogram-based Outlier Score (HBOS), Robust Principal Component Analysis (PCA), Minimum Covariance Determinant (MCD), and K-Nearest Neighbors (KNN) to identify anomalies in federal spending patterns. The research addresses the growing need for efficient and accurate anomaly detection in large-scale governmental datasets, where traditional auditing methods may fall short. Our methodology involves data preparation, algorithm implementation, and performance evaluation using precision, recall, and F1 scores. Results indicate that a hybrid approach, combining multiple detection strategies, enhances the robustness and accuracy of outlier identification in complex financial data. This study contributes to the field of audit analytics by providing insights into the comparative effectiveness of various outlier detection models and demonstrating the potential of unsupervised learning techniques in improving audit quality and efficiency. The findings have implications for auditors, policymakers, and researchers seeking to leverage advanced analytics in governmental financial oversight and risk management.", "date": "2025-09-19", "categories": []}
{"id": "2509.19997", "title": "Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixture", "abstract": "In this work, we leverage informative embeddings from foundational models for unsupervised anomaly detection in medical imaging. For small datasets, a memory-bank of normative features can directly be used for anomaly detection which has been demonstrated recently. However, this is unsuitable for large medical datasets as the computational burden increases substantially. Therefore, we propose to model the distribution of normative DINOv2 embeddings with a Dirichlet Process Mixture model (DPMM), a non-parametric mixture model that automatically adjusts the number of mixture components to the data at hand. Rather than using a memory bank, we use the similarity between the component centers and the embeddings as anomaly score function to create a coarse anomaly segmentation mask. Our experiments show that through DPMM embeddings of DINOv2, despite being trained on natural images, achieve very competitive anomaly detection performance on medical imaging benchmarks and can do this while at least halving the computation time at inference. Our analysis further indicates that normalized DINOv2 embeddings are generally more aligned with anatomical structures than unnormalized features, even in the presence of anomalies, making them great representations for anomaly detection. The code is available at https://github.com/NicoSchulthess/anomalydino-dpmm.", "date": "2025-09-24", "categories": []}
{"id": "2509.20068", "title": "A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network", "abstract": "Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framework for short-term anomaly detection that uses an SDN-based DT. Using a comprehensive dataset, time-aware labeling of features, and a comprehensive evaluation of various machine learning models, we propose a novel SD-TWIN-based anomaly detection algorithm. According to the performance of a new real-time SD-TWIN deployment, the GPU- accelerated LightGBM model is particularly effective, achieving a balance of high recall and strong classification performance.", "date": "2025-09-24", "categories": []}
{"id": "2509.20184", "title": "An Improved Time Series Anomaly Detection by Applying Structural Similarity", "abstract": "Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual labeling, reconstruction-based unsupervised approaches have garnered considerable attention. However, accurate anomaly detection remains an unsettled challenge, since the optimization objectives of reconstruction-based methods merely rely on point-by-point distance measures, ignoring the potential structural characteristics of time series and thus failing to tackle complex pattern-wise anomalies. In this paper, we propose StrAD, a novel structure-enhanced anomaly detection approach to enrich the optimization objective by incorporating structural information hidden in the time series and steering the data reconstruction procedure to better capture such structural features. StrAD accommodates the trend, seasonality, and shape in the optimization objective of the reconstruction model to learn latent structural characteristics and capture the intrinsic pattern variation of time series. The proposed structure-aware optimization objective mechanism can assure the alignment between the original data and the reconstructed data in terms of structural features, thereby keeping consistency in global fluctuation and local characteristics. The mechanism is pluggable and applicable to any reconstruction-based methods, enhancing the model sensitivity to both point-wise anomalies and pattern-wise anomalies. Experimental results show that StrAD improves the performance of state-of-the-art reconstruction-based models across five real-world anomaly detection datasets.", "date": "2025-09-24", "categories": []}
{"id": "2412.20201", "title": "Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems", "abstract": "Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the real-time and interpretability requirements of edge devices due to their complexity. This paper presents TCVADS (Two-stage Cross-modal Video Anomaly Detection System), which leverages knowledge distillation and cross-modal contrastive learning to enable efficient, accurate, and interpretable anomaly detection on edge devices.TCVADS operates in two stages: coarse-grained rapid classification and fine-grained detailed analysis. In the first stage, TCVADS extracts features from video frames and inputs them into a time series analysis module, which acts as the teacher model. Insights are then transferred via knowledge distillation to a simplified convolutional network (student model) for binary classification. Upon detecting an anomaly, the second stage is triggered, employing a fine-grained multi-class classification model. This stage uses CLIP for cross-modal contrastive learning with text and images, enhancing interpretability and achieving refined classification through specially designed triplet textual relationships. Experimental results demonstrate that TCVADS significantly outperforms existing methods in model performance, detection efficiency, and interpretability, offering valuable contributions to smart city monitoring applications.", "date": "2024-12-28", "categories": []}
{"id": "2502.19200", "title": "HDM: Hybrid Diffusion Model for Unified Image Anomaly Detection", "abstract": "Image anomaly detection plays a vital role in applications such as industrial quality inspection and medical imaging, where it directly contributes to improving product quality and system reliability. However, existing methods often struggle with complex and diverse anomaly patterns. In particular, the separation between generation and discrimination tasks limits the effective coordination between anomaly sample generation and anomaly region detection. To address these challenges, we propose a novel hybrid diffusion model (HDM) that integrates generation and discrimination into a unified framework. The model consists of three key modules: the Diffusion Anomaly Generation Module (DAGM), the Diffusion Discriminative Module (DDM), and the Probability Optimization Module (POM). DAGM generates realistic and diverse anomaly samples, improving their representativeness. DDM then applies a reverse diffusion process to capture the differences between generated and normal samples, enabling precise anomaly region detection and localization based on probability distributions. POM refines the probability distributions during both the generation and discrimination phases, ensuring high-quality samples are used for training. Extensive experiments on multiple industrial image datasets demonstrate that our method outperforms state-of-the-art approaches, significantly improving both image-level and pixel-level anomaly detection performance, as measured by AUROC.", "date": "2025-02-26", "categories": []}
{"id": "2507.07838", "title": "3D-ADAM: A Dataset for 3D Anomaly Detection in Additive Manufacturing", "abstract": "Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative datasets. To overcome this, we introduce 3D-ADAM, a 3D Anomaly Detection in Additive Manufacturing dataset, that is the first large-scale, industry-relevant dataset for RGB+3D surface defect detection in additive manufacturing. 3D-ADAM comprises 14,120 high-resolution scans of 217 unique parts, captured with four industrial depth sensors, and includes 27,346 annotated defects across 12 categories along with 27,346 annotations of machine element features in 16 classes. 3D-ADAM is captured in a real industrial environment and as such reflects real production conditions, including variations in part placement, sensor positioning, lighting, and partial occlusion. Benchmarking state-of-the-art models demonstrates that 3D-ADAM presents substantial challenges beyond existing datasets. Validation through expert labelling surveys with industry partners further confirms its industrial relevance. By providing this benchmark, 3D-ADAM establishes a foundation for advancing robust 3D anomaly detection capable of meeting manufacturing demands.", "date": "2025-07-10", "categories": []}
{"id": "2509.18354", "title": "A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data", "abstract": "Anomaly detection in images is typically addressed by learning from collections of training data or relying on reference samples. In many real-world scenarios, however, such training data may be unavailable, and only the test image itself is provided. We address this zero-shot setting by proposing a single-image anomaly localization method that leverages the inductive bias of convolutional neural networks, inspired by Deep Image Prior (DIP). Our method is named Single Shot Decomposition Network (SSDnet). Our key assumption is that natural images often exhibit unified textures and patterns, and that anomalies manifest as localized deviations from these repetitive or stochastic patterns. To learn the deep image prior, we design a patch-based training framework where the input image is fed directly into the network for self-reconstruction, rather than mapping random noise to the image as done in DIP. To avoid the model simply learning an identity mapping, we apply masking, patch shuffling, and small Gaussian noise. In addition, we use a perceptual loss based on inner-product similarity to capture structure beyond pixel fidelity. Our approach needs no external training data, labels, or references, and remains robust in the presence of noise or missing pixels. SSDnet achieves 0.99 AUROC and 0.60 AUPRC on MVTec-AD and 0.98 AUROC and 0.67 AUPRC on the fabric dataset, outperforming state-of-the-art methods. The implementation code will be released at https://github.com/mehrdadmoradi124/SSDnet", "date": "2025-09-22", "categories": []}
{"id": "2509.18386", "title": "Graph Enhanced Trajectory Anomaly Detection", "abstract": "Trajectory anomaly detection is essential for identifying unusual and unexpected movement patterns in applications ranging from intelligent transportation systems to urban safety and fraud prevention.\n  Existing methods only consider limited aspects of the trajectory nature and its movement space by treating trajectories as sequences of sampled locations, with sampling determined by positioning technology, e.g., GPS, or by high-level abstractions such as staypoints. Trajectories are analyzed in Euclidean space, neglecting the constraints and connectivity information of the underlying movement network, e.g., road or transit networks.\n  The proposed Graph Enhanced Trajectory Anomaly Detection (GETAD) framework tightly integrates road network topology, segment semantics, and historical travel patterns to model trajectory data. GETAD uses a Graph Attention Network to learn road-aware embeddings that capture both physical attributes and transition behavior, and augments these with graph-based positional encodings that reflect the spatial layout of the road network.\n  A Transformer-based decoder models sequential movement, while a multiobjective loss function combining autoregressive prediction and supervised link prediction ensures realistic and structurally coherent representations.\n  To improve the robustness of anomaly detection, we introduce Confidence Weighted Negative Log Likelihood (CW NLL), an anomaly scoring function that emphasizes high-confidence deviations.\n  Experiments on real-world and synthetic datasets demonstrate that GETAD achieves consistent improvements over existing methods, particularly in detecting subtle anomalies in road-constrained environments. These results highlight the benefits of incorporating graph structure and contextual semantics into trajectory modeling, enabling more precise and context-aware anomaly detection.", "date": "2025-09-22", "categories": []}
{"id": "2509.14693", "title": "RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning", "abstract": "Logs constitute a form of evidence signaling the operational status of software systems. Automated log anomaly detection is crucial for ensuring the reliability of modern software systems. However, existing approaches face significant limitations: traditional deep learning models lack interpretability and generalization, while methods leveraging Large Language Models are often hindered by unreliability and factual inaccuracies. To address these issues, we propose RationAnomaly, a novel framework that enhances log anomaly detection by synergizing Chain-of-Thought (CoT) fine-tuning with reinforcement learning. Our approach first instills expert-like reasoning patterns using CoT-guided supervised fine-tuning, grounded in a high-quality dataset corrected through a rigorous expert-driven process. Subsequently, a reinforcement learning phase with a multi-faceted reward function optimizes for accuracy and logical consistency, effectively mitigating hallucinations. Experimentally, RationAnomaly outperforms state-of-the-art baselines, achieving superior F1-scores on key benchmarks while providing transparent, step-by-step analytical outputs. We have released the corresponding resources, including code and datasets.", "date": "2025-09-18", "categories": []}
{"id": "2509.17068", "title": "Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detection", "abstract": "Long-term trajectory anomaly detection is a challenging problem due to the diversity and complex spatiotemporal dependencies in trajectory data. Existing trajectory anomaly detection methods fail to simultaneously consider both the high-level intentions of agents as well as the low-level details of the agent's navigation when analysing an agent's trajectories. This limits their ability to capture the full diversity of normal trajectories. In this paper, we propose an unsupervised trajectory anomaly detection method named Intention-aware Hierarchical Diffusion model (IHiD), which detects anomalies through both high-level intent evaluation and low-level sub-trajectory analysis. Our approach leverages Inverse Q Learning as the high-level model to assess whether a selected subgoal aligns with an agent's intention based on predicted Q-values. Meanwhile, a diffusion model serves as the low-level model to generate sub-trajectories conditioned on subgoal information, with anomaly detection based on reconstruction error. By integrating both models, IHiD effectively utilises subgoal transition knowledge and is designed to capture the diverse distribution of normal trajectories. Our experiments show that the proposed method IHiD achieves up to 30.2% improvement in anomaly detection performance in terms of F1 score over state-of-the-art baselines.", "date": "2025-09-21", "categories": []}
{"id": "2509.17235", "title": "Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection", "abstract": "Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have increasingly leveraged graph structures to model inter-variable relationships, typically employing Graph Neural Networks (GNNs). Despite their promising results, existing methods often rely on a single graph representation, which are insufficient for capturing the complex, diverse relationships inherent in multivariate time series. To address this, we propose the Prospective Multi-Graph Cohesion (PMGC) framework for multivariate TSAD. PMGC exploits spatial correlations by integrating a long-term static graph with a series of short-term instance-wise dynamic graphs, regulated through a graph cohesion loss function. Our theoretical analysis shows that this loss function promotes diversity among dynamic graphs while aligning them with the stable long-term relationships encapsulated by the static graph. Additionally, we introduce a \"prospective graphing\" strategy to mitigate the limitations of traditional forecasting-based TSAD methods, which often struggle with unpredictable future variations. This strategy allows the model to accurately reflect concurrent inter-series relationships under normal conditions, thereby enhancing anomaly detection efficacy. Empirical evaluations on real-world datasets demonstrate the superior performance of our method compared to existing TSAD techniques.", "date": "2025-09-21", "categories": []}
{"id": "2509.17400", "title": "Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs", "abstract": "Anomaly detection in dynamic graphs is a critical task with broad real-world applications, including social networks, e-commerce, and cybersecurity. Most existing methods assume that normal patterns remain stable over time; however, this assumption often fails in practice due to the phenomenon we refer to as normality distribution shift (NDS), where normal behaviors evolve over time. Ignoring NDS can lead models to misclassify shifted normal instances as anomalies, degrading detection performance. To tackle this issue, we propose WhENDS, a novel unsupervised anomaly detection method that aligns normal edge embeddings across time by estimating distributional statistics and applying whitening transformations. Extensive experiments on four widely-used dynamic graph datasets show that WhENDS consistently outperforms nine strong baselines, achieving state-of-the-art results and underscoring the importance of addressing NDS in dynamic graph anomaly detection.", "date": "2025-09-22", "categories": []}
{"id": "2509.17472", "title": "Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector", "abstract": "Multivariate time series (MTS) anomaly detection commonly encounters in various domains like finance, healthcare, and industrial monitoring. However, existing MTS anomaly detection methods are mostly defined on the static graph structure, which fails to perform an accurate representation of complex spatio-temporal correlations in MTS. To address this issue, this study proposes a Periodic Graph-Enhanced Multivariate Time Series Anomaly Detector (PGMA) with the following two-fold ideas: a) designing a periodic time-slot allocation strategy based Fast Fourier Transform (FFT), which enables the graph structure to reflect dynamic changes in MTS; b) utilizing graph neural network and temporal extension convolution to accurate extract the complex spatio-temporal correlations from the reconstructed periodic graphs. Experiments on four real datasets from real applications demonstrate that the proposed PGMA outperforms state-of-the-art models in MTS anomaly detection.", "date": "2025-09-22", "categories": []}
{"id": "2509.17615", "title": "From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge", "abstract": "Visual anomaly detection is a strongly application-driven field of research. Consequently, the connection between academia and industry is of paramount importance. In this regard, we present the VAND 3.0 Challenge to showcase current progress in anomaly detection across different practical settings whilst addressing critical issues in the field. The challenge hosted two tracks, fostering the development of anomaly detection methods robust against real-world distribution shifts (Category 1) and exploring the capabilities of Vision Language Models within the few-shot regime (Category 2), respectively. The participants' solutions reached significant improvements over previous baselines by combining or adapting existing approaches and fusing them with novel pipelines. While for both tracks the progress in large pre-trained vision (language) backbones played a pivotal role for the performance increase, scaling up anomaly detection methods more efficiently needs to be addressed by future research to meet real-time and computational constraints on-site.", "date": "2025-09-22", "categories": []}
{"id": "2509.17670", "title": "Tailored Transformation Invariance for Industrial Anomaly Detection", "abstract": "Industrial Anomaly Detection (IAD) is a subproblem within Computer Vision Anomaly Detection that has been receiving increasing amounts of attention due to its applicability to real-life scenarios. Recent research has focused on how to extract the most informative features, contrasting older kNN-based methods that use only pretrained features. These recent methods are much more expensive to train however and could complicate real-life application. Careful study of related work with regards to transformation invariance leads to the idea that popular benchmarks require robustness to only minor translations. With this idea we then formulate LWinNN, a local window based approach that creates a middle ground between kNN based methods that have either complete or no translation invariance. Our experiments demonstrate that this small change increases accuracy considerably, while simultaneously decreasing both train and test time. This teaches us two things: first, the gap between kNN-based approaches and more complex state-of-the-art methodology can still be narrowed by effective usage of the limited data available. Second, our assumption of requiring only limited translation invariance highlights potential areas of interest for future work and the need for more spatially diverse benchmarks, for which our method can hopefully serve as a new baseline. Our code can be found at https://github.com/marietteschonfeld/LWinNN .", "date": "2025-09-22", "categories": []}
{"id": "2311.04190", "title": "Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter", "abstract": "The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system. Code: https://github.com/muleina/CMS_HCAL_ML_OnlineDQM .", "date": "2023-11-07", "categories": []}
{"id": "2405.18848", "title": "Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection", "abstract": "Anomaly detection focuses on identifying samples that deviate from the norm. Discovering informative representations of normal samples is crucial to detecting anomalies effectively. Recent self-supervised methods have successfully learned such representations by employing prior knowledge about anomalies to create synthetic outliers during training. However, we often do not know what to expect from unseen data in specialized real-world applications. In this work, we address this limitation with our new approach Con$_2$, which leverages prior knowledge about symmetries in normal samples to observe the data in different contexts. Con$_2$ consists of two parts: Context Contrasting clusters representations according to their context, while Content Alignment encourages the model to capture semantic information by aligning the positions of normal samples across clusters. The resulting representation space allows us to detect anomalies as outliers of the learned context clusters. We demonstrate the benefit of this approach in extensive experiments on specialized medical datasets, outperforming competitive baselines based on self-supervised learning and pretrained models and presenting competitive performance on natural imaging benchmarks.", "date": "2024-05-29", "categories": []}
{"id": "2503.04997", "title": "ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects", "abstract": "Automatic visual inspection using machine learning plays a key role in achieving zero-defect policies in industry. Research on anomaly detection is constrained by the availability of datasets that capture complex defect appearances and imperfect imaging conditions, which are typical of production processes. Recent benchmarks indicate that most publicly available datasets are biased towards optimal imaging conditions, leading to an overestimation of their applicability in real-world industrial scenarios. To address this gap, we introduce the Industrial Screen Printing Anomaly Detection Dataset (ISP-AD). It presents challenging small and weakly contrasted surface defects embedded within structured patterns exhibiting high permitted design variability. To the best of our knowledge, it is the largest publicly available industrial dataset to date, including both synthetic and real defects collected directly from the factory floor. Beyond benchmarking recent unsupervised anomaly detection methods, experiments on a mixed supervised training strategy, incorporating both synthesized and real defects, were conducted. Experiments show that even a small amount of injected, weakly labeled real defects improves generalization. Furthermore, starting from training on purely synthetic defects, emerging real defective samples can be efficiently integrated into subsequent scalable training. Overall, our findings indicate that model-free synthetic defects can provide a cold-start baseline, whereas a small number of injected real defects refine the decision boundary for previously unseen defect characteristics. The presented unsupervised and supervised dataset splits are designed to emphasize research on unsupervised, self-supervised, and supervised approaches, enhancing their applicability to industrial settings.", "date": "2025-03-06", "categories": []}
{"id": "2504.06320", "title": "Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems", "abstract": "Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization and the integration of IoT devices and industrial control systems (ICS). These cyber-physical systems (CPS) introduce new vulnerabilities, requiring robust and automated intrusion detection systems (IDS) to mitigate potential threats. This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data, integrating physical principles into machine learning models, and optimizing computational efficiency for edge applications. We build upon the concept of temporal differential consistency (TDC) loss to capture the dynamics of the system, ensuring meaningful relationships between dynamic states. Expanding on this foundation, we propose a hybrid autoencoder-based approach, referred to as hybrid TDC-AE, which extends TDC by incorporating both deterministic nodes and conventional statistical nodes. This hybrid structure enables the model to account for non-deterministic processes. Our approach achieves state-of-the-art classification performance while improving time to detect anomalies by 3%, outperforming the BATADAL challenge leader without requiring domain-specific knowledge, making it broadly applicable. Additionally, it maintains the computational efficiency of conventional autoencoders while reducing the number of fully connected layers, resulting in a more sustainable and efficient solution. The method demonstrates how leveraging physics-inspired consistency principles enhances anomaly detection and strengthens the resilience of cyber-physical systems.", "date": "2025-04-08", "categories": []}
{"id": "2509.12845", "title": "Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training", "abstract": "Anomalous Sound Detection (ASD) is often formulated as a machine attribute classification task, a strategy necessitated by the common scenario where only normal data is available for training. However, the exhaustive collection of machine attribute labels is laborious and impractical. To address the challenge of missing attribute labels, this paper proposes an agglomerative hierarchical clustering method for the assignment of pseudo-attribute labels using representations derived from a domain-adaptive pre-trained model, which are expected to capture machine attribute characteristics. We then apply model adaptation to this pre-trained model through supervised fine-tuning for machine attribute classification, resulting in a new state-of-the-art performance. Evaluation on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge dataset demonstrates that our proposed approach yields significant performance gains, ultimately outperforming our previous top-ranking system in the challenge.", "date": "2025-09-16", "categories": []}
{"id": "2509.15570", "title": "Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection", "abstract": "The outlier exposure method is an effective approach to address the unsupervised anomaly sound detection problem. The key focus of this method is how to make the model learn the distribution space of normal data. Based on biological perception and data analysis, it is found that anomalous audio and noise often have higher frequencies. Therefore, we propose a data augmentation method for high-frequency information in contrastive learning. This enables the model to pay more attention to the low-frequency information of the audio, which represents the normal operational mode of the machine. We evaluated the proposed method on the DCASE 2020 Task 2. The results showed that our method outperformed other contrastive learning methods used on this dataset. We also evaluated the generalizability of our method on the DCASE 2022 Task 2 dataset.", "date": "2025-09-19", "categories": []}
{"id": "2404.08453", "title": "Lightweight Multi-System Multivariate Interconnection and Divergence Discovery", "abstract": "Identifying outlier behavior among sensors and subsystems is essential for discovering faults and facilitating diagnostics in large systems. At the same time, exploring large systems with numerous multivariate data sets is challenging. This study presents a lightweight interconnection and divergence discovery mechanism (LIDD) to identify abnormal behavior in multi-system environments. The approach employs a multivariate analysis technique that first estimates the similarity heatmaps among the sensors for each system and then applies information retrieval algorithms to provide relevant multi-level interconnection and discrepancy details. Our experiment on the readout systems of the Hadron Calorimeter of the Compact Muon Solenoid (CMS) experiment at CERN demonstrates the effectiveness of the proposed method. Our approach clusters readout systems and their sensors consistent with the expected calorimeter interconnection configurations, while capturing unusual behavior in divergent clusters and estimating their root causes.", "date": "2024-04-12", "categories": []}
{"id": "2501.19237", "title": "DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments", "abstract": "Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive task has been handled by human shifters who struggle with frequent changes in operational conditions. We present DINAMO: a novel, interpretable, robust, and scalable DQM framework designed to automate anomaly detection in time-dependent settings. Our approach constructs evolving histogram templates with built-in uncertainties, featuring both a statistical variant - extending the classical Exponentially Weighted Moving Average (EWMA) - and a machine learning (ML)-enhanced version that leverages a transformer encoder for improved adaptability. Experimental validations on synthetic datasets demonstrate the high accuracy, adaptability, and interpretability of these methods. The statistical variant is being commissioned in the LHCb experiment at the Large Hadron Collider, underscoring its real-world impact. The code used in this study is available at https://github.com/ArseniiGav/DINAMO.", "date": "2025-01-31", "categories": []}
{"id": "2505.10876", "title": "Preference Isolation Forest for Structure-based Anomaly Detection", "abstract": "We address the problem of detecting anomalies as samples that do not conform to structured patterns represented by low-dimensional manifolds. To this end, we conceive a general anomaly detection framework called Preference Isolation Forest (PIF), that combines the benefits of adaptive isolation-based methods with the flexibility of preference embedding. The key intuition is to embed the data into a high-dimensional preference space by fitting low-dimensional manifolds, and to identify anomalies as isolated points. We propose three isolation approaches to identify anomalies: $i$) Voronoi-iForest, the most general solution, $ii$) RuzHash-iForest, that avoids explicit computation of distances via Local Sensitive Hashing, and $iii$) Sliding-PIF, that leverages a locality prior to improve efficiency and effectiveness.", "date": "2025-05-16", "categories": []}
{"id": "2509.13853", "title": "Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection", "abstract": "Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This paper introduces a novel training technique called one-stage supervised contrastive learning (OS-SCL), which significantly addresses this problem by perturbing features in the embedding space and employing a one-stage noisy supervised contrastive learning approach. On the DCASE 2020 Challenge Task 2, it achieved 94.64\\% AUC, 88.42\\% pAUC, and 89.24\\% mAUC using only Log-Mel features. Additionally, a time-frequency feature named TFgram is proposed, which is extracted from raw audio. This feature effectively captures critical information for anomalous sound detection, ultimately achieving 95.71\\% AUC, 90.23\\% pAUC, and 91.23\\% mAUC. The source code is available at: \\underline{www.github.com/huangswt/OS-SCL}.", "date": "2025-09-17", "categories": []}
{"id": "2509.14084", "title": "AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration", "abstract": "Zero-Shot Anomaly Detection (ZSAD) seeks to identify anomalies from arbitrary novel categories, offering a scalable and annotation-efficient solution. Traditionally, most ZSAD works have been based on the CLIP model, which performs anomaly detection by calculating the similarity between visual and text embeddings. Recently, vision foundation models such as DINOv3 have demonstrated strong transferable representation capabilities. In this work, we are the first to adapt DINOv3 for ZSAD. However, this adaptation presents two key challenges: (i) the domain bias between large-scale pretraining data and anomaly detection tasks leads to feature misalignment; and (ii) the inherent bias toward global semantics in pretrained representations often leads to subtle anomalies being misinterpreted as part of the normal foreground objects, rather than being distinguished as abnormal regions. To overcome these challenges, we introduce AD-DINOv3, a novel vision-language multimodal framework designed for ZSAD. Specifically, we formulate anomaly detection as a multimodal contrastive learning problem, where DINOv3 is employed as the visual backbone to extract patch tokens and a CLS token, and the CLIP text encoder provides embeddings for both normal and abnormal prompts. To bridge the domain gap, lightweight adapters are introduced in both modalities, enabling their representations to be recalibrated for the anomaly detection task. Beyond this baseline alignment, we further design an Anomaly-Aware Calibration Module (AACM), which explicitly guides the CLS token to attend to anomalous regions rather than generic foreground semantics, thereby enhancing discriminability. Extensive experiments on eight industrial and medical benchmarks demonstrate that AD-DINOv3 consistently matches or surpasses state-of-the-art methods.The code will be available at https://github.com/Kaisor-Yuan/AD-DINOv3.", "date": "2025-09-17", "categories": []}
{"id": "2509.14472", "title": "H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations", "abstract": "The plethora of space-borne and ground-based observatories has provided astrophysicists with an unprecedented volume of data, which can only be processed at scale using advanced computing algorithms. Consequently, ensuring the quality of data fed into machine learning (ML) models is critical. The H$\\alpha$ observations from the GONG network represent one such data stream, producing several observations per minute, 24/7, since 2010. In this study, we introduce a lightweight (non-ML) anomaly-detection algorithm, called H-Alpha Anomalyzer, designed to identify anomalous observations based on user-defined criteria. Unlike many black-box algorithms, our approach highlights exactly which regions triggered the anomaly flag and quantifies the corresponding anomaly likelihood. For our comparative analysis, we also created and released a dataset of 2,000 observations, equally divided between anomalous and non-anomalous cases. Our results demonstrate that the proposed model not only outperforms existing methods but also provides explainability, enabling qualitative evaluation by domain experts.", "date": "2025-09-17", "categories": []}
{"id": "2509.15033", "title": "Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection", "abstract": "In this paper, we aim to improve multivariate anomaly detection (AD) by modeling the \\textit{time-varying non-linear spatio-temporal correlations} found in multivariate time series data . In multivariate time series data, an anomaly may be indicated by the simultaneous deviation of interrelated time series from their expected collective behavior, even when no individual time series exhibits a clearly abnormal pattern on its own. In many existing approaches, time series variables are assumed to be (conditionally) independent, which oversimplifies real-world interactions. Our approach addresses this by modeling joint dependencies in the latent space and decoupling the modeling of \\textit{marginal distributions, temporal dynamics, and inter-variable dependencies}. We use a transformer encoder to capture temporal patterns, and to model spatial (inter-variable) dependencies, we fit a multi-variate likelihood and a copula. The temporal and the spatial components are trained jointly in a latent space using a self-supervised contrastive learning objective to learn meaningful feature representations to separate normal and anomaly samples.", "date": "2025-09-18", "categories": []}
{"id": "2509.15153", "title": "AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use", "abstract": "Multivariate time-series anomaly detection, which is critical for identifying unexpected events, has been explored in the field of machine learning for several decades. However, directly applying these methods to data from forceful tool use tasks is challenging because streaming sensor data in the real world tends to be inherently noisy, exhibits non-stationary behavior, and varies across different tasks and tools. To address these challenges, we propose a method, AnoF-Diff, based on the diffusion model to extract force-torque features from time-series data and use force-torque features to detect anomalies. We compare our method with other state-of-the-art methods in terms of F1-score and Area Under the Receiver Operating Characteristic curve (AUROC) on four forceful tool-use tasks, demonstrating that our method has better performance and is more robust to a noisy dataset. We also propose the method of parallel anomaly score evaluation based on one-step diffusion and demonstrate how our method can be used for online anomaly detection in several forceful tool use experiments.", "date": "2025-09-18", "categories": []}
{"id": "2405.00239", "title": "IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study", "abstract": "Minimizing the need for pixel-level annotated data to train PET lesion detection and segmentation networks is highly desired and can be transformative, given time and cost constraints associated with expert annotations. Current unsupervised or weakly-supervised anomaly detection methods rely on autoencoder or generative adversarial networks (GANs) trained only on healthy data. While these approaches reduce annotation dependency, GAN-based methods are notably more challenging to train than non-GAN alternatives (such as autoencoders) due to issues such as the simultaneous optimization of two competing networks, mode collapse, and training instability. In this paper, we present the weakly-supervised $\\textbf{I}$mplicitly-$\\textbf{g}$uided $\\textbf{CO}$u$\\textbf{N}$terfactual diffusion model for $\\textbf{D}$etecting $\\textbf{A}$nomalies in $\\textbf{PET}$ images (IgCONDA-PET). The solution is developed and validated using PET scans from six retrospective cohorts consisting of a total of 2652 cases (multi-cancer, multi-tracer) containing both local and public datasets (spanning multiple centers). The training is conditioned on image class labels (healthy vs. unhealthy) via attention modules, and we employ implicit diffusion guidance. We perform counterfactual generation which facilitates \"unhealthy-to-healthy\" domain translation by generating a synthetic, healthy version of an unhealthy input image, enabling the detection of anomalies through the calculated differences. The performance of our method was compared against several other deep learning based weakly-supervised or unsupervised methods as well as traditional methods like 41% SUV$_\\text{max}$ thresholding. We also highlight the importance of incorporating attention modules in our network for the detection of small anomalies. The code is publicly available at: https://github.com/ahxmeds/IgCONDA-PET.git.", "date": "2024-04-30", "categories": []}
{"id": "2412.17586", "title": "Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles", "abstract": "Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However, OOD detection in medical imaging remains an open challenge, and we address three gaps: the underexplored potential of a simple OOD detection model, the lack of optimization of deep learning strategies specifically for OOD detection, and the selection of appropriate reconstruction metrics. In this study, we investigated the effectiveness of a reconstruction-based autoencoder for unsupervised detection of synthetic artifacts in brain MRI. We evaluated the general reconstruction capability of the model, analyzed the impact of the selected training epoch and reconstruction metrics, assessed the potential of model and/or metric ensembles, and tested the model on a dataset containing a diverse range of artifacts. Among the metrics assessed, the contrast component of SSIM and LPIPS consistently outperformed others in detecting homogeneous circular anomalies. By combining two well-converged models and using LPIPS and contrast as reconstruction metrics, we achieved a pixel-level area under the Precision-Recall curve of 0.66. Furthermore, with the more realistic OOD dataset, we observed that the detection performance varied between artifact types; local artifacts were more difficult to detect, while global artifacts showed better detection results. These findings underscore the importance of carefully selecting metrics and model configurations, and highlight the need for tailored approaches, as standard deep learning approaches do not always align with the unique needs of OOD detection.", "date": "2024-12-23", "categories": []}
{"id": "2509.13390", "title": "A Domain Knowledge Informed Approach for Anomaly Detection of Electric Vehicle Interior Sounds", "abstract": "The detection of anomalies in automotive cabin sounds is critical for ensuring vehicle quality and maintaining passenger comfort. In many real-world settings, this task is more appropriately framed as an unsupervised learning problem rather than the supervised case due to the scarcity or complete absence of labeled faulty data. In such an unsupervised setting, the model is trained exclusively on healthy samples and detects anomalies as deviations from normal behavior. However, in the absence of labeled faulty samples for validation and the limited reliability of commonly used metrics, such as validation reconstruction error, effective model selection remains a significant challenge. To overcome these limitations, a domain-knowledge-informed approach for model selection is proposed, in which proxy-anomalies engineered through structured perturbations of healthy spectrograms are used in the validation set to support model selection. The proposed methodology is evaluated on a high-fidelity electric vehicle dataset comprising healthy and faulty cabin sounds across five representative fault types viz., Imbalance, Modulation, Whine, Wind, and Pulse Width Modulation. This dataset, generated using advanced sound synthesis techniques, and validated via expert jury assessments, has been made publicly available to facilitate further research. Experimental evaluations on the five fault cases demonstrate the selection of optimal models using proxy-anomalies, significantly outperform conventional model selection strategies.", "date": "2025-09-16", "categories": []}
{"id": "2509.13621", "title": "Unsupervised Anomaly Detection in ALS EPICS Event Logs", "abstract": "This paper introduces an automated fault analysis framework for the Advanced Light Source (ALS) that processes real-time event logs from its EPICS control system. By treating log entries as natural language, we transform them into contextual vector representations using semantic embedding techniques. A sequence-aware neural network, trained on normal operational data, assigns a real-time anomaly score to each event. This method flags deviations from baseline behavior, enabling operators to rapidly identify the critical event sequences that precede complex system failures.", "date": "2025-09-17", "categories": []}
{"id": "2507.03903", "title": "Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection", "abstract": "Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds due to the large scale and complex structure. In this study, a Down-Up Sampling Network (DUS-Net) is proposed to reconstruct high-precision point clouds for 3D anomaly detection by preserving the group center geometric structure. The DUS-Net first introduces a Noise Generation module to generate noisy patches, which facilitates the diversity of training data and strengthens the feature representation for reconstruction. Then, a Down-sampling Network (Down-Net) is developed to learn an anomaly-free center point cloud from patches with noise injection. Subsequently, an Up-sampling Network (Up-Net) is designed to reconstruct high-precision point clouds by fusing multi-scale up-sampling features. Our method leverages group centers for construction, enabling the preservation of geometric structure and providing a more precise point cloud. Extensive experiments demonstrate the effectiveness of our proposed method, achieving state-of-the-art (SOTA) performance with an Object-level AUROC of 79.9% and 79.5%, and a Point-level AUROC of 71.2% and 84.7% on the Real3D-AD and Anomaly-ShapeNet datasets, respectively.", "date": "2025-07-05", "categories": []}
{"id": "2507.10715", "title": "Real-time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-negative Matrix Factorization", "abstract": "Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate, or sacrifice detection sensitivity in order to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) has been shown to be a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. We have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The Adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.", "date": "2025-07-14", "categories": []}
{"id": "2509.11605", "title": "DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) is critical for surveillance and public safety. However, existing benchmarks are limited to either frame-level or video-level tasks, restricting a holistic view of model generalization. This work first introduces a softmax-based frame allocation strategy that prioritizes anomaly-dense segments while maintaining full-video coverage, enabling balanced sampling across temporal scales. Building on this process, we construct two complementary benchmarks. The image-based benchmark evaluates frame-level reasoning with representative frames, while the video-based benchmark extends to temporally localized segments and incorporates an abnormality scoring task. Experiments on UCF-Crime demonstrate improvements at both the frame and video levels, and ablation studies confirm clear advantages of anomaly-focused sampling over uniform and random baselines.", "date": "2025-09-15", "categories": []}
{"id": "2509.12372", "title": "Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder", "abstract": "The nuclear industry is advancing toward more new reactor designs, with next-generation reactors expected to be smaller in scale and power output. These systems have the potential to produce large volumes of information in the form of multivariate time-series data, which could be used for enhanced real-time monitoring and control. In this context, the development of remote autonomous or semi-autonomous control systems for reactor operation has gained significant interest. A critical first step toward such systems is an accurate diagnostics module capable of detecting and localizing anomalies within the reactor system. Recent studies have proposed various ML and DL approaches for anomaly detection in the nuclear domain. Despite promising results, key challenges remain, including limited to no explainability, lack of access to real-world data, and scarcity of abnormal events, which impedes benchmarking and characterization. Most existing studies treat these methods as black boxes, while recent work highlights the need for greater interpretability of ML/DL outputs in safety-critical domains. Here, we propose an unsupervised methodology based on an LSTM autoencoder with a dual attention mechanism for characterization of abnormal events in a real-world reactor radiation area monitoring system. The framework includes not only detection but also localization of the event and was evaluated using real-world datasets of increasing complexity from the PUR-1 research reactor. The attention mechanisms operate in both the feature and temporal dimensions, where the feature attention assigns weights to radiation sensors exhibiting abnormal patterns, while time attention highlights the specific timesteps where irregularities occur, thus enabling localization. By combining the results, the framework can identify both the affected sensors and the duration of each anomaly within a single unified network.", "date": "2025-09-15", "categories": []}
{"id": "2509.12540", "title": "Cross-Modal Deep Metric Learning for Time Series Anomaly Detection", "abstract": "To effectively address the issues of low sensitivity and high time consumption in time series anomaly detection, we propose an anomaly detection method based on cross-modal deep metric learning. A cross-modal deep metric learning feature clustering model is constructed, composed of an input layer, a triplet selection layer, and a loss function computation layer. The squared Euclidean distances between cluster centers are calculated, and a stochastic gradient descent strategy is employed to optimize the model and classify different time series features. The inner product of principal component direction vectors is used as a metric for anomaly measurement. The von Mises-Fisher (vMF) distribution is applied to describe the directional characteristics of time series data, and historical data is used to train and obtain evaluation parameters. By comparing the principal component direction vector of actual time series data with the threshold, anomaly detection is performed. Experimental results demonstrate that the proposed method accurately classifies time series data with different attributes, exhibits high sensitivity to anomalies, and achieves high detection accuracy, fast detection speed, and strong robustness.", "date": "2025-09-16", "categories": []}
{"id": "2509.12650", "title": "Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection", "abstract": "Detecting anomalies in time series data is essential for the reliable operation of many real-world systems. Recently, time series foundation models (TSFMs) have emerged as a powerful tool for anomaly detection. However, existing methods typically rely on the final layer's representations of TSFMs, computing the anomaly score as a reconstruction or forecasting error via a task-specific head. Instead, we propose TimeRep, a novel anomaly detection approach that leverages the intermediate layer's representations of TSFMs, computing the anomaly score as the distance between these representations. Given a pre-trained TSFM, TimeRep selects the intermediate layer and patch-token position that yield the most informative representation. TimeRep forms a reference collection of intermediate representations from the training data and applies a core-set strategy to reduce its size while maintaining distributional coverage. During inference, TimeRep computes the anomaly score for incoming data by measuring the distance between its intermediate representations and those of the collection. To address concept drift, TimeRep integrates an adaptation mechanism that, at inference time, augments the collection exclusively with non-redundant intermediate representations from incoming data. We conducted extensive experiments on the UCR Anomaly Archive, which contains 250 univariate time series. TimeRep consistently outperforms a broad spectrum of state-of-the-art baselines, including non-DL, DL, and foundation model-based methods.", "date": "2025-09-16", "categories": []}
{"id": "2509.12787", "title": "Double Helix Diffusion for Cross-Domain Anomaly Image Generation", "abstract": "Visual anomaly inspection is critical in manufacturing, yet hampered by the scarcity of real anomaly samples for training robust detectors. Synthetic data generation presents a viable strategy for data augmentation; however, current methods remain constrained by two principal limitations: 1) the generation of anomalies that are structurally inconsistent with the normal background, and 2) the presence of undesirable feature entanglement between synthesized images and their corresponding annotation masks, which undermines the perceptual realism of the output. This paper introduces Double Helix Diffusion (DH-Diff), a novel cross-domain generative framework designed to simultaneously synthesize high-fidelity anomaly images and their pixel-level annotation masks, explicitly addressing these challenges. DH-Diff employs a unique architecture inspired by a double helix, cycling through distinct modules for feature separation, connection, and merging. Specifically, a domain-decoupled attention mechanism mitigates feature entanglement by enhancing image and annotation features independently, and meanwhile a semantic score map alignment module ensures structural authenticity by coherently integrating anomaly foregrounds. DH-Diff offers flexible control via text prompts and optional graphical guidance. Extensive experiments demonstrate that DH-Diff significantly outperforms state-of-the-art methods in diversity and authenticity, leading to significant improvements in downstream anomaly detection performance.", "date": "2025-09-16", "categories": []}
{"id": "2509.12905", "title": "AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring", "abstract": "Early detection of newly emerging diseases, lesion severity assessment, differentiation of medical conditions and automated screening are examples for the wide applicability and importance of anomaly detection (AD) and unsupervised segmentation in medicine. Normal fine-grained tissue variability such as present in pulmonary anatomy is a major challenge for existing generative AD methods. Here, we propose a novel generative AD approach addressing this issue. It consists of an image-to-image translation for anomaly-free reconstruction and a subsequent patch similarity scoring between observed and generated image-pairs for precise anomaly localization. We validate the new method on chest computed tomography (CT) scans for the detection and segmentation of infectious disease lesions. To assess generalizability, we evaluate the method on an ischemic stroke lesion segmentation task in T1-weighted brain MRI. Results show improved pixel-level anomaly segmentation in both chest CTs and brain MRIs, with relative DICE score improvements of +1.9% and +4.4%, respectively, compared to other state-of-the-art reconstruction-based methods.", "date": "2025-09-16", "categories": []}
{"id": "2508.12278", "title": "CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision", "abstract": "Graph Neural Networks (GNNs) are widely used as the engine for various graph-related tasks, with their effectiveness in analyzing graph-structured data. However, training robust GNNs often demands abundant labeled data, which is a critical bottleneck in real-world applications. This limitation severely impedes progress in Graph Anomaly Detection (GAD), where anomalies are inherently rare, costly to label, and may actively camouflage their patterns to evade detection. To address these problems, we propose Context Refactoring Contrast (CRoC), a simple yet effective framework that trains GNNs for GAD by jointly leveraging limited labeled and abundant unlabeled data. Different from previous works, CRoC exploits the class imbalance inherent in GAD to refactor the context of each node, which builds augmented graphs by recomposing the attributes of nodes while preserving their interaction patterns. Furthermore, CRoC encodes heterogeneous relations separately and integrates them into the message-passing process, enhancing the model's capacity to capture complex interaction semantics. These operations preserve node semantics while encouraging robustness to adversarial camouflage, enabling GNNs to uncover intricate anomalous cases. In the training stage, CRoC is further integrated with the contrastive learning paradigm. This allows GNNs to effectively harness unlabeled data during joint training, producing richer, more discriminative node embeddings. CRoC is evaluated on seven real-world GAD datasets with varying scales. Extensive experiments demonstrate that CRoC achieves up to 14% AUC improvement over baseline GNNs and outperforms state-of-the-art GAD methods under limited-label settings.", "date": "2025-08-17", "categories": []}
{"id": "2509.10869", "title": "GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation", "abstract": "Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their structural and behavioral characteristics. Existing methods, such as those based on graph convolutional networks (GCNs), often suffer from over-smoothing, which causes the learned node representations to become indistinguishable. Furthermore, graph reconstruction-based approaches are vulnerable to anomalous node interference during the reconstruction process, leading to inaccurate anomaly detection. In this work, we propose a novel and holistic anomaly evaluation framework that integrates three key components: a local-global Transformer encoder, a memory-guided reconstruction mechanism, and a multi-scale representation matching strategy. These components work synergistically to enhance the model's ability to capture both local and global structural dependencies, suppress the influence of anomalous nodes, and assess anomalies from multiple levels of granularity. Anomaly scores are computed by combining reconstruction errors and memory matching signals, resulting in a more robust evaluation. Extensive experiments on seven benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches, offering a comprehensive and generalizable solution for anomaly detection across various graph domains.", "date": "2025-09-13", "categories": []}
{"id": "2509.11633", "title": "Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay", "abstract": "Anomaly detection in dynamic graphs is essential for identifying malicious activities, fraud, and unexpected behaviors in real-world systems such as cybersecurity and power grids. However, existing approaches struggle with scalability, probabilistic interpretability, and adaptability to evolving traffic patterns. In this paper, we propose ADAPTIVE-GRAPHSKETCH, a lightweight and scalable framework for real-time anomaly detection in streaming edge data. Our method integrates temporal multi-tensor sketching with Count-Min Sketch using Conservative Update (CMS-CU) to compactly track edge frequency patterns with bounded memory, while mitigating hash collision issues. We incorporate Bayesian inference for probabilistic anomaly scoring and apply Exponentially Weighted Moving Average (EWMA) for adaptive thresholding tuned to burst intensity. Extensive experiments on four real-world intrusion detection datasets demonstrate that ADAPTIVE-GRAPHSKETCH outperforms state-of-the-art baselines such as ANOEDGE-G/L, MIDAS-R, and F-FADE, achieving up to 6.5% AUC gain on CIC-IDS2018 and up to 15.6% on CIC-DDoS2019, while processing 20 million edges in under 3.4 seconds using only 10 hash functions. Our results show that ADAPTIVE-GRAPHSKETCH is practical and effective for fast, accurate anomaly detection in large-scale streaming graphs.\n  Keywords: Anomaly Detection, Streaming, Real-time, Dynamic Graphs, Edge Streams, Tensor Sketching", "date": "2025-09-15", "categories": []}
{"id": "2509.10282", "title": "MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection", "abstract": "Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by data scarcity, privacy, or high annotation cost. However, most existing methods focus exclusively on point clouds, neglecting the rich semantic cues available from complementary modalities such as RGB images and texts priors. This paper introduces MCL-AD, a novel framework that leverages multimodal collaboration learning across point clouds, RGB images, and texts semantics to achieve superior zero-shot 3D anomaly detection. Specifically, we propose a Multimodal Prompt Learning Mechanism (MPLM) that enhances the intra-modal representation capability and inter-modal collaborative learning by introducing an object-agnostic decoupled text prompt and a multimodal contrastive loss. In addition, a collaborative modulation mechanism (CMM) is proposed to fully leverage the complementary representations of point clouds and RGB images by jointly modulating the RGB image-guided and point cloud-guided branches. Extensive experiments demonstrate that the proposed MCL-AD framework achieves state-of-the-art performance in ZS-3D anomaly detection.", "date": "2025-09-12", "categories": []}
{"id": "2509.09375", "title": "Unsupervised Integrated-Circuit Defect Segmentation via Image-Intrinsic Normality", "abstract": "Modern Integrated-Circuit(IC) manufacturing introduces diverse, fine-grained defects that depress yield and reliability. Most industrial defect segmentation compares a test image against an external normal set, a strategy that is brittle for IC imagery where layouts vary across products and accurate alignment is difficult. We observe that defects are predominantly local, while each image still contains rich, repeatable normal patterns. We therefore propose an unsupervised IC defect segmentation framework that requires no external normal support. A learnable normal-information extractor aggregates representative normal features from the test image, and a coherence loss enforces their association with normal regions. Guided by these features, a decoder reconstructs only normal content; the reconstruction residual then segments defects. Pseudo-anomaly augmentation further stabilizes training. Experiments on datasets from three IC process stages show consistent improvements over existing approaches and strong robustness to product variability.", "date": "2025-09-11", "categories": []}
{"id": "2509.08232", "title": "GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation", "abstract": "Recent advancements in video anomaly detection (VAD) have enabled identification of various criminal activities in surveillance videos, but detecting fatal incidents such as shootings and stabbings remains difficult due to their rarity and ethical issues in data collection. Recognizing this limitation, we introduce GTA-Crime, a fatal video anomaly dataset and generation framework using Grand Theft Auto 5 (GTA5). Our dataset contains fatal situations such as shootings and stabbings, captured from CCTV multiview perspectives under diverse conditions including action types, weather, time of day, and viewpoints. To address the rarity of such scenarios, we also release a framework for generating these types of videos. Additionally, we propose a snippet-level domain adaptation strategy using Wasserstein adversarial training to bridge the gap between synthetic GTA-Crime features and real-world features like UCF-Crime. Experimental results validate our GTA-Crime dataset and demonstrate that incorporating GTA-Crime with our domain adaptation strategy consistently enhances real world fatal violence detection accuracy. Our dataset and the data generation framework are publicly available at https://github.com/ta-ho/GTA-Crime.", "date": "2025-09-10", "categories": []}
{"id": "2509.08698", "title": "A layered architecture for log analysis in complex IT systems", "abstract": "In the evolving IT landscape, stability and reliability of systems are essential, yet their growing complexity challenges DevOps teams in implementation and maintenance. Log analysis, a core element of AIOps, provides critical insights into complex behaviors and failures. This dissertation introduces a three-layered architecture to support DevOps in failure resolution. The first layer, Log Investigation, performs autonomous log labeling and anomaly classification. We propose a method that labels log data without manual effort, enabling supervised training and precise evaluation of anomaly detection. Additionally, we define a taxonomy that groups anomalies into three categories, ensuring appropriate method selection. The second layer, Anomaly Detection, detects behaviors deviating from the norm. We propose a flexible Anomaly Detection method adaptable to unsupervised, weakly supervised, and supervised training. Evaluations on public and industry datasets show F1-scores between 0.98 and 1.0, ensuring reliable anomaly detection. The third layer, Root Cause Analysis, identifies minimal log sets describing failures, their origin, and event sequences. By balancing training data and identifying key services, our Root Cause Analysis method consistently detects 90-98% of root cause log lines within the top 10 candidates, providing actionable insights for mitigation. Our research addresses how log analysis methods can be designed and optimized to help DevOps resolve failures efficiently. By integrating these three layers, the architecture equips teams with robust methods to enhance IT system reliability.", "date": "2025-08-29", "categories": []}
{"id": "2509.07392", "title": "Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions", "abstract": "Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines.", "date": "2025-09-09", "categories": []}
{"id": "2404.11269", "title": "DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series", "abstract": "In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective Center-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD.", "date": "2024-04-17", "categories": []}
{"id": "2509.05766", "title": "Ensemble of Precision-Recall Curve (PRC) Classification Trees with Autoencoders", "abstract": "Anomaly detection underpins critical applications from network security and intrusion detection to fraud prevention, where recognizing aberrant patterns rapidly is indispensable. Progress in this area is routinely impeded by two obstacles: extreme class imbalance and the curse of dimensionality. To combat the former, we previously introduced Precision-Recall Curve (PRC) classification trees and their ensemble extension, the PRC Random Forest (PRC-RF). Building on that foundation, we now propose a hybrid framework that integrates PRC-RF with autoencoders, unsupervised machine learning methods that learn compact latent representations, to confront both challenges simultaneously. Extensive experiments across diverse benchmark datasets demonstrate that the resulting Autoencoder-PRC-RF model achieves superior accuracy, scalability, and interpretability relative to prior methods, affirming its potential for high-stakes anomaly-detection tasks.", "date": "2025-09-06", "categories": []}
{"id": "2509.05936", "title": "ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection", "abstract": "Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled data and significant human effort. To address these challenges, we propose ALPHA, the first Active Learning Pipeline for Human-free log Analysis. ALPHA integrates semantic embedding, clustering-based representative sampling, and large language model (LLM)-assisted few-shot annotation to automate the anomaly detection process. The LLM annotated labels are propagated across clusters, enabling large-scale training of an anomaly detector with minimal supervision. To enhance the annotation accuracy, we propose a two-step few-shot refinement strategy that adaptively selects informative prompts based on the LLM's observed error patterns. Extensive experiments on real-world log datasets demonstrate that ALPHA achieves detection accuracy comparable to fully supervised methods while mitigating human efforts in the loop. ALPHA also supports interpretable analysis through LLM-driven root cause explanations in the post-detection stage. These capabilities make ALPHA a scalable and cost-efficient solution for truly automated log-based anomaly detection.", "date": "2025-09-07", "categories": []}
{"id": "2509.06419", "title": "CAPMix: Robust Time Series Anomaly Detection Based on Abnormal Assumptions with Dual-Space Mixup", "abstract": "Time series anomaly detection (TSAD) is a vital yet challenging task, particularly in scenarios where labeled anomalies are scarce and temporal dependencies are complex. Recent anomaly assumption (AA) approaches alleviate the lack of anomalies by injecting synthetic samples and training discriminative models. Despite promising results, these methods often suffer from two fundamental limitations: patchy generation, where scattered anomaly knowledge leads to overly simplistic or incoherent anomaly injection, and Anomaly Shift, where synthetic anomalies either resemble normal data too closely or diverge unrealistically from real anomalies, thereby distorting classification boundaries. In this paper, we propose CAPMix, a controllable anomaly augmentation framework that addresses both issues. First, we design a CutAddPaste mechanism to inject diverse and complex anomalies in a targeted manner, avoiding patchy generation. Second, we introduce a label revision strategy to adaptively refine anomaly labels, reducing the risk of anomaly shift. Finally, we employ dual-space mixup within a temporal convolutional network to enforce smoother and more robust decision boundaries. Extensive experiments on five benchmark datasets, including AIOps, UCR, SWaT, WADI, and ESA, demonstrate that CAPMix achieves significant improvements over state-of-the-art baselines, with enhanced robustness against contaminated training data. The code is available at https://github.com/alsike22/CAPMix.", "date": "2025-09-08", "categories": []}
{"id": "2509.06609", "title": "A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models", "abstract": "Graph anomaly detection (GAD) has attracted increasing attention in recent years for identifying malicious samples in a wide range of graph-based applications, such as social media and e-commerce. However, most GAD methods assume identical training and testing distributions and are tailored to specific tasks, resulting in limited adaptability to real-world scenarios such as shifting data distributions and scarce training samples in new applications. To address the limitations, recent work has focused on improving the generalization capability of GAD models through transfer learning that leverages knowledge from related domains to enhance detection performance, or developing \"one-for-all\" GAD foundation models that generalize across multiple applications. Since a systematic understanding of generalization in GAD is still lacking, in this paper, we provide a comprehensive review of generalization in GAD. We first trace the evolution of generalization in GAD and formalize the problem settings, which further leads to our systematic taxonomy. Rooted in this fine-grained taxonomy, an up-to-date and comprehensive review is conducted for the existing generalized GAD methods. Finally, we identify current open challenges and suggest future directions to inspire future research in this emerging field.", "date": "2025-09-08", "categories": []}
{"id": "2509.06693", "title": "STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment", "abstract": "Segmentation-oriented Industrial Anomaly Synthesis (SIAS) plays a pivotal role in enhancing the performance of downstream anomaly segmentation, as it provides an effective means of expanding abnormal data. However, existing SIAS methods face several critical limitations: (i) the synthesized anomalies often lack intricate texture details and fail to align precisely with the surrounding background, and (ii) they struggle to generate fine-grained, pixel-level anomalies. To address these challenges, we propose Segmentation-oriented Anomaly synthesis via Graded diffusion with Explicit mask alignment, termed STAGE. STAGE introduces a novel anomaly inference strategy that incorporates clean background information as a prior to guide the denoising distribution, enabling the model to more effectively distinguish and highlight abnormal foregrounds. Furthermore, it employs a graded diffusion framework with an anomaly-only branch to explicitly record local anomalies during both the forward and reverse processes, ensuring that subtle anomalies are not overlooked. Finally, STAGE incorporates the explicit mask alignment (EMA) strategy to progressively align the synthesized anomalies with the background, resulting in context-consistent and structurally coherent generations. Extensive experiments on the MVTec and BTAD datasets demonstrate that STAGE achieves state-of-the-art performance in SIAS, which in turn enhances downstream anomaly segmentation.", "date": "2025-09-08", "categories": []}
{"id": "2509.06911", "title": "Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection", "abstract": "We propose HyGLAD, a novel algorithm that automatically builds a set of interpretable patterns that model event data. These patterns can then be used to detect event-based anomalies in a stationary system, where any deviation from past behavior may indicate malicious activity. The algorithm infers equivalence classes of entities with similar behavior observed from the events, and then builds regular expressions that capture the values of those entities. As opposed to deep-learning approaches, the regular expressions are directly interpretable, which also translates to interpretable anomalies. We evaluate HyGLAD against all 7 unsupervised anomaly detection methods from DeepOD on five datasets from real-world systems. The experimental results show that on average HyGLAD outperforms existing deep-learning methods while being an order of magnitude more efficient in training and inference (single CPU vs GPU). Precision improved by 1.2x and recall by 1.3x compared to the second-best baseline.", "date": "2025-09-08", "categories": []}
{"id": "2509.04999", "title": "Adversarial Augmentation and Active Sampling for Robust Cyber Anomaly Detection", "abstract": "Advanced Persistent Threats (APTs) present a considerable challenge to cybersecurity due to their stealthy, long-duration nature. Traditional supervised learning methods typically require large amounts of labeled data, which is often scarce in real-world scenarios. This paper introduces a novel approach that combines AutoEncoders for anomaly detection with active learning to iteratively enhance APT detection. By selectively querying an oracle for labels on uncertain or ambiguous samples, our method reduces labeling costs while improving detection accuracy, enabling the model to effectively learn with minimal data and reduce reliance on extensive manual labeling. We present a comprehensive formulation of the Attention Adversarial Dual AutoEncoder-based anomaly detection framework and demonstrate how the active learning loop progressively enhances the model's performance. The framework is evaluated on real-world, imbalanced provenance trace data from the DARPA Transparent Computing program, where APT-like attacks account for just 0.004\\% of the data. The datasets, which cover multiple operating systems including Android, Linux, BSD, and Windows, are tested in two attack scenarios. The results show substantial improvements in detection rates during active learning, outperforming existing methods.", "date": "2025-09-05", "categories": []}
{"id": "2509.05034", "title": "Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization", "abstract": "Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during production, leveraging them usually requires pixel-level annotations, limiting scalability. To address this, we propose ADClick, an Interactive Image Segmentation (IIS) algorithm for industrial anomaly detection. ADClick generates pixel-wise anomaly annotations from only a few user clicks and a brief textual description, enabling precise and efficient labeling that significantly improves AD model performance (e.g., AP = 96.1\\% on MVTec AD). We further introduce ADClick-Seg, a cross-modal framework that aligns visual features and textual prompts via a prototype-based approach for anomaly detection and localization. By combining pixel-level priors with language-guided cues, ADClick-Seg achieves state-of-the-art results on the challenging ``Multi-class'' AD task (AP = 80.0\\%, PRO = 97.5\\%, Pixel-AUROC = 99.1\\% on MVTec AD).", "date": "2025-09-05", "categories": []}
{"id": "2502.05719", "title": "Extended Histogram-based Outlier Score (EHBOS)", "abstract": "Histogram-Based Outlier Score (HBOS) is a widely used outlier or anomaly detection method known for its computational efficiency and simplicity. However, its assumption of feature independence limits its ability to detect anomalies in datasets where interactions between features are critical. In this paper, we propose the Extended Histogram-Based Outlier Score (EHBOS), which enhances HBOS by incorporating two-dimensional histograms to capture dependencies between feature pairs. This extension allows EHBOS to identify contextual and dependency-driven anomalies that HBOS fails to detect. We evaluate EHBOS on 17 benchmark datasets, demonstrating its effectiveness and robustness across diverse anomaly detection scenarios. EHBOS outperforms HBOS on several datasets, particularly those where feature interactions are critical in defining the anomaly structure, achieving notable improvements in ROC AUC. These results highlight that EHBOS can be a valuable extension to HBOS, with the ability to model complex feature dependencies. EHBOS offers a powerful new tool for anomaly detection, particularly in datasets where contextual or relational anomalies play a significant role.", "date": "2025-02-08", "categories": []}
{"id": "2506.05138", "title": "Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems", "abstract": "Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low-resource, unsupervised method well-suited for edge deployment and continuous learning. In this paper, we present an application of Isolation Forest-based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 96% accuracy in distinguishing normal from abnormal readings and above 78% precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 160 KB during model training. These results highlight its suitability for resource-constrained environments and edge systems, while upholding federated learning principles of data privacy and collaborative learning.", "date": "2025-06-05", "categories": []}
{"id": "2509.04326", "title": "Efficient Odd-One-Out Anomaly Detection", "abstract": "The recently introduced odd-one-out anomaly detection task involves identifying the odd-looking instances within a multi-object scene. This problem presents several challenges for modern deep learning models, demanding spatial reasoning across multiple views and relational reasoning to understand context and generalize across varying object categories and layouts. We argue that these challenges must be addressed with efficiency in mind. To this end, we propose a DINO-based model that reduces the number of parameters by one third and shortens training time by a factor of three compared to the current state-of-the-art, while maintaining competitive performance. Our experimental evaluation also introduces a Multimodal Large Language Model baseline, providing insights into its current limitations in structured visual reasoning tasks. The project page can be found at https://silviochito.github.io/EfficientOddOneOut/", "date": "2025-09-04", "categories": []}
{"id": "2502.05779", "title": "A 3D Multimodal Feature for Infrastructure Anomaly Detection", "abstract": "Ageing structures require periodic inspections to identify structural defects. Previous work has used geometric distortions to locate cracks in synthetic masonry bridge point clouds but has struggled to detect small cracks. To address this limitation, this study proposes a novel 3D multimodal feature, 3DMulti-FPFHI, that combines a customized Fast Point Feature Histogram (FPFH) with an intensity feature. This feature is integrated into the PatchCore anomaly detection algorithm and evaluated through statistical and parametric analyses. The method is further evaluated using point clouds of a real masonry arch bridge and a full-scale experimental model of a concrete tunnel. Results show that the 3D intensity feature enhances inspection quality by improving crack detection; it also enables the identification of water ingress which introduces intensity anomalies. The 3DMulti-FPFHI outperforms FPFH and a state-of-the-art multimodal anomaly detection method. The potential of the method to address diverse infrastructure anomaly detection scenarios is highlighted by the minimal requirements for data compared to learning-based methods. The code and related point cloud dataset are available at https://github.com/Jingyixiong/3D-Multi-FPFHI.", "date": "2025-02-09", "categories": []}
{"id": "2506.18368", "title": "Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection", "abstract": "Detecting anomalous human behaviour is an important visual task in safety-critical applications such as healthcare monitoring, workplace safety, or public surveillance. In these contexts, abnormalities are often reflected with unusual human poses. Thus, we propose SeeKer, a method for detecting anomalies in sequences of human skeletons. Our method formulates the skeleton sequence density through autoregressive factorization at the keypoint level. The corresponding conditional distributions represent probable keypoint locations given prior skeletal motion. We formulate the joint distribution of the considered skeleton as causal prediction of conditional Gaussians across its constituent keypoints. A skeleton is flagged as anomalous if its keypoint locations surprise our model (i.e. receive a low density). In practice, our anomaly score is a weighted sum of per-keypoint log-conditionals, where the weights account for the confidence of the underlying keypoint detector. Despite its conceptual simplicity, SeeKer surpasses all previous methods on the UBnormal and MSAD-HR datasets while delivering competitive performance on the ShanghaiTech dataset.", "date": "2025-06-23", "categories": []}
{"id": "2509.03260", "title": "HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling", "abstract": "Abnormal cryptocurrency transactions - such as mixing services, fraudulent transfers, and pump-and-dump operations -- pose escalating risks to financial integrity but remain notoriously difficult to detect due to class imbalance, temporal volatility, and complex network dependencies. Existing approaches are predominantly model-centric and post hoc, flagging anomalies only after they occur and thus offering limited preventive value. This paper introduces HyPV-LEAD (Hyperbolic Peak-Valley Lead-time Enabled Anomaly Detection), a data-driven early-warning framework that explicitly incorporates lead time into anomaly detection. Unlike prior methods, HyPV-LEAD integrates three innovations: (1) window-horizon modeling to guarantee actionable lead-time alerts, (2) Peak-Valley (PV) sampling to mitigate class imbalance while preserving temporal continuity, and (3) hyperbolic embedding to capture the hierarchical and scale-free properties of blockchain transaction networks. Empirical evaluation on large-scale Bitcoin transaction data demonstrates that HyPV-LEAD consistently outperforms state-of-the-art baselines, achieving a PR-AUC of 0.9624 with significant gains in precision and recall. Ablation studies further confirm that each component - PV sampling, hyperbolic embedding, and structural-temporal modeling - provides complementary benefits, with the full framework delivering the highest performance. By shifting anomaly detection from reactive classification to proactive early-warning, HyPV-LEAD establishes a robust foundation for real-time risk management, anti-money laundering (AML) compliance, and financial security in dynamic blockchain environments.", "date": "2025-09-03", "categories": []}
{"id": "2509.03290", "title": "Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics", "abstract": "The ever-increasing reliance of critical services on network infrastructure coupled with the increased operational complexity of beyond-5G/6G networks necessitate the need for proactive and automated network fault management. The provision for open interfaces among different radio access network\\,(RAN) elements and the integration of AI/ML into network architecture enabled by the Open RAN\\,(O-RAN) specifications bring new possibilities for active network health monitoring and anomaly detection. In this paper we leverage these advantages and develop an anomaly detection framework that proactively detect the possible throughput drops for a UE and minimize the post-handover failures. We propose two actionable anomaly detection algorithms tailored for real-world deployment. The first algorithm identifies user equipment (UE) at risk of severe throughput degradation by analyzing key performance indicators (KPIs) such as resource block utilization and signal quality metrics, enabling proactive handover initiation. The second algorithm evaluates neighbor cell radio coverage quality, filtering out cells with anomalous signal strength or interference levels. This reduces candidate targets for handover by 41.27\\% on average. Together, these methods mitigate post-handover failures and throughput drops while operating much faster than the near-real-time latency constraints. This paves the way for self-healing 6G networks.", "date": "2025-09-03", "categories": []}
{"id": "2404.03495", "title": "Deep Transductive Outlier Detection", "abstract": "Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly leverages unlabeled test data to boost accuracy. On the comprehensive ADBench benchmark, Doust achieves an average ROC-AUC of $89%$, outperforming all 21 competitors by roughly $10%$. Our analysis identifies both the potential and a limitation of transductive OD: while performance gains can be substantial in favorable conditions, very low contamination rates can hinder improvements unless the dataset is sufficiently large.", "date": "2024-04-04", "categories": []}
{"id": "2502.18601", "title": "Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method", "abstract": "Detecting out-of-distribution (OOD) data is a critical task for maintaining model reliability and robustness. In this study, we propose a novel anomaly detection algorithm that leverages the convex hull (CH) property of a dataset by exploiting the observation that OOD samples marginally increase the CH's volume compared to in-distribution samples. Thus, we establish a decision boundary between OOD and in-distribution data by iteratively computing the CH's volume as samples are removed, stopping when such removal does not significantly alter the CH's volume. The proposed algorithm is evaluated against seven widely used anomaly detection methods across ten datasets, demonstrating performance comparable to state-of-the-art (SOTA) techniques. Furthermore, we introduce a computationally efficient criterion for identifying datasets where the proposed method outperforms existing SOTA approaches.", "date": "2025-02-25", "categories": []}
{"id": "2507.09338", "title": "Simplifying Traffic Anomaly Detection with Video Foundation Models", "abstract": "Recent methods for ego-centric Traffic Anomaly Detection (TAD) often rely on complex multi-stage or multi-representation fusion architectures, yet it remains unclear whether such complexity is necessary. Recent findings in visual perception suggest that foundation models, enabled by advanced pre-training, allow simple yet flexible architectures to outperform specialized designs. Therefore, in this work, we investigate an architecturally simple encoder-only approach using plain Video Vision Transformers (Video ViTs) and study how pre-training enables strong TAD performance. We find that: (i) advanced pre-training enables simple encoder-only models to match or even surpass the performance of specialized state-of-the-art TAD methods, while also being significantly more efficient; (ii) although weakly- and fully-supervised pre-training are advantageous on standard benchmarks, we find them less effective for TAD. Instead, self-supervised Masked Video Modeling (MVM) provides the strongest signal; and (iii) Domain-Adaptive Pre-Training (DAPT) on unlabeled driving videos further improves downstream performance, without requiring anomalous examples. Our findings highlight the importance of pre-training and show that effective, efficient, and scalable TAD models can be built with minimal architectural complexity. We release our code, domain-adapted encoders, and fine-tuned models to support future work: https://github.com/tue-mps/simple-tad.", "date": "2025-07-12", "categories": []}
{"id": "2508.12927", "title": "Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization", "abstract": "Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in fields, such as industrial inspection or medical imaging, where acquiring labels is costly or when we want to avoid introducing biases in the type of anomalies that can be spotted. In this work, we propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings that balances a feature-based cost and a spatial-based cost. We leverage this metric to learn local and global prototypes with optimal transport from latent representations extracted with a pre-trained image encoder. We demonstrate that our approach can enforce a structural constraint when learning the prototypes, allowing to capture the underlying organization of the normal samples, thus improving the detection of incoherencies in images. Our model achieves performance that is on par with strong baselines on two reference benchmarks for anomaly detection on industrial images.", "date": "2025-08-18", "categories": []}
{"id": "2509.00042", "title": "ARTPS: Depth-Enhanced Hybrid Anomaly Detection and Learnable Curiosity Score for Autonomous Rover Target Prioritization", "abstract": "We present ARTPS (Autonomous Rover Target Prioritization System), a novel hybrid AI system that combines depth estimation, anomaly detection, and learnable curiosity scoring for autonomous exploration of planetary surfaces. Our approach integrates monocular depth estimation using Vision Transformers with multi-component anomaly detection and a weighted curiosity score that balances known value, anomaly signals, depth variance, and surface roughness. The system achieves state-of-the-art performance with AUROC of 0.94, AUPRC of 0.89, and F1-Score of 0.87 on Mars rover datasets. We demonstrate significant improvements in target prioritization accuracy through ablation studies and provide comprehensive analysis of component contributions. The hybrid fusion approach reduces false positives by 23% while maintaining high detection sensitivity across diverse terrain types.", "date": "2025-08-23", "categories": []}
{"id": "2509.00131", "title": "Self-supervised large-scale kidney abnormality detection in drug safety assessment studies", "abstract": "Kidney abnormality detection is required for all preclinical drug development. It involves a time-consuming and costly examination of hundreds to thousands of whole-slide images per drug safety study, most of which are normal, to detect any subtle changes indicating toxic effects. In this study, we present the first large-scale self-supervised abnormality detection model for kidney toxicologic pathology, spanning drug safety assessment studies from 158 compounds. We explore the complexity of kidney abnormality detection on this scale using features extracted from the UNI foundation model (FM) and show that a simple k-nearest neighbor classifier on these features performs at chance, demonstrating that the FM-generated features alone are insufficient for detecting abnormalities. We then demonstrate that a self-supervised method applied to the same features can achieve better-than-chance performance, with an area under the receiver operating characteristic curve of 0.62 and a negative predictive value of 89%. With further development, such a model can be used to rule out normal slides in drug safety assessment studies, reducing the costs and time associated with drug development.", "date": "2025-08-29", "categories": []}
{"id": "2509.00827", "title": "Surface Defect Detection with Gabor Filter Using Reconstruction-Based Blurring U-Net-ViT", "abstract": "This paper proposes a novel approach to enhance the accuracy and reliability of texture-based surface defect detection using Gabor filters and a blurring U-Net-ViT model. By combining the local feature training of U-Net with the global processing of the Vision Transformer(ViT), the model effectively detects defects across various textures. A Gaussian filter-based loss function removes background noise and highlights defect patterns, while Salt-and-Pepper(SP) masking in the training process reinforces texture-defect boundaries, ensuring robust performance in noisy environments. Gabor filters are applied in post-processing to emphasize defect orientation and frequency characteristics. Parameter optimization, including filter size, sigma, wavelength, gamma, and orientation, maximizes performance across datasets like MVTec-AD, Surface Crack Detection, and Marble Surface Anomaly Dataset, achieving an average Area Under the Curve(AUC) of 0.939. The ablation studies validate that the optimal filter size and noise probability significantly enhance defect detection performance.", "date": "2025-08-31", "categories": []}
{"id": "2509.01098", "title": "CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection", "abstract": "Time Series Anomaly Detection metrics serve as crucial tools for model evaluation. However, existing metrics suffer from several limitations: insufficient discriminative power, strong hyperparameter dependency, sensitivity to perturbations, and high computational overhead. This paper introduces Confidence-Consistency Evaluation (CCE), a novel evaluation metric that simultaneously measures prediction confidence and uncertainty consistency. By employing Bayesian estimation to quantify the uncertainty of anomaly scores, we construct both global and event-level confidence and consistency scores for model predictions, resulting in a concise CCE metric. Theoretically and experimentally, we demonstrate that CCE possesses strict boundedness, Lipschitz robustness against score perturbations, and linear time complexity $\\mathcal{O}(n)$. Furthermore, we establish RankEval, a benchmark for comparing the ranking capabilities of various metrics. RankEval represents the first standardized and reproducible evaluation pipeline that enables objective comparison of evaluation metrics. Both CCE and RankEval implementations are fully open-source.", "date": "2025-09-01", "categories": []}
{"id": "2509.01375", "title": "Anomaly detection in network flows using unsupervised online machine learning", "abstract": "Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection model for network flows using unsupervised machine learning with online learning capabilities. This approach allows the system to dynamically learn the normal behavior of the network and detect deviations without requiring labeled data, which is particularly useful in real-world environments where traffic is constantly changing and labeled data is scarce. The model was implemented using the River library with a One-Class SVM and evaluated on the NF-UNSW-NB15 dataset and its extended version v2, which contain network flows labeled with different attack categories. The results show an accuracy above 98%, a false positive rate below 3.1%, and a recall of 100% in the most advanced version of the dataset. In addition, the low processing time per flow (<0.033 ms) demonstrates the feasibility of the approach for real-time applications.", "date": "2025-09-01", "categories": []}
{"id": "2509.01512", "title": "Unsupervised Identification and Replay-based Detection (UIRD) for New Category Anomaly Detection in ECG Signal", "abstract": "In clinical practice, automatic analysis of electrocardiogram (ECG) is widely applied to identify irregular heart rhythms and other electrical anomalies of the heart, enabling timely intervention and potentially improving clinical outcomes. However, due to the limited samples in certain types of ECG signals, the class imbalance issues pose a challenge for ECG-based detection. In addition, as the volume of patient data grows, long-term storage of all historical data becomes increasingly burdensome as training samples to recognize new patterns and classify existing ECG signals accurately. Therefore, to enhance the performance of anomaly detection while addressing storage limitations, we propose a pseudo-replay based semi-supervised continual learning framework, which consists of two components: unsupervised identification and replay-based detection. For unsupervised identification, an unsupervised generative adversarial network (GAN)-based framework is integrated to detect novel patterns. Besides, instead of directly storing all historical data, a pseudo replay-based learning strategy is proposed which utilizes a generator to learn the data distribution for each individual task. When a new task arises, the generator synthesizes pseudo data representative of previous learnt classes, enabling the model to detect both the existed patterns and the newly presented anomalies. The effectiveness of the proposed framework is validated in four public ECG datasets, which leverages supervised classification problems for anomaly detection. The experimental results show that the developed approach is very promising in identifying novel anomalies while maintaining good performance on detecting existing ECG signals.", "date": "2025-09-01", "categories": []}
{"id": "2509.02101", "title": "SALAD -- Semantics-Aware Logical Anomaly Detection", "abstract": "Recent surface anomaly detection methods excel at identifying structural anomalies, such as dents and scratches, but struggle with logical anomalies, such as irregular or missing object components. The best-performing logical anomaly detection approaches rely on aggregated pretrained features or handcrafted descriptors (most often derived from composition maps), which discard spatial and semantic information, leading to suboptimal performance. We propose SALAD, a semantics-aware discriminative logical anomaly detection method that incorporates a newly proposed composition branch to explicitly model the distribution of object composition maps, consequently learning important semantic relationships. Additionally, we introduce a novel procedure for extracting composition maps that requires no hand-made labels or category-specific information, in contrast to previous methods. By effectively modelling the composition map distribution, SALAD significantly improves upon state-of-the-art methods on the standard benchmark for logical anomaly detection, MVTec LOCO, achieving an impressive image-level AUROC of 96.1%. Code: https://github.com/MaticFuc/SALAD", "date": "2025-09-02", "categories": []}
{"id": "2509.02471", "title": "ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection", "abstract": "The core challenge in industrial equipment anoma lous sound detection (ASD) lies in modeling the time-frequency coupling characteristics of acoustic features. Existing modeling methods are limited by local receptive fields, making it difficult to capture long-range temporal patterns and cross-band dynamic coupling effects in machine acoustic features. In this paper, we propose a novel framework, ESTM, which is based on a dual-path Mamba architecture with time-frequency decoupled modeling and utilizes Selective State-Space Models (SSM) for long-range sequence modeling. ESTM extracts rich feature representations from different time segments and frequency bands by fusing enhanced Mel spectrograms and raw audio features, while further improving sensitivity to anomalous patterns through the TriStat-Gating (TSG) module. Our experiments demonstrate that ESTM improves anomalous detection performance on the DCASE 2020 Task 2 dataset, further validating the effectiveness of the proposed method.", "date": "2025-09-02", "categories": []}
{"id": "2508.18474", "title": "DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection", "abstract": "Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with limited labeled data, high false-positive rates, and difficulty generalizing to novel anomaly types. To overcome these challenges, we propose a reinforcement learning-based framework that integrates dynamic reward shaping, Variational Autoencoder (VAE), and active learning, called DRTA. Our method uses an adaptive reward mechanism that balances exploration and exploitation by dynamically scaling the effect of VAE-based reconstruction error and classification rewards. This approach enables the agent to detect anomalies effectively in low-label systems while maintaining high precision and recall. Our experimental results on the Yahoo A1 and Yahoo A2 benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art unsupervised and semi-supervised approaches. These findings show that our framework is a scalable and efficient solution for real-world anomaly detection tasks.", "date": "2025-08-25", "categories": []}
{"id": "2508.21111", "title": "Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI", "abstract": "The Deep Space Network (DSN) is NASA's largest network of antenna facilities that generate a large volume of multivariate time-series data. These facilities contain DSN antennas and transmitters that undergo degradation over long periods of time, which may cause costly disruptions to the data flow and threaten the earth-connection of dozens of spacecraft that rely on the Deep Space Network for their lifeline. The purpose of this study was to experiment with different methods that would be able to assist JPL engineers with directly pinpointing anomalies and equipment degradation through collected data, and continue conducting maintenance and operations of the DSN for future space missions around our universe. As such, we have researched various machine learning techniques that can fully reconstruct data through predictive analysis, and determine anomalous data entries within real-time datasets through statistical computations and thresholds. On top of the fully trained and tested machine learning models, we have also integrated the use of a reinforcement learning subsystem that classifies identified anomalies based on severity level and a Large Language Model that labels an explanation for each anomalous data entry, all of which can be improved and fine-tuned over time through human feedback/input. Specifically, for the DSN transmitters, we have also implemented a full data pipeline system that connects the data extraction, parsing, and processing workflow all together as there was no coherent program or script for performing these tasks before. Using this data pipeline system, we were able to then also connect the models trained from DSN antenna data, completing the data workflow for DSN anomaly detection. This was all wrapped around and further connected by an agentic AI system, where complex reasoning was utilized to determine the classifications and predictions of anomalous data.", "date": "2025-08-28", "categories": []}
{"id": "2508.21273", "title": "CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams", "abstract": "The detection of anomalies in non-stationary time-series streams is a critical but challenging task across numerous industrial and scientific domains. Traditional models, trained offline, suffer significant performance degradation when faced with concept drift, where the underlying statistical properties of the data change over time. This paper introduces CALM (Continuous, Adaptive, and LLM-Mediated), a novel, end-to-end framework for real-time anomaly detection designed to address this challenge. CALM is built on the Apache Beam distributed processing framework and leverages the TimesFm foundation model for forecasting-based anomaly detection. The framework's novelty lies in two core contributions. First, it implements a closed-loop, continuous fine-tuning mechanism that allows the anomaly detection model to adapt to evolving data patterns in near real-time. Second, it introduces an LLM-as-a-Judge component, a Large Language Model that provides semantic, context-aware judgments on detected anomalies to curate a high-quality training dataset, deciding whether an anomaly represents transient noise or a meaningful pattern shift. We evaluate CALM on the comprehensive TSB-UAD benchmark. Our results demonstrate that the continuously fine-tuned model improves the ROC AUC score in most datasets compared to the static, pre-trained base model, validating the efficacy of our adaptive, LLM-guided approach to maintaining high-performance anomaly detection in dynamic streaming environments.", "date": "2025-08-29", "categories": []}
{"id": "2508.21795", "title": "TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank", "abstract": "Anomaly detection, which aims to identify anomalies deviating from normal patterns, is challenging due to the limited amount of normal data available. Unlike most existing unified methods that rely on carefully designed image feature extractors and memory banks to capture logical relationships between objects, we introduce a text memory bank to enhance the detection of logical anomalies. Specifically, we propose a Three-Memory framework for Unified structural and logical Anomaly Detection (TMUAD). First, we build a class-level text memory bank for logical anomaly detection by the proposed logic-aware text extractor, which can capture rich logical descriptions of objects from input images. Second, we construct an object-level image memory bank that preserves complete object contours by extracting features from segmented objects. Third, we employ visual encoders to extract patch-level image features for constructing a patch-level memory bank for structural anomaly detection. These three complementary memory banks are used to retrieve and compare normal images that are most similar to the query image, compute anomaly scores at multiple levels, and fuse them into a final anomaly score. By unifying structural and logical anomaly detection through collaborative memory banks, TMUAD achieves state-of-the-art performance across seven publicly available datasets involving industrial and medical domains. The model and code are available at https://github.com/SIA-IDE/TMUAD.", "date": "2025-08-29", "categories": []}
{"id": "2508.20492", "title": "IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection", "abstract": "Surface anomaly detection is pivotal for ensuring product quality in industrial manufacturing. While 2D image-based methods have achieved remarkable success, 3D point cloud-based detection remains underexplored despite its richer geometric cues. We argue that the key bottleneck is the absence of powerful pretrained foundation backbones in 3D comparable to those in 2D. To bridge this gap, we propose Importance-Aware Ensemble Network (IAENet), an ensemble framework that synergizes 2D pretrained expert with 3D expert models. However, naively fusing predictions from disparate sources is non-trivial: existing strategies can be affected by a poorly performing modality and thus degrade overall accuracy. To address this challenge, We introduce an novel Importance-Aware Fusion (IAF) module that dynamically assesses the contribution of each source and reweights their anomaly scores. Furthermore, we devise critical loss functions that explicitly guide the optimization of IAF, enabling it to combine the collective knowledge of the source experts but also preserve their unique strengths, thereby enhancing the overall performance of anomaly detection. Extensive experiments on MVTec 3D-AD demonstrate that our IAENet achieves a new state-of-the-art with a markedly lower false positive rate, underscoring its practical value for industrial deployment.", "date": "2025-08-28", "categories": []}
{"id": "2508.20829", "title": "ATM-GAD: Adaptive Temporal Motif Graph Anomaly Detection for Financial Transaction Networks", "abstract": "Financial fraud detection is essential to safeguard billions of dollars, yet the intertwined entities and fast-changing transaction behaviors in modern financial systems routinely defeat conventional machine learning models. Recent graph-based detectors make headway by representing transactions as networks, but they still overlook two fraud hallmarks rooted in time: (1) temporal motifs--recurring, telltale subgraphs that reveal suspicious money flows as they unfold--and (2) account-specific intervals of anomalous activity, when fraud surfaces only in short bursts unique to each entity. To exploit both signals, we introduce ATM-GAD, an adaptive graph neural network that leverages temporal motifs for financial anomaly detection. A Temporal Motif Extractor condenses each account's transaction history into the most informative motifs, preserving both topology and temporal patterns. These motifs are then analyzed by dual-attention blocks: IntraA reasons over interactions within a single motif, while InterA aggregates evidence across motifs to expose multi-step fraud schemes. In parallel, a differentiable Adaptive Time-Window Learner tailors the observation window for every node, allowing the model to focus precisely on the most revealing time slices. Experiments on four real-world datasets show that ATM-GAD consistently outperforms seven strong anomaly-detection baselines, uncovering fraud patterns missed by earlier methods.", "date": "2025-08-28", "categories": []}
{"id": "2508.18463", "title": "Context-Aware Zero-Shot Anomaly Detection in Surveillance Using Contrastive and Predictive Spatiotemporal Modeling", "abstract": "Detecting anomalies in surveillance footage is inherently challenging due to their unpredictable and context-dependent nature. This work introduces a novel context-aware zero-shot anomaly detection framework that identifies abnormal events without exposure to anomaly examples during training. The proposed hybrid architecture combines TimeSformer, DPC, and CLIP to model spatiotemporal dynamics and semantic context. TimeSformer serves as the vision backbone to extract rich spatial-temporal features, while DPC forecasts future representations to identify temporal deviations. Furthermore, a CLIP-based semantic stream enables concept-level anomaly detection through context-specific text prompts. These components are jointly trained using InfoNCE and CPC losses, aligning visual inputs with their temporal and semantic representations. A context-gating mechanism further enhances decision-making by modulating predictions with scene-aware cues or global video features. By integrating predictive modeling with vision-language understanding, the system can generalize to previously unseen behaviors in complex environments. This framework bridges the gap between temporal reasoning and semantic context in zero-shot anomaly detection for surveillance. The code for this research has been made available at https://github.com/NK-II/Context-Aware-Zero-Shot-Anomaly-Detection-in-Surveillance.", "date": "2025-08-25", "categories": []}
{"id": "2508.19573", "title": "DNP-Guided Contrastive Reconstruction with a Reverse Distillation Transformer for Medical Anomaly Detection", "abstract": "Anomaly detection in medical images is challenging due to limited annotations and a domain gap compared to natural images. Existing reconstruction methods often rely on frozen pre-trained encoders, which limits adaptation to domain-specific features and reduces localization accuracy. Prototype-based learning offers interpretability and clustering benefits but suffers from prototype collapse, where few prototypes dominate training, harming diversity and generalization. To address this, we propose a unified framework combining a trainable encoder with prototype-guided reconstruction and a novel Diversity-Aware Alignment Loss. The trainable encoder, enhanced by a momentum branch, enables stable domain-adaptive feature learning. A lightweight Prototype Extractor mines informative normal prototypes to guide the decoder via attention for precise reconstruction. Our loss enforces balanced prototype use through diversity constraints and per-prototype normalization, effectively preventing collapse. Experiments on multiple medical imaging benchmarks show significant improvements in representation quality and anomaly localization, outperforming prior methods. Visualizations and prototype assignment analyses further validate the effectiveness of our anti-collapse mechanism and enhanced interpretability.", "date": "2025-08-27", "categories": []}
{"id": "2508.19683", "title": "Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data", "abstract": "We study the performance of the Topological Uncertainty (TU) constructed with a trained feedforward neural network (FNN) for Anomaly Detection. Generally, meaningful information can be stored in the hidden layers of the trained FNN, and the TU implementation is one tractable recipe to extract buried information by means of the Topological Data Analysis. We explicate the concept of the TU and the numerical procedures. Then, for a concrete demonstration of the performance test, we employ the Neutron Star data used for inference of the equation of state (EoS). For the training dataset consisting of the input (Neutron Star data) and the output (EoS parameters), we can compare the inferred EoSs and the exact answers to classify the data with the label $k$. The subdataset with $k=0$ leads to the normal inference for which the inferred EoS approximates the answer well, while the subdataset with $k=1$ ends up with the unsuccessful inference. Once the TU is prepared based on the $k$-labled subdatasets, we introduce the cross-TU to quantify the uncertainty of characterizing the $k$-labeled data with the label $j$. The anomaly or unsuccessful inference is correctly detected if the cross-TU for $j=k=1$ is smaller than that for $j=0$ and $k=1$. In our numerical experiment, for various input data, we calculate the cross-TU and estimate the performance of Anomaly Detection. We find that performance depends on FNN hyperparameters, and the success rate of Anomaly Detection exceeds $90\\%$ in the best case. We finally discuss further potential of the TU application to retrieve the information hidden in the trained FNN.", "date": "2025-08-27", "categories": []}
{"id": "2508.20076", "title": "Anomaly Detection in Networked Bandits", "abstract": "The nodes' interconnections on a social network often reflect their dependencies and information-sharing behaviors. Nevertheless, abnormal nodes, which significantly deviate from most of the network concerning patterns or behaviors, can lead to grave consequences. Therefore, it is imperative to design efficient online learning algorithms that robustly learn users' preferences while simultaneously detecting anomalies.\n  We introduce a novel bandit algorithm to address this problem. Through network knowledge, the method characterizes the users' preferences and residuals of feature information. By learning and analyzing these preferences and residuals, it develops a personalized recommendation strategy for each user and simultaneously detects anomalies. We rigorously prove an upper bound on the regret of the proposed algorithm and experimentally compare it with several state-of-the-art collaborative contextual bandit algorithms on both synthetic and real-world datasets.", "date": "2025-08-27", "categories": []}
{"id": "2508.18912", "title": "HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections", "abstract": "Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT-YOLO, a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone-based thermal inspections of PV systems, addressing the unique challenges of detecting small and subtle thermal anomalies, such as hotspots and defective modules, while maintaining real-time performance. Experimental results demonstrate a mean average precision of 90.8%, reflecting a significant improvement over baseline object detection models. With a reduced computational load and robustness under diverse environmental conditions, HOTSPOT-YOLO offers a scalable and reliable solution for large-scale PV inspections. This work highlights the integration of advanced AI techniques with practical engineering applications, revolutionizing automated fault detection in renewable energy systems.", "date": "2025-08-26", "categories": []}
{"id": "2406.03262", "title": "A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection", "abstract": "Visual anomaly detection aims to identify anomalous regions in images through unsupervised learning paradigms, with increasing application demand and value in fields such as industrial inspection and medical lesion detection. Despite significant progress in recent years, there is a lack of comprehensive benchmarks to adequately evaluate the performance of various mainstream methods across different datasets under the practical multi-class setting. The absence of standardized experimental setups can lead to potential biases in training epochs, resolution, and metric results, resulting in erroneous conclusions. This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework that is highly extensible for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. Additionally, we have proposed the GPU-assisted ADEval package to address the slow evaluation problem of metrics like time-consuming mAU-PRO on large-scale data, significantly reducing evaluation time by more than 1000-fold. Through extensive experimental results, we objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection. We hope that ADer will become a valuable resource for researchers and practitioners in the field, promoting the development of more robust and generalizable anomaly detection systems. Full codes are open-sourced at https://github.com/zhangzjn/ader.", "date": "2024-06-05", "categories": []}
{"id": "2508.14503", "title": "Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services", "abstract": "This study proposes an anomaly detection method based on the Transformer architecture with integrated multiscale feature perception, aiming to address the limitations of temporal modeling and scale-aware feature representation in cloud service environments. The method first employs an improved Transformer module to perform temporal modeling on high-dimensional monitoring data, using a self-attention mechanism to capture long-range dependencies and contextual semantics. Then, a multiscale feature construction path is introduced to extract temporal features at different granularities through downsampling and parallel encoding. An attention-weighted fusion module is designed to dynamically adjust the contribution of each scale to the final decision, enhancing the model's robustness in anomaly pattern modeling. In the input modeling stage, standardized multidimensional time series are constructed, covering core signals such as CPU utilization, memory usage, and task scheduling states, while positional encoding is used to strengthen the model's temporal awareness. A systematic experimental setup is designed to evaluate performance, including comparative experiments and hyperparameter sensitivity analysis, focusing on the impact of optimizers, learning rates, anomaly ratios, and noise levels. Experimental results show that the proposed method outperforms mainstream baseline models in key metrics, including precision, recall, AUC, and F1-score, and maintains strong stability and detection performance under various perturbation conditions, demonstrating its superior capability in complex cloud environments.", "date": "2025-08-20", "categories": []}
{"id": "2508.16617", "title": "Leveraging the Christoffel Function for Outlier Detection in Data Streams", "abstract": "Outlier detection holds significant importance in the realm of data mining, particularly with the growing pervasiveness of data acquisition methods. The ability to identify outliers in data streams is essential for maintaining data quality and detecting faults. However, dealing with data streams presents challenges due to the non-stationary nature of distributions and the ever-increasing data volume. While numerous methods have been proposed to tackle this challenge, a common drawback is the lack of straightforward parameterization in many of them. This article introduces two novel methods: DyCF and DyCG. DyCF leverages the Christoffel function from the theory of approximation and orthogonal polynomials. Conversely, DyCG capitalizes on the growth properties of the Christoffel function, eliminating the need for tuning parameters. Both approaches are firmly rooted in a well-defined algebraic framework, meeting crucial demands for data stream processing, with a specific focus on addressing low-dimensional aspects and maintaining data history without memory cost. A comprehensive comparison between DyCF, DyCG, and state-of-the-art methods is presented, using both synthetic and real industrial data streams. The results show that DyCF outperforms fine-tuning methods, offering superior performance in terms of execution time and memory usage. DyCG performs less well, but has the considerable advantage of requiring no tuning at all.", "date": "2025-08-13", "categories": []}
{"id": "2508.17726", "title": "Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework", "abstract": "Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category paradigm, requiring separate training for each action category and a large number of normal samples. These constraints hinder scalability and limit applicability in real-world scenarios, where data is often scarce or novel categories frequently appear. To address these limitations, we propose a unified framework for HAAD that is compatible with few-shot scenarios. Our method constructs a category-agnostic representation space via contrastive learning, enabling AD by comparing test samples with a given small set of normal examples (referred to as the support set). To improve inter-category generalization and intra-category robustness, we introduce a generative motion augmentation strategy harnessing a diffusion-based foundation model for creating diverse and realistic training samples. Notably, to the best of our knowledge, our work is the first to introduce such a strategy specifically tailored to enhance contrastive learning for action AD. Extensive experiments on the HumanAct12 dataset demonstrate the state-of-the-art effectiveness of our approach under both seen and unseen category settings, regarding training efficiency and model scalability for few-shot HAAD.", "date": "2025-08-25", "categories": []}
{"id": "2508.17789", "title": "Robust Anomaly Detection in Industrial Environments via Meta-Learning", "abstract": "Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when training data contains mislabeled samples-a common occurrence in real-world scenarios. This paper presents RAD, a robust anomaly detection framework that integrates Normalizing Flows with Model-Agnostic Meta-Learning to address the critical challenge of label noise in industrial settings. Our approach employs a bi-level optimization strategy where meta-learning enables rapid adaptation to varying noise conditions, while uncertainty quantification guides adaptive L2 regularization to maintain model stability. The framework incorporates multiscale feature processing through pretrained feature extractors and leverages the precise likelihood estimation capabilities of Normalizing Flows for robust anomaly scoring. Comprehensive evaluation on MVTec-AD and KSDD2 datasets demonstrates superior performance, achieving I-AUROC scores of 95.4% and 94.6% respectively under clean conditions, while maintaining robust detection capabilities above 86.8% and 92.1% even when 50% of training samples are mislabeled. The results highlight RAD's exceptional resilience to noisy training conditions and its ability to detect subtle anomalies across diverse industrial scenarios, making it a practical solution for real-world anomaly detection applications where perfect data curation is challenging.", "date": "2025-08-25", "categories": []}
{"id": "2508.17827", "title": "A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection", "abstract": "Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settings. To address this, we propose CoZAD, a novel zero-shot anomaly detection framework that integrates soft confident learning with meta-learning and contrastive feature representation. Unlike traditional confident learning that discards uncertain samples, our method assigns confidence-based weights to all training data, preserving boundary information while emphasizing prototypical normal patterns. The framework quantifies data uncertainty through IQR-based thresholding and model uncertainty via covariance based regularization within a Model-Agnostic Meta-Learning. Contrastive learning creates discriminative feature spaces where normal patterns form compact clusters, enabling rapid domain adaptation. Comprehensive evaluation across 10 datasets spanning industrial and medical domains demonstrates state-of-the-art performance, outperforming existing methods on 6 out of 7 industrial benchmarks with notable improvements on texture-rich datasets (99.2% I-AUROC on DTD-Synthetic, 97.2% on BTAD) and pixellevel localization (96.3% P-AUROC on MVTec-AD). The framework eliminates dependence on vision-language alignments or model ensembles, making it valuable for resourceconstrained environments requiring rapid deployment.", "date": "2025-08-25", "categories": []}
{"id": "2508.18007", "title": "Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection", "abstract": "Fully Unsupervised Anomaly Detection (FUAD) is a practical extension of Unsupervised Anomaly Detection (UAD), aiming to detect anomalies without any labels even when the training set may contain anomalous samples. To achieve FUAD, we pioneer the introduction of Knowledge Distillation (KD) paradigm based on teacher-student framework into the FUAD setting. However, due to the presence of anomalies in the training data, traditional KD methods risk enabling the student to learn the teacher's representation of anomalies under FUAD setting, thereby resulting in poor anomaly detection performance. To address this issue, we propose a novel Cross-Domain Distillation (CDD) framework based on the widely studied reverse distillation (RD) paradigm. Specifically, we design a Domain-Specific Training, which divides the training set into multiple domains with lower anomaly ratios and train a domain-specific student for each. Cross-Domain Knowledge Aggregation is then performed, where pseudo-normal features generated by domain-specific students collaboratively guide a global student to learn generalized normal representations across all samples. Experimental results on noisy versions of the MVTec AD and VisA datasets demonstrate that our method achieves significant performance improvements over the baseline, validating its effectiveness under FUAD setting.", "date": "2025-08-25", "categories": []}
{"id": "2508.18013", "title": "Towards Continual Visual Anomaly Detection in the Medical Domain", "abstract": "Visual Anomaly Detection (VAD) seeks to identify abnormal images and precisely localize the corresponding anomalous regions, relying solely on normal data during training. This approach has proven essential in domains such as manufacturing and, more recently, in the medical field, where accurate and explainable detection is critical. Despite its importance, the impact of evolving input data distributions over time has received limited attention, even though such changes can significantly degrade model performance. In particular, given the dynamic and evolving nature of medical imaging data, Continual Learning (CL) provides a natural and effective framework to incrementally adapt models while preserving previously acquired knowledge. This study explores for the first time the application of VAD models in a CL scenario for the medical field. In this work, we utilize a CL version of the well-established PatchCore model, called PatchCoreCL, and evaluate its performance using BMAD, a real-world medical imaging dataset with both image-level and pixel-level annotations. Our results demonstrate that PatchCoreCL is an effective solution, achieving performance comparable to the task-specific models, with a forgetting value less than a 1%, highlighting the feasibility and potential of CL for adaptive VAD in medical imaging.", "date": "2025-08-25", "categories": []}
{"id": "2501.02107", "title": "Online Detection of Water Contamination Under Concept Drift", "abstract": "Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent calibration. This study introduces the Dual-Threshold Anomaly and Drift Detection (AD&DD) method, an unsupervised approach combining a dual-threshold drift detection mechanism with an LSTM-based Variational Autoencoder(LSTM-VAE) for real-time contamination detection. Tested on two realistic WDNs, AD&DD effectively identifies anomalies with sensor offsets as concept drift, and outperforms other methods. A proposed decentralized architecture enables accurate contamination detection and localization by deploying AD&DD on selected nodes.", "date": "2025-01-03", "categories": []}
{"id": "2508.15865", "title": "Securing Swarms: Cross-Domain Adaptation for ROS2-based CPS Anomaly Detection", "abstract": "Cyber-physical systems (CPS) are being increasingly utilized for critical applications. CPS combines sensing and computing elements, often having multi-layer designs with networking, computational, and physical interfaces, which provide them with enhanced capabilities for a variety of application scenarios. However, the combination of physical and computational elements also makes CPS more vulnerable to attacks compared to network-only systems, and the resulting impacts of CPS attacks can be substantial. Intelligent intrusion detection systems (IDS) are an effective mechanism by which CPS can be secured, but the majority of current solutions often train and validate on network traffic-only datasets, ignoring the distinct attacks that may occur on other system layers. In order to address this, we develop an adaptable CPS anomaly detection model that can detect attacks within CPS without the need for previously labeled data. To achieve this, we utilize domain adaptation techniques that allow us to transfer known attack knowledge from a network traffic-only environment to a CPS environment. We validate our approach using a state-of-the-art CPS intrusion dataset that combines network, operating system (OS), and Robot Operating System (ROS) data. Through this dataset, we are able to demonstrate the effectiveness of our model across network traffic-only and CPS environments with distinct attack types and its ability to outperform other anomaly detection methods.", "date": "2025-08-20", "categories": []}
{"id": "2508.16034", "title": "Wavelet-Enhanced PaDiM for Industrial Anomaly Detection", "abstract": "Anomaly detection and localization in industrial images are essential for automated quality inspection. PaDiM, a prominent method, models the distribution of normal image features extracted by pre-trained Convolutional Neural Networks (CNNs) but reduces dimensionality through random channel selection, potentially discarding structured information. We propose Wavelet-Enhanced PaDiM (WE-PaDiM), which integrates Discrete Wavelet Transform (DWT) analysis with multi-layer CNN features in a structured manner. WE-PaDiM applies 2D DWT to feature maps from multiple backbone layers, selects specific frequency subbands (e.g., LL, LH, HL), spatially aligns them, and concatenates them channel-wise before modeling with PaDiM's multivariate Gaussian framework. This DWT-before-concatenation strategy provides a principled method for feature selection based on frequency content relevant to anomalies, leveraging multi-scale wavelet information as an alternative to random selection. We evaluate WE-PaDiM on the challenging MVTec AD dataset with multiple backbones (ResNet-18 and EfficientNet B0-B6). The method achieves strong performance in anomaly detection and localization, yielding average results of 99.32% Image-AUC and 92.10% Pixel-AUC across 15 categories with per-class optimized configurations. Our analysis shows that wavelet choices affect performance trade-offs: simpler wavelets (e.g., Haar) with detail subbands (HL or LH/HL/HH) often enhance localization, while approximation bands (LL) improve image-level detection. WE-PaDiM thus offers a competitive and interpretable alternative to random feature selection in PaDiM, achieving robust results suitable for industrial inspection with comparable efficiency.", "date": "2025-08-22", "categories": []}
{"id": "2508.16157", "title": "Beyond Human-prompting: Adaptive Prompt Tuning with Semantic Alignment for Anomaly Detection", "abstract": "Pre-trained Vision-Language Models (VLMs) have recently shown promise in detecting anomalies. However, previous approaches are fundamentally limited by their reliance on human-designed prompts and the lack of accessible anomaly samples, leading to significant gaps in context-specific anomaly understanding. In this paper, we propose \\textbf{A}daptive \\textbf{P}rompt \\textbf{T}uning with semantic alignment for anomaly detection (APT), a groundbreaking prior knowledge-free, few-shot framework and overcomes the limitations of traditional prompt-based approaches. APT uses self-generated anomaly samples with noise perturbations to train learnable prompts that capture context-dependent anomalies in different scenarios. To prevent overfitting to synthetic noise, we propose a Self-Optimizing Meta-prompt Guiding Scheme (SMGS) that iteratively aligns the prompts with general anomaly semantics while incorporating diverse synthetic anomaly. Our system not only advances pixel-wise anomaly detection, but also achieves state-of-the-art performance on multiple benchmark datasets without requiring prior knowledge for prompt crafting, establishing a robust and versatile solution for real-world anomaly detection.", "date": "2025-08-22", "categories": []}
{"id": "2508.16336", "title": "Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks", "abstract": "Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational constraints such as data non-stationarity and limited labeled data. This paper proposes an unsupervised, online learning framework that aims to detect two types of faults in WDNs: pipe blockages, modeled as collective anomalies, and background leakages, modeled as concept drift. Our approach combines a Long Short-Term Memory Variational Autoencoder (LSTM-VAE) with a dual drift detection mechanism, enabling robust detection and adaptation under non-stationary conditions. Its lightweight, memory-efficient design enables real-time, edge-level monitoring. Experiments on two realistic WDNs show that the proposed approach consistently outperforms strong baselines in detecting anomalies and adapting to recurrent drift, demonstrating its effectiveness in unsupervised event detection for dynamic WDN environments.", "date": "2025-08-22", "categories": []}
{"id": "2508.13560", "title": "DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup", "abstract": "Recent vision-language models (e.g., CLIP) have demonstrated remarkable class-generalizable ability to unseen classes in few-shot anomaly segmentation (FSAS), leveraging supervised prompt learning or fine-tuning on seen classes. However, their cross-category generalization largely depends on prior knowledge of real seen anomaly samples. In this paper, we propose a novel framework, namely DictAS, which enables a unified model to detect visual anomalies in unseen object categories without any retraining on the target data, only employing a few normal reference images as visual prompts. The insight behind DictAS is to transfer dictionary lookup capabilities to the FSAS task for unseen classes via self-supervised learning, instead of merely memorizing the normal and abnormal feature patterns from the training set. Specifically, DictAS mainly consists of three components: (1) Dictionary Construction - to simulate the index and content of a real dictionary using features from normal reference images. (2) Dictionary Lookup - to retrieve queried region features from the dictionary via a sparse lookup strategy. When a query feature cannot be retrieved, it is classified as an anomaly. (3) Query Discrimination Regularization - to enhance anomaly discrimination by making abnormal features harder to retrieve from the dictionary. To achieve this, Contrastive Query Constraint and Text Alignment Constraint are further proposed. Extensive experiments on seven public industrial and medical datasets demonstrate that DictAS consistently outperforms state-of-the-art FSAS methods.", "date": "2025-08-19", "categories": []}
{"id": "2508.15236", "title": "Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis", "abstract": "Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approaches deliver high accuracy, they rely on extensively annotated datasets, suffering from data scarcity in digital pathology. Unsupervised anomaly detection, however, offers a viable alternative by identifying deviations from normal tissue distributions without requiring exhaustive annotations. Recently, denoising diffusion probabilistic models have gained popularity in unsupervised anomaly detection, achieving promising performance in both natural and medical imaging datasets. Building on this, we incorporate a vision-language model with a diffusion model for unsupervised anomaly detection in digital pathology, utilizing histopathology prompts during reconstruction. Our approach employs a set of pathology-related keywords associated with normal tissues to guide the reconstruction process, facilitating the differentiation between normal and abnormal tissues. To evaluate the effectiveness of the proposed method, we conduct experiments on a gastric lymph node dataset from a local hospital and assess its generalization ability under domain shift using a public breast lymph node dataset. The experimental results highlight the potential of the proposed method for unsupervised anomaly detection across various organs in digital pathology. Code: https://github.com/QuIIL/AnoPILaD.", "date": "2025-08-21", "categories": []}
{"id": "2508.15334", "title": "An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Models", "abstract": "Anomalous Sound Detection (ASD) aims at identifying anomalous sounds from machines and has gained extensive research interests from both academia and industry. However, the uncertainty of anomaly location and much redundant information such as noise in machine sounds hinder the improvement of ASD system performance. This paper proposes a novel audio feature of filter banks with evenly distributed intervals, ensuring equal attention to all frequency ranges in the audio, which enhances the detection of anomalies in machine sounds. Moreover, based on pre-trained models, this paper presents a parameter-free feature enhancement approach to remove redundant information in machine audio. It is believed that this parameter-free strategy facilitates the effective transfer of universal knowledge from pre-trained tasks to the ASD task during model fine-tuning. Evaluation results on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge dataset demonstrate significant improvements in ASD performance with our proposed methods.", "date": "2025-08-21", "categories": []}
{"id": "2405.07865", "title": "AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving", "abstract": "The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong focus on camera data. In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date. AnoVox incorporates large-scale multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. We propose a formal definition of normality and provide a compliant training dataset. AnoVox is the first benchmark to contain both content and temporal anomalies.", "date": "2024-05-13", "categories": []}
{"id": "2508.14088", "title": "CoBAD: Modeling Collective Behaviors for Human Mobility Anomaly Detection", "abstract": "Detecting anomalies in human mobility is essential for applications such as public safety and urban planning. While traditional anomaly detection methods primarily focus on individual movement patterns (e.g., a child should stay at home at night), collective anomaly detection aims to identify irregularities in collective mobility behaviors across individuals (e.g., a child is at home alone while the parents are elsewhere) and remains an underexplored challenge. Unlike individual anomalies, collective anomalies require modeling spatiotemporal dependencies between individuals, introducing additional complexity. To address this gap, we propose CoBAD, a novel model designed to capture Collective Behaviors for human mobility Anomaly Detection. We first formulate the problem as unsupervised learning over Collective Event Sequences (CES) with a co-occurrence event graph, where CES represents the event sequences of related individuals. CoBAD then employs a two-stage attention mechanism to model both the individual mobility patterns and the interactions across multiple individuals. Pre-trained on large-scale collective behavior data through masked event and link reconstruction tasks, CoBAD is able to detect two types of collective anomalies: unexpected co-occurrence anomalies and absence anomalies, the latter of which has been largely overlooked in prior work. Extensive experiments on large-scale mobility datasets demonstrate that CoBAD significantly outperforms existing anomaly detection baselines, achieving an improvement of 13%-18% in AUCROC and 19%-70% in AUCPR. All source code is available at https://github.com/wenhaomin/CoBAD.", "date": "2025-08-13", "categories": []}
{"id": "2508.14203", "title": "A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment", "abstract": "Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment.", "date": "2025-08-19", "categories": []}
{"id": "2508.14504", "title": "PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments", "abstract": "The detection of anomalies in manufacturing processes is crucial to ensure product quality and identify process deviations. Statistical and data-driven approaches remain the standard in industrial anomaly detection, yet their adaptability and usability are constrained by the dependence on extensive annotated datasets and limited flexibility under dynamic production conditions. Recent advances in the perception capabilities of foundation models provide promising opportunities for their adaptation to this downstream task. This paper presents PB-IAD (Prompt-based Industrial Anomaly Detection), a novel framework that leverages the multimodal and reasoning capabilities of foundation models for industrial anomaly detection. Specifically, PB-IAD addresses three key requirements of dynamic production environments: data sparsity, agile adaptability, and domain user centricity. In addition to the anomaly detection, the framework includes a prompt template that is specifically designed for iteratively implementing domain-specific process knowledge, as well as a pre-processing module that translates domain user inputs into effective system prompts. This user-centric design allows domain experts to customise the system flexibly without requiring data science expertise. The proposed framework is evaluated by utilizing GPT-4.1 across three distinct manufacturing scenarios, two data modalities, and an ablation study to systematically assess the contribution of semantic instructions. Furthermore, PB-IAD is benchmarked to state-of-the-art methods for anomaly detection such as PatchCore. The results demonstrate superior performance, particularly in data-sparse scenarios and low-shot settings, achieved solely through semantic instructions.", "date": "2025-08-20", "categories": []}
{"id": "2508.14684", "title": "Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum", "abstract": "In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous networks that most GNN-based techniques fail to address. Several works have been proposed to tackle this issue in the spatial domain. However, these methods overlook the complex relationships between node structure encoding, node features, and their contextual environment and rely on principled guidance, research on solving spectral domain heterophilic problems remains limited. This study analyzes the spectral distribution of nodes with different heterophilic degrees and discovers that the heterophily of anomalous nodes causes the spectral energy to shift from low to high frequencies. To address the above challenges, we propose a spectral neural network CES2-GAD based on causal edge separation for anomaly detection on heterophilic graphs. Firstly, CES2-GAD will separate the original graph into homophilic and heterophilic edges using causal interventions. Subsequently, various hybrid-spectrum filters are used to capture signals from the segmented graphs. Finally, representations from multiple signals are concatenated and input into a classifier to predict anomalies. Extensive experiments with real-world datasets have proven the effectiveness of the method we proposed.", "date": "2025-08-20", "categories": []}
{"id": "2407.09578", "title": "Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection", "abstract": "Reconstruction-based anomaly detection via denoising diffusion model has limitations in determining appropriate noise parameters that can degrade anomalies while preserving normal characteristics. Also, normal regions can fluctuate considerably during reconstruction, resulting in false detection. In this paper, we propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation, effectively solving the both problems that impede practical application in display inspection.", "date": "2024-07-12", "categories": []}
{"id": "2508.10594", "title": "FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection", "abstract": "Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. Despite the current advancements in deep learning-based GAD, existing approaches often suffer from high deployment costs and poor scalability due to their complex and resource-intensive training processes. Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as crucial, may actually contribute less to anomaly detection performance than expected. Inspired by this, we propose FreeGAD, a novel training-free yet effective GAD method. Specifically, it leverages an affinity-gated residual encoder to generate anomaly-aware representations. Meanwhile, FreeGAD identifies anchor nodes as pseudo-normal and anomalous guides, followed by calculating anomaly scores through anchor-guided statistical deviations. Extensive experiments demonstrate that FreeGAD achieves superior anomaly detection performance, efficiency, and scalability on multiple benchmark datasets from diverse domains, without any training or iterative optimization.", "date": "2025-08-14", "categories": []}
{"id": "2508.13406", "title": "Semi-Supervised Anomaly Detection Pipeline for SOZ Localization Using Ictal-Related Chirp", "abstract": "This study presents a quantitative framework for evaluating the spatial concordance between clinically defined seizure onset zones (SOZs) and statistically anomalous channels identified through time-frequency analysis of chirp events. The proposed pipeline employs a two-step methodology: (1) Unsupervised Outlier Detection, where Local Outlier Factor (LOF) analysis with adaptive neighborhood selection identifies anomalous channels based on spectro-temporal features of chirp (Onset frequency, offset frequency, and temporal duration); and (2) Spatial Correlation Analysis, which computes both exact co-occurrence metrics and weighted index similarity, incorporating hemispheric congruence and electrode proximity. Key findings demonstrate that the LOF-based approach (N neighbors=20, contamination=0.2) effectively detects outliers, with index matching (weighted by channel proximity) outperforming exact matching in SOZ localization. Performance metrics (precision, recall, F1) were highest for seizure-free patients (Index Precision mean: 0.903) and those with successful surgical outcomes (Index Precision mean: 0.865), whereas failure cases exhibited lower concordance (Index Precision mean: 0.460). The key takeaway is that chirp-based outlier detection, combined with weighted spatial metrics, provides a complementary method for SOZ localization, particularly in patients with successful surgical outcomes.", "date": "2025-08-18", "categories": []}
{"id": "2302.06670", "title": "Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos", "abstract": "Anomaly detection and localization in visual data, including images and videos, are crucial in machine learning and real-world applications. Despite rapid advancements in visual anomaly detection (VAD), interpreting these often black-box models and explaining why specific instances are flagged as anomalous remains challenging. This paper provides the first comprehensive survey focused specifically on explainable 2D visual anomaly detection (X-VAD), covering methods for both images (IAD) and videos (VAD). We first introduce the background of IAD and VAD. Then, as the core contribution, we present a thorough literature review of explainable methods, categorized by their underlying techniques (e.g., attention-based, generative model-based, reasoning-based, foundation model-based). We analyze the commonalities and differences in applying these methods across image and video modalities, highlighting modality-specific challenges and opportunities for explainability. Additionally, we summarize relevant datasets and evaluation metrics, discussing both standard performance metrics and emerging approaches for assessing explanation quality (e.g., faithfulness, stability). Finally, we discuss promising future directions and open problems, including quantifying explanation quality, explaining diverse AD paradigms (SSL, zero-shot), enhancing context-awareness, leveraging foundation models responsibly, and addressing real-world constraints like efficiency and robustness. A curated collection of related resources is available at https://github.com/wyzjack/Awesome-XAD.", "date": "2023-02-13", "categories": []}
{"id": "2406.17826", "title": "European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry", "abstract": "Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.", "date": "2024-06-25", "categories": []}
{"id": "2411.10729", "title": "On-device Anomaly Detection in Conveyor Belt Operations", "abstract": "Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of bulk materials over long distances, which directly impacts productivity. While detecting anomalies in specific conveyor belt components has been widely studied, identifying the root causes of these failures, such as changing production conditions and operator errors, remains critical. Continuous monitoring of mining conveyor belt work cycles is still at an early stage and requires robust solutions. Recently, an anomaly detection method for duty cycle operations of a mining conveyor belt has been proposed. Based on its limited performance and unevaluated long-term proper operation, this study proposes two novel methods for classifying normal and abnormal duty cycles. The proposed approaches are pattern recognition systems that make use of threshold-based duty-cycle detection mechanisms, manually extracted features, pattern-matching, and supervised tiny machine learning models. The explored low-computational models include decision tree, random forest, extra trees, extreme gradient boosting, Gaussian naive Bayes, and multi-layer perceptron. A comprehensive evaluation of the former and proposed approaches is carried out on two datasets. Both proposed methods outperform the former method in anomaly detection, with the best-performing approach being dataset-dependent. The heuristic rule-based approach achieves the highest F1-score in the same dataset used for algorithm training, with 97.3% for normal cycles and 80.2% for abnormal cycles. The ML-based approach performs better on a dataset including the effects of machine aging, with an F1-score scoring 91.3% for normal cycles and 67.9% for abnormal cycles. Implemented on two low-power microcontrollers, the methods demonstrate efficient, real-time operation with energy consumption of 13.3 and 20.6 \\textmu J during inference. These results ...", "date": "2024-11-16", "categories": []}
{"id": "2503.12910", "title": "AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification", "abstract": "Recently, zero-shot anomaly detection (ZSAD) has emerged as a pivotal paradigm for industrial inspection and medical diagnostics, detecting defects in novel objects without requiring any target-dataset samples during training. Existing CLIP-based ZSAD methods generate anomaly maps by measuring the cosine similarity between visual and textual features. However, CLIP's alignment with object categories instead of their anomalous states limits its effectiveness for anomaly detection. To address this limitation, we propose AFR-CLIP, a CLIP-based anomaly feature rectification framework. AFR-CLIP first performs image-guided textual rectification, embedding the implicit defect information from the image into a stateless prompt that describes the object category without indicating any anomalous state. The enriched textual embeddings are then compared with two pre-defined stateful (normal or abnormal) embeddings, and their text-on-text similarity yields the anomaly map that highlights defective regions. To further enhance perception to multi-scale features and complex anomalies, we introduce self prompting (SP) and multi-patch feature aggregation (MPFA) modules. Extensive experiments are conducted on eleven anomaly detection benchmarks across industrial and medical domains, demonstrating AFR-CLIP's superiority in ZSAD.", "date": "2025-03-17", "categories": []}
{"id": "2508.11826", "title": "From Pixels to Graphs: Deep Graph-Level Anomaly Detection on Dermoscopic Images", "abstract": "Graph Neural Networks (GNNs) have emerged as a powerful approach for graph-based machine learning tasks. Previous work applied GNNs to image-derived graph representations for various downstream tasks such as classification or anomaly detection. These transformations include segmenting images, extracting features from segments, mapping them to nodes, and connecting them. However, to the best of our knowledge, no study has rigorously compared the effectiveness of the numerous potential image-to-graph transformation approaches for GNN-based graph-level anomaly detection (GLAD). In this study, we systematically evaluate the efficacy of multiple segmentation schemes, edge construction strategies, and node feature sets based on color, texture, and shape descriptors to produce suitable image-derived graph representations to perform graph-level anomaly detection. We conduct extensive experiments on dermoscopic images using state-of-the-art GLAD models, examining performance and efficiency in purely unsupervised, weakly supervised, and fully supervised regimes. Our findings reveal, for example, that color descriptors contribute the best standalone performance, while incorporating shape and texture features consistently enhances detection efficacy. In particular, our best unsupervised configuration using OCGTL achieves a competitive AUC-ROC score of up to 0.805 without relying on pretrained backbones like comparable image-based approaches. With the inclusion of sparse labels, the performance increases substantially to 0.872 and with full supervision to 0.914 AUC-ROC.", "date": "2025-08-15", "categories": []}
{"id": "2508.12931", "title": "Towards High-Resolution Industrial Image Anomaly Detection", "abstract": "Current anomaly detection methods primarily focus on low-resolution scenarios. For high-resolution images, conventional downsampling often results in missed detections of subtle anomalous regions due to the loss of fine-grained discriminative information. Despite some progress, recent studies have attempted to improve detection resolution by employing lightweight networks or using simple image tiling and ensemble methods. However, these approaches still struggle to meet the practical demands of industrial scenarios in terms of detection accuracy and efficiency. To address the above issues, we propose HiAD, a general framework for high-resolution anomaly detection. HiAD is capable of detecting anomalous regions of varying sizes in high-resolution images under limited computational resources. Specifically, HiAD employs a dual-branch architecture that integrates anomaly cues across different scales to comprehensively capture both subtle and large-scale anomalies. Furthermore, it incorporates a multi-resolution feature fusion strategy to tackle the challenges posed by fine-grained texture variations in high-resolution images. To enhance both adaptability and efficiency, HiAD utilizes a detector pool in conjunction with various detector assignment strategies, enabling detectors to be adaptively assigned based on patch features, ensuring detection performance while effectively controlling computational costs. We conduct extensive experiments on our specifically constructed high-resolution anomaly detection benchmarks, including MVTec-HD, VisA-HD, and the real-world benchmark RealIAD-HD, demonstrating the superior performance of HiAD. The code is available at https://github.com/cnulab/HiAD.", "date": "2025-08-18", "categories": []}
{"id": "2508.13099", "title": "Outlier Detection of Poisson-Distributed Targets Using a Seabed Sensor Network", "abstract": "This paper presents a framework for classifying and detecting spatial commission outliers in maritime environments using seabed acoustic sensor networks and log Gaussian Cox processes (LGCPs). By modeling target arrivals as a mixture of normal and outlier processes, we estimate the probability that a newly observed event is an outlier. We propose a second-order approximation of this probability that incorporates both the mean and variance of the normal intensity function, providing improved classification accuracy compared to mean-only approaches. We analytically show that our method yields a tighter bound to the true probability using Jensen's inequality. To enhance detection, we integrate a real-time, near-optimal sensor placement strategy that dynamically adjusts sensor locations based on the evolving outlier intensity. The proposed framework is validated using real ship traffic data near Norfolk, Virginia, where numerical results demonstrate the effectiveness of our approach in improving both classification performance and outlier detection through sensor deployment.", "date": "2025-08-18", "categories": []}
{"id": "2505.00040", "title": "Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies", "abstract": "Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML), for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve dual functionality of data compression for more efficient off-satellite transmission, and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for a use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both novel ML-based approaches in small satellite applications along with the expansion of space technology and artificial intelligence in Africa.", "date": "2025-04-29", "categories": []}
{"id": "2508.11528", "title": "Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series", "abstract": "We propose an unsupervised anomaly detection approach based on a physics-informed diffusion model for multivariate time series data. Over the past years, diffusion model has demonstrated its effectiveness in forecasting, imputation, generation, and anomaly detection in the time series domain. In this paper, we present a new approach for learning the physics-dependent temporal distribution of multivariate time series data using a weighted physics-informed loss during diffusion model training. A weighted physics-informed loss is constructed using a static weight schedule. This approach enables a diffusion model to accurately approximate underlying data distribution, which can influence the unsupervised anomaly detection performance. Our experiments on synthetic and real-world datasets show that physics-informed training improves the F1 score in anomaly detection; it generates better data diversity and log-likelihood. Our model outperforms baseline approaches, additionally, it surpasses prior physics-informed work and purely data-driven diffusion models on a synthetic dataset and one real-world dataset while remaining competitive on others.", "date": "2025-08-15", "categories": []}
{"id": "2508.11550", "title": "Training-Free Anomaly Generation via Dual-Attention Enhancement in Diffusion Model", "abstract": "Industrial anomaly detection (AD) plays a significant role in manufacturing where a long-standing challenge is data scarcity. A growing body of works have emerged to address insufficient anomaly data via anomaly generation. However, these anomaly generation methods suffer from lack of fidelity or need to be trained with extra data. To this end, we propose a training-free anomaly generation framework dubbed AAG, which is based on Stable Diffusion (SD)'s strong generation ability for effective anomaly image generation. Given a normal image, mask and a simple text prompt, AAG can generate realistic and natural anomalies in the specific regions and simultaneously keep contents in other regions unchanged. In particular, we propose Cross-Attention Enhancement (CAE) to re-engineer the cross-attention mechanism within Stable Diffusion based on the given mask. CAE increases the similarity between visual tokens in specific regions and text embeddings, which guides these generated visual tokens in accordance with the text description. Besides, generated anomalies need to be more natural and plausible with object in given image. We propose Self-Attention Enhancement (SAE) which improves similarity between each normal visual token and anomaly visual tokens. SAE ensures that generated anomalies are coherent with original pattern. Extensive experiments on MVTec AD and VisA datasets demonstrate effectiveness of AAG in anomaly generation and its utility. Furthermore, anomaly images generated by AAG can bolster performance of various downstream anomaly inspection tasks.", "date": "2025-08-15", "categories": []}
{"id": "2508.05690", "title": "Leveraging large language models for SQL behavior-based database intrusion detection", "abstract": "Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge, making it easier to mimic employee behavior effectively. In contrast, external masqueraders may behave differently due to their lack of familiarity with the organization. Current approaches lack the granularity needed to detect anomalies at the operational level, frequently misclassifying entire sequences of operations as anomalies, even though most operations are likely to represent normal behavior. On the other hand, some anomalous behaviors often resemble normal activities, making them difficult for existing detection methods to identify. This paper introduces a two-tiered anomaly detection approach for Structured Query Language (SQL) using the Bidirectional Encoder Representations from Transformers (BERT) model, specifically DistilBERT, a more efficient, pre-trained version. Our method combines both unsupervised and supervised machine learning techniques to accurately identify anomalous activities while minimizing the need for data labeling. First, the unsupervised method uses ensemble anomaly detectors that flag embedding vectors distant from learned normal patterns of typical user behavior across the database (out-of-scope queries). Second, the supervised method uses fine-tuned transformer-based models to detect internal attacks with high precision (in-scope queries), using role-labeled classification, even on limited labeled SQL data. Our findings make a significant contribution by providing an effective solution for safeguarding critical database systems from sophisticated threats.", "date": "2025-08-06", "categories": []}
{"id": "2508.09178", "title": "IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection", "abstract": "Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific applications. Although Vision-Language Models (VLMs) demonstrate significant advantages in generalization capabilities, their performance in industrial anomaly detection remains limited. To address this challenge, we propose IAD-R1, a universal post-training framework applicable to VLMs of different architectures and parameter scales, which substantially enhances their anomaly detection capabilities. IAD-R1 employs a two-stage training strategy: the Perception Activation Supervised Fine-Tuning (PA-SFT) stage utilizes a meticulously constructed high-quality Chain-of-Thought dataset (Expert-AD) for training, enhancing anomaly perception capabilities and establishing reasoning-to-answer correlations; the Structured Control Group Relative Policy Optimization (SC-GRPO) stage employs carefully designed reward functions to achieve a capability leap from \"Anomaly Perception\" to \"Anomaly Interpretation\". Experimental results demonstrate that IAD-R1 achieves significant improvements across 7 VLMs, the largest improvement was on the DAGM dataset, with average accuracy 43.3% higher than the 0.5B baseline. Notably, the 0.5B parameter model trained with IAD-R1 surpasses commercial models including GPT-4.1 and Claude-Sonnet-4 in zero-shot settings, demonstrating the effectiveness and superiority of IAD-R1. The dataset, code, and all model weights will be publicly available at https://github.com/Yanhui-Lee/IAD-R1.", "date": "2025-08-07", "categories": []}
{"id": "2508.10255", "title": "Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling", "abstract": "This paper proposes an anomaly detection method based on federated learning to address key challenges in multi-tenant cloud environments, including data privacy leakage, heterogeneous resource behavior, and the limitations of centralized modeling. The method establishes a federated training framework involving multiple tenants. Each tenant trains the model locally using private resource usage data. Through parameter aggregation, a global model is optimized, enabling cross-tenant collaborative anomaly detection while preserving data privacy. To improve adaptability to diverse resource usage patterns, a personalized parameter adjustment mechanism is introduced. This allows the model to retain tenant-specific feature representations while sharing global knowledge. In the model output stage, the Mahalanobis distance is used to compute anomaly scores. This enhances both the accuracy and stability of anomaly detection. The experiments use real telemetry data from a cloud platform to construct a simulated multi-tenant environment. The study evaluates the model's performance under varying participation rates and noise injection levels. These comparisons demonstrate the proposed method's robustness and detection accuracy. Experimental results show that the proposed method outperforms existing mainstream models across key metrics such as Precision, Recall, and F1-Score. It also maintains stable performance in various complex scenarios. These findings highlight the method's practical potential for intelligent resource monitoring and anomaly diagnosis in cloud computing environments.", "date": "2025-08-14", "categories": []}
{"id": "2508.10681", "title": "IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning", "abstract": "Few-Shot Industrial Anomaly Detection (FS-IAD) has important applications in automating industrial quality inspection. Recently, some FS-IAD methods based on Large Vision-Language Models (LVLMs) have been proposed with some achievements through prompt learning or fine-tuning. However, existing LVLMs focus on general tasks but lack basic industrial knowledge and reasoning capabilities related to FS-IAD, making these methods far from specialized human quality inspectors. To address these challenges, we propose a unified framework, IADGPT, designed to perform FS-IAD in a human-like manner, while also handling associated localization and reasoning tasks, even for diverse and novel industrial products. To this end, we introduce a three-stage progressive training strategy inspired by humans. Specifically, the first two stages gradually guide IADGPT in acquiring fundamental industrial knowledge and discrepancy awareness. In the third stage, we design an in-context learning-based training paradigm, enabling IADGPT to leverage a few-shot image as the exemplars for improved generalization to novel products. In addition, we design a strategy that enables IADGPT to output image-level and pixel-level anomaly scores using the logits output and the attention map, respectively, in conjunction with the language output to accomplish anomaly reasoning. To support our training, we present a new dataset comprising 100K images across 400 diverse industrial product categories with extensive attribute-level textual annotations. Experiments indicate IADGPT achieves considerable performance gains in anomaly detection and demonstrates competitiveness in anomaly localization and reasoning. We will release our dataset in camera-ready.", "date": "2025-08-14", "categories": []}
{"id": "2508.10785", "title": "Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. However, fairness considerations in GAD remain largely underexplored. Indeed, GNN-based GAD models can inherit and amplify biases present in training data, potentially leading to unfair outcomes. While existing efforts have focused on developing fair GNNs, most approaches target node classification tasks, where models often rely on simple layer architectures rather than autoencoder-based structures, which are the most widely used architecturs for anomaly detection. To address fairness in autoencoder-based GAD models, we propose \\textbf{D}is\\textbf{E}ntangled \\textbf{C}ounterfactual \\textbf{A}dversarial \\textbf{F}air (DECAF)-GAD, a framework that alleviates bias while preserving GAD performance. Specifically, we introduce a structural causal model (SCM) to disentangle sensitive attributes from learned representations. Based on this causal framework, we formulate a specialized autoencoder architecture along with a fairness-guided loss function. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that DECAF-GAD not only achieves competitive anomaly detection performance but also significantly enhances fairness metrics compared to baseline GAD methods. Our code is available at https://github.com/Tlhey/decaf_code.", "date": "2025-08-14", "categories": []}
{"id": "2508.09401", "title": "Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery", "abstract": "This study proposes an unsupervised anomaly detection method for distributed backend service systems, addressing practical challenges such as complex structural dependencies, diverse behavioral evolution, and the absence of labeled data. The method constructs a dynamic graph based on service invocation relationships and applies graph convolution to extract high-order structural representations from multi-hop topologies. A Transformer is used to model the temporal behavior of each node, capturing long-term dependencies and local fluctuations. During the feature fusion stage, a learnable joint embedding mechanism integrates structural and behavioral representations into a unified anomaly vector. A nonlinear mapping is then applied to compute anomaly scores, enabling an end-to-end detection process without supervision. Experiments on real-world cloud monitoring data include sensitivity analyses across different graph depths, sequence lengths, and data perturbations. Results show that the proposed method outperforms existing models on several key metrics, demonstrating stronger expressiveness and stability in capturing anomaly propagation paths and modeling dynamic behavior sequences, with high potential for practical deployment.", "date": "2025-08-13", "categories": []}
{"id": "2508.09477", "title": "CLIP-Flow: A Universal Discriminator for AI-Generated Images Inspired by Anomaly Detection", "abstract": "With the rapid advancement of AI generative models, the visual quality of AI-generated images (AIIs) has become increasingly close to natural images, which inevitably raises security concerns. Most AII detectors often employ the conventional image classification pipeline with natural images and AIIs (generated by a generative model), which can result in limited detection performance for AIIs from unseen generative models. To solve this, we proposed a universal AI-generated image detector from the perspective of anomaly detection. Our discriminator does not need to access any AIIs and learn a generalizable representation with unsupervised learning. Specifically, we use the pre-trained CLIP encoder as the feature extractor and design a normalizing flow-like unsupervised model. Instead of AIIs, proxy images, e.g., obtained by applying a spectral modification operation on natural images, are used for training. Our models are trained by minimizing the likelihood of proxy images, optionally combined with maximizing the likelihood of natural images. Extensive experiments demonstrate the effectiveness of our method on AIIs produced by various image generators.", "date": "2025-08-13", "categories": []}
{"id": "2508.09504", "title": "Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems", "abstract": "With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns. Traditional methods -- statistical, density-based, and graph-based models struggle with distribution shifts and class imbalance in multivariate time series, often leading to high false positive rates. To address these challenges, we propose CGAD, a Causal Graph-based Anomaly Detection framework designed for reliable cyberattack detection in public infrastructure systems. CGAD follows a two-phase supervised framework -- causal profiling and anomaly scoring. First, it learns causal invariant graph structures representing the system's behavior under \"Normal\" and \"Attack\" states using Dynamic Bayesian Networks. Second, it employs structural divergence to detect anomalies via causal graph comparison by evaluating topological deviations in causal graphs over time. By leveraging causal structures, CGAD achieves superior adaptability and accuracy in non-stationary and imbalanced time series environments compared to conventional machine learning approaches. By uncovering causal structures beneath volatile sensor data, our framework not only detects cyberattacks with markedly higher precision but also redefines robustness in anomaly detection, proving resilience where traditional models falter under imbalance and drift. Our framework achieves substantial gains in F1 and ROC-AUC scores over best-performing baselines across four industrial datasets, demonstrating robust detection of delayed and structurally complex anomalies.", "date": "2025-08-13", "categories": []}
{"id": "2508.09660", "title": "Anomaly Detection for IoT Global Connectivity", "abstract": "Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it has become increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a reactive approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR -- an unsupervised anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic. We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.", "date": "2025-08-13", "categories": []}
{"id": "2508.09894", "title": "Rare anomalies require large datasets: About proving the existence of anomalies", "abstract": "Detecting whether any anomalies exist within a dataset is crucial for effective anomaly detection, yet it remains surprisingly underexplored in anomaly detection literature. This paper presents a comprehensive study that addresses the fundamental question: When can we conclusively determine that anomalies are present? Through extensive experimentation involving over three million statistical tests across various anomaly detection tasks and algorithms, we identify a relationship between the dataset size, contamination rate, and an algorithm-dependent constant $ \\alpha_{\\text{algo}} $. Our results demonstrate that, for an unlabeled dataset of size $ N $ and contamination rate $ \\nu $, the condition $ N \\ge \\frac{\\alpha_{\\text{algo}}}{\\nu^2} $ represents a lower bound on the number of samples required to confirm anomaly existence. This threshold implies a limit to how rare anomalies can be before proving their existence becomes infeasible.", "date": "2025-08-13", "categories": []}
{"id": "2503.13184", "title": "Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process", "abstract": "Although recent methods have tried to introduce large multimodal models (LMMs) into industrial anomaly detection (IAD), their generalization in the IAD field is far inferior to that for general purposes. We summarize the main reasons for this gap into two aspects. On one hand, general-purpose LMMs lack cognition of defects in the visual modality, thereby failing to sufficiently focus on defect areas. Therefore, we propose to modify the AnyRes structure of the LLaVA model, providing the potential anomalous areas identified by existing IAD models to the LMMs. On the other hand, existing methods mainly focus on identifying defects by learning defect patterns or comparing with normal samples, yet they fall short of understanding the causes of these defects. Considering that the generation of defects is closely related to the manufacturing process, we propose a manufacturing-driven IAD paradigm. An instruction-tuning dataset for IAD (InstructIAD) and a data organization approach for Chain-of-Thought with manufacturing (CoT-M) are designed to leverage the manufacturing process for IAD. Based on the above two modifications, we present Triad, a novel LMM-based method incorporating an expert-guided region-of-interest tokenizer and manufacturing process for industrial anomaly detection. Extensive experiments show that our Triad not only demonstrates competitive performance against current LMMs but also achieves further improved accuracy when equipped with manufacturing processes. Source code, training data, and pre-trained models will be publicly available at https://github.com/tzjtatata/Triad.", "date": "2025-03-17", "categories": []}
{"id": "2508.08593", "title": "Generative AI for Critical Infrastructure in Smart Grids: A Unified Framework for Synthetic Data Generation and Anomaly Detection", "abstract": "In digital substations, security events pose significant challenges to the sustained operation of power systems. To mitigate these challenges, the implementation of robust defense strategies is critically important. A thorough process of anomaly identification and detection in information and communication technology (ICT) frameworks is crucial to ensure secure and reliable communication and coordination between interconnected devices within digital substations. Hence, this paper addresses the critical cybersecurity challenges confronting IEC61850-based digital substations within modern smart grids, where the integration of advanced communication protocols, e.g., generic object-oriented substation event (GOOSE), has enhanced energy management and introduced significant vulnerabilities to cyberattacks. Focusing on the limitations of traditional anomaly detection systems (ADSs) in detecting threats, this research proposes a transformative approach by leveraging generative AI (GenAI) to develop robust ADSs. The primary contributions include the suggested advanced adversarial traffic mutation (AATM) technique to generate synthesized and balanced datasets for GOOSE messages, ensuring protocol compliance and enabling realistic zero-day attack pattern creation to address data scarcity. Then, the implementation of GenAI-based ADSs incorporating the task-oriented dialogue (ToD) processes has been explored for improved detection of attack patterns. Finally, a comparison of the GenAI-based ADS with machine learning (ML)-based ADSs has been implemented to showcase the outperformance of the GenAI-based frameworks considering the AATM-generated GOOSE datasets and standard/advanced performance evaluation metrics.", "date": "2025-08-12", "categories": []}
{"id": "2508.09058", "title": "ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds", "abstract": "Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active learning framework tailored for VAD, designed for adapting to the ever-changing real-world conditions. Our approach leverages active learning to continuously select the most informative data points for labeling, thereby enhancing model adaptability. A critical innovation is the incorporation of a human-in-the-loop mechanism, which enables the identification of actual normal and anomalous instances from pseudo-labeling results generated by AI. This collected data allows the framework to define an adaptive threshold tailored to different environments, ensuring that the system remains effective as the definition of 'normal' shifts across various settings. Implemented within a lab-based framework that simulates real-world conditions, our approach allows rigorous testing and refinement of VAD algorithms with a new metric. Experimental results show that our method achieves an EBI (Error Balance Index) of 68.91 for Q3 in real-world simulated scenarios, demonstrating its practical effectiveness and significantly enhancing the applicability of VAD in dynamic environments.", "date": "2025-08-12", "categories": []}
{"id": "2407.11935", "title": "Learning Multi-view Anomaly Detection with Efficient Adaptive Selection", "abstract": "This study explores the recently proposed and challenging multi-view Anomaly Detection (AD) task. Single-view tasks will encounter blind spots from other perspectives, resulting in inaccuracies in sample-level prediction. Therefore, we introduce the Multi-View Anomaly Detection (MVAD) approach, which learns and integrates features from multi-views. Specifically, we propose a Multi-View Adaptive Selection (MVAS) algorithm for feature learning and fusion across multiple views. The feature maps are divided into neighbourhood attention windows to calculate a semantic correlation matrix between single-view windows and all other views, which is an attention mechanism conducted for each single-view window and the top-k most correlated multi-view windows. Adjusting the window sizes and top-k can minimise the complexity to O((hw)^4/3). Extensive experiments on the Real-IAD dataset under the multi-class setting validate the effectiveness of our approach, achieving state-of-the-art performance with an average improvement of +2.5 across 10 metrics at the sample/image/pixel levels, using only 18M parameters and requiring fewer FLOPs and training time. The codes are available at https://github.com/lewandofskee/MVAD.", "date": "2024-07-16", "categories": []}
{"id": "2504.11055", "title": "Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection", "abstract": "Anomaly Detection involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection. Traditional AD methods typically require the availability of normal training samples; however, this assumption is not always feasible. Recently, the rich pretraining knowledge of CLIP has shown promising zero-shot generalization in detecting anomalies without the need for training samples from target domains. However, CLIP's coarse-grained image-text alignment limits localization and detection performance for fine-grained anomalies due to: (1) spatial misalignment, and (2) the limited sensitivity of global features to local anomalous patterns. In this paper, we propose Crane which tackles both problems. First, we introduce a correlation-based attention module to retain spatial alignment more accurately. Second, to boost the model's awareness of fine-grained anomalies, we condition the learnable prompts of the text encoder on image context extracted from the vision encoder and perform a local-to-global representation fusion. Moreover, our method can incorporate vision foundation models such as DINOv2 to further enhance spatial understanding and localization. The key insight of Crane is to balance learnable adaptations for modeling anomalous concepts with non-learnable adaptations that preserve and exploit generalized pretrained knowledge, thereby minimizing in-domain overfitting and maximizing performance on unseen domains. Extensive evaluation across 14 diverse industrial and medical datasets demonstrates that Crane consistently improves the state-of-the-art ZSAD from 2% to 28%, at both image and pixel levels, while remaining competitive in inference speed. The code is available at https://github.com/AlirezaSalehy/Crane.", "date": "2025-04-15", "categories": []}
{"id": "2506.19694", "title": "UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation", "abstract": "Precise anomaly detection in medical images is critical for clinical decision-making. While recent unsupervised or semi-supervised anomaly detection methods trained on large-scale normal data show promising results, they lack fine-grained differentiation, such as benign vs. malignant tumors. Additionally, ultrasound (US) imaging is highly sensitive to devices and acquisition parameter variations, creating significant domain gaps in the resulting US images. To address these challenges, we propose UltraAD, a vision-language model (VLM)-based approach that leverages few-shot US examples for generalized anomaly localization and fine-grained classification. To enhance localization performance, the image-level token of query visual prototypes is first fused with learnable text embeddings. This image-informed prompt feature is then further integrated with patch-level tokens, refining local representations for improved accuracy. For fine-grained classification, a memory bank is constructed from few-shot image samples and corresponding text descriptions that capture anatomical and abnormality-specific features. During training, the stored text embeddings remain frozen, while image features are adapted to better align with medical data. UltraAD has been extensively evaluated on three breast US datasets, outperforming state-of-the-art methods in both lesion localization and fine-grained medical classification. The code will be released upon acceptance.", "date": "2025-06-24", "categories": []}
{"id": "2506.19877", "title": "Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017", "abstract": "Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. Our results show that supervised MLP and CNN achieve near-perfect accuracy on familiar attacks but suffer drastic recall drops on novel attacks. Unsupervised LOF attains moderate overall accuracy and high recall on unknown threats at the cost of elevated false alarms, while boundary-based OCSVM balances precision and recall best, demonstrating robust detection across both scenarios. These findings offer practical guidance for selecting IDS models in dynamic network environments.", "date": "2025-06-23", "categories": []}
{"id": "2508.01591", "title": "Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection", "abstract": "In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within test images to enhance anomaly discrimination. Unlike conventional methods that depend solely on pre-trained features from normal training data, SNARM dynamically refines anomaly detection by iteratively comparing test patches against adaptively selected in-image references. Specifically, we first compute the ``inter-residuals'' features by contrasting test image patches with the training feature bank. Patches exhibiting small-norm residuals (indicating high normality) are then utilized as self-generated reference patches to compute ``intra-residuals'', amplifying discriminative signals. These inter- and intra-residual features are concatenated and fed into a novel Mamba module with multiple heads, which are dynamically navigated by residual properties to focus on anomalous regions. Finally, AD results are obtained by aggregating the outputs of a self-navigated Mamba in an ensemble learning paradigm. Extensive experiments on MVTec AD, MVTec 3D, and VisA benchmarks demonstrate that SNARM achieves state-of-the-art (SOTA) performance, with notable improvements in all metrics, including Image-AUROC, Pixel-AURC, PRO, and AP.", "date": "2025-08-03", "categories": []}
{"id": "2508.06638", "title": "Segmented Confidence Sequences and Multi-Scale Adaptive Confidence Segments for Anomaly Detection in Nonstationary Time Series", "abstract": "As time series data become increasingly prevalent in domains such as manufacturing, IT, and infrastructure monitoring, anomaly detection must adapt to nonstationary environments where statistical properties shift over time. Traditional static thresholds are easily rendered obsolete by regime shifts, concept drift, or multi-scale changes. To address these challenges, we introduce and empirically evaluate two novel adaptive thresholding frameworks: Segmented Confidence Sequences (SCS) and Multi-Scale Adaptive Confidence Segments (MACS). Both leverage statistical online learning and segmentation principles for local, contextually sensitive adaptation, maintaining guarantees on false alarm rates even under evolving distributions. Our experiments across Wafer Manufacturing benchmark datasets show significant F1-score improvement compared to traditional percentile and rolling quantile approaches. This work demonstrates that robust, statistically principled adaptive thresholds enable reliable, interpretable, and timely detection of diverse real-world anomalies.", "date": "2025-08-08", "categories": []}
{"id": "2508.07049", "title": "Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation", "abstract": "Anomaly detection (AD) plays a vital role across a wide range of domains, but its performance might deteriorate when applied to target domains with limited data. Domain Adaptation (DA) offers a solution by transferring knowledge from a related source domain with abundant data. However, this adaptation process can introduce additional uncertainty, making it difficult to draw statistically valid conclusions from AD results. In this paper, we propose STAND-DA -- a novel framework for statistically rigorous Autoencoder-based AD after Representation Learning-based DA. Built on the Selective Inference (SI) framework, STAND-DA computes valid $p$-values for detected anomalies and rigorously controls the false positive rate below a pre-specified level $\\alpha$ (e.g., 0.05). To address the computational challenges of applying SI to deep learning models, we develop the GPU-accelerated SI implementation, significantly enhancing both scalability and runtime performance. This advancement makes SI practically feasible for modern, large-scale deep architectures. Extensive experiments on synthetic and real-world datasets validate the theoretical results and computational efficiency of the proposed STAND-DA method.", "date": "2025-08-09", "categories": []}
{"id": "2508.07923", "title": "Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection", "abstract": "Generative AI holds great potentials to automate and enhance data synthesis in nuclear medicine. However, the high-stakes nature of biomedical imaging necessitates robust mechanisms to detect and manage unexpected or erroneous model behavior. We introduce development and implementation of a hybrid anomaly detection framework to safeguard GenAI models in BIOEMTECH's eyes(TM) systems. Two applications are demonstrated: Pose2Xray, which generates synthetic X-rays from photographic mouse images, and DosimetrEYE, which estimates 3D radiation dose maps from 2D SPECT/CT scans. In both cases, our outlier detection (OD) enhances reliability, reduces manual oversight, and supports real-time quality control. This approach strengthens the industrial viability of GenAI in preclinical settings by increasing robustness, scalability, and regulatory compliance.", "date": "2025-08-11", "categories": []}
{"id": "2508.08029", "title": "Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks", "abstract": "The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security challenges, such as data manipulation attacks on the semi-standardised Shared Data Layer (SDL) within the O-RAN platform through malicious xApps. In particular, malicious xApps can exploit this vulnerability by introducing subtle Unicode-wise alterations (hypoglyphs) into the data that are being used by traditional machine learning (ML)-based anomaly detection methods. These Unicode-wise manipulations can potentially bypass detection and cause failures in anomaly detection systems based on traditional ML, such as AutoEncoders, which are unable to process hypoglyphed data without crashing. We investigate the use of Large Language Models (LLMs) for anomaly detection within the O-RAN architecture to address this challenge. We demonstrate that LLM-based xApps maintain robust operational performance and are capable of processing manipulated messages without crashing. While initial detection accuracy requires further improvements, our results highlight the robustness of LLMs to adversarial attacks such as hypoglyphs in input data. There is potential to use their adaptability through prompt engineering to further improve the accuracy, although this requires further research. Additionally, we show that LLMs achieve low detection latency (under 0.07 seconds), making them suitable for Near-Real-Time (Near-RT) RIC deployments.", "date": "2025-08-11", "categories": []}
{"id": "2312.09478", "title": "Entropy Causal Graphs for Multivariate Time Series Anomaly Detection", "abstract": "Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal relationship among variables and degrading anomaly detection performance. This work proposes a novel framework called CGAD, an entropy Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes transfer entropy to construct graph structures that unveil the underlying causal relationships among time series data. Weighted graph convolutional networks combined with causal convolutions are employed to model both the causal graph structures and the temporal patterns within multivariate time series data. Furthermore, CGAD applies anomaly scoring, leveraging median absolute deviation-based normalization to improve the robustness of the anomaly identification process. Extensive experiments demonstrate that CGAD outperforms state-of-the-art methods on real-world datasets with a 9% average improvement in terms of three different multivariate time series anomaly detection metrics.", "date": "2023-12-15", "categories": []}
{"id": "2508.05696", "title": "Log2Sig: Frequency-Aware Insider Threat Detection via Multivariate Behavioral Signal Decomposition", "abstract": "Insider threat detection presents a significant challenge due to the deceptive nature of malicious behaviors, which often resemble legitimate user operations. However, existing approaches typically model system logs as flat event sequences, thereby failing to capture the inherent frequency dynamics and multiscale disturbance patterns embedded in user behavior. To address these limitations, we propose Log2Sig, a robust anomaly detection framework that transforms user logs into multivariate behavioral frequency signals, introducing a novel representation of user behavior. Log2Sig employs Multivariate Variational Mode Decomposition (MVMD) to extract Intrinsic Mode Functions (IMFs), which reveal behavioral fluctuations across multiple temporal scales. Based on this, the model further performs joint modeling of behavioral sequences and frequency-decomposed signals: the daily behavior sequences are encoded using a Mamba-based temporal encoder to capture long-term dependencies, while the corresponding frequency components are linearly projected to match the encoder's output dimension. These dual-view representations are then fused to construct a comprehensive user behavior profile, which is fed into a multilayer perceptron for precise anomaly detection. Experimental results on the CERT r4.2 and r5.2 datasets demonstrate that Log2Sig significantly outperforms state-of-the-art baselines in both accuracy and F1 score.", "date": "2025-08-06", "categories": []}
{"id": "2508.06203", "title": "AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection", "abstract": "Anomaly detection is a critical task across numerous domains and modalities, yet existing methods are often highly specialized, limiting their generalizability. These specialized models, tailored for specific anomaly types like textural defects or logical errors, typically exhibit limited performance when deployed outside their designated contexts. To overcome this limitation, we propose AnomalyMoE, a novel and universal anomaly detection framework based on a Mixture-of-Experts (MoE) architecture. Our key insight is to decompose the complex anomaly detection problem into three distinct semantic hierarchies: local structural anomalies, component-level semantic anomalies, and global logical anomalies. AnomalyMoE correspondingly employs three dedicated expert networks at the patch, component, and global levels, and is specialized in reconstructing features and identifying deviations at its designated semantic level. This hierarchical design allows a single model to concurrently understand and detect a wide spectrum of anomalies. Furthermore, we introduce an Expert Information Repulsion (EIR) module to promote expert diversity and an Expert Selection Balancing (ESB) module to ensure the comprehensive utilization of all experts. Experiments on 8 challenging datasets spanning industrial imaging, 3D point clouds, medical imaging, video surveillance, and logical anomaly detection demonstrate that AnomalyMoE establishes new state-of-the-art performance, significantly outperforming specialized methods in their respective domains.", "date": "2025-08-08", "categories": []}
{"id": "2508.06318", "title": "Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD) paradigm, only video-level labels are provided during training, while predictions are made at the frame level. Although state-of-the-art models perform well on simple anomalies (e.g., explosions), they struggle with complex real-world events (e.g., shoplifting). This difficulty stems from two key issues: (1) the inability of current models to address the diversity of anomaly types, as they process all categories with a shared model, overlooking category-specific features; and (2) the weak supervision signal, which lacks precise temporal information, limiting the ability to capture nuanced anomalous patterns blended with normal events. To address these challenges, we propose Gaussian Splatting-guided Mixture of Experts (GS-MoE), a novel framework that employs a set of expert models, each specialized in capturing specific anomaly types. These experts are guided by a temporal Gaussian splatting loss, enabling the model to leverage temporal consistency and enhance weak supervision. The Gaussian splatting approach encourages a more precise and comprehensive representation of anomalies by focusing on temporal segments most likely to contain abnormal events. The predictions from these specialized experts are integrated through a mixture-of-experts mechanism to model complex relationships across diverse anomaly patterns. Our approach achieves state-of-the-art performance, with a 91.58% AUC on the UCF-Crime dataset, and demonstrates superior results on XD-Violence and MSAD datasets. By leveraging category-specific expertise and temporal guidance, GS-MoE sets a new benchmark for VAD under weak supervision.", "date": "2025-08-08", "categories": []}
{"id": "2406.17537", "title": "SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder", "abstract": "Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately the 1 \\% of the population. These patients face significant risks, underscoring the need for reliable, continuous seizure monitoring in daily life. Most of the techniques discussed in the literature rely on supervised Machine Learning (ML) methods. However, the challenge of accurately labeling variations in epileptic EEG waveforms complicates the use of these approaches. Additionally, the rarity of ictal events introduces an high imbalancing within the data, which could lead to poor prediction performance in supervised learning approaches. Instead, a semi-supervised approach allows to train the model only on data not containing seizures, thus avoiding the issues related to the data imbalancing. This work proposes a semi-supervised approach for detecting epileptic seizures from EEG data, utilizing a novel Deep Learning-based method called SincVAE. This proposal incorporates the learning of an ad-hoc array of bandpass filter as a first layer of a Variational Autoencoder (VAE), potentially eliminating the preprocessing stage where informative band frequencies are identified and isolated. Results indicate that SincVAE improves seizure detection in EEG data and is capable of identifying early seizures during the preictal stage as well as monitoring patients throughout the postictal stage.", "date": "2024-06-25", "categories": []}
{"id": "2412.03930", "title": "GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model", "abstract": "Anomaly detection on text-rich graphs is widely prevalent in real life, such as detecting incorrectly assigned academic papers to authors and detecting bots in social networks. The remarkable capabilities of large language models (LLMs) pave a new revenue by utilizing rich-text information for effective anomaly detection. However, simply introducing rich texts into LLMs can obscure essential detection cues and introduce high fine-tuning costs. Moreover, LLMs often overlook the intrinsic structural bias of graphs which is vital for distinguishing normal from abnormal node patterns. To this end, this paper introduces GuARD, a text-rich and graph-informed language model that combines key structural features from graph-based methods with fine-grained semantic attributes extracted via small language models for effective anomaly detection on text-rich graphs. GuARD is optimized with the progressive multi-modal multi-turn instruction tuning framework in the task-guided instruction tuning regime tailed to incorporate both rich-text and structural modalities. Extensive experiments on four datasets reveal that GuARD outperforms graph-based and LLM-based anomaly detection methods, while offering up to 5$\\times$ times speedup in training and 5$\\times$ times speedup in inference over vanilla long-context LLMs on the large-scale WhoIsWho dataset.", "date": "2024-12-05", "categories": []}
{"id": "2504.08632", "title": "Deep Learning Methods for Detecting Thermal Runaway Events in Battery Production Lines", "abstract": "One of the key safety considerations of battery manufacturing is thermal runaway, the uncontrolled increase in temperature which can lead to fires, explosions, and emissions of toxic gasses. As such, development of automated systems capable of detecting such events is of considerable importance in both academic and industrial contexts. In this work, we investigate the use of deep learning for detecting thermal runaway in the battery production line of VDL Nedcar, a Dutch automobile manufacturer. Specifically, we collect data from the production line to represent both baseline (non thermal runaway) and thermal runaway conditions. Thermal runaway was simulated through the use of external heat and smoke sources. The data consisted of both optical and thermal images which were then preprocessed and fused before serving as input to our models. In this regard, we evaluated three deep-learning models widely used in computer vision including shallow convolutional neural networks, residual neural networks, and vision transformers on two performance metrics. Furthermore, we evaluated these models using explainability methods to gain insight into their ability to capture the relevant feature information from their inputs. The obtained results indicate that the use of deep learning is a viable approach to thermal runaway detection in battery production lines.", "date": "2025-04-11", "categories": []}
{"id": "2508.02903", "title": "RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation", "abstract": "Recent advancements in diffusion models have demonstrated significant success in unsupervised anomaly segmentation. For anomaly segmentation, these models are first trained on normal data; then, an anomalous image is noised to an intermediate step, and the normal image is reconstructed through backward diffusion. Unlike traditional statistical methods, diffusion models do not rely on specific assumptions about the data or target anomalies, making them versatile for use across different domains. However, diffusion models typically assume access to normal data for training, limiting their applicability in realistic settings. In this paper, we propose novel robust denoising diffusion models for scenarios where only contaminated (i.e., a mix of normal and anomalous) unlabeled data is available. By casting maximum likelihood estimation of the data as a nonlinear regression problem, we reinterpret the denoising diffusion probabilistic model through a regression lens. Using robust regression, we derive a robust version of denoising diffusion probabilistic models. Our novel framework offers flexibility in constructing various robust diffusion models. Our experiments show that our approach outperforms current state of the art diffusion models, for unsupervised anomaly segmentation when only contaminated data is available. Our method outperforms existing diffusion-based approaches, achieving up to 8.08\\% higher AUROC and 10.37\\% higher AUPRC on MVTec datasets. The implementation code is available at: https://github.com/mehrdadmoradi124/RDDPM", "date": "2025-08-04", "categories": []}
{"id": "2508.05503", "title": "AutoIAD: Manager-Driven Multi-Agent Collaboration for Automated Industrial Anomaly Detection", "abstract": "Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This paper introduces AutoIAD, a multi-agent collaboration framework, specifically designed for end-to-end automated development of industrial visual anomaly detection. AutoIAD leverages a Manager-Driven central agent to orchestrate specialized sub-agents (including Data Preparation, Data Loader, Model Designer, Trainer) and integrates a domain-specific knowledge base, which intelligently handles the entire pipeline using raw industrial image data to develop a trained anomaly detection model. We construct a comprehensive benchmark using MVTec AD datasets to evaluate AutoIAD across various LLM backends. Extensive experiments demonstrate that AutoIAD significantly outperforms existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks in task completion rate and model performance (AUROC), while effectively mitigating issues like hallucination through iterative refinement. Ablation studies further confirm the crucial roles of the Manager central agent and the domain knowledge base module in producing robust and high-quality IAD solutions.", "date": "2025-08-07", "categories": []}
{"id": "2503.19146", "title": "Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants", "abstract": "Efficient and reliable operation of Concentrated Solar Power (CSP) plants is essential for meeting the growing demand for sustainable energy. However, high-temperature solar receivers face severe operational risks, such as freezing, deformation, and corrosion, resulting in costly downtime and maintenance. To monitor CSP plants, cameras mounted on solar receivers record infrared images at irregular intervals ranging from one to five minutes throughout the day. Anomalous images can be detected by thresholding an anomaly score, where the threshold is chosen to optimize metrics such as the F1-score on a validation set. This work proposes a framework, using risk control, for generating more reliable decision thresholds with finite-sample coverage guarantees on any chosen risk function. Our framework also incorporates an abstention mechanism, allowing high-risk predictions to be deferred to domain experts. Second, we propose a density forecasting method to estimate the likelihood of an observed image given a sequence of previously observed images, using this likelihood as its anomaly score. Third, we analyze the deployment results of our framework across multiple training scenarios over several months for two CSP plants. This analysis provides valuable insights to our industry partner for optimizing maintenance operations. Finally, given the confidential nature of our dataset, we provide an extended simulated dataset, leveraging recent advancements in generative modeling to create diverse thermal images that simulate multiple CSP plants. Our code is publicly available.", "date": "2025-03-24", "categories": []}
{"id": "2504.06740", "title": "MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning", "abstract": "Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial to know the distinct type of defect, such as a bent, cut, or scratch. The ability to recognize the \"exact\" defect type enables automated treatments of the anomalies in modern production lines. Current methods are limited to solely detecting whether a product is defective or not without providing any insights on the defect type, nevertheless detecting and identifying multiple defects. We propose MultiADS, a zero-shot learning approach, able to perform Multi-type Anomaly Detection and Segmentation. The architecture of MultiADS comprises CLIP and extra linear layers to align the visual- and textual representation in a joint feature space. To the best of our knowledge, our proposal, is the first approach to perform a multi-type anomaly segmentation task in zero-shot learning. Contrary to the other baselines, our approach i) generates specific anomaly masks for each distinct defect type, ii) learns to distinguish defect types, and iii) simultaneously identifies multiple defect types present in an anomalous product. Additionally, our approach outperforms zero/few-shot learning SoTA methods on image-level and pixel-level anomaly detection and segmentation tasks on five commonly used datasets: MVTec-AD, Visa, MPDD, MAD and Real-IAD.", "date": "2025-04-09", "categories": []}
{"id": "2508.03921", "title": "Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data", "abstract": "This paper examines the effectiveness of combining active learning and transfer learning for anomaly detection in cross-domain time-series data. Our results indicate that there is an interaction between clustering and active learning and in general the best performance is achieved using a single cluster (in other words when clustering is not applied). Also, we find that adding new samples to the training set using active learning does improve model performance but that in general, the rate of improvement is slower than the results reported in the literature suggest. We attribute this difference to an improved experimental design where distinct data samples are used for the sampling and testing pools. Finally, we assess the ceiling performance of transfer learning in combination with active learning across several datasets and find that performance does initially improve but eventually begins to tail off as more target points are selected for inclusion in training. This tail-off in performance may indicate that the active learning process is doing a good job of sequencing data points for selection, pushing the less useful points towards the end of the selection process and that this tail-off occurs when these less useful points are eventually added. Taken together our results indicate that active learning is effective but that the improvement in model performance follows a linear flat function concerning the number of points selected and labelled.", "date": "2025-08-05", "categories": []}
{"id": "2508.04175", "title": "AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization", "abstract": "While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrained by domain adaptation challenges. Existing Group Relative Policy Optimization (GRPO) based approaches suffer from two critical limitations: inadequate training data utilization when models produce uniform responses, and insufficient supervision over reasoning processes that encourage immediate binary decisions without deliberative analysis. We propose a comprehensive framework addressing these limitations through two synergistic innovations. First, we introduce a multi-stage deliberative reasoning process that guides models from region identification to focused examination, generating diverse response patterns essential for GRPO optimization while enabling structured supervision over analytical workflows. Second, we develop a fine-grained reward mechanism incorporating classification accuracy and localization supervision, transforming binary feedback into continuous signals that distinguish genuine analytical insight from spurious correctness. Comprehensive evaluation across multiple industrial datasets demonstrates substantial performance improvements in adapting general vision-language models to specialized anomaly detection. Our method achieves superior accuracy with efficient adaptation of existing annotations, effectively bridging the gap between general-purpose MLLM capabilities and the fine-grained visual discrimination required for detecting subtle manufacturing defects and structural irregularities.", "date": "2025-08-06", "categories": []}
{"id": "2508.04630", "title": "CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series", "abstract": "Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly generation in time series data. In addition, time series anomaly detection often faces several data-related inherent challenges, i.e., label scarcity, data imbalance, and complex multi-periodicity. In this paper, we leverage causal tools and introduce a new causality-based framework, CaPulse, which tunes in to the underlying causal pulse of time series data to effectively detect anomalies. Concretely, we begin by building a structural causal model to decipher the generation processes behind anomalies. To tackle the challenges posed by the data, we propose Periodical Normalizing Flows with a novel mask mechanism and carefully designed periodical learners, creating a periodicity-aware, density-based anomaly detection approach. Extensive experiments on seven real-world datasets demonstrate that CaPulse consistently outperforms existing methods, achieving AUROC improvements of 3% to 17%, with enhanced interpretability.", "date": "2025-08-06", "categories": []}
{"id": "2501.11638", "title": "Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model", "abstract": "Class imbalance (CI) is a longstanding problem in machine learning, slowing down training and reducing performances. Although empirical remedies exist, it is often unclear which ones work best and when, due to the lack of an overarching theory. We address a common case of imbalance, that of anomaly (or outlier) detection. We provide a theoretical framework to analyze, interpret and address CI. It is based on an exact solution of the teacher-student perceptron model, through replica theory. Within this framework, one can distinguish several sources of CI: either intrinsic, train or test imbalance. Our analysis reveals that the optimal train imbalance is generally different from 50%, with a non trivial dependence on the intrinsic imbalance, the abundance of data and on the noise in the learning. Moreover, there is a crossover between a small noise training regime where results are independent of the noise level to a high noise regime where performances quickly degrade with noise. Our results challenge some of the conventional wisdom on CI and offer practical guidelines to address it.", "date": "2025-01-20", "categories": []}
{"id": "2508.02889", "title": "REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport", "abstract": "Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains challenging due to the intricate structure of brain anatomy and the scarcity of abnormal examples. In this work, we introduce REFLECT, a novel framework that leverages rectified flows to establish a direct, linear trajectory for correcting abnormal MR images toward a normal distribution. By learning a straight, one-step correction transport map, our method efficiently corrects brain anomalies and can precisely localize anomalies by detecting discrepancies between anomalous input and corrected counterpart. In contrast to the diffusion-based UAD models, which require iterative stochastic sampling, rectified flows provide a direct transport map, enabling single-step inference. Extensive experiments on popular UAD brain segmentation benchmarks demonstrate that REFLECT significantly outperforms state-of-the-art unsupervised anomaly detection methods. The code is available at https://github.com/farzad-bz/REFLECT.", "date": "2025-08-04", "categories": []}
{"id": "2508.03436", "title": "AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence", "abstract": "We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~ 22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.", "date": "2025-08-05", "categories": []}
{"id": "2508.03539", "title": "Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection", "abstract": "Despite substantial progress in anomaly synthesis methods, existing diffusion-based and coarse inpainting pipelines commonly suffer from structural deficiencies such as micro-structural discontinuities, limited semantic controllability, and inefficient generation. To overcome these limitations, we introduce ARAS, a language-conditioned, auto-regressive anomaly synthesis approach that precisely injects local, text-specified defects into normal images via token-anchored latent editing. Leveraging a hard-gated auto-regressive operator and a training-free, context-preserving masked sampling kernel, ARAS significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies. Integrated within our Quality-Aware Re-weighted Anomaly Detection (QARAD) framework, we further propose a dynamic weighting strategy that emphasizes high-quality synthetic samples by computing an image-text similarity score with a dual-encoder model. Extensive experiments across three benchmark datasets-MVTec AD, VisA, and BTAD, demonstrate that our QARAD outperforms SOTA methods in both image- and pixel-level anomaly detection tasks, achieving improved accuracy, robustness, and a 5 times synthesis speedup compared to diffusion-based alternatives. Our complete code and synthesized dataset will be publicly available.", "date": "2025-08-05", "categories": []}
{"id": "2508.03594", "title": "CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models", "abstract": "Applying machine learning to real-world medical data, e.g. from hospital archives, has the potential to revolutionize disease detection in brain images. However, detecting pathology in such heterogeneous cohorts is a difficult challenge. Normative modeling, a form of unsupervised anomaly detection, offers a promising approach to studying such cohorts where the ``normal'' behavior is modeled and can be used at subject level to detect deviations relating to disease pathology. Diffusion models have emerged as powerful tools for anomaly detection due to their ability to capture complex data distributions and generate high-quality images. Their performance relies on image restoration; differences between the original and restored images highlight potential abnormalities. However, unlike normative models, these diffusion model approaches do not incorporate clinical information which provides important context to guide the disease detection process. Furthermore, standard approaches often poorly restore healthy regions, resulting in poor reconstructions and suboptimal detection performance. We present CADD, the first conditional diffusion model for normative modeling in 3D images. To guide the healthy restoration process, we propose a novel inference inpainting strategy which balances anomaly removal with retention of subject-specific features. Evaluated on three challenging datasets, including clinical scans, which may have lower contrast, thicker slices, and motion artifacts, CADD achieves state-of-the-art performance in detecting neurological abnormalities in heterogeneous cohorts.", "date": "2025-08-05", "categories": []}
{"id": "2309.16896", "title": "Algorithmic Recourse in Abnormal Multivariate Time Series", "abstract": "Algorithmic recourse provides actionable recommendations to alter unfavorable predictions of machine learning models, enhancing transparency through counterfactual explanations. While significant progress has been made in algorithmic recourse for static data, such as tabular and image data, limited research explores recourse for multivariate time series, particularly for reversing abnormal time series. This paper introduces Recourse in time series Anomaly Detection (RecAD), a framework for addressing anomalies in multivariate time series using backtracking counterfactual reasoning. By modeling the causes of anomalies as external interventions on exogenous variables, RecAD predicts recourse actions to restore normal status as counterfactual explanations, where the recourse function, responsible for generating actions based on observed data, is trained using an end-to-end approach. Experiments on synthetic and real-world datasets demonstrate its effectiveness.", "date": "2023-09-28", "categories": []}
{"id": "2401.09793", "title": "PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection", "abstract": "Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models' representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four distinct MLP Mixers and innovative dual project constraint module, PatchAD mitigates potential model degradation and offers a lightweight solution, requiring only $0.403M$ parameters. Its efficacy is demonstrated by state-of-the-art results across $8$ datasets sourced from different application scenarios, outperforming over $30$ comparative algorithms. PatchAD significantly improves the classical F1 score by 6.84%, the Aff-F1 score by 4.27%, and the V-ROC by 2.49%. Simultaneously, an in-depth analysis of the mechanisms underlying PatchAD has been conducted from both theoretical and experimental perspectives, validating the design motivations of the model. The code is publicly available at https://github.com/EmorZz1G/PatchAD.", "date": "2024-01-18", "categories": []}
{"id": "2406.09016", "title": "Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark", "abstract": "Anomaly detection in complex industrial processes plays a pivotal role in ensuring efficient, stable, and secure operation. Existing anomaly detection methods primarily focus on analyzing dominant anomalies using the process variables (such as arc current) or constructing neural networks based on abnormal visual features, while overlooking the intrinsic correlation of cross-modal information. This paper proposes a cross-modal Transformer (dubbed FmFormer), designed to facilitate anomaly detection by exploring the correlation between visual features (video) and process variables (current) in the context of the fused magnesium smelting process. Our approach introduces a novel tokenization paradigm to effectively bridge the substantial dimensionality gap between the 3D video modality and the 1D current modality in a multiscale manner, enabling a hierarchical reconstruction of pixel-level anomaly detection. Subsequently, the FmFormer leverages self-attention to learn internal features within each modality and bidirectional cross-attention to capture correlations across modalities. By decoding the bidirectional correlation features, we obtain the final detection result and even locate the specific anomaly region. To validate the effectiveness of the proposed method, we also present a pioneering cross-modal benchmark of the fused magnesium smelting process, featuring synchronously acquired video and current data for over 2.2 million samples. Leveraging cross-modal learning, the proposed FmFormer achieves state-of-the-art performance in detecting anomalies, particularly under extreme interferences such as current fluctuations and visual occlusion caused by heavy water mist. The presented methodology and benchmark may be applicable to other industrial applications with some amendments. The benchmark will be released at https://github.com/GaochangWu/FMF-Benchmark.", "date": "2024-06-13", "categories": []}
{"id": "2412.00560", "title": "Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection", "abstract": "Overfitting has traditionally been viewed as detrimental to anomaly detection, where excessive generalization often limits models' sensitivity to subtle anomalies. Our work challenges this conventional view by introducing Controllable Overfitting-based Anomaly Detection (COAD), a novel framework that strategically leverages overfitting to enhance anomaly discrimination capabilities. We propose the Aberrance Retention Quotient (ARQ), a novel metric that systematically quantifies the extent of overfitting, enabling the identification of an optimal golden overfitting interval wherein model sensitivity to anomalies is maximized without sacrificing generalization. To comprehensively capture how overfitting affects detection performance, we further propose the Relative Anomaly Distribution Index (RADI), a metric superior to traditional AUROC by explicitly modeling the separation between normal and anomalous score distributions. Theoretically, RADI leverages ARQ to track and evaluate how overfitting impacts anomaly detection, offering an integrated approach to understanding the relationship between overfitting dynamics and model efficacy. We also rigorously validate the statistical efficacy of Gaussian noise as pseudo-anomaly generators, reinforcing the method's broad applicability. Empirical evaluations demonstrate that our controllable overfitting method achieves State-Of-The-Art(SOTA) performance in both one-class and multi-class anomaly detection tasks, thus redefining overfitting as a powerful strategy rather than a limitation.", "date": "2024-11-30", "categories": []}
{"id": "2501.12723", "title": "Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach", "abstract": "Anomaly detection is crucial in financial auditing, and effective detection requires large volumes of data from multiple organizations. However, journal entry data is highly sensitive, making it infeasible to share them directly across audit firms. To address this challenge, journal entry anomaly detection methods based on model share-type federated learning (FL) have been proposed. These methods require multiple rounds of communication with external servers to exchange model parameters, which necessitates connecting devices storing confidential data to external networks -- a practice not recommended for sensitive data such as journal entries. To overcome these limitations, a novel anomaly detection framework based on data collaboration (DC) analysis, a non-model share-type FL approach, is proposed. The method first transforms raw journal entry data into secure intermediate representations via dimensionality reduction and then constructs a collaboration representation used to train an anomaly detection autoencoder. Notably, the approach does not require raw data to be exposed or devices to be connected to external networks, and the entire process needs only a single round of communication. The proposed method was evaluated on both synthetic and real-world journal entry data collected from eight healthcare organizations. The experimental results demonstrated that the framework not only outperforms the baseline trained on individual data but also achieves higher detection performance than model-sharing FL methods such as FedAvg and FedProx, particularly under non-i.i.d. settings that simulate practical audit environments. This study addresses the critical need to integrate organizational knowledge while preserving data confidentiality, contributing to the development of practical intelligent auditing systems.", "date": "2025-01-22", "categories": []}
{"id": "2504.12970", "title": "MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection", "abstract": "Currently, industrial anomaly detection suffers from two bottlenecks: (i) the rarity of real-world defect images and (ii) the opacity of sample quality when synthetic data are used. Existing synthetic strategies (e.g., cut-and-paste) overlook the underlying physical causes of defects, leading to inconsistent, low-fidelity anomalies that hamper model generalization to real-world complexities. In this paper, we introduce a novel and lightweight pipeline that generates synthetic anomalies through Math-Phys model guidance, refines them via a Coarse-to-Fine approach and employs a bi-level optimization strategy with a Synthesis Quality Estimator (SQE). By combining physical modeling of the three most typical physics-driven defect mechanisms: Fracture Line (FL), Pitting Loss (PL), and Plastic Warpage (PW), our method produces realistic defect masks, which are subsequently enhanced in two phases. The first stage (npcF) enforces a PDE-based consistency to achieve a globally coherent anomaly structure, while the second stage (npcF++) further improves local fidelity. Additionally, we leverage SQE-driven weighting, ensuring that high-quality synthetic samples receive greater emphasis during training. To validate our method, we conduct experiments on three anomaly detection benchmarks: MVTec AD, VisA, and BTAD. Across these datasets, our method achieves state-of-the-art results in both image- and pixel-AUROC, confirming the effectiveness of our MaPhC2F dataset and BiSQAD method. All code will be released.", "date": "2025-04-17", "categories": []}
{"id": "2506.21549", "title": "SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark", "abstract": "We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the task is to produce a voxel-based Anomaly Volume. Moreover, SiM3D focuses on a scenario of high interest in manufacturing: single-instance anomaly detection, where only one object, either real or synthetic, is available for training. In this respect, SiM3D stands out as the first ADS benchmark that addresses the challenge of generalising from synthetic training data to real test data. SiM3D includes a novel multimodal multiview dataset acquired using top-tier industrial sensors and robots. The dataset features multiview high-resolution images (12 Mpx) and point clouds (7M points) for 333 instances of eight types of objects, alongside a CAD model for each type. We also provide manually annotated 3D segmentation GTs for anomalous test samples. To establish reference baselines for the proposed multiview 3D ADS task, we adapt prominent singleview methods and assess their performance using novel metrics that operate on Anomaly Volumes.", "date": "2025-06-26", "categories": []}
{"id": "2507.23449", "title": "Manifold-regularised Large-Margin $\\ell_p$-SVDD for Multidimensional Time Series Anomaly Detection", "abstract": "We generalise the recently introduced large-margin $\\ell_p$-SVDD approach to exploit the geometry of data distribution via manifold regularising for time series anomaly detection. Specifically, we formulate a manifold-regularised variant of the $\\ell_p$-SVDD method to encourage label smoothness on the underlying manifold to capture structural information for improved detection performance. Drawing on an existing Representer theorem, we then provide an effective optimisation technique for the proposed method.\n  We theoretically study the proposed approach using Rademacher complexities to analyse its generalisation performance and also provide an experimental assessment of the proposed method across various data sets to compare its performance against other methods.", "date": "2025-07-31", "categories": []}
{"id": "2508.00879", "title": "GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines", "abstract": "The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome these limitations, this paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines. The focus is on detecting multiple fault types -- including eccentricity, bearing defects, and broken rotor bars -- under varying severity levels and load conditions. Unlike traditional approaches, raw current and vibration signals are used as direct inputs, eliminating the need for signal preprocessing or manual feature extraction. The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs, leveraging the graph structure to capture complex relationships between signal types and fault patterns. It is evaluated for both individual fault detection and multi-class classification of combined fault conditions. Experimental results demonstrate the effectiveness of the proposed model, achieving 92.5\\% accuracy for eccentricity defects, 91.2\\% for bearing faults, and 93.1\\% for broken rotor bar detection. These findings highlight the model's robustness and generalization capability across different operational scenarios. The proposed GNN-based framework offers a lightweight yet powerful solution that simplifies implementation while maintaining high diagnostic performance. It stands as a promising alternative to conventional model-based diagnostic techniques for real-world induction machine monitoring and predictive maintenance.", "date": "2025-07-23", "categories": []}
{"id": "2508.00909", "title": "NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection", "abstract": "Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learning has gained traction for modeling normal behavior without the need of labeled data. However, many existing methods rely on a single proxy task, limiting their ability to capture meaningful patterns in normal data. Moreover, they often depend on handcrafted transformations tailored specific domains, hindering their generalization accross diverse problems. To address these limitations, we introduce NeuCoReClass AD, a self-supervised multi-task time series anomaly detection framework that combines contrastive, reconstruction, and classification proxy tasks. Our method employs neural transformation learning to generate augmented views that are informative, diverse, and coherent, without requiring domain-specific knowledge. We evaluate NeuCoReClass AD across a wide range of benchmarks, demonstrating that it consistently outperforms both classical baselines and most deep-learning alternatives. Furthermore, it enables the characterization of distinct anomaly profiles in a fully unsupervised manner.", "date": "2025-07-29", "categories": []}
{"id": "2508.01137", "title": "Semi-Supervised Anomaly Detection in Brain MRI Using a Domain-Agnostic Deep Reinforcement Learning Approach", "abstract": "To develop a domain-agnostic, semi-supervised anomaly detection framework that integrates deep reinforcement learning (DRL) to address challenges such as large-scale data, overfitting, and class imbalance, focusing on brain MRI volumes. This retrospective study used publicly available brain MRI datasets collected between 2005 and 2021. The IXI dataset provided 581 T1-weighted and 578 T2-weighted MRI volumes (from healthy subjects) for training, while the BraTS 2021 dataset provided 251 volumes for validation and 1000 for testing (unhealthy subjects with Glioblastomas). Preprocessing included normalization, skull-stripping, and co-registering to a uniform voxel size. Experiments were conducted on both T1- and T2-weighted modalities. Additional experiments and ablation analyses were also carried out on the industrial datasets. The proposed method integrates DRL with feature representations to handle label scarcity, large-scale data and overfitting. Statistical analysis was based on several detection and segmentation metrics including AUROC and Dice score. The proposed method achieved an AUROC of 88.7% (pixel-level) and 96.7% (image-level) on brain MRI datasets, outperforming State-of-The-Art (SOTA) methods. On industrial surface datasets, the model also showed competitive performance (AUROC = 99.8% pixel-level, 99.3% image-level) on MVTec AD dataset, indicating strong cross-domain generalization. Studies on anomaly sample size showed a monotonic increase in AUROC as more anomalies were seen, without evidence of overfitting or additional computational cost. The domain-agnostic semi-supervised approach using DRL shows significant promise for MRI anomaly detection, achieving strong performance on both medical and industrial datasets. Its robustness, generalizability and efficiency highlight its potential for real-world clinical applications.", "date": "2025-08-02", "categories": []}
{"id": "2508.01311", "title": "C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor", "abstract": "3D Anomaly Detection (AD) has shown great potential in detecting anomalies or defects of high-precision industrial products. However, existing methods are typically trained in a class-specific manner and also lack the capability of learning from emerging classes. In this study, we proposed a continual learning framework named Continual 3D Anomaly Detection (C3D-AD), which can not only learn generalized representations for multi-class point clouds but also handle new classes emerging over time.Specifically, in the feature extraction module, to extract generalized local features from diverse product types of different tasks efficiently, Kernel Attention with random feature Layer (KAL) is introduced, which normalizes the feature space. Then, to reconstruct data correctly and continually, an efficient Kernel Attention with learnable Advisor (KAA) mechanism is proposed, which learns the information from new categories while discarding redundant old information within both the encoder and decoder. Finally, to keep the representation consistency over tasks, a Reconstruction with Parameter Perturbation (RPP) module is proposed by designing a representation rehearsal loss function, which ensures that the model remembers previous category information and returns category-adaptive representation.Extensive experiments on three public datasets demonstrate the effectiveness of the proposed method, achieving an average performance of 66.4%, 83.1%, and 63.4% AUROC on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, respectively.", "date": "2025-08-02", "categories": []}
{"id": "2508.02477", "title": "Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions", "abstract": "Recent advances in image anomaly detection have extended unsupervised learning-based models from single-class settings to multi-class frameworks, aiming to improve efficiency in training time and model storage. When a single model is trained to handle multiple classes, it often underperforms compared to class-specific models in terms of per-class detection accuracy. Accordingly, previous studies have primarily focused on narrowing this performance gap. However, the way class information is used, or not used, remains a relatively understudied factor that could influence how detection thresholds are defined in multi-class image anomaly detection. These thresholds, whether class-specific or class-agnostic, significantly affect detection outcomes. In this study, we identify and formalize the requirements that a multi-class image anomaly detection model must satisfy under different conditions, depending on whether class labels are available during training and evaluation. We then re-examine existing methods under these criteria. To meet these challenges, we propose Hierarchical Coreset (HierCore), a novel framework designed to satisfy all defined requirements. HierCore operates effectively even without class labels, leveraging a hierarchical memory bank to estimate class-wise decision criteria for anomaly detection. We empirically validate the applicability and robustness of existing methods and HierCore under four distinct scenarios, determined by the presence or absence of class labels in the training and evaluation phases. The experimental results demonstrate that HierCore consistently meets all requirements and maintains strong, stable performance across all settings, highlighting its practical potential for real-world multi-class anomaly detection tasks.", "date": "2025-08-04", "categories": []}
{"id": "2312.01046", "title": "Bagged Regularized $k$-Distances for Anomaly Detection", "abstract": "We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled examples. Though distance-based methods are top-performing for unsupervised anomaly detection, they suffer heavily from the sensitivity to the choice of the number of the nearest neighbors. In this paper, we propose a new distance-based algorithm called bagged regularized $k$-distances for anomaly detection (BRDAD), converting the unsupervised anomaly detection problem into a convex optimization problem. Our BRDAD algorithm selects the weights by minimizing the surrogate risk, i.e., the finite sample bound of the empirical risk of the bagged weighted $k$-distances for density estimation (BWDDE). This approach enables us to successfully address the sensitivity challenge of the hyperparameter choice in distance-based algorithms. Moreover, when dealing with large-scale datasets, the efficiency issues can be addressed by the incorporated bagging technique in our BRDAD algorithm. On the theoretical side, we establish fast convergence rates of the AUC regret of our algorithm and demonstrate that the bagging technique significantly reduces the computational complexity. On the practical side, we conduct numerical experiments to illustrate the insensitivity of the parameter selection of our algorithm compared with other state-of-the-art distance-based methods. Furthermore, our method achieves superior performance on real-world datasets with the introduced bagging technique compared to other approaches.", "date": "2023-12-02", "categories": []}
{"id": "2508.00312", "title": "GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection", "abstract": "Video anomaly detection (VAD) plays a critical role in public safety applications such as intelligent surveillance. However, the rarity, unpredictability, and high annotation cost of real-world anomalies make it difficult to scale VAD datasets, which limits the performance and generalization ability of existing models. To address this challenge, we propose a generative video-enhanced weakly-supervised video anomaly detection (GV-VAD) framework that leverages text-conditioned video generation models to produce semantically controllable and physically plausible synthetic videos. These virtual videos are used to augment training data at low cost. In addition, a synthetic sample loss scaling strategy is utilized to control the influence of generated synthetic samples for efficient training. The experiments show that the proposed framework outperforms state-of-the-art methods on UCF-Crime datasets. The code is available at https://github.com/Sumutan/GV-VAD.git.", "date": "2025-08-01", "categories": []}
{"id": "2508.00473", "title": "HyPCV-Former: Hyperbolic Spatio-Temporal Transformer for 3D Point Cloud Video Anomaly Detection", "abstract": "Video anomaly detection is a fundamental task in video surveillance, with broad applications in public safety and intelligent monitoring systems. Although previous methods leverage Euclidean representations in RGB or depth domains, such embeddings are inherently limited in capturing hierarchical event structures and spatio-temporal continuity. To address these limitations, we propose HyPCV-Former, a novel hyperbolic spatio-temporal transformer for anomaly detection in 3D point cloud videos. Our approach first extracts per-frame spatial features from point cloud sequences via point cloud extractor, and then embeds them into Lorentzian hyperbolic space, which better captures the latent hierarchical structure of events. To model temporal dynamics, we introduce a hyperbolic multi-head self-attention (HMHA) mechanism that leverages Lorentzian inner products and curvature-aware softmax to learn temporal dependencies under non-Euclidean geometry. Our method performs all feature transformations and anomaly scoring directly within full Lorentzian space rather than via tangent space approximation. Extensive experiments demonstrate that HyPCV-Former achieves state-of-the-art performance across multiple anomaly categories, with a 7\\% improvement on the TIMo dataset and a 5.6\\% gain on the DAD dataset compared to benchmarks. The code will be released upon paper acceptance.", "date": "2025-08-01", "categories": []}
{"id": "2508.00507", "title": "Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection", "abstract": "The natural combination of intricate topological structures and rich textual information in text-attributed graphs (TAGs) opens up a novel perspective for graph anomaly detection (GAD). However, existing GAD methods primarily focus on designing complex optimization objectives within the graph domain, overlooking the complementary value of the textual modality, whose features are often encoded by shallow embedding techniques, such as bag-of-words or skip-gram, so that semantic context related to anomalies may be missed. To unleash the enormous potential of textual modality, large language models (LLMs) have emerged as promising alternatives due to their strong semantic understanding and reasoning capabilities. Nevertheless, their application to TAG anomaly detection remains nascent, and they struggle to encode high-order structural information inherent in graphs due to input length constraints. For high-quality anomaly detection in TAGs, we propose CoLL, a novel framework that combines LLMs and graph neural networks (GNNs) to leverage their complementary strengths. CoLL employs multi-LLM collaboration for evidence-augmented generation to capture anomaly-relevant contexts while delivering human-readable rationales for detected anomalies. Moreover, CoLL integrates a GNN equipped with a gating mechanism to adaptively fuse textual features with evidence while preserving high-order topological information. Extensive experiments demonstrate the superiority of CoLL, achieving an average improvement of 13.37% in AP. This study opens a new avenue for incorporating LLMs in advancing GAD.", "date": "2025-08-01", "categories": []}
{"id": "2508.00513", "title": "Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning", "abstract": "The widespread application of graph data in various high-risk scenarios has increased attention to graph anomaly detection (GAD). Faced with real-world graphs that often carry node descriptions in the form of raw text sequences, termed text-attributed graphs (TAGs), existing graph anomaly detection pipelines typically involve shallow embedding techniques to encode such textual information into features, and then rely on complex self-supervised tasks within the graph domain to detect anomalies. However, this text encoding process is separated from the anomaly detection training objective in the graph domain, making it difficult to ensure that the extracted textual features focus on GAD-relevant information, seriously constraining the detection capability. How to seamlessly integrate raw text and graph topology to unleash the vast potential of cross-modal data in TAGs for anomaly detection poses a challenging issue. This paper presents a novel end-to-end paradigm for text-attributed graph anomaly detection, named CMUCL. We simultaneously model data from both text and graph structures, and jointly train text and graph encoders by leveraging cross-modal and uni-modal multi-scale consistency to uncover potential anomaly-related information. Accordingly, we design an anomaly score estimator based on inconsistency mining to derive node-specific anomaly scores. Considering the lack of benchmark datasets tailored for anomaly detection on TAGs, we release 8 datasets to facilitate future research. Extensive evaluations show that CMUCL significantly advances in text-attributed graph anomaly detection, delivering an 11.13% increase in average accuracy (AP) over the suboptimal.", "date": "2025-08-01", "categories": []}
{"id": "2508.00664", "title": "DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes", "abstract": "Dynamic graph anomaly detection (DGAD) is essential for identifying anomalies in evolving graphs across domains such as finance, traffic, and social networks. Recently, generalist graph anomaly detection (GAD) models have shown promising results. They are pretrained on multiple source datasets and generalize across domains. While effective on static graphs, they struggle to capture evolving anomalies in dynamic graphs. Moreover, the continuous emergence of new domains and the lack of labeled data further challenge generalist DGAD. Effective cross-domain DGAD requires both domain-specific and domain-agnostic anomalous patterns. Importantly, these patterns evolve temporally within and across domains. Building on these insights, we propose a DGAD model with Dynamic Prototypes (DP) to capture evolving domain-specific and domain-agnostic patterns. Firstly, DP-DGAD extracts dynamic prototypes, i.e., evolving representations of normal and anomalous patterns, from temporal ego-graphs and stores them in a memory buffer. The buffer is selectively updated to retain general, domain-agnostic patterns while incorporating new domain-specific ones. Then, an anomaly scorer compares incoming data with dynamic prototypes to flag both general and domain-specific anomalies. Finally, DP-DGAD employs confidence-based pseudo-labeling for effective self-supervised adaptation in target domains. Extensive experiments demonstrate state-of-the-art performance across ten real-world datasets from different domains.", "date": "2025-08-01", "categories": []}
{"id": "2508.00758", "title": "Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data", "abstract": "Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noise, limiting adaptability to diverse data distributions. Diffusion models introduce scheduled noise and iterative denoising, but lack explicit reconstruction mappings. We propose the Diffusion-Scheduled Denoising Autoencoder (DDAE), a framework that integrates diffusion-based noise scheduling and contrastive learning into the encoding process to improve anomaly detection. We evaluated DDAE on 57 datasets from ADBench. Our method outperforms in semi-supervised settings and achieves competitive results in unsupervised settings, improving PR-AUC by up to 65% (9%) and ROC-AUC by 16% (6%) over state-of-the-art autoencoder (diffusion) model baselines. We observed that higher noise levels benefit unsupervised training, while lower noise with linear scheduling is optimal in semi-supervised settings. These findings underscore the importance of principled noise strategies in tabular anomaly detection.", "date": "2025-08-01", "categories": []}
{"id": "2507.23411", "title": "Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories", "abstract": "In medical imaging, unsupervised out-of-distribution (OOD) detection offers an attractive approach for identifying pathological cases with extremely low incidence rates. In contrast to supervised methods, OOD-based approaches function without labels and are inherently robust to data imbalances. Current generative approaches often rely on likelihood estimation or reconstruction error, but these methods can be computationally expensive, unreliable, and require retraining if the inlier data changes. These limitations hinder their ability to distinguish nominal from anomalous inputs efficiently, consistently, and robustly. We propose a reconstruction-free OOD detection method that leverages the forward diffusion trajectories of a Stein score-based denoising diffusion model (SBDDM). By capturing trajectory curvature via the estimated Stein score, our approach enables accurate anomaly scoring with only five diffusion steps. A single SBDDM pre-trained on a large, semantically aligned medical dataset generalizes effectively across multiple Near-OOD and Far-OOD benchmarks, achieving state-of-the-art performance while drastically reducing computational cost during inference. Compared to existing methods, SBDDM achieves a relative improvement of up to 10.43% and 18.10% for Near-OOD and Far-OOD detection, making it a practical building block for real-time, reliable computer-aided diagnosis.", "date": "2025-07-31", "categories": []}
{"id": "2507.23712", "title": "Anomalous Samples for Few-Shot Anomaly Detection", "abstract": "Several anomaly detection and classification methods rely on large amounts of non-anomalous or \"normal\" samples under the assump- tion that anomalous data is typically harder to acquire. This hypothesis becomes questionable in Few-Shot settings, where as little as one anno- tated sample can make a significant difference. In this paper, we tackle the question of utilizing anomalous samples in training a model for bi- nary anomaly classification. We propose a methodology that incorporates anomalous samples in a multi-score anomaly detection score leveraging recent Zero-Shot and memory-based techniques. We compare the utility of anomalous samples to that of regular samples and study the benefits and limitations of each. In addition, we propose an augmentation-based validation technique to optimize the aggregation of the different anomaly scores and demonstrate its effectiveness on popular industrial anomaly detection datasets.", "date": "2025-07-31", "categories": []}
{"id": "2503.22743", "title": "Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection", "abstract": "State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks such as natural language processing, time-series forecasting, and signal processing. In this work, we propose an \\emph{Adaptive State-Space Mamba} (\\textbf{ASSM}) framework for real-time sensor data anomaly detection. While state-space models have been previously employed for image processing applications (e.g., style transfer \\cite{wang2024stylemamba}), our approach leverages the core idea of sequential hidden states to tackle a significantly different domain: detecting anomalies on streaming sensor data.\n  In particular, we introduce an adaptive gating mechanism that dynamically modulates the hidden state update based on contextual and learned statistical cues. This design ensures that our model remains computationally efficient and scalable, even under rapid data arrival rates. Extensive experiments on real-world and synthetic sensor datasets demonstrate that our method achieves superior detection performance compared to existing baselines. Our approach is easily extensible to other time-series tasks that demand rapid and reliable detection capabilities.", "date": "2025-03-26", "categories": []}
{"id": "2507.22692", "title": "Zero-Shot Image Anomaly Detection Using Generative Foundation Models", "abstract": "Detecting out-of-distribution (OOD) inputs is pivotal for deploying safe vision systems in open-world environments. We revisit diffusion models, not as generators, but as universal perceptual templates for OOD detection. This research explores the use of score-based generative models as foundational tools for semantic anomaly detection across unseen datasets. Specifically, we leverage the denoising trajectories of Denoising Diffusion Models (DDMs) as a rich source of texture and semantic information. By analyzing Stein score errors, amplified through the Structural Similarity Index Metric (SSIM), we introduce a novel method for identifying anomalous samples without requiring re-training on each target dataset. Our approach improves over state-of-the-art and relies on training a single model on one dataset -- CelebA -- which we find to be an effective base distribution, even outperforming more commonly used datasets like ImageNet in several settings. Experimental results show near-perfect performance on some benchmarks, with notable headroom on others, highlighting both the strength and future potential of generative foundation models in anomaly detection.", "date": "2025-07-30", "categories": []}
{"id": "2403.18201", "title": "Few-shot Online Anomaly Detection and Segmentation", "abstract": "Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance. Consequently, this paper focuses on addressing the challenging yet practical few-shot online anomaly detection and segmentation (FOADS) task. Under the FOADS framework, models are trained on a few-shot normal dataset, followed by inspection and improvement of their capabilities by leveraging unlabeled streaming data containing both normal and abnormal samples simultaneously.\n  To tackle this issue, we propose modeling the feature distribution of normal images using a Neural Gas network, which offers the flexibility to adapt the topology structure to identify outliers in the data flow. In order to achieve improved performance with limited training samples, we employ multi-scale feature embedding extracted from a CNN pre-trained on ImageNet to obtain a robust representation. Furthermore, we introduce an algorithm that can incrementally update parameters without the need to store previous samples. Comprehensive experimental results demonstrate that our method can achieve substantial performance under the FOADS setting, while ensuring that the time complexity remains within an acceptable range on MVTec AD and BTAD datasets.", "date": "2024-03-27", "categories": []}
{"id": "2505.03134", "title": "Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control", "abstract": "Visual defect detection in industrial glass manufacturing remains a critical challenge due to the low frequency of defective products, leading to imbalanced datasets that limit the performance of deep learning models and computer vision systems. This paper presents a novel approach using Denoising Diffusion Probabilistic Models (DDPMs) to generate synthetic defective glass product images for data augmentation, effectively addressing class imbalance issues in manufacturing quality control and automated visual inspection. The methodology significantly enhances image classification performance of standard CNN architectures (ResNet50V2, EfficientNetB0, and MobileNetV2) in detecting anomalies by increasing the minority class representation. Experimental results demonstrate substantial improvements in key machine learning metrics, particularly in recall for defective samples across all tested deep neural network architectures while maintaining perfect precision on the validation set. The most dramatic improvement was observed in ResNet50V2's overall classification accuracy, which increased from 78\\% to 93\\% when trained with the augmented data. This work provides a scalable, cost-effective approach to enhancing automated defect detection in glass manufacturing that can potentially be extended to other industrial quality assurance systems and industries with similar class imbalance challenges.", "date": "2025-05-06", "categories": []}
{"id": "2507.21193", "title": "Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs", "abstract": "Next generation Radio Access Networks (RANs) introduce programmability, intelligence, and near real-time control through intelligent controllers, enabling enhanced security within the RAN and across broader 5G/6G infrastructures. This paper presents a comprehensive survey highlighting opportunities, challenges, and research gaps for Large Language Models (LLMs)-assisted explainable (XAI) intrusion detection (IDS) for secure future RAN environments. Motivated by this, we propose an LLM interpretable anomaly-based detection system for distributed denial-of-service (DDoS) attacks using multivariate time series key performance measures (KPMs), extracted from E2 nodes, within the Near Real-Time RAN Intelligent Controller (Near-RT RIC). An LSTM-based model is trained to identify malicious User Equipment (UE) behavior based on these KPMs. To enhance transparency, we apply post-hoc local explainability methods such as LIME and SHAP to interpret individual predictions. Furthermore, LLMs are employed to convert technical explanations into natural-language insights accessible to non-expert users. Experimental results on real 5G network KPMs demonstrate that our framework achieves high detection accuracy (F1-score > 0.96) while delivering actionable and interpretable outputs.", "date": "2025-07-27", "categories": []}
{"id": "2507.21203", "title": "An empirical comparison of some outlier detection methods with longitudinal data", "abstract": "This note investigates the problem of detecting outliers in longitudinal data. It compares well-known methods used in official statistics with proposals from the fields of data mining and machine learning that are based on the distance between observations or binary partitioning trees. This is achieved by applying the methods to panel survey data related to different types of statistical units. Traditional methods are quite simple, enabling the direct identification of potential outliers, but they require specific assumptions. In contrast, recent methods provide only a score whose magnitude is directly related to the likelihood of an outlier being present. All the methods require the user to set a number of tuning parameters. However, the most recent methods are more flexible and sometimes more effective than traditional methods. In addition, these methods can be applied to multidimensional data.", "date": "2025-07-28", "categories": []}
{"id": "2507.21507", "title": "VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understanding", "abstract": "Video Anomaly Detection (VAD) aims to identify anomalous events in videos and accurately determine their time intervals. Current VAD methods mainly fall into two categories: traditional DNN-based approaches that focus on temporal localization, and LLM-based approaches that emphasize semantic understanding. Both anomaly understanding and grounding are essential for comprehensive video anomaly detection and can complement each other. However, no existing model or dataset supports both tasks simultaneously. To address this, we introduce VAGU (Video Anomaly Grounding and Understanding), the first benchmark to integrate both tasks. Each VAGU instance includes annotations for anomaly category, semantic explanation, precise temporal grounding and Video QA. We also provide multiple-choice Video QA for objective evaluation. Based on this dataset, we propose Glance then Scrutinize (GtS), a training-free framework guided by textual prompts. The framework first enables coarse localization of high-probability anomalous regions, followed by detailed anomaly interpretation and temporal boundary refinement. Additionally, we propose the JeAUG metric, which jointly evaluates semantic interpretability and temporal precision, overcoming the limitations of traditional metrics. Extensive experiments verify the effectiveness of our benchmark, framework, and evaluation metric.", "date": "2025-07-29", "categories": []}
{"id": "2507.21555", "title": "Multi-View Reconstruction with Global Context for 3D Anomaly Detection", "abstract": "3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6\\% object-wise AU-ROC and 95.7\\% point-wise AU-ROC on the Real3D-AD benchmark.", "date": "2025-07-29", "categories": []}
{"id": "2507.21619", "title": "EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO", "abstract": "Industrial anomaly detection (IAD) plays a crucial role in maintaining the safety and reliability of manufacturing systems. While multimodal large language models (MLLMs) show strong vision-language reasoning abilities, their effectiveness in IAD remains limited without domain-specific adaptation. In this work, we propose EMIT, a unified framework that enhances MLLMs for IAD via difficulty-aware group relative policy optimization (GRPO). EMIT constructs a multi-task IAD dataset and utilizes GPT-generated object text descriptions to compensate for missing defective images. For few-shot anomaly detection, it integrates a soft prompt and heatmap-guided contrastive embeddings derived from patch-level comparisons. To better handle difficult data samples, i.e., cases where the MLLM struggles to generate correct answers, we propose a difficulty-aware GRPO that extends the original GRPO by incorporating a response resampling strategy to ensure the inclusion of correct answers in the sampled responses, as well as an advantage reweighting mechanism to strengthen learning from such difficult data samples. Extensive experiments on the MMAD benchmark demonstrate that EMIT significantly enhances the IAD performance of MLLMs, achieving an average improvement of 7.77\\% over the base model (InternVL3-8B) across seven tasks.", "date": "2025-07-29", "categories": []}
{"id": "2507.21649", "title": "The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM", "abstract": "Video anomaly detection (VAD) aims to identify and ground anomalous behaviors or events in videos, serving as a core technology in the fields of intelligent surveillance and public safety. With the advancement of deep learning, the continuous evolution of deep model architectures has driven innovation in VAD methodologies, significantly enhancing feature representation and scene adaptability, thereby improving algorithm generalization and expanding application boundaries. More importantly, the rapid development of multi-modal large language (MLLMs) and large language models (LLMs) has introduced new opportunities and challenges to the VAD field. Under the support of MLLMs and LLMs, VAD has undergone significant transformations in terms of data annotation, input modalities, model architectures, and task objectives. The surge in publications and the evolution of tasks have created an urgent need for systematic reviews of recent advancements. This paper presents the first comprehensive survey analyzing VAD methods based on MLLMs and LLMs, providing an in-depth discussion of the changes occurring in the VAD field in the era of large models and their underlying causes. Additionally, this paper proposes a unified framework that encompasses both deep neural network (DNN)-based and LLM-based VAD methods, offering a thorough analysis of the new VAD paradigms empowered by LLMs, constructing a classification system, and comparing their strengths and weaknesses. Building on this foundation, this paper focuses on current VAD methods based on MLLMs/LLMs. Finally, based on the trajectory of technological advancements and existing bottlenecks, this paper distills key challenges and outlines future research directions, offering guidance for the VAD community.", "date": "2025-07-29", "categories": []}
{"id": "2411.04004", "title": "Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images", "abstract": "Anomaly detection in medical imaging plays a crucial role in identifying pathological regions across various imaging modalities, such as brain MRI, liver CT, and carotid ultrasound (US). However, training fully supervised segmentation models is often hindered by the scarcity of expert annotations and the complexity of diverse anatomical structures. To address these issues, we propose a novel unsupervised anomaly detection framework based on a diffusion model that incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process. Synomaly noise introduces synthetic anomalies into healthy images during training, allowing the model to effectively learn anomaly removal. The multi-stage diffusion process is introduced to progressively denoise images, preserving fine details while improving the quality of anomaly-free reconstructions. The generated high-fidelity counterfactual healthy images can further enhance the interpretability of the segmentation models, as well as provide a reliable baseline for evaluating the extent of anomalies and supporting clinical decision-making. Notably, the unsupervised anomaly detection model is trained purely on healthy images, eliminating the need for anomalous training samples and pixel-level annotations. We validate the proposed approach on brain MRI, liver CT datasets, and carotid US. The experimental results demonstrate that the proposed framework outperforms existing state-of-the-art unsupervised anomaly detection methods, achieving performance comparable to fully supervised segmentation models in the US dataset. Ablation studies further highlight the contributions of Synomaly noise and the multi-stage diffusion process in improving anomaly segmentation. These findings underscore the potential of our approach as a robust and annotation-efficient alternative for medical anomaly detection.", "date": "2024-11-06", "categories": []}
{"id": "2502.03876", "title": "Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data", "abstract": "Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing where only one sample can be collected without any additional labels and historical datasets. Identifying anomalies accurately based on one 3D point cloud sample is a critical challenge in both industrial applications and the field of machine learning. This paper aims to provide a formal definition of the untrained anomaly detection problem based on 3D point cloud data, discuss the differences between untrained anomaly detection and current unsupervised anomaly detection problems. Unlike trained unsupervised learning, untrained unsupervised learning does not rely on any data, including unlabeled data. Instead, they leverage prior knowledge about the surfaces and anomalies.\n  We propose three complementary methodological frameworks: the Latent Variable Inference Framework that employs probabilistic modeling to distinguish anomalies; the Decomposition Framework that separates point clouds into reference, anomaly, and noise components through sparse learning; and the Local Geometry Framework that leverages neighborhood information for anomaly identification. Experimental results demonstrate that untrained methods achieve competitive detection performance while offering significant computational advantages, demonstrating up to a 15-fold increase in execution speed. The proposed methods provide viable solutions for scenarios with extreme data scarcity, addressing critical challenges in personalized manufacturing and healthcare applications where collecting multiple samples or historical data is infeasible.", "date": "2025-02-06", "categories": []}
{"id": "2504.13092", "title": "EventVAD: Training-Free Event-Aware Video Anomaly Detection", "abstract": "Video Anomaly Detection~(VAD) focuses on identifying anomalies within videos. Supervised methods require an amount of in-domain training data and often struggle to generalize to unseen anomalies. In contrast, training-free methods leverage the intrinsic world knowledge of large language models (LLMs) to detect anomalies but face challenges in localizing fine-grained visual transitions and diverse events. Therefore, we propose EventVAD, an event-aware video anomaly detection framework that combines tailored dynamic graph architectures and multimodal LLMs through temporal-event reasoning. Specifically, EventVAD first employs dynamic spatiotemporal graph modeling with time-decay constraints to capture event-aware video features. Then, it performs adaptive noise filtering and uses signal ratio thresholding to detect event boundaries via unsupervised statistical features. The statistical boundary detection module reduces the complexity of processing long videos for MLLMs and improves their temporal reasoning through event consistency. Finally, it utilizes a hierarchical prompting strategy to guide MLLMs in performing reasoning before determining final decisions. We conducted extensive experiments on the UCF-Crime and XD-Violence datasets. The results demonstrate that EventVAD with a 7B MLLM achieves state-of-the-art (SOTA) in training-free settings, outperforming strong baselines that use 7B or larger MLLMs.", "date": "2025-04-17", "categories": []}
{"id": "2507.19806", "title": "From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning", "abstract": "Log anomaly detection plays a critical role in ensuring the stability and reliability of software systems. However, existing approaches rely on large amounts of labeled log data, which poses significant challenges in real-world applications. To address this issue, cross-system transfer has been identified as a key research direction. State-of-the-art cross-system approaches achieve promising performance with only a few labels from the target system. However, their reliance on labeled target logs makes them susceptible to the cold-start problem when labeled logs are insufficient. To overcome this limitation, we explore a novel yet underexplored setting: zero-label cross-system log anomaly detection, where the target system logs are entirely unlabeled. To this end, we propose FreeLog, a system-agnostic representation meta-learning method that eliminates the need for labeled target system logs, enabling cross-system log anomaly detection under zero-label conditions. Experimental results on three public log datasets demonstrate that FreeLog achieves performance comparable to state-of-the-art methods that rely on a small amount of labeled data from the target system.", "date": "2025-07-26", "categories": []}
{"id": "2507.19949", "title": "AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation", "abstract": "Visual anomaly detection has been widely used in industrial inspection and medical diagnosis. Existing methods typically demand substantial training samples, limiting their utility in zero-/few-shot scenarios. While recent efforts have leveraged CLIP's zero-shot recognition capability for this task, they often ignore optimizing visual features to focus on local anomalies, reducing their efficacy. In this work, we propose AF-CLIP (Anomaly-Focused CLIP) by dramatically enhancing its visual representations to focus on local defects. Our approach introduces a lightweight adapter that emphasizes anomaly-relevant patterns in visual features, simultaneously optimizing both class-level features for image classification and patch-level features for precise localization. To capture anomalies of different sizes and improve detection accuracy, prior to the adapter, we develop a multi-scale spatial aggregation mechanism to effectively consolidate neighborhood context. Complementing these visual enhancements, we design learnable textual prompts that generically characterize normal and abnormal states. After optimization on auxiliary datasets using a composite objective function, AF-CLIP demonstrates strong zero-shot detection capability. Our method is also extended to few-shot scenarios by extra memory banks. Experimental results across diverse industrial and medical datasets demonstrate the effectiveness and generalization of our proposed method. Code is available at https://github.com/Faustinaqq/AF-CLIP.", "date": "2025-07-26", "categories": []}
{"id": "2507.20019", "title": "Anomaly Detection in Human Language via Meta-Learning: A Few-Shot Approach", "abstract": "We propose a meta learning framework for detecting anomalies in human language across diverse domains with limited labeled data. Anomalies in language ranging from spam and fake news to hate speech pose a major challenge due to their sparsity and variability. We treat anomaly detection as a few shot binary classification problem and leverage meta-learning to train models that generalize across tasks. Using datasets from domains such as SMS spam, COVID-19 fake news, and hate speech, we evaluate model generalization on unseen tasks with minimal labeled anomalies. Our method combines episodic training with prototypical networks and domain resampling to adapt quickly to new anomaly detection tasks. Empirical results show that our method outperforms strong baselines in F1 and AUC scores. We also release the code and benchmarks to facilitate further research in few-shot text anomaly detection.", "date": "2025-07-26", "categories": []}
{"id": "2507.20373", "title": "WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks", "abstract": "We propose the Wasserstein Black Hole Transformer (WBHT) framework for detecting black hole (BH) anomalies in communication networks. These anomalies cause packet loss without failure notifications, disrupting connectivity and leading to financial losses. WBHT combines generative modeling, sequential learning, and attention mechanisms to improve BH anomaly detection. It integrates a Wasserstein generative adversarial network with attention mechanisms for stable training and accurate anomaly identification. The model uses long-short-term memory layers to capture long-term dependencies and convolutional layers for local temporal patterns. A latent space encoding mechanism helps distinguish abnormal network behavior. Tested on real-world network data, WBHT outperforms existing models, achieving significant improvements in F1 score (ranging from 1.65% to 58.76%). Its efficiency and ability to detect previously undetected anomalies make it a valuable tool for proactive network monitoring and security, especially in mission-critical networks.", "date": "2025-07-27", "categories": []}
{"id": "2507.20629", "title": "DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection", "abstract": "The goal of video anomaly detection is tantamount to performing spatio-temporal localization of abnormal events in the video. The multiscale temporal dependencies, visual-semantic heterogeneity, and the scarcity of labeled data exhibited by video anomalies collectively present a challenging research problem in computer vision. This study offers a dual-path architecture called the Dual-Branch Adaptive Multiscale Spatiotemporal Framework (DAMS), which is based on multilevel feature decoupling and fusion, enabling efficient anomaly detection modeling by integrating hierarchical feature learning and complementary information. The main processing path of this framework integrates the Adaptive Multiscale Time Pyramid Network (AMTPN) with the Convolutional Block Attention Mechanism (CBAM). AMTPN enables multigrained representation and dynamically weighted reconstruction of temporal features through a three-level cascade structure (time pyramid pooling, adaptive feature fusion, and temporal context enhancement). CBAM maximizes the entropy distribution of feature channels and spatial dimensions through dual attention mapping. Simultaneously, the parallel path driven by CLIP introduces a contrastive language-visual pre-training paradigm. Cross-modal semantic alignment and a multiscale instance selection mechanism provide high-order semantic guidance for spatio-temporal features. This creates a complete inference chain from the underlying spatio-temporal features to high-level semantic concepts. The orthogonal complementarity of the two paths and the information fusion mechanism jointly construct a comprehensive representation and identification capability for anomalous events. Extensive experimental results on the UCF-Crime and XD-Violence benchmarks establish the effectiveness of the DAMS framework.", "date": "2025-07-28", "categories": []}
{"id": "2507.20666", "title": "MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection", "abstract": "This paper proposes a method for generating machine-type-specific anomalies to evaluate the relative performance of unsupervised anomalous sound detection (UASD) systems across different machine types, even in the absence of real anomaly sound data. Conventional keyword-based data augmentation methods often produce unrealistic sounds due to their reliance on manually defined labels, limiting scalability as machine types and anomaly patterns diversify. Advanced audio generative models, such as MIMII-Gen, show promise but typically depend on anomalous training data, making them less effective when diverse anomalous examples are unavailable. To address these limitations, we propose a novel synthesis approach leveraging large language models (LLMs) to interpret textual descriptions of faults and automatically select audio transformation functions, converting normal machine sounds into diverse and plausible anomalous sounds. We validate this approach by evaluating a UASD system trained only on normal sounds from five machine types, using both real and synthetic anomaly data. Experimental results reveal consistent trends in relative detection difficulty across machine types between synthetic and real anomalies. This finding supports our hypothesis and highlights the effectiveness of the proposed LLM-based synthesis approach for relative evaluation of UASD systems.", "date": "2025-07-28", "categories": []}
{"id": "2412.08949", "title": "Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners", "abstract": "Knowledge distillation (KD) has been widely studied in unsupervised image Anomaly Detection (AD), but its application to unsupervised multimodal AD remains underexplored. Existing KD-based methods for multimodal AD that use fused multimodal features to obtain teacher representations face challenges. Anomalies that only exist in one modality may not be effectively captured in the fused teacher features, leading to detection failures. Besides, these methods do not fully leverage the rich intra- and inter-modality information that are critical for effective anomaly detection. In this paper, we propose Tuned Reverse Distillation (TRD) based on Multi-branch design to realize Multimodal Industrial AD. By assigning independent branches to each modality, our method enables finer detection of anomalies within each modality. Furthermore, we enhance the interaction between modalities during the distillation process by designing two Crossmodal Tuners including Crossmodal Filter and Amplifier. With the idea of crossmodal mapping, the student network is allowed to better learn normal features while anomalies in all modalities are ensured to be effectively detected. Experimental verifications on multimodal AD datasets demonstrate that our method achieves state-of-the-art performance in multimodal anomaly detection and localization. Code is available at https://github.com/hito2448/TRD.", "date": "2024-12-12", "categories": []}
{"id": "2507.19076", "title": "SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection", "abstract": "Radiography imaging protocols target on specific anatomical regions, resulting in highly consistent images with recurrent structural patterns across patients. Recent advances in medical anomaly detection have demonstrated the effectiveness of CNN- and transformer-based approaches. However, CNNs exhibit limitations in capturing long-range dependencies, while transformers suffer from quadratic computational complexity. In contrast, Mamba-based models, leveraging superior long-range modeling, structural feature extraction, and linear computational efficiency, have emerged as a promising alternative. To capitalize on the inherent structural regularity of medical images, this study introduces SP-Mamba, a spatial-perception Mamba framework for unsupervised medical anomaly detection. The window-sliding prototype learning and Circular-Hilbert scanning-based Mamba are introduced to better exploit consistent anatomical patterns and leverage spatial information for medical anomaly detection. Furthermore, we excavate the concentration and contrast characteristics of anomaly maps for improving anomaly detection. Extensive experiments on three diverse medical anomaly detection benchmarks confirm the proposed method's state-of-the-art performance, validating its efficacy and robustness. The code is available at https://github.com/Ray-RuiPan/SP-Mamba.", "date": "2025-07-25", "categories": []}
{"id": "2507.19168", "title": "Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers", "abstract": "Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfunctioning mechanisms and usually can be monitored only if the CB is disconnected from the grid. To facilitate online condition monitoring while CBs remain connected, non-intrusive measurement techniques such as vibration or acoustic signals are necessary. Currently, CB condition monitoring studies using these signals typically utilize supervised methods for fault diagnostics, where ground-truth fault types are known due to artificially introduced faults in laboratory settings. This supervised approach is however not feasible in real-world applications, where fault labels are unavailable. In this work, we propose a novel unsupervised fault detection and segmentation framework for CBs based on vibration and acoustic signals. This framework can detect deviations from the healthy state. The explainable artificial intelligence (XAI) approach is applied to the detected faults for fault diagnostics. The specific contributions are: (1) we propose an integrated unsupervised fault detection and segmentation framework that is capable of detecting faults and clustering different faults with only healthy data required during training (2) we provide an unsupervised explainability-guided fault diagnostics approach using XAI to offer domain experts potential indications of the aged or faulty components, achieving fault diagnostics without the prerequisite of ground-truth fault labels. These contributions are validated using an experimental dataset from a high-voltage CB under healthy and artificially introduced fault conditions, contributing to more reliable CB system operation.", "date": "2025-07-25", "categories": []}
{"id": "2507.19253", "title": "BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection", "abstract": "Industrial anomaly detection for 2D objects has gained significant attention and achieved progress in anomaly detection (AD) methods. However, identifying 3D depth anomalies using only 2D information is insufficient. Despite explicitly fusing depth information into RGB images or using point cloud backbone networks to extract depth features, both approaches struggle to adequately represent 3D information in multimodal scenarios due to the disparities among different modal information. Additionally, due to the scarcity of abnormal samples in industrial data, especially in multimodal scenarios, it is necessary to perform anomaly generation to simulate real-world abnormal samples. Therefore, we propose a novel unified multimodal anomaly detection framework to address these issues. Our contributions consist of 3 key aspects. (1) We extract visible depth information from 3D point cloud data simply and use 2D RGB images to represent appearance, which disentangles depth and appearance to support unified anomaly generation. (2) Benefiting from the flexible input representation, the proposed Multi-Scale Gaussian Anomaly Generator and Unified Texture Anomaly Generator can generate richer anomalies in RGB and depth. (3) All modules share parameters for both RGB and depth data, effectively bridging 2D and 3D anomaly detection. Subsequent modules can directly leverage features from both modalities without complex fusion. Experiments show our method outperforms state-of-the-art (SOTA) on MVTec-3D AD and Eyecandies datasets. Code available at: https://github.com/Xantastic/BridgeNet", "date": "2025-07-25", "categories": []}
{"id": "2502.16943", "title": "MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection", "abstract": "Unsupervised anomaly detection in brain images is crucial for identifying injuries and pathologies without access to labels. However, the accurate localization of anomalies in medical images remains challenging due to the inherent complexity and variability of brain structures and the scarcity of annotated abnormal data. To address this challenge, we propose a novel approach that incorporates masking within diffusion models, leveraging their generative capabilities to learn robust representations of normal brain anatomy. During training, our model processes only normal brain MRI scans and performs a forward diffusion process in the latent space that adds noise to the features of randomly-selected patches. Following a dual objective, the model learns to identify which patches are noisy and recover their original features. This strategy ensures that the model captures intricate patterns of normal brain structures while isolating potential anomalies as noise in the latent space. At inference, the model identifies noisy patches corresponding to anomalies and generates a normal counterpart for these patches by applying a reverse diffusion process. Our method surpasses existing unsupervised anomaly detection techniques, demonstrating superior performance in generating accurate normal counterparts and localizing anomalies. The code is available at hhttps://github.com/farzad-bz/MAD-AD.", "date": "2025-02-24", "categories": []}
{"id": "2507.18481", "title": "Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection", "abstract": "Anomaly detection in medical images is an important yet challenging task due to the diversity of possible anomalies and the practical impossibility of collecting comprehensively annotated data sets. In this work, we tackle unsupervised medical anomaly detection proposing a modernized autoencoder-based framework, the Q-Former Autoencoder, that leverages state-of-the-art pretrained vision foundation models, such as DINO, DINOv2 and Masked Autoencoder. Instead of training encoders from scratch, we directly utilize frozen vision foundation models as feature extractors, enabling rich, multi-stage, high-level representations without domain-specific fine-tuning. We propose the usage of the Q-Former architecture as the bottleneck, which enables the control of the length of the reconstruction sequence, while efficiently aggregating multiscale features. Additionally, we incorporate a perceptual loss computed using features from a pretrained Masked Autoencoder, guiding the reconstruction towards semantically meaningful structures. Our framework is evaluated on four diverse medical anomaly detection benchmarks, achieving state-of-the-art results on BraTS2021, RESC, and RSNA. Our results highlight the potential of vision foundation model encoders, pretrained on natural images, to generalize effectively to medical image analysis tasks without further fine-tuning. We release the code and models at https://github.com/emirhanbayar/QFAE.", "date": "2025-07-24", "categories": []}
{"id": "2507.16946", "title": "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts", "abstract": "Anomaly detection (AD) identifies the defect regions of a given image. Recent works have studied AD, focusing on learning AD without abnormal images, with long-tailed distributed training data, and using a unified model for all classes. In addition, online AD learning has also been explored. In this work, we expand in both directions to a realistic setting by considering the novel task of long-tailed online AD (LTOAD). We first identified that the offline state-of-the-art LTAD methods cannot be directly applied to the online setting. Specifically, LTAD is class-aware, requiring class labels that are not available in the online setting. To address this challenge, we propose a class-agnostic framework for LTAD and then adapt it to our online learning setting. Our method outperforms the SOTA baselines in most offline LTAD settings, including both the industrial manufacturing and the medical domain. In particular, we observe +4.63% image-AUROC on MVTec even compared to methods that have access to class labels and the number of classes. In the most challenging long-tailed online setting, we achieve +0.53% image-AUROC compared to baselines. Our LTOAD benchmark is released here: https://doi.org/10.5281/zenodo.16283852 .", "date": "2025-07-22", "categories": []}
{"id": "2408.07114", "title": "Investigation of unsupervised and supervised hyperspectral anomaly detection", "abstract": "Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.", "date": "2024-08-13", "categories": []}
{"id": "2502.19848", "title": "One-for-More: Continual Diffusion Model for Anomaly Detection", "abstract": "With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to generate or reconstruct normal samples when given arbitrary anomaly images. However, our study found that the diffusion model suffers from severe ``faithfulness hallucination'' and ``catastrophic forgetting'', which can't meet the unpredictable pattern increments. To mitigate the above problems, we propose a continual diffusion model that uses gradient projection to achieve stable continual learning. Gradient projection deploys a regularization on the model updating by modifying the gradient towards the direction protecting the learned knowledge. But as a double-edged sword, it also requires huge memory costs brought by the Markov process. Hence, we propose an iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss. Finally, considering the risk of ``over-fitting'' to normal images of the diffusion model, we propose an anomaly-masked network to enhance the condition mechanism of the diffusion model. For continual anomaly detection, ours achieves first place in 17/18 settings on MVTec and VisA. Code is available at https://github.com/FuNz-0/One-for-More", "date": "2025-02-27", "categories": []}
{"id": "2503.04502", "title": "Interpretable Transformation and Analysis of Timelines through Learning via Surprisability", "abstract": "The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medical data, historical records, and global statistics. However, conventional analysis techniques often struggle with challenges such as high dimensionality, complex distributions, and sparsity. These limitations hinder the ability to extract meaningful insights from complex temporal datasets, making it difficult to identify trending features, outliers, and anomalies effectively. Inspired by surprisability -- a cognitive science concept describing how humans instinctively focus on unexpected deviations - we propose Learning via Surprisability (LvS), a novel approach for transforming high-dimensional timeline data. LvS quantifies and prioritizes anomalies in time-series data by formalizing deviations from expected behavior. LvS bridges cognitive theories of attention with computational methods, enabling the detection of anomalies and shifts in a way that preserves critical context, offering a new lens for interpreting complex datasets. We demonstrate the usefulness of LvS on three high-dimensional timeline use cases: a time series of sensor data, a global dataset of mortality causes over multiple years, and a textual corpus containing over two centuries of State of the Union Addresses by U.S. presidents. Our results show that the LvS transformation enables efficient and interpretable identification of outliers, anomalies, and the most variable features along the timeline.", "date": "2025-03-06", "categories": []}
{"id": "2506.11815", "title": "Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection", "abstract": "Electrocardiography (ECG) signals are frequently degraded by noise, limiting their clinical reliability in both conventional and wearable settings. Existing methods for addressing ECG noise, relying on artifact classification or denoising, are constrained by annotation inconsistencies and poor generalizability. Here, we address these limitations by reframing ECG noise quantification as an anomaly detection task. We propose a diffusion-based framework trained to model the normative distribution of clean ECG signals, identifying deviations as noise without requiring explicit artifact labels. To robustly evaluate performance and mitigate label inconsistencies, we introduce a distribution-based metric using the Wasserstein-1 distance ($W_1$). Our model achieved a macro-average $W_1$ score of 1.308, outperforming the next-best method by over 48\\%. External validation confirmed strong generalizability, facilitating the exclusion of noisy segments to improve diagnostic accuracy and support timely clinical intervention. This approach enhances real-time ECG monitoring and broadens ECG applicability in digital health technologies.", "date": "2025-06-13", "categories": []}
{"id": "2507.07714", "title": "Adaptive Gaussian Mixture Models-based Anomaly Detection for under-constrained Cable-Driven Parallel Robots", "abstract": "Cable-Driven Parallel Robots (CDPRs) are increasingly used for load manipulation tasks involving predefined toolpaths with intermediate stops. At each stop, where the platform maintains a fixed pose and the motors keep the cables under tension, the system must evaluate whether it is safe to proceed by detecting anomalies that could compromise performance (e.g., wind gusts or cable impacts). This paper investigates whether anomalies can be detected using only motor torque data, without additional sensors. It introduces an adaptive, unsupervised outlier detection algorithm based on Gaussian Mixture Models (GMMs) to identify anomalies from torque signals. The method starts with a brief calibration period, just a few seconds, during which a GMM is fit on known anomaly-free data. Real-time torque measurements are then evaluated using Mahalanobis distance from the GMM, with statistically derived thresholds triggering anomaly flags. Model parameters are periodically updated using the latest segments identified as anomaly-free to adapt to changing conditions. Validation includes 14 long-duration test sessions simulating varied wind intensities. The proposed method achieves a 100% true positive rate and 95.4% average true negative rate, with 1-second detection latency. Comparative evaluation against power threshold and non-adaptive GMM methods indicates higher robustness to drift and environmental variation.", "date": "2025-07-10", "categories": []}
{"id": "2507.07939", "title": "SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment", "abstract": "While Vision-Language Models (VLMs) have shown promising progress in general multimodal tasks, they often struggle in industrial anomaly detection and reasoning, particularly in delivering interpretable explanations and generalizing to unseen categories. This limitation stems from the inherently domain-specific nature of anomaly detection, which hinders the applicability of existing VLMs in industrial scenarios that require precise, structured, and context-aware analysis. To address these challenges, we propose SAGE, a VLM-based framework that enhances anomaly reasoning through Self-Guided Fact Enhancement (SFE) and Entropy-aware Direct Preference Optimization (E-DPO). SFE integrates domain-specific knowledge into visual reasoning via fact extraction and fusion, while E-DPO aligns model outputs with expert preferences using entropy-aware optimization. Additionally, we introduce AD-PL, a preference-optimized dataset tailored for industrial anomaly reasoning, consisting of 28,415 question-answering instances with expert-ranked responses. To evaluate anomaly reasoning models, we develop Multiscale Logical Evaluation (MLE), a quantitative framework analyzing model logic and consistency. SAGE demonstrates superior performance on industrial anomaly datasets under zero-shot and one-shot settings. The code, model and dataset are available at https://github.com/amoreZgx1n/SAGE.", "date": "2025-07-10", "categories": []}
{"id": "2507.15905", "title": "Foundation Models and Transformers for Anomaly Detection: A Survey", "abstract": "In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We explore how these architectures, with their global receptive fields and adaptability, address challenges such as long-range dependency modeling, contextual modeling and data scarcity. The survey categorizes VAD methods into reconstruction-based, feature-based and zero/few-shot approaches, highlighting the paradigm shift brought about by foundation models. By integrating attention mechanisms and leveraging large-scale pre-training, Transformers and foundation models enable more robust, interpretable, and scalable anomaly detection solutions. This work provides a comprehensive review of state-of-the-art techniques, their strengths, limitations, and emerging trends in leveraging these architectures for VAD.", "date": "2025-07-21", "categories": []}
{"id": "2306.12033", "title": "Self-Tuning Self-Supervised Image Anomaly Detection", "abstract": "Self-supervised learning (SSL) has emerged as a promising paradigm that presents supervisory signals to real-world problems, bypassing the extensive cost of manual labeling. Consequently, self-supervised anomaly detection (SSAD) has seen a recent surge of interest, since SSL is especially attractive for unsupervised tasks. However, recent works have reported that the choice of a data augmentation function has significant impact on the accuracy of SSAD, posing augmentation search as an essential but nontrivial problem due to lack of labeled validation data. In this paper, we introduce ST-SSAD, the first unsupervised approach to end-to-end augmentation tuning for SSAD. To this end, our work presents two key contributions. The first is a new unsupervised validation loss that quantifies the alignment between augmented training data and unlabeled validation data. The second is new differentiable augmentation functions, allowing data augmentation hyperparameter(s) to be tuned in an end-to-end manner. Experiments on two testbeds with semantic class anomalies and subtle industrial defects show that ST-SSAD gives significant performance gains over existing works. All our code and testbeds are available at https://github.com/jaeminyoo/ST-SSAD.", "date": "2023-06-21", "categories": []}
{"id": "2507.12049", "title": "MoViAD: A Modular Library for Visual Anomaly Detection", "abstract": "VAD is a critical field in machine learning focused on identifying deviations from normal patterns in images, often challenged by the scarcity of anomalous data and the need for unsupervised training. To accelerate research and deployment in this domain, we introduce MoViAD, a comprehensive and highly modular library designed to provide fast and easy access to state-of-the-art VAD models, trainers, datasets, and VAD utilities. MoViAD supports a wide array of scenarios, including continual, semi-supervised, few-shots, noisy, and many more. In addition, it addresses practical deployment challenges through dedicated Edge and IoT settings, offering optimized models and backbones, along with quantization and compression utilities for efficient on-device execution and distributed inference. MoViAD integrates a selection of backbones, robust evaluation VAD metrics (pixel-level and image-level) and useful profiling tools for efficiency analysis. The library is designed for fast, effortless deployment, enabling machine learning engineers to easily use it for their specific setup with custom models, datasets, and backbones. At the same time, it offers the flexibility and extensibility researchers need to develop and experiment with new methods.", "date": "2025-07-16", "categories": []}
{"id": "2507.14677", "title": "Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective", "abstract": "The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.", "date": "2025-07-19", "categories": []}
{"id": "2507.14960", "title": "A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books", "abstract": "The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26,204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management.", "date": "2025-07-20", "categories": []}
{"id": "2507.15346", "title": "RoadFusion: Latent Diffusion Model for Pavement Defect Detection", "abstract": "Pavement defect detection faces critical challenges including limited annotated data, domain shift between training and deployment environments, and high variability in defect appearances across different road conditions. We propose RoadFusion, a framework that addresses these limitations through synthetic anomaly generation with dual-path feature adaptation. A latent diffusion model synthesizes diverse, realistic defects using text prompts and spatial masks, enabling effective training under data scarcity. Two separate feature adaptors specialize representations for normal and anomalous inputs, improving robustness to domain shift and defect variability. A lightweight discriminator learns to distinguish fine-grained defect patterns at the patch level. Evaluated on six benchmark datasets, RoadFusion achieves consistently strong performance across both classification and localization tasks, setting new state-of-the-art in multiple metrics relevant to real-world road inspection.", "date": "2025-07-21", "categories": []}
{"id": "2507.15492", "title": "An aerial color image anomaly dataset for search missions in complex forested terrain", "abstract": "After a family murder in rural Germany, authorities failed to locate the suspect in a vast forest despite a massive search. To aid the search, a research aircraft captured high-resolution aerial imagery. Due to dense vegetation obscuring small clues, automated analysis was ineffective, prompting a crowd-search initiative. This effort produced a unique dataset of labeled, hard-to-detect anomalies under occluded, real-world conditions. It can serve as a benchmark for improving anomaly detection approaches in complex forest environments, supporting manhunts and rescue operations. Initial benchmark tests showed existing methods performed poorly, highlighting the need for context-aware approaches. The dataset is openly accessible for offline processing. An additional interactive web interface supports online viewing and dynamic growth by allowing users to annotate and submit new findings.", "date": "2025-07-21", "categories": []}
{"id": "2507.15643", "title": "Towards Explainable Anomaly Detection in Shared Mobility Systems", "abstract": "Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations, improving service reliability, and enhancing user experience. This paper presents an interpretable anomaly detection framework that integrates multi-source data, including bike-sharing trip records, weather conditions, and public transit availability. The Isolation Forest algorithm is employed for unsupervised anomaly detection, along with the Depth-based Isolation Forest Feature Importance (DIFFI) algorithm providing interpretability. Results show that station-level analysis offers a robust understanding of anomalies, highlighting the influence of external factors such as adverse weather and limited transit availability. Our findings contribute to improving decision-making in shared mobility operations.", "date": "2025-07-21", "categories": []}
{"id": "2507.15676", "title": "Agentic AI for autonomous anomaly management in complex systems", "abstract": "This paper explores the potential of agentic AI in autonomously detecting and responding to anomalies within complex systems, emphasizing its ability to transform traditional, human-dependent anomaly management methods.", "date": "2025-07-21", "categories": []}
{"id": "2507.15718", "title": "Explainable Anomaly Detection for Electric Vehicles Charging Stations", "abstract": "Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability and efficiency requires effective anomaly detection to identify irregularities in charging behavior. However, in such a productive scenario, it is also crucial to determine the underlying cause behind the detected anomalies. To achieve this goal, this study investigates unsupervised anomaly detection techniques for EV charging infrastructure, integrating eXplainable Artificial Intelligence techniques to enhance interpretability and uncover root causes of anomalies.\n  Using real-world sensors and charging session data, this work applies Isolation Forest to detect anomalies and employs the Depth-based Isolation Forest Feature Importance (DIFFI) method to identify the most important features contributing to such anomalies. The efficacy of the proposed approach is evaluated in a real industrial case.", "date": "2025-07-21", "categories": []}
{"id": "2507.13378", "title": "A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects", "abstract": "Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.", "date": "2025-07-15", "categories": []}
{"id": "2507.13954", "title": "Robust Anomaly Detection with Graph Neural Networks using Controllability", "abstract": "Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples. Graph-based machine learning models have emerged as a promising solution that combines attribute and relational data to uncover intricate patterns. However, the scarcity of anomalous data exacerbates the challenge, which requires innovative strategies to enhance model learning with limited information. In this paper, we hypothesize that the incorporation of the influence of the nodes, quantified through average controllability, can significantly improve the performance of anomaly detection. We propose two novel approaches to integrate average controllability into graph-based frameworks: (1) using average controllability as an edge weight and (2) encoding it as a one-hot edge attribute vector. Through rigorous evaluation on real-world and synthetic networks with six state-of-the-art baselines, our proposed methods demonstrate improved performance in identifying anomalies, highlighting the critical role of controllability measures in enhancing the performance of graph machine learning models. This work underscores the potential of integrating average controllability as additional metrics to address the challenges of anomaly detection in sparse and imbalanced datasets.", "date": "2025-07-18", "categories": []}
{"id": "2507.14083", "title": "Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection", "abstract": "Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this paper, we present a comprehensive analysis of anomaly detection performance under four human anonymization techniques, including blurring, masking, encryption, and avatar replacement, applied to the UCF-Crime dataset. We evaluate four anomaly detection methods, MGFN, UR-DMU, BN-WVAD, and PEL4VAD, on the anonymized UCF-Crime to reveal how each method responds to different obfuscation techniques. Experimental results demonstrate that anomaly detection remains viable under anonymized data and is dependent on the algorithmic design and the learning strategy. For instance, under certain anonymization patterns, such as encryption and masking, some models inadvertently achieve higher AUC performance compared to raw data, due to the strong responsiveness of their algorithmic components to these noise patterns. These results highlight the algorithm-specific sensitivities to anonymization and emphasize the trade-off between preserving privacy and maintaining detection utility. Furthermore, we compare these conventional anonymization techniques with the emerging privacy-by-design solutions, highlighting an often overlooked trade-off between robust privacy protection and utility flexibility. Through comprehensive experiments and analyses, this study provides a compelling benchmark and insights into balancing human privacy with the demands of anomaly detection.", "date": "2025-07-18", "categories": []}
{"id": "2410.14987", "title": "SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning", "abstract": "We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of $+8.66\\%$ pixel-level AP for synthesis-based AD approaches, $+1.10\\%$ image-level AP for unsupervised AD methods, and $+12.79\\%$ IoU for supervised segmentation models. Code is available at \\href{https://github.com/HUST-SLOW/SeaS}{https://github.com/HUST-SLOW/SeaS}.", "date": "2024-10-19", "categories": []}
{"id": "2412.04769", "title": "Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection", "abstract": "For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single model capable of handling multiple classes. However, directly extending early AD methods to multi-class settings often results in degraded performance. In this paper, we investigate this performance degradation observed in reconstruction-based methods, identifying the key issue: inter-class confusion. This confusion emerges when a model trained in multi-class scenarios incorrectly reconstructs samples from one class as those of another, thereby exacerbating reconstruction errors. To this end, we propose a simple yet effective modification, called class-aware contrastive learning (CCL). By explicitly leveraging raw object category information (\\eg carpet or wood) as supervised signals, we introduce local CL to refine multiscale dense features, and global CL to obtain more compact feature representations of normal patterns, thereby effectively adapting the models to multi-class settings. Experiments across five datasets validate the effectiveness of our approach, demonstrating significant improvements and superior performance compared to state-of-the-art methods. Notably, ablation studies indicate that pseudo-class labels can achieve comparable performance.", "date": "2024-12-06", "categories": []}
{"id": "2502.12086", "title": "Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems", "abstract": "Dynamical systems, prevalent in various scientific and engineering domains, are susceptible to anomalies that can significantly impact their performance and reliability. This paper addresses the critical challenges of anomaly detection, root cause localization, and anomaly type classification in dynamical systems governed by ordinary differential equations (ODEs). We define two categories of anomalies: cyber anomalies, which propagate through interconnected variables, and measurement anomalies, which remain localized to individual variables. To address these challenges, we propose the Interpretable Causality Ordinary Differential Equation (ICODE) Networks, a model-intrinsic explainable learning framework. ICODE leverages Neural ODEs for anomaly detection while employing causality inference through an explanation channel to perform root cause analysis (RCA), elucidating why specific time periods are flagged as anomalous. ICODE is designed to simultaneously perform anomaly detection, RCA, and anomaly type classification within a single, interpretable framework. Our approach is grounded in the hypothesis that anomalies alter the underlying ODEs of the system, manifesting as changes in causal relationships between variables. We provide a theoretical analysis of how perturbations in learned model parameters can be utilized to identify anomalies and their root causes in time series data. Comprehensive experimental evaluations demonstrate the efficacy of ICODE across various dynamical systems, showcasing its ability to accurately detect anomalies, classify their types, and pinpoint their origins.", "date": "2025-02-17", "categories": []}
{"id": "2505.02179", "title": "ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications", "abstract": "Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling this via two synergistic components. The Prototype Interaction Layer (PIL) provides controlled normality modeling using a small set of learnable prototypes, establishing a robust baseline without being overwhelmed by dominant normal data. The Pseudo-Instance Discriminative Enhancement (PIDE) loss boosts separability by applying targeted contrastive learning exclusively to the most reliable extreme-scoring instances (highest/lowest scores). ProDisc-VAD achieves strong AUCs (97.98% ShanghaiTech, 87.12% UCF-Crime) using only 0.4M parameters, over 800x fewer than recent ViT-based methods like VadCLIP. Code is available at https://github.com/modadundun/ProDisc-VAD.", "date": "2025-05-04", "categories": []}
{"id": "2507.13022", "title": "Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers", "abstract": "In the context of the health monitoring for the next generation of reusable space launchers, we outline a first step toward developing an onboard fault detection and diagnostic capability for the electrical system that controls the engine valves. Unlike existing approaches in the literature, our solution is designed to meet a broader range of key requirements. This includes estimating confidence levels for predictions, detecting out-of-distribution (OOD) cases, and controlling false alarms. The proposed solution is based on a temporal convolutional autoencoder to automatically extract low-dimensional features from raw sensor data. Fault detection and diagnosis are respectively carried out using a binary and a multiclass classifier trained on the autoencoder latent and residual spaces. The classifiers are histogram-based gradient boosting models calibrated to output probabilities that can be interpreted as confidence levels. A relatively simple technique, based on inductive conformal anomaly detection, is used to identify OOD data. We leverage other simple yet effective techniques, such as cumulative sum control chart (CUSUM) to limit the false alarms, and threshold moving to address class imbalance in fault detection. The proposed framework is highly configurable and has been evaluated on simulated data, covering both nominal and anomalous operational scenarios. The results indicate that our solution is a promising first step, though testing with real data will be necessary to ensure that it achieves the required maturity level for operational use.", "date": "2025-07-17", "categories": []}
{"id": "2507.13110", "title": "3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering", "abstract": "High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computational cost, sensitivity to spatial misalignment, and difficulty in capturing localized structural differences. This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis to enable precise 3D anomaly localization. Specifically, each test sample is first registered to multiple normal prototypes to enable direct structural comparison. To evaluate anomalies at a local level, clustering is performed over the point cloud, and similarity is computed between features from the test sample and the prototypes within each cluster. Rather than selecting cluster centroids randomly, a keypoint-guided strategy is employed, where geometrically informative points are chosen as centroids. This ensures that clusters are centered on feature-rich regions, enabling more meaningful and stable distance-based comparisons. Extensive experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection, even using only raw features.", "date": "2025-07-17", "categories": []}
{"id": "2507.12295", "title": "Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding", "abstract": "Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation identification, spam detection and content moderation, etc. Despite significant advances in large language models (LLMs) and anomaly detection algorithms, the absence of standardized and comprehensive benchmarks for evaluating the existing anomaly detection methods on text data limits rigorous comparison and development of innovative approaches. This work performs a comprehensive empirical study and introduces a benchmark for text anomaly detection, leveraging embeddings from diverse pre-trained language models across a wide array of text datasets. Our work systematically evaluates the effectiveness of embedding-based text anomaly detection by incorporating (1) early language models (GloVe, BERT); (2) multiple LLMs (LLaMa-2, LLama-3, Mistral, OpenAI (small, ada, large)); (3) multi-domain text datasets (news, social media, scientific publications); (4) comprehensive evaluation metrics (AUROC, AUPRC). Our experiments reveal a critical empirical insight: embedding quality significantly governs anomaly detection efficacy, and deep learning-based approaches demonstrate no performance advantage over conventional shallow algorithms (e.g., KNN, Isolation Forest) when leveraging LLM-derived embeddings.In addition, we observe strongly low-rank characteristics in cross-model performance matrices, which enables an efficient strategy for rapid model evaluation (or embedding evaluation) and selection in practical applications. Furthermore, by open-sourcing our benchmark toolkit that includes all embeddings from different models and code at https://github.com/jicongfan/Text-Anomaly-Detection-Benchmark, this work provides a foundation for future research in robust and scalable text anomaly detection systems.", "date": "2025-07-16", "categories": []}
{"id": "2405.11238", "title": "SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection", "abstract": "Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains a tremendous challenge. Existing approaches often struggle with limited temporal contexts, insufficient representation of normal patterns, and flawed evaluation metrics, all of which hinder their effectiveness in detecting anomalous behavior. To address these issues, we introduce a $\\textbf{Sim}$ple dissimilarity-based approach for time series $\\textbf{A}$nomaly $\\textbf{D}$etection, referred to as $\\textbf{SimAD}$. Specifically, SimAD first incorporates a patching-based feature extractor capable of processing extended temporal windows and employs the EmbedPatch encoder to fully integrate normal behavioral patterns. Second, we design an innovative ContrastFusion module in SimAD, which strengthens the robustness of anomaly detection by highlighting the distributional differences between normal and abnormal data. Third, we introduce two robust enhanced evaluation metrics, Unbiased Affiliation (UAff) and Normalized Affiliation (NAff), designed to overcome the limitations of existing metrics by providing better distinctiveness and semantic clarity. The reliability of these two metrics has been demonstrated by both theoretical and experimental analyses. Experiments conducted on seven diverse time series datasets clearly demonstrate SimAD's superior performance compared to state-of-the-art methods, achieving relative improvements of $\\textbf{19.85%}$ on F1, $\\textbf{4.44%}$ on Aff-F1, $\\textbf{77.79%}$ on NAff-F1, and $\\textbf{9.69%}$ on AUC on six multivariate datasets. Code and pre-trained models are available at https://github.com/EmorZz1G/SimAD.", "date": "2024-05-18", "categories": []}
{"id": "2410.09126", "title": "Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors", "abstract": "Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task.", "date": "2024-10-11", "categories": []}
{"id": "2506.18046", "title": "TAB: Unified Benchmarking of Time Series Anomaly Detection Methods", "abstract": "Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods. Besides, all datasets and code are available at https://github.com/decisionintelligence/TAB.", "date": "2025-06-22", "categories": []}
{"id": "2507.11003", "title": "Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection", "abstract": "With the advent of vision-language models (e.g., CLIP) in zero- and few-shot settings, CLIP has been widely applied to zero-shot anomaly detection (ZSAD) in recent research, where the rare classes are essential and expected in many applications. This study introduces \\textbf{FiSeCLIP} for ZSAD with training-free \\textbf{CLIP}, combining the feature matching with the cross-modal alignment. Testing with the entire dataset is impractical, while batch-based testing better aligns with real industrial needs, and images within a batch can serve as mutual reference points. Accordingly, FiSeCLIP utilizes other images in the same batch as reference information for the current image. However, the lack of labels for these references can introduce ambiguity, we apply text information to \\textbf{fi}lter out noisy features. In addition, we further explore CLIP's inherent potential to restore its local \\textbf{se}mantic correlation, adapting it for fine-grained anomaly detection tasks to enable a more accurate filtering process. Our approach exhibits superior performance for both anomaly classification and segmentation on anomaly detection benchmarks, building a stronger baseline for the direction, e.g., on MVTec-AD, FiSeCLIP outperforms the SOTA AdaCLIP by +4.6\\%$\\uparrow$/+5.7\\%$\\uparrow$ in segmentation metrics AU-ROC/$F_1$-max.", "date": "2025-07-15", "categories": []}
{"id": "2507.11071", "title": "LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection", "abstract": "Log anomaly detection using traditional rule based or deep learning based methods is often challenging due to the large volume and highly complex nature of log sequence. So effective way of detection of anomalous sequence of logs is crucial for system maintenance and development. This paper proposes parameter efficient finetuning specifically low rank adaptation (LoRA) and adapter based approaches for finding contextual anomalies in sequence of logs in large log data set. It compares different tiny large language models (LLMs) on the Thunderbird dataset. The results show that LoRA based finetuning provides substantial performance improvements of 18 to 19 percentage over LogBert based full finetuning approach, achieving accuracy scores between 97.76% and 98.83% compared to 79.37%.", "date": "2025-07-15", "categories": []}
{"id": "2507.11106", "title": "A Mathematical Optimization Approach to Multisphere Support Vector Data Description", "abstract": "We present a novel mathematical optimization framework for outlier detection in multimodal datasets, extending Support Vector Data Description approaches. We provide a primal formulation, in the shape of a Mixed Integer Second Order Cone model, that constructs Euclidean hyperspheres to identify anomalous observations. Building on this, we develop a dual model that enables the application of the kernel trick, thus allowing for the detection of outliers within complex, non-linear data structures. An extensive computational study demonstrates the effectiveness of our exact method, showing clear advantages over existing heuristic techniques in terms of accuracy and robustness.", "date": "2025-07-15", "categories": []}
{"id": "2502.01391", "title": "Learning Traffic Anomalies from Generative Models on Real-Time Observations", "abstract": "Accurate detection of traffic anomalies is crucial for effective urban traffic management and congestion mitigation. We use the Spatiotemporal Generative Adversarial Network (STGAN) framework combining Graph Neural Networks and Long Short-Term Memory networks to capture complex spatial and temporal dependencies in traffic data. We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020. The images are processed to compute a flow metric representing vehicle density, which serves as input for the model. Training is conducted on data from April to November 2020, and validation is performed on a separate dataset from November 14 to 23, 2020. Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates. The detected anomalies include camera signal interruptions, visual artifacts, and extreme weather conditions affecting traffic flow.", "date": "2025-02-03", "categories": []}
{"id": "2507.09619", "title": "Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection", "abstract": "Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as localization and classification. While several anomaly synthesis approaches have been introduced for data augmentation, they often struggle with low realism, inaccurate mask alignment, and poor generalization. To overcome these limitations, we propose Generate Aligned Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework. GAA leverages the strong priors of a pretrained latent diffusion model to generate realistic, diverse, and semantically aligned anomalies using only a small number of samples. The framework first employs Localized Concept Decomposition to jointly model the semantic features and spatial information of anomalies, enabling flexible control over the type and location of anomalies. It then utilizes Adaptive Multi-Round Anomaly Clustering to perform fine-grained semantic clustering of anomaly concepts, thereby enhancing the consistency of anomaly representations. Subsequently, a region-guided mask generation strategy ensures precise alignment between anomalies and their corresponding masks, while a low-quality sample filtering module is introduced to further improve the overall quality of the generated samples. Extensive experiments on the MVTec AD and LOCO datasets demonstrate that GAA achieves superior performance in both anomaly synthesis quality and downstream tasks such as localization and classification.", "date": "2025-07-13", "categories": []}
{"id": "2507.09742", "title": "Causality-informed Anomaly Detection in Partially Observable Sensor Networks: Moving beyond Correlations", "abstract": "Nowadays, as AI-driven manufacturing becomes increasingly popular, the volume of data streams requiring real-time monitoring continues to grow. However, due to limited resources, it is impractical to place sensors at every location to detect unexpected shifts. Therefore, it is necessary to develop an optimal sensor placement strategy that enables partial observability of the system while detecting anomalies as quickly as possible. Numerous approaches have been proposed to address this challenge; however, most existing methods consider only variable correlations and neglect a crucial factor: Causality. Moreover, although a few techniques incorporate causal analysis, they rely on interventions-artificially creating anomalies-to identify causal effects, which is impractical and might lead to catastrophic losses. In this paper, we introduce a causality-informed deep Q-network (Causal DQ) approach for partially observable sensor placement in anomaly detection. By integrating causal information at each stage of Q-network training, our method achieves faster convergence and tighter theoretical error bounds. Furthermore, the trained causal-informed Q-network significantly reduces the detection time for anomalies under various settings, demonstrating its effectiveness for sensor placement in large-scale, real-world data streams. Beyond the current implementation, our technique's fundamental insights can be applied to various reinforcement learning problems, opening up new possibilities for real-world causality-informed machine learning methods in engineering applications.", "date": "2025-07-13", "categories": []}
{"id": "2507.05730", "title": "Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study", "abstract": "Hyperspectral images are high-dimensional datasets comprising hundreds of contiguous spectral bands, enabling detailed analysis of materials and surfaces. Hyperspectral anomaly detection (HAD) refers to the technique of identifying and locating anomalous targets in such data without prior information about a hyperspectral scene or target spectrum. This technology has seen rapid advancements in recent years, with applications in agriculture, defence, military surveillance, and environmental monitoring. Despite this significant progress, existing HAD methods continue to face challenges such as high computational complexity, sensitivity to noise, and limited generalisation across diverse datasets. This study presents a comprehensive comparison of various HAD techniques, categorising them into statistical models, representation-based methods, classical machine learning approaches, and deep learning models. We evaluated these methods across 17 benchmarking datasets using different performance metrics, such as ROC, AUC, and separability map to analyse detection accuracy, computational efficiency, their strengths, limitations, and directions for future research. Our findings highlight that deep learning models achieved the highest detection accuracy, while statistical models demonstrated exceptional speed across all datasets. This survey aims to provide valuable insights for researchers and practitioners working to advance the field of hyperspectral anomaly detection methods.", "date": "2025-07-08", "categories": []}
{"id": "2507.08177", "title": "Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity", "abstract": "As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an important role in ensuring system security and operational integrity. However, current data-driven approaches, largely driven by black-box deep learning, face challenges in interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. In this paper, we advocate for a causal learning perspective to advance anomaly detection in spatially distributed infrastructures that grounds detection in structural cause-effect relationships. We identify and formalize three key directions: causal graph profiling, multi-view fusion, and continual causal graph learning, each offering distinct advantages in uncovering dynamic cause-effect structures across time and space. Drawing on real-world insights from systems such as water treatment infrastructures, we illustrate how causal models provide early warning signals and root cause attribution, addressing the limitations of black-box detectors. Looking ahead, we outline the future research agenda centered on multi-modality, generative AI-driven, and scalable adaptive causal frameworks. Our objective is to lay a new research trajectory toward scalable, adaptive, explainable, and spatially grounded anomaly detection systems. We hope to inspire a paradigm shift in cybersecurity research, promoting causality-driven approaches to address evolving threats in interconnected infrastructures.", "date": "2025-07-10", "categories": []}
{"id": "2507.08518", "title": "Data Depth as a Risk", "abstract": "Data depths are score functions that quantify in an unsupervised fashion how central is a point inside a distribution, with numerous applications such as anomaly detection, multivariate or functional data analysis, arising across various fields. The halfspace depth was the first depth to aim at generalising the notion of quantile beyond the univariate case. Among the existing variety of depth definitions, it remains one of the most used notions of data depth. Taking a different angle from the quantile point of view, we show that the halfspace depth can also be regarded as the minimum loss of a set of classifiers for a specific labelling of the points. By changing the loss or the set of classifiers considered, this new angle naturally leads to a family of \"loss depths\", extending to well-studied classifiers such as, e.g., SVM or logistic regression, among others. This framework directly inherits computational efficiency of existing machine learning algorithms as well as their fast statistical convergence rates, and opens the data depth realm to the high-dimensional setting. Furthermore, the new loss depths highlight a connection between the dataset and the right amount of complexity or simplicity of the classifiers. The simplicity of classifiers as well as the interpretation as a risk makes our new kind of data depth easy to explain, yet efficient for anomaly detection, as is shown by experiments.", "date": "2025-07-11", "categories": []}
{"id": "2506.23446", "title": "User-Based Sequential Modeling with Transformer Encoders for Insider Threat Detection", "abstract": "Insider threat detection presents unique challenges due to the authorized status of malicious actors and the subtlety of anomalous behaviors. Existing machine learning methods often treat user activity as isolated events, thereby failing to leverage sequential dependencies in user behavior. In this study, we propose a User-Based Sequencing (UBS) methodology, transforming the CERT insider threat dataset into structured temporal sequences suitable for deep sequential modeling. We deploy a Transformer Encoder architecture to model benign user activity and employ its reconstruction errors as anomaly scores. These scores are subsequently evaluated using three unsupervised outlier detection algorithms: One-Class SVM (OCSVM), Local Outlier Factor (LOF), and Isolation Forest (iForest). Across four rigorously designed test sets, including combinations of multiple CERT dataset releases, our UBS-Transformer pipeline consistently achieves state-of-the-art performance - notably 96.61% accuracy, 99.43% recall, 96.38% F1-score, 95.00% AUROC, and exceptionally low false negative (0.0057) and false positive (0.0571) rates. Comparative analyses demonstrate that our approach substantially outperforms tabular and conventional autoencoder baselines, underscoring the efficacy of sequential user modeling and advanced anomaly detection in the insider threat domain.", "date": "2025-06-30", "categories": []}
{"id": "2507.07435", "title": "Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects", "abstract": "In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1\\% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.", "date": "2025-07-10", "categories": []}
{"id": "2507.07559", "title": "Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series", "abstract": "Anomaly detection (AD) plays a vital role across a wide range of real-world domains by identifying data instances that deviate from expected patterns, potentially signaling critical events such as system failures, fraudulent activities, or rare medical conditions. The demand for real-time AD has surged with the rise of the (Industrial) Internet of Things, where massive volumes of multivariate sensor data must be processed instantaneously. Real-time AD requires methods that not only handle high-dimensional streaming data but also operate in a single-pass manner, without the burden of storing historical instances, thereby ensuring minimal memory usage and fast decision-making. We propose DAD, a novel real-time decorrelation-based anomaly detection method for multivariate time series, based on an online decorrelation learning approach. Unlike traditional proximity-based or reconstruction-based detectors that process entire data or windowed instances, DAD dynamically learns and monitors the correlation structure of data sample by sample in a single pass, enabling efficient and effective detection. To support more realistic benchmarking practices, we also introduce a practical hyperparameter tuning strategy tailored for real-time anomaly detection scenarios. Extensive experiments on widely used benchmark datasets demonstrate that DAD achieves the most consistent and superior performance across diverse anomaly types compared to state-of-the-art methods. Crucially, its robustness to increasing dimensionality makes it particularly well-suited for real-time, high-dimensional data streams. Ultimately, DAD not only strikes an optimal balance between detection efficacy and computational efficiency but also sets a new standard for real-time, memory-constrained anomaly detection.", "date": "2025-07-10", "categories": []}
{"id": "2507.07579", "title": "NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning", "abstract": "This paper presents a novel few-shot cross-domain anomaly detection framework, Nexus Vision Transformer for Anomaly Detection (NexViTAD), based on vision foundation models, which effectively addresses domain-shift challenges in industrial anomaly detection through innovative shared subspace projection mechanisms and multi-task learning (MTL) module. The main innovations include: (1) a hierarchical adapter module that adaptively fuses complementary features from Hiera and DINO-v2 pre-trained models, constructing more robust feature representations; (2) a shared subspace projection strategy that enables effective cross-domain knowledge transfer through bottleneck dimension constraints and skip connection mechanisms; (3) a MTL Decoder architecture supports simultaneous processing of multiple source domains, significantly enhancing model generalization capabilities; (4) an anomaly score inference method based on Sinkhorn-K-means clustering, combined with Gaussian filtering and adaptive threshold processing for precise pixel level. Valuated on the MVTec AD dataset, NexViTAD delivers state-of-the-art performance with an AUC of 97.5%, AP of 70.4%, and PRO of 95.2% in the target domains, surpassing other recent models, marking a transformative advance in cross-domain defect detection.", "date": "2025-07-10", "categories": []}
{"id": "2507.06733", "title": "MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport", "abstract": "Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapters and prompt learning with Partial Optimal Transport (POT) and contrastive learning (CL) to improve CLIP's adaptability to medical images, particularly for AD. Unlike standard prompt learning, which often yields a single representation, our method employs multiple prompts aligned with local features via POT to capture subtle abnormalities. CL further enforces intra-class cohesion and inter-class separation. Our method achieves state-of-the-art results in few-shot, zero-shot, and cross-dataset scenarios without synthetic data or memory banks. The code is available at https://github.com/mahshid1998/MADPOT.", "date": "2025-07-09", "categories": []}
{"id": "2502.12793", "title": "Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport", "abstract": "Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods.", "date": "2025-02-18", "categories": []}
{"id": "2507.06217", "title": "What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23", "abstract": "We present results from the SNAD VIII Workshop, during which we conducted the first systematic anomaly search in the ZTF fields also observed by LSSTComCam during Rubin Scientific Pipeline commissioning. Using the PineForest active anomaly detection algorithm, we analysed four selected fields (two galactic and two extragalactic) and visually inspected 400 candidates. As a result, we discovered six previously uncatalogued variable stars, including RS~CVn, BY Draconis, ellipsoidal, and solar-type variables, and refined classifications and periods for six known objects. These results demonstrate the effectiveness of the SNAD anomaly detection pipeline and provide a preview of the discovery potential in the upcoming LSST data.", "date": "2025-07-08", "categories": []}
{"id": "2502.08593", "title": "Toward Universal Laws of Outlier Propagation", "abstract": "When a variety of anomalous features motivate flagging different samples as outliers, Algorithmic Information Theory (AIT) offers a principled way to unify them in terms of a sample's randomness deficiency. Subject to the algorithmic Markov condition on a causal Bayesian network, we show that the randomness deficiency of a joint sample decomposes into a sum of randomness deficiencies at each causal mechanism. Consequently, anomalous observations can be attributed to their root causes, i.e., the mechanisms that behaved anomalously. As an extension of Levin's law of randomness conservation, we show that weak outliers cannot cause strong ones. We show how these information theoretic laws clarify our understanding of outlier detection and attribution, in the context of more specialized outlier scores from prior literature.", "date": "2025-02-12", "categories": []}
{"id": "2506.23577", "title": "StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection", "abstract": "Enhancing the alignment between text and image features in the CLIP model is a critical challenge in zero-shot industrial anomaly detection tasks. Recent studies predominantly utilize specific category prompts during pretraining, which can cause overfitting to the training categories and limit model generalization. To address this, we propose a method that transforms category names through multicategory name stacking to create stacked prompts, forming the basis of our StackCLIP model. Our approach introduces two key components. The Clustering-Driven Stacked Prompts (CSP) module constructs generic prompts by stacking semantically analogous categories, while utilizing multi-object textual feature fusion to amplify discriminative anomalies among similar objects. The Ensemble Feature Alignment (EFA) module trains knowledge-specific linear layers tailored for each stack cluster and adaptively integrates them based on the attributes of test categories. These modules work together to deliver superior training speed, stability, and convergence, significantly boosting anomaly segmentation performance. Additionally, our stacked prompt framework offers robust generalization across classification tasks. To further improve performance, we introduce the Regulating Prompt Learning (RPL) module, which leverages the generalization power of stacked prompts to refine prompt learning, elevating results in anomaly detection classification tasks. Extensive testing on seven industrial anomaly detection datasets demonstrates that our method achieves state-of-the-art performance in both zero-shot anomaly detection and segmentation tasks.", "date": "2025-06-30", "categories": []}
{"id": "2507.01455", "title": "OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes", "abstract": "Anomaly segmentation aims to identify Out-of-Distribution (OoD) anomalous objects within images. Existing pixel-wise methods typically assign anomaly scores individually and employ a global thresholding strategy to segment anomalies. Despite their effectiveness, these approaches encounter significant challenges in real-world applications: (1) neglecting spatial correlations among pixels within the same object, resulting in fragmented segmentation; (2) variabil ity in anomaly score distributions across image regions, causing global thresholds to either generate false positives in background areas or miss segments of anomalous objects. In this work, we introduce OoDDINO, a novel multi-level anomaly segmentation framework designed to address these limitations through a coarse-to-fine anomaly detection strategy. OoDDINO combines an uncertainty-guided anomaly detection model with a pixel-level segmentation model within a two-stage cascade architecture. Initially, we propose an Orthogonal Uncertainty-Aware Fusion Strategy (OUAFS) that sequentially integrates multiple uncertainty metrics with visual representations, employing orthogonal constraints to strengthen the detection model's capacity for localizing anomalous regions accurately. Subsequently, we develop an Adaptive Dual-Threshold Network (ADT-Net), which dynamically generates region-specific thresholds based on object-level detection outputs and pixel-wise anomaly scores. This approach allows for distinct thresholding strategies within foreground and background areas, achieving fine-grained anomaly segmentation. The proposed framework is compatible with other pixel-wise anomaly detection models, which acts as a plug-in to boost the performance. Extensive experiments on two benchmark datasets validate our framework's superiority and compatibility over state-of-the-art methods.", "date": "2025-07-02", "categories": []}
{"id": "2507.03656", "title": "MLASDO: a software tool to detect and explain clinical and omics inconsistencies applied to the Parkinson's Progression Markers Initiative cohort", "abstract": "Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-based patients or individuals may become crucial to better reshape the disease, e.g., by detecting early onsets signaled by the omics and undetectable from observable symptoms. Here, we developed MLASDO (Machine Learning based Anomalous Sample Detection on Omics), a new method and software tool to identify, characterize and automatically describe anomalous samples based on omics data. Its workflow is based on three steps: (1) classification of healthy and cases individuals using a support vector machine algorithm; (2) detection of anomalous samples within groups; (3) explanation of anomalous individuals based on clinical data and expert knowledge. We showcase MLASDO using transcriptomics data of 317 healthy controls (HC) and 465 Parkinson's disease (PD) cases from the Parkinson's Progression Markers Initiative. In this cohort, MLASDO detected 15 anomalous HC with a PD-like transcriptomic signature and PD-like clinical features, including a lower proportion of CD4/CD8 naive T-cells and CD4 memory T-cells compared to HC (P<3.5*10^-3). MLASDO also identified 22 anomalous PD cases with a transcriptomic signature more similar to that of HC and some clinical features more similar to HC, including a lower proportion of mature neutrophils compared to PD cases (P<6*10^-3). In summary, MLASDO is a powerful tool that can help the clinician to detect and explain anomalous HC and cases of interest to be followed up. MLASDO is an open-source R package available at: https://github.com/JoseAdrian3/MLASDO.", "date": "2025-07-04", "categories": []}
{"id": "2507.03995", "title": "Fast Re-Trainable Attention Autoencoder for Liquid Sensor Anomaly Detection at the Edge", "abstract": "A lightweight, edge-deployable pipeline is proposed for detecting sensor anomalies in chemistry and biology laboratories. A custom PCB captures seven sensor channels and streams them over the local network. An Attention-based One-Class Autoencoder reaches a usable state after training on only thirty minutes of normal data. Despite the small data set, the model already attains an F1 score of 0.72, a precision of 0.89, and a recall of 0.61 when tested on synthetic micro-anomalies. The trained network is converted into a TensorFlow-Lite binary of about 31 kB and runs on an Advantech ARK-1221L, a fan-less x86 edge device without AVX instructions; end-to-end inference latency stays below two seconds. The entire collect-train-deploy workflow finishes within one hour, which demonstrates that the pipeline adapts quickly whenever a new liquid or sensor is introduced.", "date": "2025-07-05", "categories": []}
{"id": "2507.01999", "title": "Continuous Wavelet Transform and Siamese Network-Based Anomaly Detection in Multi-variate Semiconductor Process Time Series", "abstract": "Semiconductor manufacturing is an extremely complex process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate time-series (MTS) analysis has emerged as a critical methodology for enabling real-time monitoring, fault detection, and predictive maintenance in such environments. However, anomaly prediction in semiconductor fabrication presents several critical challenges, including high data dimensionality, severe class imbalance due to the rarity of true faults, noisy and missing measurements, and non-stationary behavior of production systems. Furthermore, the complex interdependencies between variables and the delayed emergence of faults across downstream stages complicate both anomaly detection and root-cause-analysis. This paper presents a novel and generic approach for anomaly detection in MTS data using machine learning. The proposed methodology consists of three main steps: a) converting MTS data into image-based representations using the Continuous Wavelet Transform, b) developing a multi-class image classifier by fine-tuning a pretrained VGG-16 architecture on custom CWT image datasets, and c) constructing a Siamese network composed of two identical sub-networks, each utilizing the fine-tuned VGG-16 as a backbone. The network takes pairs of CWT images as input -one serving as a reference or anchor (representing a known-good signal), and the other as a query (representing an unknown signal). The model then compares the embeddings of both inputs to determine whether they belong to the same class at a given time step. Our approach demonstrates high accuracy in identifying anomalies on a real FAB process time-series dataset, offering a promising solution for offline anomaly detection in process and tool trace data. Moreover, the approach is flexible and can be applied in both supervised and semi-supervised settings.", "date": "2025-07-01", "categories": []}
{"id": "2411.12556", "title": "UMGAD: Unsupervised Multiplex Graph Anomaly Detection", "abstract": "Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely applied in various real-world scenarios, including fraud detection and social network analysis. However, existing GAD methods still face two major challenges: (1) They are often limited to detecting anomalies in single-type interaction graphs and struggle with multiple interaction types in multiplex heterogeneous graphs. (2) In unsupervised scenarios, selecting appropriate anomaly score thresholds remains a significant challenge for accurate anomaly detection. To address the above challenges, we propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD. We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs and capture anomaly information during node attribute and structure reconstruction through graph-masked autoencoder (GMAE). Then, to further extract abnormal information, we generate attribute-level and subgraph-level augmented-view graphs, respectively, and perform attribute and structure reconstruction through GMAE. Finally, we learn to optimize node attributes and structural features through contrastive learning between original-view and augmented-view graphs to improve the model's ability to capture anomalies. Meanwhile, we propose a new anomaly score threshold selection strategy, which allows the model to be independent of ground truth information in real unsupervised scenarios. Extensive experiments on six datasets show that our UMGAD significantly outperforms state-of-the-art methods, achieving average improvements of 12.25% in AUC and 11.29% in Macro-F1 across all datasets.", "date": "2024-11-19", "categories": []}
{"id": "2507.01077", "title": "Good Enough to Learn: LLM-based Anomaly Detection in ECU Logs without Reliable Labels", "abstract": "Anomaly detection often relies on supervised or clustering approaches, with limited success in specialized domains like automotive communication systems where scalable solutions are essential. We propose a novel decoder-only Large Language Model (LLM) to detect anomalies in Electronic Control Unit (ECU) communication logs. Our approach addresses two key challenges: the lack of LLMs tailored for ECU communication and the complexity of inconsistent ground truth data. By learning from UDP communication logs, we formulate anomaly detection simply as identifying deviations in time from normal behavior. We introduce an entropy regularization technique that increases model's uncertainty in known anomalies while maintaining consistency in similar scenarios. Our solution offers three novelties: a decoder-only anomaly detection architecture, a way to handle inconsistent labeling, and an adaptable LLM for different ECU communication use cases. By leveraging the generative capabilities of decoder-only models, we present a new technique that addresses the high cost and error-prone nature of manual labeling through a more scalable system that is able to learn from a minimal set of examples, while improving detection accuracy in complex communication environments.", "date": "2025-07-01", "categories": []}
{"id": "2507.01875", "title": "Towards Foundation Auto-Encoders for Time-Series Anomaly Detection", "abstract": "We investigate a novel approach to time-series modeling, inspired by the successes of large pretrained foundation models. We introduce FAE (Foundation Auto-Encoders), a foundation generative-AI model for anomaly detection in time-series data, based on Variational Auto-Encoders (VAEs). By foundation, we mean a model pretrained on massive amounts of time-series data which can learn complex temporal patterns useful for accurate modeling, forecasting, and detection of anomalies on previously unseen datasets. FAE leverages VAEs and Dilated Convolutional Neural Networks (DCNNs) to build a generic model for univariate time-series modeling, which could eventually perform properly in out-of-the-box, zero-shot anomaly detection applications. We introduce the main concepts of FAE, and present preliminary results in different multi-dimensional time-series datasets from various domains, including a real dataset from an operational mobile ISP, and the well known KDD 2021 Anomaly Detection dataset.", "date": "2025-07-02", "categories": []}
{"id": "2507.01924", "title": "Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection", "abstract": "The complexity of mental healthcare billing enables anomalies, including fraud. While machine learning methods have been applied to anomaly detection, they often struggle with class imbalance, label scarcity, and complex sequential patterns. This study explores a hybrid deep learning approach combining Long Short-Term Memory (LSTM) networks and Transformers, with pseudo-labeling via Isolation Forests (iForest) and Autoencoders (AE). Prior work has not evaluated such hybrid models trained on pseudo-labeled data in the context of healthcare billing. The approach is evaluated on two real-world billing datasets related to mental healthcare. The iForest LSTM baseline achieves the highest recall (0.963) on declaration-level data. On the operation-level data, the hybrid iForest-based model achieves the highest recall (0.744), though at the cost of lower precision. These findings highlight the potential of combining pseudo-labeling with hybrid deep learning in complex, imbalanced anomaly detection settings.", "date": "2025-07-02", "categories": []}
{"id": "2406.00772", "title": "Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models", "abstract": "Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). In the context of brain MRIs, existing CA approaches rely on supervised contrastive learning or variational autoencoders (VAEs) using both healthy and unhealthy data, but such reliance on target samples is challenging in clinical settings. Unsupervised Anomaly Detection (UAD) offers an alternative by learning a reference representation of healthy anatomy without the need for target samples. Deviations from this reference distribution can indicate potential anomalies. In this context, diffusion models have been increasingly adopted in UAD due to their superior performance in image generation compared to VAEs. Nonetheless, precisely reconstructing the anatomy of the brain remains a challenge. In this work, we propose an unsupervised framework to improve the reconstruction quality by training a self-supervised contrastive encoder on healthy images to extract meaningful anatomical features. These features are used to condition a diffusion model to reconstruct the healthy appearance of a given image, enabling interpretable anomaly localization via pixel-wise comparison. We validate our approach through a proof-of-concept on a facial image dataset and further demonstrate its effectiveness on four brain MRI datasets, achieving state-of-the-art anomaly localization performance on the NOVA benchmark.", "date": "2024-06-02", "categories": []}
{"id": "2503.02424", "title": "Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection", "abstract": "Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at:https://github.com/luow23/INP-Former.", "date": "2025-03-04", "categories": []}
{"id": "2506.02007", "title": "eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems", "abstract": "We present eACGM, a full-stack AI/ML system monitoring framework based on eBPF. eACGM collects real-time performance data from key hardware components, including the GPU and network communication layer, as well as from key software stacks such as CUDA, Python, and PyTorch, all without requiring any code instrumentation or modifications. Additionally, it leverages libnvml to gather process-level GPU resource usage information. By applying a Gaussian Mixture Model (GMM) to the collected multidimensional performance metrics for statistical modeling and clustering analysis, eACGM effectively identifies complex failure modes, such as latency anomalies, hardware failures, and communication inefficiencies, enabling rapid diagnosis of system bottlenecks and abnormal behaviors.\n  To evaluate eACGM's effectiveness and practicality, we conducted extensive empirical studies and case analyses in multi-node distributed training scenarios. The results demonstrate that eACGM, while maintaining a non-intrusive and low-overhead profile, successfully captures critical performance anomalies during model training and inference. Its stable anomaly detection performance and comprehensive monitoring capabilities validate its applicability and scalability in real-world production environments, providing strong support for performance optimization and fault diagnosis in large-scale AI/ML systems.", "date": "2025-05-25", "categories": []}
{"id": "2507.00304", "title": "MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic", "abstract": "The abnormal fluctuations in network traffic may indicate potential security threats or system failures. Therefore, efficient network traffic prediction and anomaly detection methods are crucial for network security and traffic management. This paper proposes a novel network traffic prediction and anomaly detection model, MamNet, which integrates time-domain modeling and frequency-domain feature extraction. The model first captures the long-term dependencies of network traffic through the Mamba module (time-domain modeling), and then identifies periodic fluctuations in the traffic using Fourier Transform (frequency-domain feature extraction). In the feature fusion layer, multi-scale information is integrated to enhance the model's ability to detect network traffic anomalies. Experiments conducted on the UNSW-NB15 and CAIDA datasets demonstrate that MamNet outperforms several recent mainstream models in terms of accuracy, recall, and F1-Score. Specifically, it achieves an improvement of approximately 2% to 4% in detection performance for complex traffic patterns and long-term trend detection. The results indicate that MamNet effectively captures anomalies in network traffic across different time scales and is suitable for anomaly detection tasks in network security and traffic management. Future work could further optimize the model structure by incorporating external network event information, thereby improving the model's adaptability and stability in complex network environments.", "date": "2025-06-30", "categories": []}
{"id": "2405.16258", "title": "Deep Multi-Manifold Transformation Based Multivariate Time Series Fault Detection", "abstract": "Unsupervised fault detection in multivariate time series plays a vital role in ensuring the stable operation of complex systems. Traditional methods often assume that normal data follow a single Gaussian distribution and identify anomalies as deviations from this distribution. {\\color{black} However, this simplified assumption fails to capture the diversity and structural complexity of real-world time series, which can lead to misjudgments and reduced detection performance in practical applications. To address this issue, we propose a new method that combines a neighborhood-driven data augmentation strategy with a multi-manifold representation learning framework.} By incorporating information from local neighborhoods, the augmentation module can simulate contextual variations of normal data, enhancing the model's adaptability to distributional changes. In addition, we design a structure-aware feature learning approach that encourages natural clustering of similar patterns in the feature space while maintaining sufficient distinction between different operational states. Extensive experiments on several public benchmark datasets demonstrate that our method achieves superior performance in terms of both accuracy and robustness, showing strong potential for generalization and real-world deployment.", "date": "2024-05-25", "categories": []}
{"id": "2411.14565", "title": "Privacy-Preserving Video Anomaly Detection: A Survey", "abstract": "Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm, such as fighting, stealing, and car accidents. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in the AI community. However, current research in P2VAD is fragmented, and prior reviews have mostly focused on methods using RGB sequences, overlooking privacy leakage and appearance bias considerations. To address this gap, this article is the first to systematically reviews the progress of P2VAD, defining its scope and providing an intuitive taxonomy. We outline the basic assumptions, learning frameworks, and optimization objectives of various approaches, analyzing their strengths, weaknesses, and potential correlations. Additionally, we provide open access to research resources such as benchmark datasets and available code. Finally, we discuss key challenges and future opportunities from the perspectives of AI development and P2VAD deployment, aiming to guide future work in the field.", "date": "2024-11-21", "categories": []}
{"id": "2501.14694", "title": "Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection", "abstract": "Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph anomaly detection. However, we empirically found that three important factors can substantially impact detection performance across datasets: 1) the specific SSL strategy employed; 2) the tuning of the strategy's hyperparameters; and 3) the allocation of combination weights when using multiple strategies. Most SSL-based graph anomaly detection methods circumvent these issues by arbitrarily or selectively (i.e., guided by label information) choosing SSL strategies, hyperparameter settings, and combination weights. While an arbitrary choice may lead to subpar performance, using label information in an unsupervised setting is label information leakage and leads to severe overestimation of a method's performance. Leakage has been criticized as \"one of the top ten data mining mistakes\", yet many recent studies on SSL-based graph anomaly detection have been using label information to select hyperparameters. To mitigate this issue, we propose to use an internal evaluation strategy (with theoretical analysis) to select hyperparameters in SSL for unsupervised anomaly detection. We perform extensive experiments using 10 recent SSL-based graph anomaly detection algorithms on various benchmark datasets, demonstrating both the prior issues with hyperparameter selection and the effectiveness of our proposed strategy.", "date": "2025-01-24", "categories": []}
{"id": "2502.19122", "title": "Random Similarity Isolation Forests", "abstract": "With predictive models becoming prevalent, companies are expanding the types of data they gather. As a result, the collected datasets consist not only of simple numerical features but also more complex objects such as time series, images, or graphs. Such multi-modal data have the potential to improve performance in predictive tasks like outlier detection, where the goal is to identify objects deviating from the main data distribution. However, current outlier detection algorithms are dedicated to individual types of data. Consequently, working with mixed types of data requires either fusing multiple data-specific models or transforming all of the representations into a single format, both of which can hinder predictive performance. In this paper, we propose a multi-modal outlier detection algorithm called Random Similarity Isolation Forest. Our method combines the notions of isolation and similarity-based projection to handle datasets with mixtures of features of arbitrary data types. Experiments performed on 47 benchmark datasets demonstrate that Random Similarity Isolation Forest outperforms five state-of-the-art competitors. Our study shows that the use of multiple modalities can indeed improve the detection of anomalies and highlights the need for new outlier detection benchmarks tailored for multi-modal algorithms.", "date": "2025-02-26", "categories": []}
{"id": "2506.03660", "title": "INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning", "abstract": "Anomaly detection (AD) is essential for industrial inspection and medical diagnosis, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Furthermore, we propose a soft version of the INP Coherence Loss and enhance INP-Former by incorporating residual learning, leading to the development of INP-Former++. The proposed method significantly improves detection performance across single-class, multi-class, semi-supervised, few-shot, and zero-shot settings.", "date": "2025-06-04", "categories": []}
{"id": "2506.22984", "title": "Cybersecurity-Focused Anomaly Detection in Connected Autonomous Vehicles Using Machine Learning", "abstract": "Anomaly detection in connected autonomous vehicles (CAVs) is crucial for maintaining safe and reliable transportation networks, as CAVs can be susceptible to sensor malfunctions, cyber-attacks, and unexpected environmental disruptions. This study explores an anomaly detection approach by simulating vehicle behavior, generating a dataset that represents typical and atypical vehicular interactions. The dataset includes time-series data of position, speed, and acceleration for multiple connected autonomous vehicles. We utilized machine learning models to effectively identify abnormal driving patterns. First, we applied a stacked Long Short-Term Memory (LSTM) model to capture temporal dependencies and sequence-based anomalies. The stacked LSTM model processed the sequential data to learn standard driving behaviors. Additionally, we deployed a Random Forest model to support anomaly detection by offering ensemble-based predictions, which enhanced model interpretability and performance. The Random Forest model achieved an R2 of 0.9830, MAE of 5.746, and a 95th percentile anomaly threshold of 14.18, while the stacked LSTM model attained an R2 of 0.9998, MAE of 82.425, and a 95th percentile anomaly threshold of 265.63. These results demonstrate the models' effectiveness in accurately predicting vehicle trajectories and detecting anomalies in autonomous driving scenarios.", "date": "2025-06-28", "categories": []}
{"id": "2506.22994", "title": "Kernel Outlier Detection", "abstract": "A new anomaly detection method called kernel outlier detection (KOD) is proposed. It is designed to address challenges of outlier detection in high-dimensional settings. The aim is to overcome limitations of existing methods, such as dependence on distributional assumptions or on hyperparameters that are hard to tune. KOD starts with a kernel transformation, followed by a projection pursuit approach. Its novelties include a new ensemble of directions to search over, and a new way to combine results of different direction types. This provides a flexible and lightweight approach for outlier detection. Our empirical evaluations illustrate the effectiveness of KOD on three small datasets with challenging structures, and on four large benchmark datasets.", "date": "2025-06-28", "categories": []}
{"id": "2506.23282", "title": "Autoregressive Denoising Score Matching is a Good Video Anomaly Detector", "abstract": "Video anomaly detection (VAD) is an important computer vision problem. Thanks to the mode coverage capabilities of generative models, the likelihood-based paradigm is catching growing interest, as it can model normal distribution and detect out-of-distribution anomalies. However, these likelihood-based methods are blind to the anomalies located in local modes near the learned distribution. To handle these ``unseen\" anomalies, we dive into three gaps uniquely existing in VAD regarding scene, motion and appearance. Specifically, we first build a noise-conditioned score transformer for denoising score matching. Then, we introduce a scene-dependent and motion-aware score function by embedding the scene condition of input sequences into our model and assigning motion weights based on the difference between key frames of input sequences. Next, to solve the problem of blindness in principle, we integrate unaffected visual information via a novel autoregressive denoising score matching mechanism for inference. Through autoregressively injecting intensifying Gaussian noise into the denoised data and estimating the corresponding score function, we compare the denoised data with the original data to get a difference and aggregate it with the score function for an enhanced appearance perception and accumulate the abnormal context. With all three gaps considered, we can compute a more comprehensive anomaly indicator. Experiments on three popular VAD benchmarks demonstrate the state-of-the-art performance of our method.", "date": "2025-06-29", "categories": []}
{"id": "2506.23469", "title": "Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection", "abstract": "Graph anomaly detection is critical in domains such as healthcare and economics, where identifying deviations can prevent substantial losses. Existing unsupervised approaches strive to learn a single model capable of detecting both attribute and structural anomalies. However, they confront the tug-of-war problem between two distinct types of anomalies, resulting in suboptimal performance. This work presents TripleAD, a mutual distillation-based triple-channel graph anomaly detection framework. It includes three estimation modules to identify the attribute, structural, and mixed anomalies while mitigating the interference between different types of anomalies. In the first channel, we design a multiscale attribute estimation module to capture extensive node interactions and ameliorate the over-smoothing issue. To better identify structural anomalies, we introduce a link-enhanced structure estimation module in the second channel that facilitates information flow to topologically isolated nodes. The third channel is powered by an attribute-mixed curvature, a new indicator that encapsulates both attribute and structural information for discriminating mixed anomalies. Moreover, a mutual distillation strategy is introduced to encourage communication and collaboration between the three channels. Extensive experiments demonstrate the effectiveness of the proposed TripleAD model against strong baselines.", "date": "2025-06-30", "categories": []}
{"id": "2506.23596", "title": "When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series", "abstract": "Recently, forecasting future abnormal events has emerged as an important scenario to tackle real-world necessities. However, the solution of predicting specific future time points when anomalies will occur, known as Anomaly Prediction (AP), remains under-explored. Existing methods dealing with time series data fail in AP, focusing only on immediate anomalies or failing to provide precise predictions for future anomalies. To address the AP task, we propose a novel framework called Anomaly to Prompt (A2P), comprised of Anomaly-Aware Forecasting (AAF) and Synthetic Anomaly Prompting (SAP). To enable the forecasting model to forecast abnormal time points, we adopt a strategy to learn the relationships of anomalies. For the robust detection of anomalies, our proposed SAP introduces a learnable Anomaly Prompt Pool (APP) that simulates diverse anomaly patterns using signal adaptive prompt. Comprehensive experiments on multiple real-world datasets demonstrate the superiority of A2P over state-of-the-art methods, showcasing its ability to predict future anomalies. Our implementation code is available at https://github.com/KU-VGI/AP.", "date": "2025-06-30", "categories": []}
{"id": "2506.23810", "title": "MadCLIP: Few-shot Medical Anomaly Detection with CLIP", "abstract": "An innovative few-shot anomaly detection approach is presented, leveraging the pre-trained CLIP model for medical data, and adapting it for both image-level anomaly classification (AC) and pixel-level anomaly segmentation (AS). A dual-branch design is proposed to separately capture normal and abnormal features through learnable adapters in the CLIP vision encoder. To improve semantic alignment, learnable text prompts are employed to link visual features. Furthermore, SigLIP loss is applied to effectively handle the many-to-one relationship between images and unpaired text prompts, showcasing its adaptation in the medical field for the first time. Our approach is validated on multiple modalities, demonstrating superior performance over existing methods for AC and AS, in both same-dataset and cross-dataset evaluations. Unlike prior work, it does not rely on synthetic data or memory banks, and an ablation study confirms the contribution of each component. The code is available at https://github.com/mahshid1998/MadCLIP.", "date": "2025-06-30", "categories": []}
{"id": "2407.04092", "title": "Learning to Be a Transformer to Pinpoint Anomalies", "abstract": "To efficiently deploy strong, often pre-trained feature extractors, recent Industrial Anomaly Detection and Segmentation (IADS) methods process low-resolution images, e.g., 224x224 pixels, obtained by downsampling the original input images. However, while numerous industrial applications demand the identification of both large and small defects, downsampling the input image to a low resolution may hinder a method's ability to pinpoint tiny anomalies. We propose a novel Teacher--Student paradigm to leverage strong pre-trained features while processing high-resolution input images very efficiently. The core idea concerns training two shallow MLPs (the Students) by nominal images so as to mimic the mappings between the patch embeddings induced by the self-attention layers of a frozen vision Transformer (the Teacher). Indeed, learning these mappings sets forth a challenging pretext task that small-capacity models are unlikely to accomplish on out-of-distribution data such as anomalous images. Our method can spot anomalies from high-resolution images and runs way faster than competitors, achieving state-of-the-art performance on MVTec AD and the best segmentation results on VisA. We also propose novel evaluation metrics to capture robustness to defect size, i.e., the ability to preserve good localisation from large anomalies to tiny ones. Evaluating our method also by these metrics reveals its neatly superior performance.", "date": "2024-07-04", "categories": []}
{"id": "2506.20693", "title": "E-ABIN: an Explainable module for Anomaly detection in BIological Networks", "abstract": "The increasing availability of large-scale omics data calls for robust analytical frameworks capable of handling complex gene expression datasets while offering interpretable results. Recent advances in artificial intelligence have enabled the identification of aberrant molecular patterns distinguishing disease states from healthy controls. Coupled with improvements in model interpretability, these tools now support the identification of genes potentially driving disease phenotypes. However, current approaches to gene anomaly detection often remain limited to single datasets and lack accessible graphical interfaces. Here, we introduce E-ABIN, a general-purpose, explainable framework for Anomaly detection in Biological Networks. E-ABIN combines classical machine learning and graph-based deep learning techniques within a unified, user-friendly platform, enabling the detection and interpretation of anomalies from gene expression or methylation-derived networks. By integrating algorithms such as Support Vector Machines, Random Forests, Graph Autoencoders (GAEs), and Graph Adversarial Attributed Networks (GAANs), E-ABIN ensures a high predictive accuracy while maintaining interpretability. We demonstrate the utility of E-ABIN through case studies of bladder cancer and coeliac disease, where it effectively uncovers biologically relevant anomalies and offers insights into disease mechanisms.", "date": "2025-06-25", "categories": []}
{"id": "2506.21398", "title": "FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection", "abstract": "Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approaches predominantly focus on deriving prototypes from limited normal samples, they typically neglect to systematically incorporate query image statistics to enhance prototype representativeness. To address this issue, we propose FastRef, a novel and efficient prototype refinement framework for FS-IAD. Our method operates through an iterative two-stage process: (1) characteristic transfer from query features to prototypes via an optimizable transformation matrix, and (2) anomaly suppression through prototype alignment. The characteristic transfer is achieved through linear reconstruction of query features from prototypes, while the anomaly suppression addresses a key observation in FS-IAD that unlike conventional IAD with abundant normal prototypes, the limited-sample setting makes anomaly reconstruction more probable. Therefore, we employ optimal transport (OT) for non-Gaussian sampled features to measure and minimize the gap between prototypes and their refined counterparts for anomaly suppression. For comprehensive evaluation, we integrate FastRef with three competitive prototype-based FS-IAD methods: PatchCore, FastRecon, WinCLIP, and AnomalyDINO. Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and RealIAD demonstrate both the effectiveness and computational efficiency of our approach under 1/2/4-shots.", "date": "2025-06-26", "categories": []}
{"id": "2411.16556", "title": "Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches", "abstract": "The search for radio technosignatures is an anomaly detection problem: Candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised learning provides an algorithmic way to winnow the most anomalous signals from the chaff, as well as group together RFI signals that bear morphological similarities. We present GLOBULAR (Grouping Low-frequency Observations By Unsupervised Learning After Reduction) clustering, a signal processing method that uses HDBSCAN to reduce the false-positive rate and isolate outlier signals for further analysis. When combined with a standard narrowband signal detection and spatial filtering pipeline, such as turboSETI, GLOBULAR clustering offers significant improvements in the false-positive rate over the standard pipeline alone, suggesting dramatic potential for the amelioration of manual follow-up requirements for future large surveys. By removing RFI signals in regions of high spectral occupancy, GLOBULAR clustering may also enable the detection of signals missed by the standard pipeline. We benchmark our method against the Choza et al. turboSETI-only search of 97 nearby galaxies at the L band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.", "date": "2024-11-25", "categories": []}
{"id": "2506.20574", "title": "Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series", "abstract": "Anomaly detection in multivariate time series is an important problem across various fields such as healthcare, financial services, manufacturing or physics detector monitoring. Accurately identifying when unexpected errors or faults occur is essential, yet challenging, due to the unknown nature of anomalies and the complex interdependencies between time series dimensions. In this paper, we investigate transformer-based approaches for time series anomaly detection, focusing on the recently proposed iTransformer architecture. Our contributions are fourfold: (i) we explore the application of the iTransformer to time series anomaly detection, and analyse the influence of key parameters such as window size, step size, and model dimensions on performance; (ii) we examine methods for extracting anomaly labels from multidimensional anomaly scores and discuss appropriate evaluation metrics for such labels; (iii) we study the impact of anomalous data present during training and assess the effectiveness of alternative loss functions in mitigating their influence; and (iv) we present a comprehensive comparison of several transformer-based models across a diverse set of datasets for time series anomaly detection.", "date": "2025-06-25", "categories": []}
{"id": "2506.13097", "title": "Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection", "abstract": "Prototype-based reconstruction methods for unsupervised anomaly detection utilize a limited set of learnable prototypes which only aggregates insufficient normal information, resulting in undesirable reconstruction. However, increasing the number of prototypes may lead to anomalies being well reconstructed through the attention mechanism, which we refer to as the \"Soft Identity Mapping\" problem. In this paper, we propose Pro-AD to address these issues and fully utilize the prototypes to boost the performance of anomaly detection. Specifically, we first introduce an expanded set of learnable prototypes to provide sufficient capacity for semantic information. Then we employ a Dynamic Bidirectional Decoder which integrates the process of the normal information aggregation and the target feature reconstruction via prototypes, with the aim of allowing the prototypes to aggregate more comprehensive normal semantic information from different levels of the image features and the target feature reconstruction to not only utilize its contextual information but also dynamically leverage the learned comprehensive prototypes. Additionally, to prevent the anomalies from being well reconstructed using sufficient semantic information through the attention mechanism, Pro-AD introduces a Prototype-based Constraint that applied within the target feature reconstruction process of the decoder, which further improves the performance of our approach. Extensive experiments on multiple challenging benchmarks demonstrate that our Pro-AD achieve state-of-the-art performance, highlighting its superior robustness and practical effectiveness for Multi-class Unsupervised Anomaly Detection task.", "date": "2025-06-16", "categories": []}
{"id": "2506.19234", "title": "Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology", "abstract": "Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, anomaly detection holds significant potential for applications such as rare disease identification, artifact detection, and biomarker discovery. However, the unique characteristics of pathology images, such as their large size, multi-scale structures, stain variability, and repetitive patterns, introduce new challenges that current anomaly detection algorithms struggle to address. In this quantitative study, we benchmark over 20 classical and prevalent anomaly detection methods through extensive experiments. We curated five digital pathology datasets, both real and synthetic, to systematically evaluate these approaches. Our experiments investigate the influence of image scale, anomaly pattern types, and training epoch selection strategies on detection performance. The results provide a detailed comparison of each method's strengths and limitations, establishing a comprehensive benchmark to guide future research in anomaly detection for digital pathology images.", "date": "2025-06-24", "categories": []}
{"id": "2506.19246", "title": "Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning", "abstract": "This paper addresses the increasingly prominent problem of anomaly detection in distributed systems. It proposes a detection method based on federated contrastive learning. The goal is to overcome the limitations of traditional centralized approaches in terms of data privacy, node heterogeneity, and anomaly pattern recognition. The proposed method combines the distributed collaborative modeling capabilities of federated learning with the feature discrimination enhancement of contrastive learning. It builds embedding representations on local nodes and constructs positive and negative sample pairs to guide the model in learning a more discriminative feature space. Without exposing raw data, the method optimizes a global model through a federated aggregation strategy. Specifically, the method uses an encoder to represent local behavior data in high-dimensional space. This includes system logs, operational metrics, and system calls. The model is trained using both contrastive loss and classification loss to improve its ability to detect fine-grained anomaly patterns. The method is evaluated under multiple typical attack types. It is also tested in a simulated real-time data stream scenario to examine its responsiveness. Experimental results show that the proposed method outperforms existing approaches across multiple performance metrics. It demonstrates strong detection accuracy and adaptability, effectively addressing complex anomalies in distributed environments. Through careful design of key modules and optimization of the training mechanism, the proposed method achieves a balance between privacy preservation and detection performance. It offers a feasible technical path for intelligent security management in distributed systems.", "date": "2025-06-24", "categories": []}
{"id": "2307.16714", "title": "A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection", "abstract": "Growth in system complexity increases the need for automated log analysis techniques, such as Log-based Anomaly Detection (LAD). While deep learning (DL) methods have been widely used for LAD, traditional machine learning (ML) techniques can also perform well depending on the context and dataset. Semi-supervised techniques deserve the same attention as they offer practical advantages over fully supervised methods. Current evaluations mainly focus on detection accuracy, but this alone is insufficient to determine the suitability of a technique for a given LAD task. Other aspects to consider include training and prediction times as well as the sensitivity to hyperparameter tuning, which in practice matters to engineers.\n  This paper presents a comprehensive empirical study evaluating a wide range of supervised and semi-supervised, traditional and deep ML techniques across four criteria: detection accuracy, time performance, and sensitivity to hyperparameter tuning in both detection accuracy and time performance. The experimental results show that supervised traditional and deep ML techniques fare similarly in terms of their detection accuracy and prediction time on most of the benchmark datasets considered in our study. Moreover, overall, sensitivity analysis to hyperparameter tuning with respect to detection accuracy shows that supervised traditional ML techniques are less sensitive than deep learning techniques. Further, semi-supervised techniques yield significantly worse detection accuracy than supervised techniques.", "date": "2023-07-31", "categories": []}
{"id": "2504.11170", "title": "A real-time anomaly detection method for robots based on a flexible and sparse latent space", "abstract": "The growing demand for robots to operate effectively in diverse environments necessitates the need for robust real-time anomaly detection techniques during robotic operations. However, deep learning-based models in robotics face significant challenges due to limited training data and highly noisy signal features. In this paper, we present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoder model to address these problems. This approach integrates Masked Autoregressive Flow model into Adversarial AutoEncoders to construct a flexible latent space and utilize Sparse autoencoder to efficiently focus on important features, even in scenarios with limited feature space. Our experiments demonstrate that the proposed model achieves a 4.96% to 9.75% higher area under the receiver operating characteristic curve for pick-and-place robotic operations with randomly placed cans, compared to existing state-of-the-art methods. Notably, it showed up to 19.67% better performance in scenarios involving collisions with lightweight objects. Additionally, unlike the existing state-of-the-art model, our model performs inferences within 1 millisecond, ensuring real-time anomaly detection. These capabilities make our model highly applicable to machine learning-based robotic safety systems in dynamic environments. The code is available at https://github.com/twkang43/sparse-maf-aae.", "date": "2025-04-15", "categories": []}
{"id": "2506.17457", "title": "When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network", "abstract": "Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is critical in time-sensitive driving scenarios. In this paper, we introduce real-time anomaly detection for autonomous driving, prioritizing both minimal response time and high accuracy. We propose a novel multimodal asynchronous hybrid network that combines event streams from event cameras with image data from RGB cameras. Our network utilizes the high temporal resolution of event cameras through an asynchronous Graph Neural Network and integrates it with spatial features extracted by a CNN from RGB images. This combination effectively captures both the temporal dynamics and spatial details of the driving environment, enabling swift and precise anomaly detection. Extensive experiments on benchmark datasets show that our approach outperforms existing methods in both accuracy and response time, achieving millisecond-level real-time performance.", "date": "2025-06-20", "categories": []}
{"id": "2506.18288", "title": "Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals", "abstract": "This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations.", "date": "2025-06-23", "categories": []}
{"id": "2506.18544", "title": "Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection", "abstract": "Recently, detecting logical anomalies is becoming a more challenging task compared to detecting structural ones. Existing encoder decoder based methods typically compress inputs into low-dimensional bottlenecks on the assumption that the compression process can effectively suppress the transmission of logical anomalies to the decoder. However, logical anomalies present a particular difficulty because, while their local features often resemble normal semantics, their global semantics deviate significantly from normal patterns. Thanks to the generalisation capabilities inherent in neural networks, these abnormal semantic features can propagate through low-dimensional bottlenecks. This ultimately allows the decoder to reconstruct anomalous images with misleading fidelity. To tackle the above challenge, we propose a novel normality prior guided multi-semantic fusion network for unsupervised anomaly detection. Instead of feeding the compressed bottlenecks to the decoder directly, we introduce the multi-semantic features of normal samples into the reconstruction process. To this end, we first extract abstract global semantics of normal cases by a pre-trained vision-language network, then the learnable semantic codebooks are constructed to store representative feature vectors of normal samples by vector quantisation. Finally, the above multi-semantic features are fused and employed as input to the decoder to guide the reconstruction of anomalies to approximate normality. Extensive experiments are conducted to validate the effectiveness of our proposed method, and it achieves the SOTA performance on the MVTec LOCO AD dataset with improvements of 5.7% in pixel-sPRO and 2.6% in image-AUROC. The source code is available at https://github.com/Xmh-L/NPGMF.", "date": "2025-06-23", "categories": []}
{"id": "2311.06835", "title": "Open-Set Graph Anomaly Detection via Normal Structure Regularisation", "abstract": "This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (referred to as seen anomalies) to detect both seen anomalies and unseen anomalies (i.e., anomalies that cannot be illustrated the training anomalies). Those labelled training data provide crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. Further, existing open-set AD models were introduced to handle Euclidean data, failing to effectively capture discriminative features from graph structure and node attributes for GAD. In this work, we propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to achieve generalised detection ability to unseen anomalies, while maintaining its effectiveness on detecting seen anomalies. The key idea in NSReg is to introduce a regularisation term that enforces the learning of compact, semantically-rich representations of normal nodes based on their structural relations to other nodes. When being optimised with supervised anomaly detection losses, the regularisation term helps incorporate strong normality into the modelling, and thus, it effectively avoids over-fitting the seen anomalies and learns a better normality decision boundary, largely reducing the false negatives of detecting unseen anomalies as normal. Extensive empirical results on seven real-world datasets show that NSReg significantly outperforms state-of-the-art competing methods by at least 14% AUC-ROC on the unseen anomaly classes and by 10% AUC-ROC on all anomaly classes.", "date": "2023-11-12", "categories": []}
{"id": "2506.10730", "title": "IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain", "abstract": "Recently, the rapid advancements of vision-language models, such as CLIP, leads to significant progress in zero-/few-shot anomaly detection (ZFSAD) tasks. However, most existing CLIP-based ZFSAD methods commonly assume prior knowledge of categories and rely on carefully crafted prompts tailored to specific scenarios. While such meticulously designed text prompts effectively capture semantic information in the textual space, they fall short of distinguishing normal and anomalous instances within the joint embedding space. Moreover, these ZFSAD methods are predominantly explored in industrial scenarios, with few efforts conducted to medical tasks. To this end, we propose an innovative framework for ZFSAD tasks in medical domain, denoted as IQE-CLIP. We reveal that query embeddings, which incorporate both textual and instance-aware visual information, are better indicators for abnormalities. Specifically, we first introduce class-based prompting tokens and learnable prompting tokens for better adaptation of CLIP to the medical domain. Then, we design an instance-aware query module (IQM) to extract region-level contextual information from both text prompts and visual features, enabling the generation of query embeddings that are more sensitive to anomalies. Extensive experiments conducted on six medical datasets demonstrate that IQE-CLIP achieves state-of-the-art performance on both zero-shot and few-shot tasks. We release our code and data at https://github.com/hongh0/IQE-CLIP/.", "date": "2025-06-12", "categories": []}
{"id": "2506.15712", "title": "BatteryBERT for Realistic Battery Fault Detection Using Point-Masked Signal Modeling", "abstract": "Accurate fault detection in lithium-ion batteries is essential for the safe and reliable operation of electric vehicles and energy storage systems. However, existing methods often struggle to capture complex temporal dependencies and cannot fully leverage abundant unlabeled data. Although large language models (LLMs) exhibit strong representation capabilities, their architectures are not directly suited to the numerical time-series data common in industrial settings. To address these challenges, we propose a novel framework that adapts BERT-style pretraining for battery fault detection by extending the standard BERT architecture with a customized time-series-to-token representation module and a point-level Masked Signal Modeling (point-MSM) pretraining task tailored to battery applications. This approach enables self-supervised learning on sequential current, voltage, and other charge-discharge cycle data, yielding distributionally robust, context-aware temporal embeddings. We then concatenate these embeddings with battery metadata and feed them into a downstream classifier for accurate fault classification. Experimental results on a large-scale real-world dataset show that models initialized with our pretrained parameters significantly improve both representation quality and classification accuracy, achieving an AUROC of 0.945 and substantially outperforming existing approaches. These findings validate the effectiveness of BERT-style pretraining for time-series fault detection.", "date": "2025-05-31", "categories": []}
{"id": "2506.16050", "title": "Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments", "abstract": "Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detecting surface defects in pre-defined or controlled imaging environments. However, accurately detecting workpiece defects in complex and unstructured industrial environments with varying views, poses and illumination remains challenging. We propose a novel anomaly detection and localization method specifically designed to handle inputs with perturbative patterns. Our approach introduces a new framework based on a collaborative distillation heterogeneous teacher network (HetNet), an adaptive local-global feature fusion module, and a local multivariate Gaussian noise generation module. HetNet can learn to model the complex feature distribution of normal patterns using limited information about local disruptive changes. We conducted extensive experiments on mainstream benchmarks. HetNet demonstrates superior performance with approximately 10% improvement across all evaluation metrics on MSC-AD under industrial conditions, while achieving state-of-the-art results on other datasets, validating its resilience to environmental fluctuations and its capability to enhance the reliability of industrial anomaly detection systems across diverse scenarios. Tests in real-world environments further confirm that HetNet can be effectively integrated into production lines to achieve robust and real-time anomaly detection. Codes, images and videos are published on the project website at: https://zihuatanejoyu.github.io/HetNet/", "date": "2025-06-19", "categories": []}
{"id": "2506.16314", "title": "Signatures to help interpretability of anomalies", "abstract": "Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. We introduce here idea of anomaly signature, whose aim is to help the interpretability of anomalies by highlighting which features contributed to the decision.", "date": "2025-06-19", "categories": []}
{"id": "2506.16815", "title": "Robust Group Anomaly Detection for Quasi-Periodic Network Time Series", "abstract": "Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.", "date": "2025-06-20", "categories": []}
{"id": "2506.16855", "title": "Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learning", "abstract": "Time series analysis has achieved great success in cyber security such as intrusion detection and device identification. Learning similarities among multiple time series is a crucial problem since it serves as the foundation for downstream analysis. Due to the complex temporal dynamics of the event-triggered time series, it often remains unclear which similarity metric is appropriate for security-related tasks, such as anomaly detection and clustering. The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning similarities among a set of event-triggered time series. From the machine learning vantage point, the proposed framework harnesses the power of both hierarchical multi-resolution sequential autoencoders and the Gaussian Mixture Model (GMM) to effectively learn the low-dimensional representations from the time series. Finally, the obtained similarity measure can be easily visualized for the explanation. The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series. Through extensive qualitative and quantitative experiments, it is revealed that the proposed method outperforms state-of-the-art methods considerably.", "date": "2025-06-20", "categories": []}
{"id": "2506.16890", "title": "From Lab to Factory: Pitfalls and Guidelines for Self-/Unsupervised Defect Detection on Low-Quality Industrial Images", "abstract": "The detection and localization of quality-related problems in industrially mass-produced products has historically relied on manual inspection, which is costly and error-prone. Machine learning has the potential to replace manual handling. As such, the desire is to facilitate an unsupervised (or self-supervised) approach, as it is often impossible to specify all conceivable defects ahead of time. A plethora of prior works have demonstrated the aptitude of common reconstruction-, embedding-, and synthesis-based methods in laboratory settings. However, in practice, we observe that most methods do not handle low data quality well or exude low robustness in unfavorable, but typical real-world settings. For practitioners it may be very difficult to identify the actual underlying problem when such methods underperform. Worse, often-reported metrics (e.g., AUROC) are rarely suitable in practice and may give misleading results. In our setting, we attempt to identify subtle anomalies on the surface of blasted forged metal parts, using rather low-quality RGB imagery only, which is a common industrial setting. We specifically evaluate two types of state-of-the-art models that allow us to identify and improve quality issues in production data, without having to obtain new data. Our contribution is to provide guardrails for practitioners that allow them to identify problems related to, e.g., (lack of) robustness or invariance, in either the chosen model or the data reliably in similar scenarios. Furthermore, we exemplify common pitfalls in and shortcomings of likelihood-based approaches and outline a framework for proper empirical risk estimation that is more suitable for real-world scenarios.", "date": "2025-06-20", "categories": []}
{"id": "2302.10719", "title": "Memory-augmented Online Video Anomaly Detection", "abstract": "The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs). This paper presents a system capable to work in an online fashion, giving an immediate response to the arise of anomalies surrounding the AV, exploiting only the videos captured by a dash-mounted camera. Our architecture, called MOVAD, relies on two main modules: a Short-Term Memory Module to extract information related to the ongoing action, implemented by a Video Swin Transformer (VST), and a Long-Term Memory Module injected inside the classifier that considers also remote past information and action context thanks to the use of a Long-Short Term Memory (LSTM) network. The strengths of MOVAD are not only linked to its excellent performance, but also to its straightforward and modular architecture, trained in a end-to-end fashion with only RGB frames with as less assumptions as possible, which makes it easy to implement and play with. We evaluated the performance of our method on Detection of Traffic Anomaly (DoTA) dataset, a challenging collection of dash-mounted camera videos of accidents. After an extensive ablation study, MOVAD is able to reach an AUC score of 82.17\\%, surpassing the current state-of-the-art by +2.87 AUC. Our code will be available on https://github.com/IMPLabUniPr/movad/tree/movad_vad", "date": "2023-02-21", "categories": []}
{"id": "2409.09957", "title": "Deep Graph Anomaly Detection: A Survey and New Perspectives", "abstract": "Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation, we also summarize a collection of widely-used GAD datasets and empirical comparison. We further discuss multiple open problems to inspire more future high-quality research. A continuously updated repository for datasets, links to the codes of algorithms, and empirical comparison is available at https://github.com/mala-lab/Awesome-Deep-Graph-Anomaly-Detection.", "date": "2024-09-16", "categories": []}
{"id": "2504.08049", "title": "Patch distribution modeling framework adaptive cosine estimator (PaDiM-ACE) for anomaly detection and localization in synthetic aperture radar imagery", "abstract": "This work presents a new approach to anomaly detection and localization in synthetic aperture radar imagery (SAR), expanding upon the existing patch distribution modeling framework (PaDiM). We introduce the adaptive cosine estimator (ACE) detection statistic. PaDiM uses the Mahalanobis distance at inference, an unbounded metric. ACE instead uses the cosine similarity metric, providing bounded anomaly detection scores. The proposed method is evaluated across multiple SAR datasets, with performance metrics including the area under the receiver operating curve (AUROC) at the image and pixel level, aiming for increased performance in anomaly detection and localization of SAR imagery. The code is publicly available: https://github.com/Advanced-Vision-and-Learning-Lab/PaDiM-ACE.", "date": "2025-04-10", "categories": []}
{"id": "2506.15388", "title": "Evaluation Pipeline for systematically searching for Anomaly Detection Systems", "abstract": "Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using FPGAs. Overall system performance is achieved via the presented holistic system evaluation.", "date": "2025-06-18", "categories": []}
{"id": "2506.13828", "title": "Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles", "abstract": "We propose a hybrid meta-learning framework for forecasting and anomaly detection in nonlinear dynamical systems characterized by nonstationary and stochastic behavior. The approach integrates a physics-inspired simulator that captures nonlinear growth-relaxation dynamics with random perturbations, representative of many complex physical, industrial, and cyber-physical systems. We use CNN-LSTM architectures for spatio-temporal feature extraction, Variational Autoencoders (VAE) for unsupervised anomaly scoring, and Isolation Forests for residual-based outlier detection in addition to a Dual-Stage Attention Recurrent Neural Network (DA-RNN) for one-step forecasting on top of the generated simulation data. To create composite anomaly forecasts, these models are combined using a meta-learner that combines forecasting outputs, reconstruction errors, and residual scores. The hybrid ensemble performs better than standalone models in anomaly localization, generalization, and robustness to nonlinear deviations, according to simulation-based experiments. The framework provides a broad, data-driven approach to early defect identification and predictive monitoring in nonlinear systems, which may be applied to a variety of scenarios where complete physical models might not be accessible.", "date": "2025-06-15", "categories": []}
{"id": "2506.13955", "title": "Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies", "abstract": "Anomaly detection (AD) is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from (synthetic) anomalies. We extend this principle to semi-supervised AD, where training data also include a limited labeled subset of anomalies possibly present in test time. We propose a theoretically-grounded and empirically effective framework for semi-supervised AD that combines known and synthetic anomalies during training. To analyze semi-supervised AD, we introduce the first mathematical formulation of semi-supervised AD, which generalizes unsupervised AD. Here, we show that synthetic anomalies enable (i) better anomaly modeling in low-density regions and (ii) optimal convergence guarantees for neural network classifiers -- the first theoretical result for semi-supervised AD. We empirically validate our framework on five diverse benchmarks, observing consistent performance gains. These improvements also extend beyond our theoretical framework to other classification-based AD methods, validating the generalizability of the synthetic anomaly principle in AD.", "date": "2025-06-16", "categories": []}
{"id": "2407.15247", "title": "TimeInf: Time Series Data Contribution via Influence Functions", "abstract": "Evaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance. Existing data contribution methods have been applied to various data types, including tabular data, images, and text; however, their primary focus has been on i.i.d. settings. Despite the pressing need for principled approaches tailored to time series datasets, the problem of estimating data contribution in such settings remains under-explored, possibly due to challenges associated with handling inherent temporal dependencies. This paper introduces TimeInf, a model-agnostic data contribution estimation method for time-series datasets. By leveraging influence scores, TimeInf attributes model predictions to individual time points while preserving temporal structures between the time points. Our empirical results show that TimeInf effectively detects time series anomalies and outperforms existing data attribution techniques as well as state-of-the-art anomaly detection methods. Moreover, TimeInf offers interpretable attributions of data values, allowing us to distinguish diverse anomalous patterns through visualizations. We also showcase a potential application of TimeInf in identifying mislabeled anomalies in the ground truth annotations.", "date": "2024-07-21", "categories": []}
{"id": "2502.04807", "title": "Robust Conformal Outlier Detection under Contaminated Reference Data", "abstract": "Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibration relies on a reference set of labeled inlier data to control the type-I error rate. However, obtaining a perfectly labeled inlier reference set is often unrealistic, and a more practical scenario involves access to a contaminated reference set containing a small fraction of outliers. This paper analyzes the impact of such contamination on the validity of conformal methods. We prove that under realistic, non-adversarial settings, calibration on contaminated data yields conservative type-I error control, shedding light on the inherent robustness of conformal methods. This conservativeness, however, typically results in a loss of power. To alleviate this limitation, we propose a novel, active data-cleaning framework that leverages a limited labeling budget and an outlier detection model to selectively annotate data points in the contaminated reference set that are suspected as outliers. By removing only the annotated outliers in this ``suspicious'' subset, we can effectively enhance power while mitigating the risk of inflating the type-I error rate, as supported by our theoretical analysis. Experiments on real datasets validate the conservative behavior of conformal methods under contamination and show that the proposed data-cleaning strategy improves power without sacrificing validity.", "date": "2025-02-07", "categories": []}
{"id": "2503.04178", "title": "Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset", "abstract": "In modern world the importance of cybersecurity of various systems is increasing from year to year. The number of information security events generated by information security tools grows up with the development of the IT infrastructure. At the same time, the cyber threat landscape does not remain constant, and monitoring should take into account both already known attack indicators and those for which there are no signature rules in information security products of various classes yet. Detecting anomalies in large cybersecurity data streams is a complex task that, if properly addressed, can allow for timely response to atypical and previously unknown cyber threats. The possibilities of using of offline algorithms may be limited for a number of reasons related to the time of training and the frequency of retraining. Using stream learning algorithms for solving this task is capable of providing near-real-time data processing. This article examines the results of ten algorithms from three Python stream machine-learning libraries on BETH dataset with cybersecurity events, which contains information about the creation, cloning, and destruction of operating system processes collected using extended eBPF. ROC-AUC metric and total processing time of processing with these algorithms are presented. Several combinations of features and the order of events are considered. In conclusion, some mentions are given about the most promising algorithms and possible directions for further research are outlined.", "date": "2025-03-06", "categories": []}
{"id": "2506.06999", "title": "Towards Physics-informed Diffusion for Anomaly Detection in Trajectories", "abstract": "Given trajectory data, a domain-specific study area, and a user-defined threshold, we aim to find anomalous trajectories indicative of possible GPS spoofing (e.g., fake trajectory). The problem is societally important to curb illegal activities in international waters, such as unauthorized fishing and illicit oil transfers. The problem is challenging due to advances in AI generated in deep fakes generation (e.g., additive noise, fake trajectories) and lack of adequate amount of labeled samples for ground-truth verification. Recent literature shows promising results for anomalous trajectory detection using generative models despite data sparsity. However, they do not consider fine-scale spatiotemporal dependencies and prior physical knowledge, resulting in higher false-positive rates. To address these limitations, we propose a physics-informed diffusion model that integrates kinematic constraints to identify trajectories that do not adhere to physical laws. Experimental results on real-world datasets in the maritime and urban domains show that the proposed framework results in higher prediction accuracy and lower estimation error rate for anomaly detection and trajectory generation methods, respectively. Our implementation is available at https://github.com/arunshar/Physics-Informed-Diffusion-Probabilistic-Model.", "date": "2025-06-08", "categories": []}
{"id": "2506.12183", "title": "Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation", "abstract": "Evaluating anomaly detection in multivariate time series (MTS) requires careful consideration of temporal dependencies, particularly when detecting subsequence anomalies common in fault detection scenarios. While time series cross-validation (TSCV) techniques aim to preserve temporal ordering during model evaluation, their impact on classifier performance remains underexplored. This study systematically investigates the effect of TSCV strategy on the precision-recall characteristics of classifiers trained to detect fault-like anomalies in MTS datasets. We compare walk-forward (WF) and sliding window (SW) methods across a range of validation partition configurations and classifier types, including shallow learners and deep learning (DL) classifiers. Results show that SW consistently yields higher median AUC-PR scores and reduced fold-to-fold performance variance, particularly for deep architectures sensitive to localized temporal continuity. Furthermore, we find that classifier generalization is sensitive to the number and structure of temporal partitions, with overlapping windows preserving fault signatures more effectively at lower fold counts. A classifier-level stratified analysis reveals that certain algorithms, such as random forests (RF), maintain stable performance across validation schemes, whereas others exhibit marked sensitivity. This study demonstrates that TSCV design in benchmarking anomaly detection models on streaming time series and provide guidance for selecting evaluation strategies in temporally structured learning environments.", "date": "2025-06-13", "categories": []}
{"id": "2506.12992", "title": "SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models", "abstract": "Video anomaly detection (VAD) is essential for enhancing safety and security by identifying unusual events across different environments. Existing VAD benchmarks, however, are primarily designed for general-purpose scenarios, neglecting the specific characteristics of smart home applications. To bridge this gap, we introduce SmartHome-Bench, the first comprehensive benchmark specially designed for evaluating VAD in smart home scenarios, focusing on the capabilities of multi-modal large language models (MLLMs). Our newly proposed benchmark consists of 1,203 videos recorded by smart home cameras, organized according to a novel anomaly taxonomy that includes seven categories, such as Wildlife, Senior Care, and Baby Monitoring. Each video is meticulously annotated with anomaly tags, detailed descriptions, and reasoning. We further investigate adaptation methods for MLLMs in VAD, assessing state-of-the-art closed-source and open-source models with various prompting techniques. Results reveal significant limitations in the current models' ability to detect video anomalies accurately. To address these limitations, we introduce the Taxonomy-Driven Reflective LLM Chain (TRLC), a new LLM chaining framework that achieves a notable 11.62% improvement in detection accuracy. The benchmark dataset and code are publicly available at https://github.com/Xinyi-0724/SmartHome-Bench-LLM.", "date": "2025-06-15", "categories": []}
{"id": "2506.13012", "title": "Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss", "abstract": "Wind energy significantly contributes to the global shift towards renewable energy, yet operational challenges, such as Leading-Edge Erosion on wind turbine blades, notably reduce energy output. This study introduces an advanced, scalable machine learning framework for condition monitoring of wind turbines, specifically targeting improved detection of anomalies using Supervisory Control and Data Acquisition data. The framework effectively isolates normal turbine behavior through rigorous preprocessing, incorporating domain-specific rules and anomaly detection filters, including Gaussian Mixture Models and a predictive power score. The data cleaning and feature selection process enables identification of deviations indicative of performance degradation, facilitating estimates of annual energy production losses. The data preprocessing methods resulted in significant data reduction, retaining on average 31% of the original SCADA data per wind farm. Notably, 24 out of 35 turbines exhibited clear performance declines. At the same time, seven improved, and four showed no significant changes when employing the power curve feature set, which consisted of wind speed and ambient temperature. Models such as Random Forest, XGBoost, and KNN consistently captured subtle but persistent declines in turbine performance. The developed framework provides a novel approach to existing condition monitoring methodologies by isolating normal operational data and estimating annual energy loss, which can be a key part in reducing maintenance expenditures and mitigating economic impacts from turbine downtime.", "date": "2025-06-16", "categories": []}
{"id": "2506.13095", "title": "Learning Event Completeness for Weakly Supervised Video Anomaly Detection", "abstract": "Weakly supervised video anomaly detection (WS-VAD) is tasked with pinpointing temporal intervals containing anomalous events within untrimmed videos, utilizing only video-level annotations. However, a significant challenge arises due to the absence of dense frame-level annotations, often leading to incomplete localization in existing WS-VAD methods. To address this issue, we present a novel LEC-VAD, Learning Event Completeness for Weakly Supervised Video Anomaly Detection, which features a dual structure designed to encode both category-aware and category-agnostic semantics between vision and language. Within LEC-VAD, we devise semantic regularities that leverage an anomaly-aware Gaussian mixture to learn precise event boundaries, thereby yielding more complete event instances. Besides, we develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories. This innovation bolsters the text's expressiveness, which is crucial for advancing WS-VAD. Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime.", "date": "2025-06-16", "categories": []}
{"id": "2506.13282", "title": "Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling", "abstract": "Recycling steel scrap can reduce carbon dioxide (CO2) emissions from the steel industry. However, a significant challenge in steel scrap recycling is the inclusion of impurities other than steel. To address this issue, we propose vision-language-model-based anomaly detection where a model is finetuned in a supervised manner, enabling it to handle niche objects effectively. This model enables automated detection of anomalies at a fine-grained level within steel scrap. Specifically, we finetune the image encoder, equipped with multi-scale mechanism and text prompts aligned with both normal and anomaly images. The finetuning process trains these modules using a multiclass classification as the supervision.", "date": "2025-06-16", "categories": []}
{"id": "2405.09933", "title": "MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection", "abstract": "Previous industrial anomaly detection methods often struggle to handle the extensive diversity in training sets, particularly when they contain stylistically diverse and feature-rich samples, which we categorize as feature-rich anomaly detection datasets (FRADs). This challenge is evident in applications such as multi-view and multi-class scenarios. To address this challenge, we developed MiniMaxAD, a efficient autoencoder designed to efficiently compress and memorize extensive information from normal images. Our model employs a technique that enhances feature diversity, thereby increasing the effective capacity of the network. It also utilizes large kernel convolution to extract highly abstract patterns, which contribute to efficient and compact feature embedding. Moreover, we introduce an Adaptive Contraction Hard Mining Loss (ADCLoss), specifically tailored to FRADs. In our methodology, any dataset can be unified under the framework of feature-rich anomaly detection, in a way that the benefits far outweigh the drawbacks. Our approach has achieved state-of-the-art performance in multiple challenging benchmarks. Code is available at: \\href{https://github.com/WangFengJiee/MiniMaxAD}{https://github.com/WangFengJiee/MiniMaxAD}", "date": "2024-05-16", "categories": []}
{"id": "2502.06911", "title": "Foundation Models for Anomaly Detection: Vision and Challenges", "abstract": "As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM-based anomaly detection. We propose a novel taxonomy that classifies FMs into three categories based on their roles in anomaly detection tasks, i.e., as encoders, detectors, or interpreters. We provide a systematic analysis of state-of-the-art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field.", "date": "2025-02-10", "categories": []}
{"id": "2506.10233", "title": "Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization", "abstract": "Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scenarios, for example, in the case of rare diseases. Reconstruction-based unsupervised anomaly detection, in particular using diffusion models, has gained popularity in the medical field as it allows for training on healthy images alone, eliminating the need for large disease-specific cohorts. These methods assume that a model trained on normal data cannot accurately represent or reconstruct anomalies. However, this assumption often fails with models failing to reconstruct healthy tissue or accurately reconstruct abnormal regions i.e., failing to remove anomalies. In this work, we introduce a novel conditional diffusion model framework for anomaly detection and healthy image reconstruction in brain MRI. Our weakly supervised approach integrates synthetically generated pseudo-pathology images into the modeling process to better guide the reconstruction of healthy images. To generate these pseudo-pathologies, we apply fluid-driven anomaly randomization to augment real pathology segmentation maps from an auxiliary dataset, ensuring that the synthetic anomalies are both realistic and anatomically coherent. We evaluate our model's ability to detect pathology, using both synthetic anomaly datasets and real pathology from the ATLAS dataset. In our extensive experiments, our model: (i) consistently outperforms variational autoencoders, and conditional and unconditional latent diffusion; and (ii) surpasses on most datasets, the performance of supervised inpainting methods with access to paired diseased/healthy images.", "date": "2025-06-11", "categories": []}
{"id": "2506.10842", "title": "Advanced fraud detection using machine learning models: enhancing financial transaction security", "abstract": "The rise of digital payments has accelerated the need for intelligent and scalable systems to detect fraud. This research presents an end-to-end, feature-rich machine learning framework for detecting credit card transaction anomalies and fraud using real-world data. The study begins by merging transactional, cardholder, merchant, and merchant category datasets from a relational database to create a unified analytical view. Through the feature engineering process, we extract behavioural signals such as average spending, deviation from historical patterns, transaction timing irregularities, and category frequency metrics. These features are enriched with temporal markers such as hour, day of week, and weekend indicators to expose all latent patterns that indicate fraudulent behaviours. Exploratory data analysis reveals contextual transaction trends across all the dataset features. Using the transactional data, we train and evaluate a range of unsupervised models: Isolation Forest, One Class SVM, and a deep autoencoder trained to reconstruct normal behavior. These models flag the top 1% of reconstruction errors as outliers. PCA visualizations illustrate each models ability to separate anomalies into a two-dimensional latent space. We further segment the transaction landscape using K-Means clustering and DBSCAN to identify dense clusters of normal activity and isolate sparse, suspicious regions.", "date": "2025-06-12", "categories": []}
{"id": "2406.01078", "title": "Unseen Visual Anomaly Generation", "abstract": "Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive training data, limiting their applicability in real-world scenarios. In this work, we propose Anomaly Anything (AnomalyAny), a novel framework that leverages Stable Diffusion (SD)'s image generation capabilities to generate diverse and realistic unseen anomalies. By conditioning on a single normal sample during test time, AnomalyAny is able to generate unseen anomalies for arbitrary object types with text descriptions. Within AnomalyAny, we propose attention-guided anomaly optimization to direct SD attention on generating hard anomaly concepts. Additionally, we introduce prompt-guided anomaly refinement, incorporating detailed descriptions to further improve the generation quality. Extensive experiments on MVTec AD and VisA datasets demonstrate AnomalyAny's ability in generating high-quality unseen anomalies and its effectiveness in enhancing downstream AD performance.", "date": "2024-06-03", "categories": []}
{"id": "2502.01920", "title": "Anomaly Detection via Autoencoder Composite Features and NCE", "abstract": "Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently identify anomalous, out-of-distribution inputs by high reconstruction error or low likelihood, respectively. However, AEs may generalize and achieve small reconstruction errors on abnormal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE). After training the AE, NCE estimates a probability density function, to serve as the anomaly score, on the joint space of the AE's latent representation combined with features of the reconstruction quality. To further reduce the false negative rate in NCE we systematically varying the reconstruction features to augment the training and optimize the contrastive Gaussian noise distribution. Experimental assessments on multiple benchmark datasets demonstrate that the proposed approach matches the performance of prevalent state-of-the-art anomaly detection algorithms.", "date": "2025-02-04", "categories": []}
{"id": "2505.16223", "title": "MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network", "abstract": "In this paper, we propose MADCluster, a novel model-agnostic anomaly detection framework utilizing self-supervised clustering. MADCluster is applicable to various deep learning architectures and addresses the 'hypersphere collapse' problem inherent in existing deep learning-based anomaly detection methods. The core idea is to cluster normal pattern data into a 'single cluster' while simultaneously learning the cluster center and mapping data close to this center. Also, to improve expressiveness and enable effective single clustering, we propose a new 'One-directed Adaptive loss'. The optimization of this loss is mathematically proven. MADCluster consists of three main components: Base Embedder capturing high-dimensional temporal dynamics, Cluster Distance Mapping, and Sequence-wise Clustering for continuous center updates. Its model-agnostic characteristics are achieved by applying various architectures to the Base Embedder. Experiments on four time series benchmark datasets demonstrate that applying MADCluster improves the overall performance of comparative models. In conclusion, the compatibility of MADCluster shows potential for enhancing model performance across various architectures.", "date": "2025-05-22", "categories": []}
{"id": "2506.09368", "title": "Anomaly Detection and Generation with Diffusion Models: A Survey", "abstract": "Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributions and generate high-fidelity samples, offering a robust framework for unsupervised AD. In this survey, we comprehensively review anomaly detection and generation with diffusion models (ADGDM), presenting a tutorial-style analysis of the theoretical foundations and practical implementations and spanning images, videos, time series, tabular, and multimodal data. Crucially, unlike existing surveys that often treat anomaly detection and generation as separate problems, we highlight their inherent synergistic relationship. We reveal how DMs enable a reinforcing cycle where generation techniques directly address the fundamental challenge of anomaly data scarcity, while detection methods provide critical feedback to improve generation fidelity and relevance, advancing both capabilities beyond their individual potential. A detailed taxonomy categorizes ADGDM methods based on anomaly scoring mechanisms, conditioning strategies, and architectural designs, analyzing their strengths and limitations. We final discuss key challenges including scalability and computational efficiency, and outline promising future directions such as efficient architectures, conditioning strategies, and integration with foundation models (e.g., visual-language models and large language models). By synthesizing recent advances and outlining open research questions, this survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.", "date": "2025-06-11", "categories": []}
{"id": "2504.08827", "title": "PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection", "abstract": "Time series anomaly detection (TSAD) focuses on identifying whether observations in streaming data deviate significantly from normal patterns. With the prevalence of connected devices, anomaly detection on time series has become paramount, as it enables real-time monitoring and early detection of irregular behaviors across various application domains. In this work, we introduce PatchTrAD, a Patch-based Transformer model for time series anomaly detection. Our approach leverages a Transformer encoder along with the use of patches under a reconstructionbased framework for anomaly detection. Empirical evaluations on multiple benchmark datasets show that PatchTrAD is on par, in terms of detection performance, with state-of-the-art deep learning models for anomaly detection while being time efficient during inference.", "date": "2025-04-10", "categories": []}
{"id": "2506.08784", "title": "HomographyAD: Deep Anomaly Detection Using Self Homography Learning", "abstract": "Anomaly detection (AD) is a task that distinguishes normal and abnormal data, which is important for applying automation technologies of the manufacturing facilities. For MVTec dataset that is a representative AD dataset for industrial environment, many recent works have shown remarkable performances. However, the existing anomaly detection works have a limitation of showing good performance for fully-aligned datasets only, unlike real-world industrial environments. To solve this limitation, we propose HomographyAD, a novel deep anomaly detection methodology based on the ImageNet-pretrained network, which is specially designed for actual industrial dataset. Specifically, we first suggest input foreground alignment using the deep homography estimation method. In addition, we fine-tune the model by self homography learning to learn additional shape information from normal samples. Finally, we conduct anomaly detection based on the measure of how far the feature of test sample is from the distribution of the extracted normal features. By applying our proposed method to various existing AD approaches, we show performance enhancement through extensive experiments.", "date": "2025-06-10", "categories": []}
{"id": "2312.04398", "title": "Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning", "abstract": "The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can be misleading to human drivers and consequently contribute to unsafe driving conditions. In response to this concern and to accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline consisting of data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy based loss with label smoothing, and post-processing to tackle it leveraging state-of-the-art deep learning techniques, especially those involving Transformer models. Various experiments verify the effectiveness of the proposed pipeline. Results indicate that the proposed pipeline exhibits superior performance in lane rendering image anomaly detection, and notably, the self-supervised pre-training with MiM can greatly enhance the detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the original 280. In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems.", "date": "2023-12-07", "categories": []}
{"id": "2410.14886", "title": "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts", "abstract": "Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight in UNPrompt is that i) the predictability of latent node attributes can serve as a generalized anomaly measure and ii) generalized normal and abnormal graph patterns can be learned via latent node attribute prediction in a properly normalized node attribute space. UNPrompt achieves a generalist mode for GAD through two main modules: one module aligns the dimensionality and semantics of node attributes across different graphs via coordinate-wise normalization, while another module learns generalized neighborhood prompts that support the use of latent node attribute predictability as an anomaly score across different datasets. Extensive experiments on real-world GAD datasets show that UNPrompt significantly outperforms diverse competing methods under the generalist GAD setting, and it also has strong superiority under the one-model-for-one-dataset setting. Code is available at https://github.com/mala-lab/UNPrompt.", "date": "2024-10-18", "categories": []}
{"id": "2410.21991", "title": "A Lightweight Dual-Branch System for Weakly-Supervised Video Anomaly Detection on Consumer Edge Devices", "abstract": "The growing demand for intelligent security in consumer electronics, such as smart home cameras and personal monitoring systems, is often hindered by the high computational cost and large model sizes of advanced AI. These limitations prevent the effective deployment of real-time Video Anomaly Detection (VAD) on resource-constrained edge devices. To bridge this gap, this paper introduces Rule-based Video Anomaly Detection (RuleVAD), a novel, lightweight system engineered for high-efficiency and low-complexity threat detection directly on consumer hardware. RuleVAD features an innovative decoupled dual-branch architecture to minimize computational load. An implicit branch uses visual features for rapid, coarse-grained binary classification, efficiently filtering out normal activity to avoid unnecessary processing. For potentially anomalous or complex events, a multimodal explicit branch takes over. This branch leverages YOLO-World to detect objects and applies data mining to generate interpretable, text-based association rules from the scene. By aligning these rules with visual data, RuleVAD achieves a more nuanced, fine-grained classification, significantly reducing the false alarms common in vision-only systems. Extensive experiments on the XD-Violence and UCF-Crime benchmark datasets show that RuleVAD achieves superior performance, surpassing existing state-of-the-art methods in both accuracy and speed. Crucially, the entire system is optimized for low-power operation and is fully deployable on an NVIDIA Jetson Nano board, demonstrating its practical feasibility for bringing advanced, real-time security monitoring to everyday consumer electronic devices.", "date": "2024-10-29", "categories": []}
{"id": "2312.16139", "title": "Abnormal component analysis", "abstract": "At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds or crisis events, their on-time identification and isolation constitute an important task in almost any area of industry and science. While a substantial body of literature is devoted to detection of anomalies, little attention is payed to their explanation. This is the case mostly due to intrinsically non-supervised nature of the task and non-robustness of the exploratory methods like principal component analysis (PCA).\n  We introduce a new statistical tool dedicated for exploratory analysis of abnormal observations using data depth as a score. Abnormal component analysis (shortly ACA) is a method that searches a low-dimensional data representation that best visualises and explains anomalies. This low-dimensional representation not only allows to distinguish groups of anomalies better than the methods of the state of the art, but as well provides a -- linear in variables and thus easily interpretable -- explanation for anomalies. In a comparative simulation and real-data study, ACA also proves advantageous for anomaly analysis with respect to methods present in the literature.", "date": "2023-12-26", "categories": []}
{"id": "2410.23158", "title": "Monotonic anomaly detection", "abstract": "Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies that correspond to high attribute values (or low, but not both). We present two asymmetrical distance measures that take this monotonicity into account: ramp distance and signed distance. Through experiments on synthetic and real-life datasets, we show that ramp distance increases anomaly detection performance over the traditional absolute distance. While signed distance also performs well on synthetic data, it performs substantially poorer on real-life datasets. We argue that this is a consequence of the fact that when using signed distance, low values of certain attributes automatically compensate for high values of other attributes, such that anomaly detection is reduced to counting the total attribute value sum, which is too simplistic in practice.", "date": "2024-10-30", "categories": []}
{"id": "2502.08605", "title": "CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection", "abstract": "Does the intrinsic curvature of complex networks hold the key to unveiling graph anomalies that conventional approaches overlook? Reconstruction-based graph anomaly detection (GAD) methods overlook such geometric outliers, focusing only on structural and attribute-level anomalies. To this end, we propose CurvGAD - a mixed-curvature graph autoencoder that introduces the notion of curvature-based geometric anomalies. CurvGAD introduces two parallel pipelines for enhanced anomaly interpretability: (1) Curvature-equivariant geometry reconstruction, which focuses exclusively on reconstructing the edge curvatures using a mixed-curvature, Riemannian encoder and Gaussian kernel-based decoder; and (2) Curvature-invariant structure and attribute reconstruction, which decouples structural and attribute anomalies from geometric irregularities by regularizing graph curvature under discrete Ollivier-Ricci flow, thereby isolating the non-geometric anomalies. By leveraging curvature, CurvGAD refines the existing anomaly classifications and identifies new curvature-driven anomalies. Extensive experimentation over 10 real-world datasets (both homophilic and heterophilic) demonstrates an improvement of up to 6.5% over state-of-the-art GAD methods. The code is available at: https://github.com/karish-grover/curvgad.", "date": "2025-02-12", "categories": []}
{"id": "2506.04429", "title": "An AI-Based Public Health Data Monitoring System", "abstract": "Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.", "date": "2025-06-04", "categories": []}
{"id": "2506.05175", "title": "Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline", "abstract": "Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Although existing methods have primarily focused on detecting anomalous objects in videos -- either by identifying anomalous frames or objects -- they often neglect finer-grained analysis, such as anomalous pixels, which limits their ability to capture a broader range of anomalies. To address this challenge, we propose a new framework called Track Any Anomalous Object (TAO), which introduces a granular video anomaly detection pipeline that, for the first time, integrates the detection of multiple fine-grained anomalous objects into a unified framework. Unlike methods that assign anomaly scores to every pixel, our approach transforms the problem into pixel-level tracking of anomalous objects. By linking anomaly scores to downstream tasks such as segmentation and tracking, our method removes the need for threshold tuning and achieves more precise anomaly localization in long and complex video sequences. Experiments demonstrate that TAO sets new benchmarks in accuracy and robustness. Project page available online.", "date": "2025-06-05", "categories": []}
{"id": "2410.09821", "title": "DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection", "abstract": "Synthesizing anomaly samples has proven to be an effective strategy for self-supervised 2D industrial anomaly detection. However, this approach has been rarely explored in multi-modality anomaly detection, particularly involving 3D and RGB images. In this paper, we propose a novel dual-modality augmentation method for 3D anomaly synthesis, which is simple and capable of mimicking the characteristics of 3D defects. Incorporating with our anomaly synthesis method, we introduce a reconstruction-based discriminative anomaly detection network, in which a dual-modal discriminator is employed to fuse the original and reconstructed embedding of two modalities for anomaly detection. Additionally, we design an augmentation dropout mechanism to enhance the generalizability of the discriminator. Extensive experiments show that our method outperforms the state-of-the-art methods on detection precision and achieves competitive segmentation performance on both MVTec 3D-AD and Eyescandies datasets.", "date": "2024-10-13", "categories": []}
{"id": "2505.21420", "title": "Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning", "abstract": "Multimodal feature reconstruction is a promising approach for 3D anomaly detection, leveraging the complementary information from dual modalities. We further advance this paradigm by utilizing multi-modal mentor learning, which fuses intermediate features to further distinguish normal from feature differences. To address these challenges, we propose a novel method called Mentor3AD, which utilizes multi-modal mentor learning. By leveraging the shared features of different modalities, Mentor3AD can extract more effective features and guide feature reconstruction, ultimately improving detection performance. Specifically, Mentor3AD includes a Mentor of Fusion Module (MFM) that merges features extracted from RGB and 3D modalities to create a mentor feature. Additionally, we have designed a Mentor of Guidance Module (MGM) to facilitate cross-modal reconstruction, supported by the mentor feature. Lastly, we introduce a Voting Module (VM) to more accurately generate the final anomaly score. Extensive comparative and ablation studies on MVTec 3D-AD and Eyecandies have verified the effectiveness of the proposed method.", "date": "2025-05-27", "categories": []}
{"id": "2506.03964", "title": "Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection", "abstract": "Utilizing the complex inter-variable causal relationships within multivariate time-series provides a promising avenue toward more robust and reliable multivariate time-series anomaly detection (MTSAD) but remains an underexplored area of research. This paper proposes Causality-Aware contrastive learning for RObust multivariate Time-Series (CAROTS), a novel MTSAD pipeline that incorporates the notion of causality into contrastive learning. CAROTS employs two data augmentors to obtain causality-preserving and -disturbing samples that serve as a wide range of normal variations and synthetic anomalies, respectively. With causality-preserving and -disturbing samples as positives and negatives, CAROTS performs contrastive learning to train an encoder whose latent space separates normal and abnormal samples based on causality. Moreover, CAROTS introduces a similarity-filtered one-class contrastive loss that encourages the contrastive learning process to gradually incorporate more semantically diverse samples with common causal relationships. Extensive experiments on five real-world and two synthetic datasets validate that the integration of causal relationships endows CAROTS with improved MTSAD capabilities. The code is available at https://github.com/kimanki/CAROTS.", "date": "2025-06-04", "categories": []}
{"id": "2506.04190", "title": "How to Use Graph Data in the Wild to Help Graph Anomaly Detection?", "abstract": "In recent years, graph anomaly detection has found extensive applications in various domains such as social, financial, and communication networks. However, anomalies in graph-structured data present unique challenges, including label scarcity, ill-defined anomalies, and varying anomaly types, making supervised or semi-supervised methods unreliable. Researchers often adopt unsupervised approaches to address these challenges, assuming that anomalies deviate significantly from the normal data distribution. Yet, when the available data is insufficient, capturing the normal distribution accurately and comprehensively becomes difficult. To overcome this limitation, we propose to utilize external graph data (i.e., graph data in the wild) to help anomaly detection tasks. This naturally raises the question: How can we use external data to help graph anomaly detection tasks? To answer this question, we propose a framework called Wild-GAD. It is built upon a unified database, UniWildGraph, which comprises a large and diverse collection of graph data with broad domain coverage, ample data volume, and a unified feature space. Further, we develop selection criteria based on representativity and diversity to identify the most suitable external data for anomaly detection task. Extensive experiments on six real-world datasets demonstrate the effectiveness of Wild-GAD. Compared to the baseline methods, our framework has an average 18% AUCROC and 32% AUCPR improvement over the best-competing methods.", "date": "2025-06-04", "categories": []}
{"id": "2503.10080", "title": "Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection", "abstract": "Recently, vision-language models (e.g. CLIP) have demonstrated remarkable performance in zero-shot anomaly detection (ZSAD). By leveraging auxiliary data during training, these models can directly perform cross-category anomaly detection on target datasets, such as detecting defects on industrial product surfaces or identifying tumors in organ tissues. Existing approaches typically construct text prompts through either manual design or the optimization of learnable prompt vectors. However, these methods face several challenges: 1) handcrafted prompts require extensive expert knowledge and trial-and-error; 2) single-form learnable prompts struggle to capture complex anomaly semantics; and 3) an unconstrained prompt space limits generalization to unseen categories. To address these issues, we propose Bayesian Prompt Flow Learning (Bayes-PFL), which models the prompt space as a learnable probability distribution from a Bayesian perspective. Specifically, a prompt flow module is designed to learn both image-specific and image-agnostic distributions, which are jointly utilized to regularize the text prompt space and improve the model's generalization on unseen categories. These learned distributions are then sampled to generate diverse text prompts, effectively covering the prompt space. Additionally, a residual cross-model attention (RCA) module is introduced to better align dynamic text embeddings with fine-grained image features. Extensive experiments on 15 industrial and medical datasets demonstrate our method's superior performance. The code is available at https://github.com/xiaozhen228/Bayes-PFL.", "date": "2025-03-13", "categories": []}
{"id": "2506.02081", "title": "RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection", "abstract": "Inspired by the success of large language models (LLMs) in natural language processing, recent research has explored the building of time series foundation models and applied them to tasks such as forecasting, classification, and anomaly detection. However, their performances vary between different domains and tasks. In LLM-based approaches, test-time adaptation using example-based prompting has become common, owing to the high cost of retraining. In the context of anomaly detection, which is the focus of this study, providing normal examples from the target domain can also be effective. However, time series foundation models do not naturally acquire the ability to interpret or utilize examples or instructions, because the nature of time series data used during training does not encourage such capabilities. To address this limitation, we propose a retrieval augmented time series foundation model (RATFM), which enables pretrained time series foundation models to incorporate examples of test-time adaptation. We show that RATFM achieves a performance comparable to that of in-domain fine-tuning while avoiding domain-dependent fine-tuning. Experiments on the UCR Anomaly Archive, a multi-domain dataset including nine domains, confirms the effectiveness of the proposed approach.", "date": "2025-06-02", "categories": []}
{"id": "2506.02757", "title": "Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection", "abstract": "Tabular anomaly detection, which aims at identifying deviant samples, has been crucial in a variety of real-world applications, such as medical disease identification, financial fraud detection, intrusion monitoring, etc. Although recent deep learning-based methods have achieved competitive performances, these methods suffer from representation entanglement and the lack of global correlation modeling, which hinders anomaly detection performance. To tackle the problem, we incorporate mask modeling and prototype learning into tabular anomaly detection. The core idea is to design learnable masks by disentangled representation learning within a projection space and extracting normal dependencies as explicit global prototypes. Specifically, the overall model involves two parts: (i) During encoding, we perform mask modeling in both the data space and projection space with orthogonal basis vectors for learning shared disentangled normal patterns; (ii) During decoding, we decode multiple masked representations in parallel for reconstruction and learn association prototypes to extract normal characteristic correlations. Our proposal derives from a distribution-matching perspective, where both projection space learning and association prototype learning are formulated as optimal transport problems, and the calibration distances are utilized to refine the anomaly scores. Quantitative and qualitative experiments on 20 tabular benchmarks demonstrate the effectiveness and interpretability of our model.", "date": "2025-06-03", "categories": []}
{"id": "2407.04248", "title": "Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method", "abstract": "This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.", "date": "2024-07-05", "categories": []}
{"id": "2411.10918", "title": "INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection", "abstract": "Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and physics-based models require extensive manual work. We propose INVARLLM, a hybrid framework that uses large language models (LLMs) to extract semantic information from CPS documentation and generate physical invariants, then validates these against real system data using a PCMCI+-inspired K-means method. This approach combines LLM semantic understanding with empirical validation to ensure both interpretability and reliability. We evaluate INVARLLM on SWaT and WADI datasets, achieving 100% precision in anomaly detection with no false alarms, outperforming all existing methods. Our results demonstrate that integrating LLM-derived semantics with statistical validation provides a scalable and dependable solution for CPS security.", "date": "2024-11-17", "categories": []}
{"id": "2502.09254", "title": "AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection", "abstract": "Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. One key insight is that graph-agnostic representations for normal and abnormal classes are required to support effective zero/few-shot GAD across different graphs. Motivated by this, AnomalyGFM is pre-trained to align data-independent, learnable normal and abnormal class prototypes with node representation residuals (i.e., representation deviation of a node from its neighbors). The residual features essentially project the node information into a unified feature space where we can effectively measure the abnormality of nodes from different graphs in a consistent way. This provides a driving force for the learning of graph-agnostic, discriminative prototypes for the normal and abnormal classes, which can be used to enable zero-shot GAD on new graphs, including very large-scale graphs. If there are few-shot labeled normal nodes available in the new graphs, AnomalyGFM can further support prompt tuning to leverage these nodes for better adaptation. Comprehensive experiments on 11 widely-used GAD datasets with real anomalies, demonstrate that AnomalyGFM significantly outperforms state-of-the-art competing methods under both zero- and few-shot GAD settings.", "date": "2025-02-13", "categories": []}
{"id": "2504.04495", "title": "AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection", "abstract": "With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-based detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances text embeddings with key multimodal information based on the semantic correlation between audio-visual features and textual labels, significantly improving CLIP's generalization for the video anomaly detection task. Moreover, to enhance robustness against modality deficiency during inference, we further develop an uncertainty-driven feature distillation module that synthesizes audio-visual representations from visual-only inputs. This module employs uncertainty modeling based on the diversity of audio-visual features to dynamically emphasize challenging features during the distillation process. Our framework demonstrates superior performance across multiple benchmarks, with audio integration significantly boosting anomaly detection accuracy in various scenarios. Notably, with unimodal data enhanced by uncertainty-driven distillation, our approach consistently outperforms current unimodal VAD methods.", "date": "2025-04-06", "categories": []}
{"id": "2506.00420", "title": "A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks", "abstract": "Detecting anomalies in the data collected by WSNs can provide crucial evidence for assessing the reliability and stability of WSNs. Existing methods for WSN anomaly detection often face challenges such as the limited extraction of spatiotemporal correlation features, the absence of sample labels, few anomaly samples, and an imbalanced sample distribution. To address these issues, a spatiotemporal correlation detection model (MTAD-RD) considering both model architecture and a two-stage training strategy perspective is proposed. In terms of model structure design, the proposed MTAD-RD backbone network includes a retentive network (RetNet) enhanced by a cross-retention (CR) module, a multigranular feature fusion module, and a graph attention network module to extract internode correlation information. This proposed model can integrate the intermodal correlation features and spatial features of WSN neighbor nodes while extracting global information from time series data. Moreover, its serialized inference characteristic can remarkably reduce inference overhead. For model training, a two-stage training approach was designed. First, a contrastive learning proxy task was designed for time series data with graph structure information in WSNs, enabling the backbone network to learn transferable features from unlabeled data using unsupervised contrastive learning methods, thereby addressing the issue of missing sample labels in the dataset. Then, a caching-based sample sampler was designed to divide samples into few-shot and contrastive learning data. A specific joint loss function was developed to jointly train the dual-graph discriminator network to address the problem of sample imbalance effectively. In experiments carried out on real public datasets, the designed MTAD-RD anomaly detection method achieved an F1 score of 90.97%, outperforming existing supervised WSN anomaly detection methods.", "date": "2025-05-31", "categories": []}
{"id": "2506.00594", "title": "Graph Evidential Learning for Anomaly Detection", "abstract": "Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (GAEs) have emerged as a dominant approach by reconstructing graph structures and node features while deriving anomaly scores from reconstruction errors. However, relying solely on reconstruction error for anomaly detection has limitations, as it increases the sensitivity to noise and overfitting. To address these issues, we propose Graph Evidential Learning (GEL), a probabilistic framework that redefines the reconstruction process through evidential learning. By modeling node features and graph topology using evidential distributions, GEL quantifies two types of uncertainty: graph uncertainty and reconstruction uncertainty, incorporating them into the anomaly scoring mechanism. Extensive experiments demonstrate that GEL achieves state-of-the-art performance while maintaining high robustness against noise and structural perturbations.", "date": "2025-05-31", "categories": []}
{"id": "2506.01466", "title": "Towards Scalable Video Anomaly Retrieval: A Synthetic Video-Text Benchmark", "abstract": "Video anomaly retrieval aims to localize anomalous events in videos using natural language queries to facilitate public safety. However, existing datasets suffer from severe limitations: (1) data scarcity due to the long-tail nature of real-world anomalies, and (2) privacy constraints that impede large-scale collection. To address the aforementioned issues in one go, we introduce SVTA (Synthetic Video-Text Anomaly benchmark), the first large-scale dataset for cross-modal anomaly retrieval, leveraging generative models to overcome data availability challenges. Specifically, we collect and generate video descriptions via the off-the-shelf LLM (Large Language Model) covering 68 anomaly categories, e.g., throwing, stealing, and shooting. These descriptions encompass common long-tail events. We adopt these texts to guide the video generative model to produce diverse and high-quality videos. Finally, our SVTA involves 41,315 videos (1.36M frames) with paired captions, covering 30 normal activities, e.g., standing, walking, and sports, and 68 anomalous events, e.g., falling, fighting, theft, explosions, and natural disasters. We adopt three widely-used video-text retrieval baselines to comprehensively test our SVTA, revealing SVTA's challenging nature and its effectiveness in evaluating a robust cross-modal retrieval method. SVTA eliminates privacy risks associated with real-world anomaly collection while maintaining realistic scenarios. The dataset demo is available at: [https://svta-mm.github.io/SVTA.github.io/].", "date": "2025-06-02", "categories": []}
{"id": "2505.23781", "title": "Unified AI for Accurate Audio Anomaly Detection", "abstract": "This paper presents a unified AI framework for high-accuracy audio anomaly detection by integrating advanced noise reduction, feature extraction, and machine learning modeling techniques. The approach combines spectral subtraction and adaptive filtering to enhance audio quality, followed by feature extraction using traditional methods like MFCCs and deep embeddings from pre-trained models such as OpenL3. The modeling pipeline incorporates classical models (SVM, Random Forest), deep learning architectures (CNNs), and ensemble methods to boost robustness and accuracy. Evaluated on benchmark datasets including TORGO and LibriSpeech, the proposed framework demonstrates superior performance in precision, recall, and classification of slurred vs. normal speech. This work addresses challenges in noisy environments and real-time applications and provides a scalable solution for audio-based anomaly detection.", "date": "2025-05-20", "categories": []}
{"id": "2505.24201", "title": "SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems", "abstract": "The rise of large language model (LLM)-based multi-agent systems (MAS) introduces new security and reliability challenges. While these systems show great promise in decomposing and coordinating complex tasks, they also face multi-faceted risks across prompt manipulation, unsafe tool usage, and emergent agent miscoordination. Existing guardrail mechanisms offer only partial protection, primarily at the input-output level, and fall short in addressing systemic or multi-point failures in MAS. In this work, we present a system-level anomaly detection framework tailored for MAS, integrating structural modeling with runtime behavioral oversight. Our approach consists of two components. First, we propose a graph-based framework that models agent interactions as dynamic execution graphs, enabling semantic anomaly detection at node, edge, and path levels. Second, we introduce a pluggable SentinelAgent, an LLM-powered oversight agent that observes, analyzes, and intervenes in MAS execution based on security policies and contextual reasoning. By bridging abstract detection logic with actionable enforcement, our method detects not only single-point faults and prompt injections but also multi-agent collusion and latent exploit paths. We validate our framework through two case studies, including an email assistant and Microsoft's Magentic-One system, demonstrating its ability to detect covert risks and provide explainable root-cause attribution. Our work lays the foundation for more trustworthy, monitorable, and secure agent-based AI ecosystems.", "date": "2025-05-30", "categories": []}
{"id": "2505.24334", "title": "KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices", "abstract": "In the era of intelligent manufacturing, anomaly detection has become essential for maintaining quality control on modern production lines. However, while many existing models show promising performance, they are often too large, computationally demanding, and impractical to deploy on resource-constrained embedded devices that can be easily installed on the production lines of Small and Medium Enterprises (SMEs). To bridge this gap, we present KairosAD, a novel supervised approach that uses the power of the Mobile Segment Anything Model (MobileSAM) for image-based anomaly detection. KairosAD has been evaluated on the two well-known industrial anomaly detection datasets, i.e., MVTec-AD and ViSA. The results show that KairosAD requires 78% fewer parameters and boasts a 4x faster inference time compared to the leading state-of-the-art model, while maintaining comparable AUROC performance. We deployed KairosAD on two embedded devices, the NVIDIA Jetson NX, and the NVIDIA Jetson AGX. Finally, KairosAD was successfully installed and tested on the real production line of the Industrial Computer Engineering Laboratory (ICE Lab) at the University of Verona. The code is available at https://github.com/intelligolabs/KairosAD.", "date": "2025-05-30", "categories": []}
{"id": "2505.24365", "title": "Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm", "abstract": "This paper introduces a unified approach to cluster refinement and anomaly detection in datasets. We propose a novel algorithm that iteratively reduces the intra-cluster variance of N clusters until a global minimum is reached, yielding tighter clusters than the standard k-means algorithm. We evaluate the method using intrinsic measures for unsupervised learning, including the silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin index, and extend it to anomaly detection by identifying points whose assignment causes a significant variance increase. External validation on synthetic data and the UCI Breast Cancer and UCI Wine Quality datasets employs the Jaccard similarity score, V-measure, and F1 score. Results show variance reductions of 18.7% and 88.1% on the synthetic and Wine Quality datasets, respectively, along with accuracy and F1 score improvements of 22.5% and 20.8% on the Wine Quality dataset.", "date": "2025-05-30", "categories": []}
{"id": "2505.24534", "title": "HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection", "abstract": "In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to capture changes in higher-order interactions that are crucial for identifying complex structural anomalies. These higher-order interactions can arise either directly from the underlying data itself or through graph lifting techniques. Our approach leverages the spectral properties of Hodge Laplacians of simplicial complexes to effectively model multi-way interactions among data points. By incorporating higher-dimensional simplicial structures into our method, our method enhances both detection accuracy and computational efficiency. Through comprehensive experiments on both synthetic and real-world datasets, we demonstrate that our approach outperforms existing graph methods in detecting both events and change points.", "date": "2025-05-30", "categories": []}
{"id": "2505.22762", "title": "MIAS-SAM: Medical Image Anomaly Segmentation without thresholding", "abstract": "This paper presents MIAS-SAM, a novel approach for the segmentation of anomalous regions in medical images. MIAS-SAM uses a patch-based memory bank to store relevant image features, which are extracted from normal data using the SAM encoder. At inference time, the embedding patches extracted from the SAM encoder are compared with those in the memory bank to obtain the anomaly map. Finally, MIAS-SAM computes the center of gravity of the anomaly map to prompt the SAM decoder, obtaining an accurate segmentation from the previously extracted features. Differently from prior works, MIAS-SAM does not require to define a threshold value to obtain the segmentation from the anomaly map. Experimental results conducted on three publicly available datasets, each with a different imaging modality (Brain MRI, Liver CT, and Retina OCT) show accurate anomaly segmentation capabilities measured using DICE score. The code is available at: https://github.com/warpcut/MIAS-SAM", "date": "2025-05-28", "categories": []}
{"id": "2505.22805", "title": "Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation", "abstract": "In order to navigate safely and reliably in off-road and unstructured environments, robots must detect anomalies that are out-of-distribution (OOD) with respect to the training data. We present an analysis-by-synthesis approach for pixel-wise anomaly detection without making any assumptions about the nature of OOD data. Given an input image, we use a generative diffusion model to synthesize an edited image that removes anomalies while keeping the remaining image unchanged. Then, we formulate anomaly detection as analyzing which image segments were modified by the diffusion model. We propose a novel inference approach for guided diffusion by analyzing the ideal guidance gradient and deriving a principled approximation that bootstraps the diffusion model to predict guidance gradients. Our editing technique is purely test-time that can be integrated into existing workflows without the need for retraining or fine-tuning. Finally, we use a combination of vision-language foundation models to compare pixels in a learned feature space and detect semantically meaningful edits, enabling accurate anomaly detection for off-road navigation. Project website: https://siddancha.github.io/anomalies-by-diffusion-synthesis/", "date": "2025-05-28", "categories": []}
{"id": "2505.21563", "title": "Fog Intelligence for Network Anomaly Detection", "abstract": "Anomalies are common in network system monitoring. When manifested as network threats to be mitigated, service outages to be prevented, and security risks to be ameliorated, detecting such anomalous network behaviors becomes of great importance. However, the growing scale and complexity of the mobile communication networks, as well as the ever-increasing amount and dimensionality of the network surveillance data, make it extremely difficult to monitor a mobile network and discover abnormal network behaviors. Recent advances in machine learning allow for obtaining near-optimal solutions to complicated decision-making problems with many sources of uncertainty that cannot be accurately characterized by traditional mathematical models. However, most machine learning algorithms are centralized, which renders them inapplicable to a large-scale distributed wireless networks with tens of millions of mobile devices. In this article, we present fog intelligence, a distributed machine learning architecture that enables intelligent wireless network management. It preserves the advantage of both edge processing and centralized cloud computing. In addition, the proposed architecture is scalable, privacy-preserving, and well suited for intelligent management of a distributed wireless network.", "date": "2025-05-27", "categories": []}
{"id": "2505.21703", "title": "A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks", "abstract": "Internet of Vehicles (IoV) systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and cloud services and present a highly distributed framework with a wide attack surface. In considering network-centered attacks on IoV systems, attacks such as Denial-of-Service (DoS) can prohibit the communication of essential physical traffic safety information between system elements, illustrating that the security concerns for these systems go beyond the traditional confidentiality, integrity, and availability concerns of enterprise systems. Given the complexity and volume of data generated by IoV systems, traditional security mechanisms are often inadequate for accurately detecting sophisticated and evolving cyberattacks. Here, we present an unsupervised autoencoder method trained entirely on benign network data for the purpose of unseen attack detection in IoV networks. We leverage a weighted combination of reconstruction and triplet margin loss to guide the autoencoder training and develop a diverse representation of the benign training set. We conduct extensive experiments on recent network intrusion datasets from two different application domains, industrial IoT and home IoT, that represent the modern IoV task. We show that our method performs robustly for all unseen attack types, with roughly 99% accuracy on benign data and between 97% and 100% performance on anomaly data. We extend these results to show that our model is adaptable through the use of transfer learning, achieving similarly high results while leveraging domain features from one domain to another.", "date": "2025-05-27", "categories": []}
{"id": "2505.22039", "title": "OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning", "abstract": "While anomaly detection has made significant progress, generating detailed analyses that incorporate industrial knowledge remains a challenge. To address this gap, we introduce OmniAD, a novel framework that unifies anomaly detection and understanding for fine-grained analysis. OmniAD is a multimodal reasoner that combines visual and textual reasoning processes. The visual reasoning provides detailed inspection by leveraging Text-as-Mask Encoding to perform anomaly detection through text generation without manually selected thresholds. Following this, Visual Guided Textual Reasoning conducts comprehensive analysis by integrating visual perception. To enhance few-shot generalization, we employ an integrated training strategy that combines supervised fine-tuning (SFT) with reinforcement learning (GRPO), incorporating three sophisticated reward functions. Experimental results demonstrate that OmniAD achieves a performance of 79.1 on the MMAD benchmark, surpassing models such as Qwen2.5-VL-7B and GPT-4o. It also shows strong results across multiple anomaly detection benchmarks. These results highlight the importance of enhancing visual perception for effective reasoning in anomaly understanding. All codes and models will be publicly available.", "date": "2025-05-28", "categories": []}
{"id": "2412.10792", "title": "Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description", "abstract": "The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their high bandwidth and capability to detect subtle anomalies that other sensors might have less sensitivity. In this study, we investigate malfunctioning industrial machines to evaluate and compare anomaly detection performance across different machine types and fault conditions. Log-Mel spectrograms of machinery sound are used as input, and the performance is evaluated using the area under the curve (AUC) score for two different methods: baseline dense autoencoder (AE) and one-class deep Support Vector Data Description (deep SVDD) with different subspace dimensions. Our results over the MIMII sound dataset demonstrate that the deep SVDD method with a subspace dimension of 2 provides superior anomaly detection performance, achieving average AUC scores of 0.84, 0.80, and 0.69 for 6 dB, 0 dB, and -6 dB signal-to-noise ratios (SNRs), respectively, compared to 0.82, 0.72, and 0.64 for the baseline model. Moreover, deep SVDD requires 7.4 times fewer trainable parameters than the baseline dense AE, emphasizing its advantage in both effectiveness and computational efficiency.", "date": "2024-12-14", "categories": []}
{"id": "2505.19750", "title": "SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect", "abstract": "In this technical report, we present our solution to the CVPR 2025 Visual Anomaly and Novelty Detection (VAND) 3.0 Workshop Challenge Track 1: Adapt & Detect: Robust Anomaly Detection in Real-World Applications. In real-world industrial anomaly detection, it is crucial to accurately identify anomalies with physical complexity, such as transparent or reflective surfaces, occlusions, and low-contrast contaminations. The recently proposed MVTec AD 2 dataset significantly narrows the gap between publicly available benchmarks and anomalies found in real-world industrial environments. To address the challenges posed by this dataset--such as complex and varying lighting conditions and real anomalies with large scale differences--we propose a fully training-free anomaly detection and segmentation method based on feature extraction using the DINOv2 model named SuperAD. Our method carefully selects a small number of normal reference images and constructs a memory bank by leveraging the strong representational power of DINOv2. Anomalies are then segmented by performing nearest neighbor matching between test image features and the memory bank. Our method achieves competitive results on both test sets of the MVTec AD 2 dataset.", "date": "2025-05-26", "categories": []}
{"id": "2505.20765", "title": "Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies", "abstract": "Unsupervised anomaly detection in time series has been a pivotal research area for decades. Current mainstream approaches focus on learning normality, on the assumption that all or most of the samples in the training set are normal. However, anomalies in the training set (i.e., anomaly contamination) can be misleading. Recent studies employ data augmentation to generate pseudo-anomalies and learn the boundary separating the training samples from the augmented samples. Although this approach mitigates anomaly contamination if augmented samples mimic unseen real anomalies, it suffers from several limitations. (1) Covering a wide range of time series anomalies is challenging. (2) It disregards augmented samples that resemble normal samples (i.e., false anomalies). (3) It places too much trust in the labels of training and augmented samples. In response, we propose RedLamp, which employs diverse data augmentations to generate multiclass pseudo-anomalies and learns the multiclass boundary. Such multiclass pseudo-anomalies cover a wide variety of time series anomalies. We conduct multiclass classification using soft labels, which prevents the model from being overconfident and ensures its robustness against contaminated/false anomalies. The learned latent space is inherently explainable as it is trained to separate pseudo-anomalies into multiclasses. Extensive experiments demonstrate the effectiveness of RedLamp in anomaly detection and its robustness against anomaly contamination.", "date": "2025-05-27", "categories": []}
{"id": "2505.21152", "title": "RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images", "abstract": "Robust unsupervised anomaly detection (AD) in real-world scenarios is an important task. Current methods exhibit severe performance degradation on the MVTec AD 2 benchmark due to its complex real-world challenges. To solve this problem, we propose a robust framework RoBiS, which consists of three core modules: (1) Swin-Cropping, a high-resolution image pre-processing strategy to preserve the information of small anomalies through overlapping window cropping. (2) The data augmentation of noise addition and lighting simulation is carried out on the training data to improve the robustness of AD model. We use INP-Former as our baseline, which could generate better results on the various sub-images. (3) The traditional statistical-based binarization strategy (mean+3std) is combined with our previous work, MEBin (published in CVPR2025), for joint adaptive binarization. Then, SAM is further employed to refine the segmentation results. Compared with some methods reported by the MVTec AD 2, our RoBiS achieves a 29.2% SegF1 improvement (from 21.8% to 51.00%) on Test_private and 29.82% SegF1 gains (from 16.7% to 46.52%) on Test_private_mixed. Code is available at https://github.com/xrli-U/RoBiS.", "date": "2025-05-27", "categories": []}
{"id": "2505.21228", "title": "Is Hyperbolic Space All You Need for Medical Anomaly Detection?", "abstract": "Medical anomaly detection has emerged as a promising solution to challenges in data availability and labeling constraints. Traditional methods extract features from different layers of pre-trained networks in Euclidean space; however, Euclidean representations fail to effectively capture the hierarchical relationships within these features, leading to suboptimal anomaly detection performance. We propose a novel yet simple approach that projects feature representations into hyperbolic space, aggregates them based on confidence levels, and classifies samples as healthy or anomalous. Our experiments demonstrate that hyperbolic space consistently outperforms Euclidean-based frameworks, achieving higher AUROC scores at both image and pixel levels across multiple medical benchmark datasets. Additionally, we show that hyperbolic space exhibits resilience to parameter variations and excels in few-shot scenarios, where healthy images are scarce. These findings underscore the potential of hyperbolic space as a powerful alternative for medical anomaly detection. The project website can be found at https://hyperbolic-anomalies.github.io", "date": "2025-05-27", "categories": []}
{"id": "2308.06663", "title": "ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN", "abstract": "Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data.", "date": "2023-08-13", "categories": []}
{"id": "2404.16366", "title": "Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection", "abstract": "Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods. This is motivated by the hypothesis that nodes in the graph tend to exhibit consistent behaviors with their neighborhoods. However, such consistency can be disrupted by graph anomalies in multiple ways. Most existing methods directly employ GNNs to learn representations, disregarding the negative impact of graph anomalies on GNNs, resulting in sub-optimal node representations and anomaly detection performance. While a few recent approaches have redesigned GNNs for graph anomaly detection under semi-supervised label guidance, how to address the adverse effects of graph anomalies on GNNs in unsupervised scenarios and learn effective representations for anomaly detection are still under-explored. To bridge this gap, in this paper, we propose a simple yet effective framework for Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD). Specifically, G3AD first introduces two auxiliary networks along with correlation constraints to guard the GNNs against inconsistent information encoding. Furthermore, G3AD introduces an adaptive caching module to guard the GNNs from directly reconstructing the observed graph data that contains anomalies. Extensive experiments demonstrate that our G3AD can outperform twenty state-of-the-art methods on both synthetic and real-world graph anomaly datasets, with flexible generalization ability in different GNN backbones.", "date": "2024-04-25", "categories": []}
{"id": "2406.18815", "title": "MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation", "abstract": "In the context of escalating safety concerns across various domains, the tasks of Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR) have emerged as critically important for applications in intelligent surveillance, evidence investigation, violence alerting, etc. These tasks, aimed at identifying and classifying deviations from normal behavior in video data, face significant challenges due to the rarity of anomalies which leads to extremely imbalanced data and the impracticality of extensive frame-level data annotation for supervised learning. This paper introduces a novel hierarchical graph neural network (GNN) based model MissionGNN that addresses these challenges by leveraging a state-of-the-art large language model and a comprehensive knowledge graph for efficient weakly supervised learning in VAR. Our approach circumvents the limitations of previous methods by avoiding heavy gradient computations on large multimodal models and enabling fully frame-level training without fixed video segmentation. Utilizing automated, mission-specific knowledge graph generation, our model provides a practical and efficient solution for real-time video analysis without the constraints of previous segmentation-based or multimodal approaches. Experimental validation on benchmark datasets demonstrates our model's performance in VAD and VAR, highlighting its potential to redefine the landscape of anomaly detection and recognition in video surveillance systems. The code is available here: https://github.com/c0510gy/MissionGNN.", "date": "2024-06-27", "categories": []}
{"id": "2505.18934", "title": "Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection", "abstract": "Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters. Our code is available at https://github.com/HsipingLi/ChiGAD.", "date": "2025-05-25", "categories": []}
{"id": "2505.19877", "title": "Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought", "abstract": "Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based Video Anomaly Detection (VAD) methods remain limited to shallow anomaly descriptions without deep reasoning. In this paper, we propose a new task named Video Anomaly Reasoning (VAR), which aims to enable deep analysis and understanding of anomalies in the video by requiring MLLMs to think explicitly before answering. To this end, we propose Vad-R1, an end-to-end MLLM-based framework for VAR. Specifically, we design a Perception-to-Cognition Chain-of-Thought (P2C-CoT) that simulates the human process of recognizing anomalies, guiding the MLLM to reason anomaly step-by-step. Based on the structured P2C-CoT, we construct Vad-Reasoning, a dedicated dataset for VAR. Furthermore, we propose an improved reinforcement learning algorithm AVA-GRPO, which explicitly incentivizes the anomaly reasoning capability of MLLMs through a self-verification mechanism with limited annotations. Experimental results demonstrate that Vad-R1 achieves superior performance, outperforming both open-source and proprietary models on VAD and VAR tasks. Codes and datasets will be released at https://github.com/wbfwonderful/Vad-R1.", "date": "2025-05-26", "categories": []}
{"id": "2501.11960", "title": "TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection", "abstract": "Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications.", "date": "2025-01-21", "categories": []}
{"id": "2505.15205", "title": "Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection", "abstract": "Video Anomaly Detection (VAD) automatically identifies anomalous events from video, mitigating the need for human operators in large-scale surveillance deployments. However, two fundamental obstacles hinder real-world adoption: domain dependency and real-time constraints -- requiring near-instantaneous processing of incoming video. To this end, we propose Flashback, a zero-shot and real-time video anomaly detection paradigm. Inspired by the human cognitive mechanism of instantly judging anomalies and reasoning in current scenes based on past experience, Flashback operates in two stages: Recall and Respond. In the offline recall stage, an off-the-shelf LLM builds a pseudo-scene memory of both normal and anomalous captions without any reliance on real anomaly data. In the online respond stage, incoming video segments are embedded and matched against this memory via similarity search. By eliminating all LLM calls at inference time, Flashback delivers real-time VAD even on a consumer-grade GPU. On two large datasets from real-world surveillance scenarios, UCF-Crime and XD-Violence, we achieve 87.3 AUC (+7.0 pp) and 75.1 AP (+13.1 pp), respectively, outperforming prior zero-shot VAD methods by large margins.", "date": "2025-05-21", "categories": []}
{"id": "2505.17311", "title": "Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays", "abstract": "Unsupervised anomaly detection (UAD) in medical imaging is crucial for identifying pathological abnormalities without requiring extensive labeled data. However, existing diffusion-based UAD models rely solely on imaging features, limiting their ability to distinguish between normal anatomical variations and pathological anomalies. To address this, we propose Diff3M, a multi-modal diffusion-based framework that integrates chest X-rays and structured Electronic Health Records (EHRs) for enhanced anomaly detection. Specifically, we introduce a novel image-EHR cross-attention module to incorporate structured clinical context into the image generation process, improving the model's ability to differentiate normal from abnormal features. Additionally, we develop a static masking strategy to enhance the reconstruction of normal-like images from anomalies. Extensive evaluations on CheXpert and MIMIC-CXR/IV demonstrate that Diff3M achieves state-of-the-art performance, outperforming existing UAD methods in medical imaging. Our code is available at this http URL https://github.com/nth221/Diff3M", "date": "2025-05-22", "categories": []}
{"id": "2505.17551", "title": "Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection", "abstract": "Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.", "date": "2025-05-23", "categories": []}
{"id": "2505.17614", "title": "PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings", "abstract": "Unsupervised pathology detection trains models on non-pathological data to flag deviations as pathologies, offering strong generalizability for identifying novel diseases and avoiding costly annotations. However, building reliable normality models requires vast healthy datasets, as hospitals' data is inherently biased toward symptomatic populations, while privacy regulations hinder the assembly of representative healthy cohorts. To address this limitation, we propose PathoSCOPE, a few-shot unsupervised pathology detection framework that requires only a small set of non-pathological samples (minimum 2 shots), significantly improving data efficiency. We introduce Global-Local Contrastive Loss (GLCL), comprised of a Local Contrastive Loss to reduce the variability of non-pathological embeddings and a Global Contrastive Loss to enhance the discrimination of pathological regions. We also propose a Pathology-informed Embedding Generation (PiEG) module that synthesizes pathological embeddings guided by the global loss, better exploiting the limited non-pathological samples. Evaluated on the BraTS2020 and ChestXray8 datasets, PathoSCOPE achieves state-of-the-art performance among unsupervised methods while maintaining computational efficiency (2.48 GFLOPs, 166 FPS).", "date": "2025-05-23", "categories": []}
{"id": "2505.18002", "title": "Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective", "abstract": "Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.", "date": "2025-05-23", "categories": []}
{"id": "2408.15519", "title": "Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Cameras", "abstract": "The behavioural and psychological symptoms of dementia, such as agitation and aggression, present a significant health and safety risk in residential care settings. Many care facilities have video cameras in place for digital monitoring of public spaces, which can be leveraged to develop an automated behaviours of risk detection system that can alert the staff to enable timely intervention and prevent the situation from escalating. However, one of the challenges in our previous study was the presence of false alarms due to disparate importance of events based on distance. To address this issue, we proposed a novel depth-weighted loss to enforce equivalent importance to the events happening both near and far from the cameras; thus, helping to reduce false alarms. We further propose to utilize the training outliers to determine the anomaly threshold. The data from nine dementia participants across three cameras in a specialized dementia unit were used for training. The proposed approach obtained the best area under receiver operating characteristic curve performance of 0.852, 0.81 and 0.768, respectively, for the three cameras. Ablation analysis was conducted for the individual components of the proposed approach and effect of frame size and frame rate. The performance of the proposed approach was investigated for cross-camera, participant-specific and sex-specific behaviours of risk detection. The proposed approach performed reasonably well in reducing false alarms. This motivates further research to make the system more suitable for deployment in care facilities.", "date": "2024-08-28", "categories": []}
{"id": "2505.16659", "title": "SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images", "abstract": "Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical imaging data, caused by privacy concerns and data silos. Few-shot learning has emerged as a promising approach to alleviate these limitations by leveraging the large-scale prior knowledge embedded in vision-language models (VLMs). Recent advancements in few-shot medical AD have treated normal and abnormal cases as a one-class classification problem, often overlooking the distinction among multiple anomaly categories. Thus, in this paper, we propose a framework tailored for few-shot medical anomaly detection in the scenario where the identification of multiple anomaly categories is required. To capture the detailed radiological signs of medical anomaly categories, our framework incorporates diverse textual descriptions for each category generated by a Large-Language model, under the assumption that different anomalies in medical images may share common radiological signs in each category. Specifically, we introduce SD-MAD, a two-stage Sign-Driven few-shot Multi-Anomaly Detection framework: (i) Radiological signs are aligned with anomaly categories by amplifying inter-anomaly discrepancy; (ii) Aligned signs are selected further to mitigate the effect of the under-fitting and uncertain-sample issue caused by limited medical data, employing an automatic sign selection strategy at inference. Moreover, we propose three protocols to comprehensively quantify the performance of multi-anomaly detection. Extensive experiments illustrate the effectiveness of our method.", "date": "2025-05-22", "categories": []}
{"id": "2505.16674", "title": "Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge", "abstract": "Batteries are essential for various applications, including electric vehicles and renewable energy storage, making safety and efficiency critical concerns. Anomaly detection in battery thermal images helps identify failures early, but traditional deep learning methods require extensive labeled data, which is difficult to obtain, especially for anomalies due to safety risks and high data collection costs. To overcome this, we explore zero-shot anomaly detection using Visual Question Answering (VQA) models, which leverage pretrained knowledge and textbased prompts to generalize across vision tasks. By incorporating prior knowledge of normal battery thermal behavior, we design prompts to detect anomalies without battery-specific training data. We evaluate three VQA models (ChatGPT-4o, LLaVa-13b, and BLIP-2) analyzing their robustness to prompt variations, repeated trials, and qualitative outputs. Despite the lack of finetuning on battery data, our approach demonstrates competitive performance compared to state-of-the-art models that are trained with the battery data. Our findings highlight the potential of VQA-based zero-shot learning for battery anomaly detection and suggest future directions for improving its effectiveness.", "date": "2025-05-22", "categories": []}
{"id": "2505.16872", "title": "A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams", "abstract": "The rapid expansion of Internet of Things (IoT) devices has introduced critical security challenges, underscoring the need for accurate anomaly detection. Although numerous studies have proposed machine learning (ML) methods for this purpose, limited research systematically examines how different preprocessing steps--normalization, transformation, and feature selection--interact with distinct model architectures. To address this gap, this paper presents a multi-step evaluation framework assessing the combined impact of preprocessing choices on three ML algorithms: RNN-LSTM, autoencoder neural networks (ANN), and Gradient Boosting (GBoosting). Experiments on the IoTID20 dataset shows that GBoosting consistently delivers superior accuracy across preprocessing configurations, while RNN-LSTM shows notable gains with z-score normalization and autoencoders excel in recall, making them well-suited for unsupervised scenarios. By offering a structured analysis of preprocessing decisions and their interplay with various ML techniques, the proposed framework provides actionable guidance to enhance anomaly detection performance in IoT environments.", "date": "2025-05-22", "categories": []}
{"id": "2505.14967", "title": "Anomaly Detection Based on Critical Paths for Deep Neural Networks", "abstract": "Deep neural networks (DNNs) are notoriously hard to understand and difficult to defend. Extracting representative paths (including the neuron activation values and the connections between neurons) from DNNs using software engineering approaches has recently shown to be a promising approach in interpreting the decision making process of blackbox DNNs, as the extracted paths are often effective in capturing essential features. With this in mind, this work investigates a novel approach that extracts critical paths from DNNs and subsequently applies the extracted paths for the anomaly detection task, based on the observation that outliers and adversarial inputs do not usually induce the same activation pattern on those paths as normal (in-distribution) inputs.\n  In our approach, we first identify critical detection paths via genetic evolution and mutation. Since different paths in a DNN often capture different features for the same target class, we ensemble detection results from multiple paths by integrating random subspace sampling and a voting mechanism. Compared with state-of-the-art methods, our experimental results suggest that our method not only outperforms them, but it is also suitable for the detection of a broad range of anomaly types with high accuracy.", "date": "2025-05-20", "categories": []}
{"id": "2403.11083", "title": "Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning", "abstract": "Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for quality control. Existing techniques tend to be specialized in individual scenarios and lack generalization capacities. In this study, our objective is to develop a generic anomaly detection model that can be applied in multiple scenarios. To achieve this, we custom-build generic visual language foundation models that possess extensive knowledge and robust reasoning abilities as anomaly detectors and reasoners. Specifically, we introduce a multi-modal prompting strategy that incorporates domain knowledge from experts as conditions to guide the models. Our approach considers diverse prompt types, including task descriptions, class context, normality rules, and reference images. In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning. Our preliminary studies demonstrate that combining visual and language prompts as conditions for customizing the models enhances anomaly detection performance. The customized models showcase the ability to detect anomalies across different data modalities such as images, point clouds, and videos. Qualitative case studies further highlight the anomaly detection and reasoning capabilities, particularly for multi-object scenes and temporal data. Our code is publicly available at https://github.com/Xiaohao-Xu/Customizable-VLM", "date": "2024-03-17", "categories": []}
{"id": "2406.18012", "title": "View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis", "abstract": "The built environment, encompassing critical infrastructure such as bridges and buildings, requires diligent monitoring of unexpected anomalies or deviations from a normal state in captured imagery. Anomaly detection methods could aid in automating this task; however, deploying anomaly detection effectively in such environments presents significant challenges that have not been evaluated before. These challenges include camera viewpoints that vary, the presence of multiple objects within a scene, and the absence of labeled anomaly data for training. To address these comprehensively, we introduce and formalize Scene Anomaly Detection (Scene AD) as the task of unsupervised, pixel-wise anomaly localization under these specific real-world conditions. Evaluating progress in Scene AD required the development of ToyCity, the first multi-object, multi-view real-image dataset, for unsupervised anomaly detection. Our initial evaluations using ToyCity revealed that established anomaly detection baselines struggle to achieve robust pixel-level localization. To address this, two data augmentation strategies were created to generate additional synthetic images of non-anomalous regions to enhance generalizability. However, the addition of these synthetic images alone only provided minor improvements. Thus, OmniAD, a refinement of the Reverse Distillation methodology, was created to establish a stronger baseline. Our experiments demonstrate that OmniAD, when used with augmented views, yields a 64.33\\% increase in pixel-wise \\(F_1\\) score over Reverse Distillation with no augmentation. Collectively, this work offers the Scene AD task definition, the ToyCity benchmark, the view synthesis augmentation approaches, and the OmniAD method. Project Page: https://drags99.github.io/OmniAD/", "date": "2024-06-26", "categories": []}
{"id": "2503.20252", "title": "LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions", "abstract": "Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it a vital tool in process control. Logical anomalies may appear visually normal yet violate predefined constraints on object presence, arrangement, or quantity, depending on reasoning and explainability. We introduce LogicQA, a framework that enhances AD by providing industrial operators with explanations for logical anomalies. LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints. LogicQA is training-free, annotation-free, and operates in a few-shot setting. We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6 percent and an F1-max of 87.0 percent along with the explanations of anomalies. Also, our approach has shown outstanding performance on semiconductor SEM corporate data, further validating its effectiveness in industrial applications.", "date": "2025-03-26", "categories": []}
{"id": "2505.13828", "title": "Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models", "abstract": "Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel multimodal Retrieval-Augmented Generation-based framework that automates anomaly detection across various Additive Manufacturing processes leveraging retrieved information from literature, including images and descriptive text, rather than training datasets. This framework integrates text and image retrieval from scientific literature and multimodal generation models to perform zero-shot anomaly identification, classification, and explanation generation in a Laser Powder Bed Fusion setting. The proposed framework is evaluated on four L-PBF manufacturing datasets from Oak Ridge National Laboratory, featuring various printer makes, models, and materials. This evaluation demonstrates the framework's adaptability and generalizability across diverse images without requiring additional training. Comparative analysis using Qwen2-VL-2B and GPT-4o-mini as MLLM within the proposed framework highlights that GPT-4o-mini outperforms Qwen2-VL-2B and proportional random baseline in manufacturing anomalies classification. Additionally, the evaluation of the RAG system confirms that incorporating retrieval mechanisms improves average accuracy by 12% by reducing the risk of hallucination and providing additional information. The proposed framework can be continuously updated by integrating emerging research, allowing seamless adaptation to the evolving landscape of AM technologies. This scalable, automated, and zero-shot-capable framework streamlines AM anomaly analysis, enhancing efficiency and accuracy.", "date": "2025-05-20", "categories": []}
{"id": "2505.14064", "title": "NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI", "abstract": "In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-world recognition flags such inputs to ensure the system remains robust as ever-emerging, previously $unknown$ categories appear and must be addressed without retraining. Foundation and vision-language models are pre-trained on large and diverse datasets with the expectation of broad generalization across domains, including medical imaging. However, benchmarking these models on test sets with only a few common outlier types silently collapses the evaluation back to a closed-set problem, masking failures on rare or truly novel conditions encountered in clinical use.\n  We therefore present $NOVA$, a challenging, real-life $evaluation-only$ benchmark of $\\sim$900 brain MRI scans that span 281 rare pathologies and heterogeneous acquisition protocols. Each case includes rich clinical narratives and double-blinded expert bounding-box annotations. Together, these enable joint assessment of anomaly localisation, visual captioning, and diagnostic reasoning. Because NOVA is never used for training, it serves as an $extreme$ stress-test of out-of-distribution generalisation: models must bridge a distribution gap both in sample appearance and in semantic space. Baseline results with leading vision-language models (GPT-4o, Gemini 2.0 Flash, and Qwen2.5-VL-72B) reveal substantial performance drops across all tasks, establishing NOVA as a rigorous testbed for advancing models that can detect, localize, and reason about truly unknown anomalies.", "date": "2025-05-20", "categories": []}
{"id": "2303.08730", "title": "DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection", "abstract": "Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD", "date": "2023-03-15", "categories": []}
{"id": "2402.07281", "title": "Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond", "abstract": "Detection of anomalous situations for complex mission-critical systems hold paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of Machine Learning (ML)-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical ML, including various tree-based approaches to Deep Learning (DL) and outlier detection methods. The inclusion of 104 publicly available enhances the diversity of the study, allowing a more realistic evaluation of algorithm performance and emphasizing the importance of adaptability to real-world scenarios.\n  The paper evaluates the general notion of DL as a universal solution, showing that, while powerful, it is not always the best fit for every scenario. The findings reveal that recently proposed tree-based evolutionary algorithms match DL methods and sometimes outperform them in many instances of univariate data where the size of the data is small and number of anomalies are less than 10%. Specifically, tree-based approaches successfully detect singleton anomalies in datasets where DL falls short. To the best of the authors' knowledge, such a study on a large number of state-of-the-art algorithms using diverse data sets, with the objective of guiding researchers and practitioners in making informed algorithmic choices, has not been attempted earlier.", "date": "2024-02-11", "categories": []}
{"id": "2502.07858", "title": "Mamba Adaptive Anomaly Transformer with association discrepancy for time series", "abstract": "Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and DCdetector have progressed, but they face limitations such as sensitivity to short-term contexts and inefficiency in noisy, non-stationary environments.\n  To overcome these issues, we introduce MAAT, an improved architecture that enhances association discrepancy modeling and reconstruction quality. MAAT features Sparse Attention, efficiently capturing long-range dependencies by focusing on relevant time steps, thereby reducing computational redundancy. Additionally, a Mamba-Selective State Space Model is incorporated into the reconstruction module, utilizing a skip connection and Gated Attention to improve anomaly localization and detection performance.\n  Extensive experiments show that MAAT significantly outperforms previous methods, achieving better anomaly distinguishability and generalization across various time series applications, setting a new standard for unsupervised time series anomaly detection in real-world scenarios.", "date": "2025-02-11", "categories": []}
{"id": "2505.08220", "title": "Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks", "abstract": "To improve the identification of potential anomaly patterns in complex user behavior, this paper proposes an anomaly detection method based on a deep mixture density network. The method constructs a Gaussian mixture model parameterized by a neural network, enabling conditional probability modeling of user behavior. It effectively captures the multimodal distribution characteristics commonly present in behavioral data. Unlike traditional classifiers that rely on fixed thresholds or a single decision boundary, this approach defines an anomaly scoring function based on probability density using negative log-likelihood. This significantly enhances the model's ability to detect rare and unstructured behaviors. Experiments are conducted on the real-world network user dataset UNSW-NB15. A series of performance comparisons and stability validation experiments are designed. These cover multiple evaluation aspects, including Accuracy, F1- score, AUC, and loss fluctuation. The results show that the proposed method outperforms several advanced neural network architectures in both performance and training stability. This study provides a more expressive and discriminative solution for user behavior modeling and anomaly detection. It strongly promotes the application of deep probabilistic modeling techniques in the fields of network security and intelligent risk control.", "date": "2025-05-13", "categories": []}
{"id": "2505.09926", "title": "AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection", "abstract": "Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and possesses a training-free manner on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods. We will make the code and model of AdaptCLIP available at https://github.com/gaobb/AdaptCLIP.", "date": "2025-05-15", "categories": []}
{"id": "2505.11793", "title": "CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection", "abstract": "Anomaly detection (AD) has attracted remarkable attention in hyperspectral image (HSI) processing fields, and most existing deep learning (DL)-based algorithms indicate dramatic potential for detecting anomaly samples through specific training process under current scenario. However, the limited prior information and the catastrophic forgetting problem indicate crucial challenges for existing DL structure in open scenarios cross-domain detection. In order to improve the detection performance, a novel continual learning-based capsule differential generative adversarial network (CL-CaGAN) is proposed to elevate the cross-scenario learning performance for facilitating the real application of DL-based structure in hyperspectral AD (HAD) task. First, a modified capsule structure with adversarial learning network is constructed to estimate the background distribution for surmounting the deficiency of prior information. To mitigate the catastrophic forgetting phenomenon, clustering-based sample replay strategy and a designed extra self-distillation regularization are integrated for merging the history and future knowledge in continual AD task, while the discriminative learning ability from previous detection scenario to current scenario is retained by the elaborately designed structure with continual learning (CL) strategy. In addition, the differentiable enhancement is enforced to augment the generation performance of the training data. This further stabilizes the training process with better convergence and efficiently consolidates the reconstruction ability of background samples. To verify the effectiveness of our proposed CL-CaGAN, we conduct experiments on several real HSIs, and the results indicate that the proposed CL-CaGAN demonstrates higher detection performance and continuous learning capacity for mitigating the catastrophic forgetting under cross-domain scenarios.", "date": "2025-05-17", "categories": []}
{"id": "2505.11796", "title": "CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection", "abstract": "Memory stability and learning flexibility in continual learning (CL) is a core challenge for cross-scene Hyperspectral Anomaly Detection (HAD) task. Biological neural networks can actively forget history knowledge that conflicts with the learning of new experiences by regulating learning-triggered synaptic expansion and synaptic convergence. Inspired by this phenomenon, we propose a novel Biologically-Inspired Continual Learning Generative Adversarial Network (CL-BioGAN) for augmenting continuous distribution fitting ability for cross-domain HAD task, where Continual Learning Bio-inspired Loss (CL-Bio Loss) and self-attention Generative Adversarial Network (BioGAN) are incorporated to realize forgetting history knowledge as well as involving replay strategy in the proposed BioGAN. Specifically, a novel Bio-Inspired Loss composed with an Active Forgetting Loss (AF Loss) and a CL loss is designed to realize parameters releasing and enhancing between new task and history tasks from a Bayesian perspective. Meanwhile, BioGAN loss with L2-Norm enhances self-attention (SA) to further balance the stability and flexibility for better fitting background distribution for open scenario HAD (OHAD) tasks. Experiment results underscore that the proposed CL-BioGAN can achieve more robust and satisfying accuracy for cross-domain HAD with fewer parameters and computation cost. This dual contribution not only elevates CL performance but also offers new insights into neural adaptation mechanisms in OHAD task.", "date": "2025-05-17", "categories": []}
{"id": "2505.12594", "title": "AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection", "abstract": "Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing number of specialized AD libraries pose challenges for non-expert users who lack in-depth library-specific knowledge and advanced programming skills. To tackle this, we present AD-AGENT, an LLM-driven multi-agent framework that turns natural-language instructions into fully executable AD pipelines. AD-AGENT coordinates specialized agents for intent parsing, data preparation, library and model selection, documentation mining, and iterative code generation and debugging. Using a shared short-term workspace and a long-term cache, the agents integrate popular AD libraries like PyOD, PyGOD, and TSLib into a unified workflow. Experiments demonstrate that AD-AGENT produces reliable scripts and recommends competitive models across libraries. The system is open-sourced to support further research and practical applications in AD.", "date": "2025-05-19", "categories": []}
{"id": "2505.12751", "title": "Structure-based Anomaly Detection and Clustering", "abstract": "Anomaly detection is a fundamental problem in domains such as healthcare, manufacturing, and cybersecurity. This thesis proposes new unsupervised methods for anomaly detection in both structured and streaming data settings. In the first part, we focus on structure-based anomaly detection, where normal data follows low-dimensional manifolds while anomalies deviate from them. We introduce Preference Isolation Forest (PIF), which embeds data into a high-dimensional preference space via manifold fitting, and isolates outliers using two variants: Voronoi-iForest, based on geometric distances, and RuzHash-iForest, leveraging Locality Sensitive Hashing for scalability. We also propose Sliding-PIF, which captures local manifold information for streaming scenarios. Our methods outperform existing techniques on synthetic and real datasets. We extend this to structure-based clustering with MultiLink, a novel method for recovering multiple geometric model families in noisy data. MultiLink merges clusters via a model-aware linkage strategy, enabling robust multi-class structure recovery. It offers key advantages over existing approaches, such as speed, reduced sensitivity to thresholds, and improved robustness to poor initial sampling. The second part of the thesis addresses online anomaly detection in evolving data streams. We propose Online Isolation Forest (Online-iForest), which uses adaptive, multi-resolution histograms and dynamically updates tree structures to track changes over time. It avoids retraining while achieving accuracy comparable to offline models, with superior efficiency for real-time applications. Finally, we tackle anomaly detection in cybersecurity via open-set recognition for malware classification. We enhance a Gradient Boosting classifier with MaxLogit to detect unseen malware families, a method now integrated into Cleafy's production system.", "date": "2025-05-19", "categories": []}
{"id": "2505.13123", "title": "Just Dance with $\\pi$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection", "abstract": "Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact that RGB-features are not sufficiently distinctive in setting apart categories such as shoplifting from visually similar events. Therefore, towards robust complex real-world VAD, it is essential to augment RGB spatio-temporal features by additional modalities. Motivated by this, we introduce the Poly-modal Induced framework for VAD: \"PI-VAD\", a novel approach that augments RGB representations by five additional modalities. Specifically, the modalities include sensitivity to fine-grained motion (Pose), three dimensional scene and entity representation (Depth), surrounding objects (Panoptic masks), global motion (optical flow), as well as language cues (VLM). Each modality represents an axis of a polygon, streamlined to add salient cues to RGB. PI-VAD includes two plug-in modules, namely Pseudo-modality Generation module and Cross Modal Induction module, which generate modality-specific prototypical representation and, thereby, induce multi-modal information into RGB cues. These modules operate by performing anomaly-aware auxiliary tasks and necessitate five modality backbones -- only during training. Notably, PI-VAD achieves state-of-the-art accuracy on three prominent VAD datasets encompassing real-world scenarios, without requiring the computational overhead of five modality backbones at inference.", "date": "2025-05-19", "categories": []}
{"id": "2502.18002", "title": "A Radon-Nikod\\'ym Perspective on Anomaly Detection: Theory and Implications", "abstract": "Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikod\\'ym theorem, a fundamental concept in measure theory. The key insight from this article is: Multiplying the vanilla loss function with the Radon-Nikod\\'ym derivative improves the performance across the board. We refer to this as RN-Loss. We prove this using the setting of PAC (Probably Approximately Correct) learnability.\n  Depending on the context a Radon-Nikod\\'ym derivative takes different forms. In the simplest case of supervised anomaly detection, Radon-Nikod\\'ym derivative takes the form of a simple weighted loss. In the case of unsupervised anomaly detection (with distributional assumptions), Radon-Nikod\\'ym derivative takes the form of the popular cluster based local outlier factor. We evaluate our algorithm on 96 datasets, including univariate and multivariate data from diverse domains, including healthcare, cybersecurity, and finance. We show that RN-Derivative algorithms outperform state-of-the-art methods on 68% of Multivariate datasets (based on F1 scores) and also achieves peak F1-scores on 72% of time series (Univariate) datasets.", "date": "2025-02-25", "categories": []}
{"id": "2503.14162", "title": "EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models", "abstract": "Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. This decoupling is achieved through independent optimization objectives and tailored learning strategies. Additionally, we contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA), encompassing a wide range of defect types and industrial scenarios. Unlike conventional datasets that rely on GPT-generated data, DDQA ensures authenticity and reliability and offers a robust foundation for model training. Experimental results demonstrate that our proposed method, Explainable Industrial Anomaly Detection Assistant (EIAD), achieves outstanding performance in defect detection and localization tasks. It not only significantly enhances accuracy but also improves interpretability. These advancements highlight the potential of EIAD for practical applications in industrial settings.", "date": "2025-03-18", "categories": []}
{"id": "2505.10873", "title": "Hashing for Structure-based Anomaly Detection", "abstract": "We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.", "date": "2025-05-16", "categories": []}
{"id": "2505.10996", "title": "Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark", "abstract": "The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between viewpoint and illumination which drastically alters defect visibility. Current benchmarks largely overlook this critical challenge. We introduce Multi-View Multi-Illumination Anomaly Detection (M2AD), a new large-scale benchmark comprising 119,880 high-resolution images designed explicitly to probe VAD robustness under such interacting conditions. By systematically capturing 999 specimens across 10 categories using 12 synchronized views and 10 illumination settings (120 configurations total), M2AD enables rigorous evaluation. We establish two evaluation protocols: M2AD-Synergy tests the ability to fuse information across diverse configurations, and M2AD-Invariant measures single-image robustness against realistic view-illumination effects. Our extensive benchmarking shows that state-of-the-art VAD methods struggle significantly on M2AD, demonstrating the profound challenge posed by view-illumination interplay. This benchmark serves as an essential tool for developing and validating VAD methods capable of overcoming real-world complexities. Our full dataset and test suite will be released at https://hustcyq.github.io/M2AD to facilitate the field.", "date": "2025-05-16", "categories": []}
{"id": "2505.11132", "title": "Fairness-aware Anomaly Detection via Fair Projection", "abstract": "Unsupervised anomaly detection is a critical task in many high-social-impact applications such as finance, healthcare, social media, and cybersecurity, where demographics involving age, gender, race, disease, etc, are used frequently. In these scenarios, possible bias from anomaly detection systems can lead to unfair treatment for different groups and even exacerbate social bias. In this work, first, we thoroughly analyze the feasibility and necessary assumptions for ensuring group fairness in unsupervised anomaly detection. Second, we propose a novel fairness-aware anomaly detection method FairAD. From the normal training data, FairAD learns a projection to map data of different demographic groups to a common target distribution that is simple and compact, and hence provides a reliable base to estimate the density of the data. The density can be directly used to identify anomalies while the common target distribution ensures fairness between different groups. Furthermore, we propose a threshold-free fairness metric that provides a global view for model's fairness, eliminating dependence on manual threshold selection. Experiments on real-world benchmarks demonstrate that our method achieves an improved trade-off between detection accuracy and fairness under both balanced and skewed data across different groups.", "date": "2025-05-16", "categories": []}
{"id": "2505.11321", "title": "Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network", "abstract": "In this paper, an unsupervised Recurrent Wavelet Probabilistic Neural Network (RWPNN) is proposed, which aims at detecting anomalies in non-stationary environments by modelling the temporal features using a nonparametric density estimation network. The novel framework consists of two components, a Stacked Recurrent Encoder-Decoder (SREnc-Dec) module that captures temporal features in a latent space, and a Multi-Receptive-field Wavelet Probabilistic Network (MRWPN) that creates an ensemble probabilistic model to characterise the latent space. This formulation extends the standard wavelet probabilistic networks to wavelet deep probabilistic networks, which can handle higher data dimensionality. The MRWPN module can adapt to different rates of data variation in different datasets without imposing strong distribution assumptions, resulting in a more robust and accurate detection for Time Series Anomaly Detection (TSAD) tasks in the non-stationary environment. We carry out the assessment on 45 real-world time series datasets from various domains, verify the performance of RWPNN in TSAD tasks with several constraints, and show its ability to provide early warnings for anomalous events.", "date": "2025-05-16", "categories": []}
{"id": "2411.11641", "title": "TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection", "abstract": "Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this task, which learn point-wise representation via unsupervised learning. However, the unlabeled anomaly points in training data may cause these reconstruction-based methods to learn and reconstruct anomalous data, resulting in the challenge of capturing normal patterns. In this paper, we propose a time series anomaly detection method based on implicit neural representation (INR) reconstruction, named TSINR, to address this challenge. Due to the property of spectral bias, TSINR enables prioritizing low-frequency signals and exhibiting poorer performance on high-frequency abnormal data. Specifically, we adopt INR to parameterize time series data as a continuous function and employ a transformer-based architecture to predict the INR of given data. As a result, the proposed TSINR method achieves the advantage of capturing the temporal continuity and thus is more sensitive to discontinuous anomaly data. In addition, we further design a novel form of INR continuous function to learn inter- and intra-channel information, and leverage a pre-trained large language model to amplify the intense fluctuations in anomalies. Extensive experiments demonstrate that TSINR achieves superior overall performance on both univariate and multivariate time series anomaly detection benchmarks compared to other state-of-the-art reconstruction-based methods. Our codes are available.", "date": "2024-11-18", "categories": []}
{"id": "2505.05901", "title": "Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection", "abstract": "In this paper, we explore a novel approach to 3D anomaly detection (AD) that goes beyond merely identifying anomalies based on structural characteristics. Our primary perspective is that most anomalies arise from unpredictable defective forces originating from both internal and external sources. To address these anomalies, we seek out opposing forces that can help correct them. Therefore, we introduce the Mechanics Complementary Model-based Framework for the 3D-AD task (MC4AD), which generates internal and external corrective forces for each point. We first propose a Diverse Anomaly-Generation (DA-Gen) module designed to simulate various types of anomalies. Next, we present the Corrective Force Prediction Network (CFP-Net), which uses complementary representations for point-level analysis to simulate the different contributions from internal and external corrective forces. To ensure the corrective forces are constrained effectively, we have developed a combined loss function that includes a new symmetric loss and an overall loss. Notably, we implement a Hierarchical Quality Control (HQC) strategy based on a three-way decision process and contribute a dataset titled Anomaly-IntraVariance, which incorporates intraclass variance to evaluate our model. As a result, the proposed MC4AD has been proven effective through theory and experimentation. The experimental results demonstrate that our approach yields nine state-of-the-art performances, achieving optimal results with minimal parameters and the fastest inference speed across five existing datasets, in addition to the proposed Anomaly-IntraVariance dataset. The source is available at https://github.com/hzzzzzhappy/MC4AD", "date": "2025-05-09", "categories": []}
{"id": "2505.09593", "title": "Online Isolation Forest", "abstract": "The anomaly detection literature is abundant with offline methods, which require repeated access to data in memory, and impose impractical assumptions when applied to a streaming context. Existing online anomaly detection methods also generally fail to address these constraints, resorting to periodic retraining to adapt to the online context. We propose Online-iForest, a novel method explicitly designed for streaming conditions that seamlessly tracks the data generating process as it evolves over time. Experimental validation on real-world datasets demonstrated that Online-iForest is on par with online alternatives and closely rivals state-of-the-art offline anomaly detection techniques that undergo periodic retraining. Notably, Online-iForest consistently outperforms all competitors in terms of efficiency, making it a promising solution in applications where fast identification of anomalies is of primary importance such as cybersecurity, fraud and fault detection.", "date": "2025-05-14", "categories": []}
{"id": "2505.10441", "title": "PIF: Anomaly detection via preference embedding", "abstract": "We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-Forest, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-Forest is better at measuring arbitrary distances and isolate points in the preference space.", "date": "2025-05-15", "categories": []}
{"id": "2505.08882", "title": "Intelligent Road Anomaly Detection with Real-time Notification System for Enhanced Road Safety", "abstract": "This study aims to improve transportation safety, especially traffic safety. Road damage anomalies such as potholes and cracks have emerged as a significant and recurring cause for accidents. To tackle this problem and improve road safety, a comprehensive system has been developed to detect potholes, cracks (e.g. alligator, transverse, longitudinal), classify their sizes, and transmit this data to the cloud for appropriate action by authorities. The system also broadcasts warning signals to nearby vehicles warning them if a severe anomaly is detected on the road. Moreover, the system can count road anomalies in real-time. It is emulated through the utilization of Raspberry Pi, a camera module, deep learning model, laptop, and cloud service. Deploying this innovative solution aims to proactively enhance road safety by notifying relevant authorities and drivers about the presence of potholes and cracks to take actions, thereby mitigating potential accidents arising from this prevalent road hazard leading to safer road conditions for the whole community.", "date": "2025-05-13", "categories": []}
{"id": "2505.09129", "title": "WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes", "abstract": "The deployment of traditional deep learning models in high-risk security tasks in an unlabeled, data-non-exploitable video intelligence environment faces significant challenges. In this paper, we propose a lightweight anomaly detection framework based on color features for surveillance video clips in a high sensitivity tactical mission, aiming to quickly identify and interpret potential threat events under resource-constrained and data-sensitive conditions. The method fuses unsupervised KMeans clustering with RGB channel histogram modeling to achieve composite detection of structural anomalies and color mutation signals in key frames. The experiment takes an operation surveillance video occurring in an African country as a research sample, and successfully identifies multiple highly anomalous frames related to high-energy light sources, target presence, and reflective interference under the condition of no access to the original data. The results show that this method can be effectively used for tactical assassination warning, suspicious object screening and environmental drastic change monitoring with strong deployability and tactical interpretation value. The study emphasizes the importance of color features as low semantic battlefield signal carriers, and its battlefield intelligent perception capability will be further extended by combining graph neural networks and temporal modeling in the future.", "date": "2025-05-14", "categories": []}
{"id": "2505.09263", "title": "Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation", "abstract": "Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic and real-world anomalies, resulting in weak performance in anomaly detection. To solve the problem, we propose a few-shot Anomaly-driven Generation (AnoGen) method, which guides the diffusion model to generate realistic and diverse anomalies with only a few real anomalies, thereby benefiting training anomaly detection models. Specifically, our work is divided into three stages. In the first stage, we learn the anomaly distribution based on a few given real anomalies and inject the learned knowledge into an embedding. In the second stage, we use the embedding and given bounding boxes to guide the diffusion model to generate realistic and diverse anomalies on specific objects (or textures). In the final stage, we propose a weakly-supervised anomaly detection method to train a more powerful model with generated anomalies. Our method builds upon DRAEM and DesTSeg as the foundation model and conducts experiments on the commonly used industrial anomaly detection dataset, MVTec. The experiments demonstrate that our generated anomalies effectively improve the model performance of both anomaly classification and segmentation tasks simultaneously, \\eg, DRAEM and DseTSeg achieved a 5.8\\% and 1.5\\% improvement in AU-PR metric on segmentation task, respectively. The code and generated anomalous data are available at https://github.com/gaobb/AnoGen.", "date": "2025-05-14", "categories": []}
{"id": "2505.09264", "title": "Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt", "abstract": "Unsupervised reconstruction networks using self-attention transformers have achieved state-of-the-art performance for multi-class (unified) anomaly detection with a single model. However, these self-attention reconstruction models primarily operate on target features, which may result in perfect reconstruction for both normal and anomaly features due to high consistency with context, leading to failure in detecting anomalies. Additionally, these models often produce inaccurate anomaly segmentation due to performing reconstruction in a low spatial resolution latent space. To enable reconstruction models enjoying high efficiency while enhancing their generalization for unified anomaly detection, we propose a simple yet effective method that reconstructs normal features and restores anomaly features with just One Normal Image Prompt (OneNIP). In contrast to previous work, OneNIP allows for the first time to reconstruct or restore anomalies with just one normal image prompt, effectively boosting unified anomaly detection performance. Furthermore, we propose a supervised refiner that regresses reconstruction errors by using both real normal and synthesized anomalous images, which significantly improves pixel-level anomaly segmentation. OneNIP outperforms previous methods on three industry anomaly detection benchmarks: MVTec, BTAD, and VisA. The code and pre-trained models are available at https://github.com/gaobb/OneNIP.", "date": "2025-05-14", "categories": []}
{"id": "2505.09265", "title": "MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning", "abstract": "Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this paper, we explore the potential of a pure visual foundation model as an alternative to widely used vision-language models for universal visual anomaly segmentation. We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables us to leverage large-scale synthetic image pairs, featuring object-level and local region changes, derived from existing image datasets, which are independent of target anomaly datasets. We propose a one-prompt Meta-learning framework for Universal Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and then generalizes well to segment any novel or unseen visual anomalies in the real world. To handle geometrical variations between prompt and query images, we propose a soft feature alignment module that bridges paired-image change perception and single-image semantic segmentation. This is the first work to achieve universal anomaly segmentation using a pure vision model without relying on special anomaly detection datasets and pre-trained visual-language models. Our method effectively and efficiently segments any anomalies with only one normal image prompt and enjoys training-free without guidance from language. Our MetaUAS significantly outperforms previous zero-shot, few-shot, and even full-shot anomaly segmentation methods. The code and pre-trained models are available at https://github.com/gaobb/MetaUAS.", "date": "2025-05-14", "categories": []}
{"id": "2505.07998", "title": "Vision Foundation Model Embedding-Based Semantic Anomaly Detection", "abstract": "Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonomous systems. This work explores semantic anomaly detection by leveraging the semantic priors of state-of-the-art vision foundation models, operating directly on the image. We propose a framework that compares local vision embeddings from runtime images to a database of nominal scenarios in which the autonomous system is deemed safe and performant. In this work, we consider two variants of the proposed framework: one using raw grid-based embeddings, and another leveraging instance segmentation for object-centric representations. To further improve robustness, we introduce a simple filtering mechanism to suppress false positives. Our evaluations on CARLA-simulated anomalies show that the instance-based method with filtering achieves performance comparable to GPT-4o, while providing precise anomaly localization. These results highlight the potential utility of vision embeddings from foundation models for real-time anomaly detection in autonomous systems.", "date": "2025-05-12", "categories": []}
{"id": "2505.08330", "title": "Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer", "abstract": "Detecting anomalous edges in dynamic graphs is an important task in many applications over evolving triple-based data, such as social networks, transaction management, and epidemiology. A major challenge with this task is the absence of structural-temporal coupling information, which decreases the ability of the representation to distinguish anomalies from normal instances. Existing methods focus on handling independent structural and temporal features with embedding models, which ignore the deep interaction between these two types of information. In this paper, we propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model. Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns. Then, a dynamic graph transformer enhanced by two-dimensional positional encoding is implemented to capture both discrimination and contextual consistency signals. Extensive experiments on six datasets demonstrate that our method outperforms current state-of-the-art models. Finally, a case study illustrates the strength of our method when applied to a real-world task.", "date": "2025-05-13", "categories": []}
{"id": "2505.08489", "title": "Isolation Forest in Novelty Detection Scenario", "abstract": "Data mining offers a diverse toolbox for extracting meaningful structures from complex datasets, with anomaly detection emerging as a critical subfield particularly in the context of streaming or real-time data. Within anomaly detection, novelty detection focuses on identifying previously unseen patterns after training solely on regular data. While classic algorithms such as One-Class SVM or Local Outlier Factor (LOF) have been widely applied, they often lack interpretability and scalability. In this work, we explore the Half-Space Tree (HST) algorithm, originally proposed for streaming anomaly detection, and propose a novel theoretical modification to adapt it specifically for novelty detection tasks. Our approach is grounded in the idea that anomalies i.e., novelties tend to appear in the higher leaves of the tree, which are less frequently visited by regular instances. We analytically demonstrate the effectiveness of this approach using probabilistic analysis, expected depth (EXD) calculations, and combinatorial reasoning. A comparative analysis of expected depths between our modified HST and the original Isolation Forest highlights that novelty points are significantly more isolated in our approach. This supports the hypothesis that HSTs, with appropriate structural adaptation, can serve as interpretable and efficient novelty detectors. The paper contributes a theoretical foundation and supporting analysis for this adaptation, setting the stage for further application and experimentation.", "date": "2025-05-13", "categories": []}
{"id": "2308.12563", "title": "Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models", "abstract": "Mainstream unsupervised anomaly detection algorithms often excel in academic datasets, yet their real-world performance is restricted due to the controlled experimental conditions involving clean training data. Addressing the challenge of training with noise, a prevalent issue in practical anomaly detection, is frequently overlooked. In a pioneering endeavor, this study delves into the realm of label-level noise within sensory time-series anomaly detection (TSAD). This paper presents a novel and practical end-to-end unsupervised TSAD when the training data is contaminated with anomalies. The introduced approach, called TSAD-C, is devoid of access to abnormality labels during the training phase. TSAD-C encompasses three core modules: a Decontaminator to rectify anomalies (aka noise) present during training, a Long-range Variable Dependency Modeling module to capture long-term intra- and inter-variable dependencies within the decontaminated data that is considered as a surrogate of the pure normal data, and an Anomaly Scoring module to detect anomalies from all types. Our extensive experiments conducted on four reliable and diverse datasets conclusively demonstrate that TSAD-C surpasses existing methodologies, thus establishing a new state-of-the-art in the TSAD field.", "date": "2023-08-24", "categories": []}
{"id": "2405.06198", "title": "MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection", "abstract": "Large unlabeled data and difficult-to-identify anomalies are the urgent issues need to overcome in most industrial scene. In order to address this issue, a new meth-odology for detecting surface defects in in-dustrial settings is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL). The methodology first in-troduces an anomaly simulation strategy, which significantly improves the model's ability to recognize rare or unknown anom-aly types by generating simulated anomaly samples. To cope with the problem of the lack of labeling of anomalous simulated samples, a pseudo-labeler method based on a one-classifier ensemble was employed in this study, which enhances the robustness of the model in the case of limited labeling data by automatically selecting key pseudo-labeling hyperparameters. Meanwhile, a memory-enhanced learning mechanism is introduced to effectively predict abnormal regions by analyzing the difference be-tween the input samples and the normal samples in the memory pool. An end-to-end learning framework is employed by MAPL to identify the abnormal regions directly from the input data, which optimizes the ef-ficiency and real-time performance of de-tection. By conducting extensive trials on the recently developed BHAD dataset (in-cluding MVTec AD [1], Visa [2], and MDPP [3]), MAPL achieves an average im-age-level AUROC score of 86.2%, demon-strating a 5.1% enhancement compared to the original MemSeg [4] model. The source code is available at https://github.com/jzc777/MAPL.", "date": "2024-05-10", "categories": []}
{"id": "2412.08501", "title": "GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient", "abstract": "Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any label, deep UOD methods struggle with the misalignment between the model's direct optimization goal and the final performance goal of Outlier Detection (OD) task. Through the perspective of training dynamics, this paper proposes an early stopping algorithm to optimize the training of deep UOD models, ensuring they perform optimally in OD rather than overfitting the entire contaminated dataset.\n  Inspired by UOD mechanism and inlier priority phenomenon, where intuitively models fit inliers more quickly than outliers, we propose GradStop, a sampling-based label-free algorithm to estimate model's real-time performance during training. First, a sampling method generates two sets: one likely containing more outliers and the other more inliers, then a metric based on gradient cohesion is applied to probe into current training dynamics, which reflects model's performance on OD task.\n  Experimental results on 4 deep UOD algorithms and 47 real-world datasets and theoretical proofs demonstrate the effectiveness of our proposed early stopping algorithm in enhancing the performance of deep UOD models. Auto Encoder (AE) enhanced by GradStop achieves better performance than itself, other SOTA UOD methods, and even ensemble AEs. Our method provides a robust and effective solution to the problem of performance degradation during training, enabling deep UOD models to achieve better potential in anomaly detection tasks.", "date": "2024-12-11", "categories": []}
{"id": "2505.00941", "title": "FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection", "abstract": "Time series anomaly detection is critical for system monitoring and risk identification, across various domains, such as finance and healthcare. However, for most reconstruction-based approaches, detecting anomalies remains a challenge due to the complexity of sequential patterns in time series data. On the one hand, reconstruction-based techniques are susceptible to computational deviation stemming from anomalies, which can lead to impure representations of normal sequence patterns. On the other hand, they often focus on the time-domain dependencies of time series, while ignoring the alignment of frequency information beyond the time domain. To address these challenges, we propose a novel Frequency-augmented Convolutional Transformer (FreCT). FreCT utilizes patch operations to generate contrastive views and employs an improved Transformer architecture integrated with a convolution module to capture long-term dependencies while preserving local topology information. The introduced frequency analysis based on Fourier transformation could enhance the model's ability to capture crucial characteristics beyond the time domain. To protect the training quality from anomalies and improve the robustness, FreCT deploys stop-gradient Kullback-Leibler (KL) divergence and absolute error to optimize consistency information in both time and frequency domains. Extensive experiments on four public datasets demonstrate that FreCT outperforms existing methods in identifying anomalies.", "date": "2025-05-02", "categories": []}
{"id": "2505.06333", "title": "NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines", "abstract": "In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and multiple modalities. This paper proposes a neurosymbolic AI and fusion-based approach for multimodal anomaly prediction in assembly pipelines. We introduce a time series and image-based fusion model that leverages decision-level fusion techniques. Our research builds upon three primary novel approaches in multimodal learning: time series and image-based decision-level fusion modeling, transfer learning for fusion, and knowledge-infused learning. We evaluate the novel method using our derived and publicly available multimodal dataset and conduct comprehensive ablation studies to assess the impact of our preprocessing techniques and fusion model compared to traditional baselines. The results demonstrate that a neurosymbolic AI-based fusion approach that uses transfer learning can effectively harness the complementary strengths of time series and image data, offering a robust and interpretable approach for anomaly prediction in assembly pipelines with enhanced performance. \\noindent The datasets, codes to reproduce the results, supplementary materials, and demo are available at https://github.com/ChathurangiShyalika/NSF-MAP.", "date": "2025-05-09", "categories": []}
{"id": "2505.06603", "title": "ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection", "abstract": "Continual Anomaly Detection (CAD) enables anomaly detection models in learning new classes while preserving knowledge of historical classes. CAD faces two key challenges: catastrophic forgetting and segmentation of small anomalous regions. Existing CAD methods store image distributions or patch features to mitigate catastrophic forgetting, but they fail to preserve pixel-level detailed features for accurate segmentation. To overcome this limitation, we propose ReplayCAD, a novel diffusion-driven generative replay framework that replay high-quality historical data, thus effectively preserving pixel-level detailed features. Specifically, we compress historical data by searching for a class semantic embedding in the conditional space of the pre-trained diffusion model, which can guide the model to replay data with fine-grained pixel details, thus improving the segmentation performance. However, relying solely on semantic features results in limited spatial diversity. Hence, we further use spatial features to guide data compression, achieving precise control of sample space, thereby generating more diverse data. Our method achieves state-of-the-art performance in both classification and segmentation, with notable improvements in segmentation: 11.5% on VisA and 8.1% on MVTec. Our source code is available at https://github.com/HULEI7/ReplayCAD.", "date": "2025-05-10", "categories": []}
{"id": "2505.07375", "title": "Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection", "abstract": "Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, necessitating the development of multi-class unsupervised methods. However, the feature similarity between normal and anomalous points from different class data leads to the feature confusion problem, which greatly hinders the performance of multi-class methods. Therefore, we introduce a multi-class point cloud anomaly detection method, named GLFM, leveraging global-local feature matching to progressively separate data that are prone to confusion across multiple classes. Specifically, GLFM is structured into three stages: Stage-I proposes an anomaly synthesis pipeline that stretches point clouds to create abundant anomaly data that are utilized to adapt the point cloud feature extractor for better feature representation. Stage-II establishes the global and local memory banks according to the global and local feature distributions of all the training data, weakening the impact of feature confusion on the establishment of the memory bank. Stage-III implements anomaly detection of test data leveraging its feature distance from global and local memory banks. Extensive experiments on the MVTec 3D-AD, Real3D-AD and actual industry parts dataset showcase our proposed GLFM's superior point cloud anomaly detection performance. The code is available at https://github.com/hustCYQ/GLFM-Multi-class-3DAD.", "date": "2025-05-12", "categories": []}
{"id": "2505.07508", "title": "EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection", "abstract": "Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. However, existing methods are lack of efficiency that is definitely necessary for embedded devices. Towards this end, we propose an Efficient Anomaly detection model on heterogeneous Graphs via contrastive LEarning (EAGLE) by contrasting abnormal nodes with normal ones in terms of their distances to the local context. The proposed method first samples instance pairs on meta path-level for contrastive learning. Then, a graph autoencoder-based model is applied to learn informative node embeddings in an unsupervised way, which will be further combined with the discriminator to predict the anomaly scores of nodes. Experimental results show that EAGLE outperforms the state-of-the-art methods on three heterogeneous network datasets.", "date": "2025-05-12", "categories": []}
{"id": "2505.07576", "title": "Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study", "abstract": "Semiconductor manufacturing is a complex, multistage process. Automated visual inspection of Scanning Electron Microscope (SEM) images is indispensable for minimizing equipment downtime and containing costs. Most previous research considers supervised approaches, assuming a sufficient number of anomalously labeled samples. On the contrary, Visual Anomaly Detection (VAD), an emerging research domain, focuses on unsupervised learning, avoiding the costly defect collection phase while providing explanations of the predictions. We introduce a benchmark for VAD in the semiconductor domain by leveraging the MIIC dataset. Our results demonstrate the efficacy of modern VAD approaches in this field.", "date": "2025-05-12", "categories": []}
{"id": "2410.12261", "title": "CATCH: Channel-Aware multivariate Time Series Anomaly Detection via\n  Frequency Patching", "abstract": "  Anomaly detection in multivariate time series is challenging as heterogeneous\nsubsequence anomalies may occur. Reconstruction-based methods, which focus on\nlearning normal patterns in the frequency domain to detect diverse abnormal\nsubsequences, achieve promising results, while still falling short on capturing\nfine-grained frequency characteristics and channel correlations. To contend\nwith the limitations, we introduce CATCH, a framework based on frequency\npatching. We propose to patchify the frequency domain into frequency bands,\nwhich enhances its ability to capture fine-grained frequency characteristics.\nTo perceive appropriate channel correlations, we propose a Channel Fusion\nModule (CFM), which features a patch-wise mask generator and a masked-attention\nmechanism. Driven by a bi-level multi-objective optimization algorithm, the CFM\nis encouraged to iteratively discover appropriate patch-wise channel\ncorrelations, and to cluster relevant channels while isolating adverse effects\nfrom irrelevant channels. Extensive experiments on 10 real-world datasets and\n12 synthetic datasets demonstrate that CATCH achieves state-of-the-art\nperformance. We make our code and datasets available at\nhttps://github.com/decisionintelligence/CATCH.\n", "date": "2024-10-16", "categories": []}
{"id": "2504.11895", "title": "Search is All You Need for Few-shot Anomaly Detection", "abstract": "  Few-shot anomaly detection (FSAD) has emerged as a crucial yet challenging\ntask in industrial inspection, where normal distribution modeling must be\naccomplished with only a few normal images. While existing approaches typically\nemploy multi-modal foundation models combining language and vision modalities\nfor prompt-guided anomaly detection, these methods often demand sophisticated\nprompt engineering and extensive manual tuning. In this paper, we demonstrate\nthat a straightforward nearest-neighbor search framework can surpass\nstate-of-the-art performance in both single-class and multi-class FSAD\nscenarios. Our proposed method, VisionAD, consists of four simple yet essential\ncomponents: (1) scalable vision foundation models that extract universal and\ndiscriminative features; (2) dual augmentation strategies - support\naugmentation to enhance feature matching adaptability and query augmentation to\naddress the oversights of single-view prediction; (3) multi-layer feature\nintegration that captures both low-frequency global context and high-frequency\nlocal details with minimal computational overhead; and (4) a class-aware visual\nmemory bank enabling efficient one-for-all multi-class detection. Extensive\nevaluations across MVTec-AD, VisA, and Real-IAD benchmarks demonstrate\nVisionAD's exceptional performance. Using only 1 normal images as support, our\nmethod achieves remarkable image-level AUROC scores of 97.4%, 94.8%, and 70.8%\nrespectively, outperforming current state-of-the-art approaches by significant\nmargins (+1.6%, +3.2%, and +1.4%). The training-free nature and superior\nfew-shot capabilities of VisionAD make it particularly appealing for real-world\napplications where samples are scarce or expensive to obtain. Code is available\nat https://github.com/Qiqigeww/VisionAD.\n", "date": "2025-04-16", "categories": []}
{"id": "2505.02393", "title": "Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly\n  Detection", "abstract": "  Most existing video anomaly detectors rely solely on RGB frames, which lack\nthe temporal resolution needed to capture abrupt or transient motion cues, key\nindicators of anomalous events. To address this limitation, we propose\nImage-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that\nsynthesizes event representations directly from RGB videos and fuses them with\nimage features through a principled, uncertainty-aware process. The system (i)\nmodels heavy-tailed sensor noise with a Student`s-t likelihood, deriving\nvalue-level inverse-variance weights via a Laplace approximation; (ii) applies\nKalman-style frame-wise updates to balance modalities over time; and (iii)\niteratively refines the fused latent state to erase residual cross-modal noise.\nWithout any dedicated event sensor or frame-level labels, IEF-VAD sets a new\nstate of the art across multiple real-world anomaly detection benchmarks. These\nfindings highlight the utility of synthetic event representations in\nemphasizing motion cues that are often underrepresented in RGB frames, enabling\naccurate and robust video understanding across diverse applications without\nrequiring dedicated event sensors. Code and models are available at\nhttps://github.com/EavnJeong/IEF-VAD.\n", "date": "2025-05-05", "categories": []}
{"id": "2505.05137", "title": "Research on Anomaly Detection Methods Based on Diffusion Models", "abstract": "  Anomaly detection is a fundamental task in machine learning and data mining,\nwith significant applications in cybersecurity, industrial fault diagnosis, and\nclinical disease monitoring. Traditional methods, such as statistical modeling\nand machine learning-based approaches, often face challenges in handling\ncomplex, high-dimensional data distributions. In this study, we explore the\npotential of diffusion models for anomaly detection, proposing a novel\nframework that leverages the strengths of diffusion probabilistic models (DPMs)\nto effectively identify anomalies in both image and audio data. The proposed\nmethod models the distribution of normal data through a diffusion process and\nreconstructs input data via reverse diffusion, using a combination of\nreconstruction errors and semantic discrepancies as anomaly indicators. To\nenhance the framework's performance, we introduce multi-scale feature\nextraction, attention mechanisms, and wavelet-domain representations, enabling\nthe model to capture fine-grained structures and global dependencies in the\ndata. Extensive experiments on benchmark datasets, including MVTec AD and\nUrbanSound8K, demonstrate that our method outperforms state-of-the-art anomaly\ndetection techniques, achieving superior accuracy and robustness across diverse\ndata modalities. This research highlights the effectiveness of diffusion models\nin anomaly detection and provides a robust and efficient solution for\nreal-world applications.\n", "date": "2025-05-08", "categories": []}
{"id": "2409.07016", "title": "Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning\n  of Pre-Trained Audio Models", "abstract": "  Anomalous Sound Detection (ASD) has gained significant interest through the\napplication of various Artificial Intelligence (AI) technologies in industrial\nsettings. Though possessing great potential, ASD systems can hardly be readily\ndeployed in real production sites due to the generalization problem, which is\nprimarily caused by the difficulty of data collection and the complexity of\nenvironmental factors. This paper introduces a robust ASD model that leverages\naudio pre-trained models. Specifically, we fine-tune these models using machine\noperation data, employing SpecAug as a data augmentation strategy.\nAdditionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA)\ntuning instead of full fine-tuning to address the problem of limited data for\nfine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new\nbenchmark of 77.75% on the evaluation set, with a significant improvement of\n6.48% compared with previous state-of-the-art (SOTA) models, including top-tier\ntraditional convolutional networks and speech pre-trained models, which\ndemonstrates the effectiveness of audio pre-trained models with LoRA tuning.\nAblation studies are also conducted to showcase the efficacy of the proposed\nscheme.\n", "date": "2024-09-11", "categories": []}
{"id": "2505.04392", "title": "Predicting Road Surface Anomalies by Visual Tracking of a Preceding\n  Vehicle", "abstract": "  A novel approach to detect road surface anomalies by visual tracking of a\npreceding vehicle is proposed. The method is versatile, predicting any kind of\nroad anomalies, such as potholes, bumps, debris, etc., unlike direct\nobservation methods that rely on training visual detectors of those cases. The\nmethod operates in low visibility conditions or in dense traffic where the\nanomaly is occluded by a preceding vehicle. Anomalies are detected\npredictively, i.e., before a vehicle encounters them, which allows to\npre-configure low-level vehicle systems (such as chassis) or to plan an\navoidance maneuver in case of autonomous driving. A challenge is that the\nsignal coming from camera-based tracking of a preceding vehicle may be weak and\ndisturbed by camera ego motion due to vibrations affecting the ego vehicle.\nTherefore, we propose an efficient method to compensate camera pitch rotation\nby an iterative robust estimator. Our experiments on both controlled setup and\nnormal traffic conditions show that road anomalies can be detected reliably at\na distance even in challenging cases where the ego vehicle traverses imperfect\nroad surfaces. The method is effective and performs in real time on standard\nconsumer hardware.\n", "date": "2025-05-07", "categories": []}
{"id": "2404.02865", "title": "End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection", "abstract": "  Time series anomaly detection (TSAD) finds many applications such as\nmonitoring environmental sensors, industry KPIs, patient biomarkers, etc. A\ntwo-fold challenge for TSAD is a versatile and unsupervised model that can\ndetect various different types of time series anomalies (spikes,\ndiscontinuities, trend shifts, etc.) without any labeled data. Modern neural\nnetworks have outstanding ability in modeling complex time series.\nSelf-supervised models in particular tackle unsupervised TSAD by transforming\nthe input via various augmentations to create pseudo anomalies for training.\nHowever, their performance is sensitive to the choice of augmentation, which is\nhard to choose in practice, while there exists no effort in the literature on\ndata augmentation tuning for TSAD without labels. Our work aims to fill this\ngap. We introduce TSAP for TSA \"on autoPilot\", which can (self-)tune\naugmentation hyperparameters end-to-end. It stands on two key components: a\ndifferentiable augmentation architecture and an unsupervised validation loss to\neffectively assess the alignment between augmentation type and anomaly type.\nCase studies show TSAP's ability to effectively select the (discrete)\naugmentation type and associated (continuous) hyperparameters. In turn, it\noutperforms established baselines, including SOTA self-supervised models, on\ndiverse TSAD tasks exhibiting different anomaly types.\n", "date": "2024-04-03", "categories": []}
{"id": "2410.01281", "title": "Uncertainty-aware Human Mobility Modeling and Anomaly Detection", "abstract": "  Given the temporal GPS coordinates from a large set of human agents, how can\nwe model their mobility behavior toward effective anomaly (e.g. bad-actor or\nmalicious behavior) detection without any labeled data? Human mobility and\ntrajectory modeling have been extensively studied, showcasing varying abilities\nto manage complex inputs and balance performance-efficiency trade-offs. In this\nwork, we formulate anomaly detection in complex human behavior by modeling raw\nGPS data as a sequence of stay-point events, each characterized by\nspatio-temporal features, along with trips (i.e. commute) between the\nstay-points. Our problem formulation allows us to leverage modern sequence\nmodels for unsupervised training and anomaly detection. Notably, we equip our\nproposed model USTAD (for Uncertainty-aware Spatio-Temporal Anomaly Detection)\nwith aleatoric (i.e. data) uncertainty estimation to account for inherent\nstochasticity in certain individuals' behavior, as well as epistemic (i.e.\nmodel) uncertainty to handle data sparsity under a large variety of human\nbehaviors. Together, aleatoric and epistemic uncertainties unlock a robust loss\nfunction as well as uncertainty-aware decision-making in anomaly scoring.\nExtensive experiments shows that USTAD improves anomaly detection AUCROC by\n3\\%-15\\% over baselines in industry-scale data.\n", "date": "2024-10-02", "categories": []}
{"id": "2505.03412", "title": "CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection", "abstract": "  Internal defect detection constitutes a critical process in ensuring\ncomponent quality, for which anomaly detection serves as an effective solution.\nHowever, existing anomaly detection datasets predominantly focus on surface\ndefects in visible-light images, lacking publicly available X-ray datasets\ntargeting internal defects in components. To address this gap, we construct the\nfirst publicly accessible component X-ray anomaly detection (CXR-AD) dataset,\ncomprising real-world X-ray images. The dataset covers five industrial\ncomponent categories, including 653 normal samples and 561 defect samples with\nprecise pixel-level mask annotations. We systematically analyze the dataset\ncharacteristics and identify three major technical challenges: (1) strong\ncoupling between complex internal structures and defect regions, (2) inherent\nlow contrast and high noise interference in X-ray imaging, and (3) significant\nvariations in defect scales and morphologies. To evaluate dataset complexity,\nwe benchmark three state-of-the-art anomaly detection frameworks\n(feature-based, reconstruction-based, and zero-shot learning methods).\nExperimental results demonstrate a 29.78% average performance degradation on\nCXR-AD compared to MVTec AD, highlighting the limitations of current algorithms\nin handling internal defect detection tasks. To the best of our knowledge,\nCXR-AD represents the first publicly available X-ray dataset for component\nanomaly detection, providing a real-world industrial benchmark to advance\nalgorithm development and enhance precision in internal defect inspection\ntechnologies.\n", "date": "2025-05-06", "categories": []}
{"id": "2408.05347", "title": "Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case\n  Identification", "abstract": "  Unsupervised anomaly detection is a promising technique for identifying\nunusual patterns in data without the need for labeled training examples. This\napproach is particularly valuable for early case detection in epidemic\nmanagement, especially when early-stage data are scarce. This research\nintroduces a novel hybrid method for anomaly detection that combines distance\nand density measures, enhancing its applicability across various infectious\ndiseases. Our method is especially relevant in pandemic situations, as\ndemonstrated during the COVID-19 crisis, where traditional supervised\nclassification methods fall short due to limited data. The efficacy of our\nmethod is evaluated using COVID-19 chest X-ray data, where it significantly\noutperforms established unsupervised techniques. It achieves an average AUC of\n77.43%, surpassing the AUC of Isolation Forest at 73.66% and KNN at 52.93%.\nThese results highlight the potential of our hybrid anomaly detection method to\nimprove early detection capabilities in diverse epidemic scenarios, thereby\nfacilitating more effective and timely responses.\n", "date": "2024-08-09", "categories": []}
{"id": "2410.10519", "title": "AI-based particle track identification in scintillating fibres read out\n  with imaging sensors", "abstract": "  This paper presents the development and application of an AI-based method for\nparticle track identification using scintillating fibres read out with imaging\nsensors. We propose a variational autoencoder (VAE) to efficiently filter and\nidentify frames containing signal from the substantial data generated by SPAD\narray sensors. Our VAE model, trained on purely background frames, demonstrated\na high capability to distinguish frames containing particle tracks from\nbackground noise. The performance of the VAE-based anomaly detection was\nvalidated with experimental data, demonstrating the method's ability to\nefficiently identify relevant events with rapid processing time, suggesting a\nsolid prospect for deployment as a fast inference tool on hardware for\nreal-time anomaly detection. This work highlights the potential of combining\nadvanced sensor technology with machine learning techniques to enhance particle\ndetection and tracking.\n", "date": "2024-10-14", "categories": []}
{"id": "2505.01488", "title": "Explainable Machine Learning for Cyberattack Identification from Traffic\n  Flows", "abstract": "  The increasing automation of traffic management systems has made them prime\ntargets for cyberattacks, disrupting urban mobility and public safety.\nTraditional network-layer defenses are often inaccessible to transportation\nagencies, necessitating a machine learning-based approach that relies solely on\ntraffic flow data. In this study, we simulate cyberattacks in a semi-realistic\nenvironment, using a virtualized traffic network to analyze disruption\npatterns. We develop a deep learning-based anomaly detection system,\ndemonstrating that Longest Stop Duration and Total Jam Distance are key\nindicators of compromised signals. To enhance interpretability, we apply\nExplainable AI (XAI) techniques, identifying critical decision factors and\ndiagnosing misclassification errors. Our analysis reveals two primary\nchallenges: transitional data inconsistencies, where mislabeled recovery-phase\ntraffic misleads the model, and model limitations, where stealth attacks in\nlow-traffic conditions evade detection. This work enhances AI-driven traffic\nsecurity, improving both detection accuracy and trustworthiness in smart\ntransportation systems.\n", "date": "2025-05-02", "categories": []}
{"id": "2505.01783", "title": "Context-Aware Online Conformal Anomaly Detection with Prediction-Powered\n  Data Acquisition", "abstract": "  Online anomaly detection is essential in fields such as cybersecurity,\nhealthcare, and industrial monitoring, where promptly identifying deviations\nfrom expected behavior can avert critical failures or security breaches. While\nnumerous anomaly scoring methods based on supervised or unsupervised learning\nhave been proposed, current approaches typically rely on a continuous stream of\nreal-world calibration data to provide assumption-free guarantees on the false\ndiscovery rate (FDR). To address the inherent challenges posed by limited real\ncalibration data, we introduce context-aware prediction-powered conformal\nonline anomaly detection (C-PP-COAD). Our framework strategically leverages\nsynthetic calibration data to mitigate data scarcity, while adaptively\nintegrating real data based on contextual cues. C-PP-COAD utilizes conformal\np-values, active p-value statistics, and online FDR control mechanisms to\nmaintain rigorous and reliable anomaly detection performance over time.\nExperiments conducted on both synthetic and real-world datasets demonstrate\nthat C-PP-COAD significantly reduces dependency on real calibration data\nwithout compromising guaranteed FDR control.\n", "date": "2025-05-03", "categories": []}
{"id": "2505.02613", "title": "Lane-Wise Highway Anomaly Detection", "abstract": "  This paper proposes a scalable and interpretable framework for lane-wise\nhighway traffic anomaly detection, leveraging multi-modal time series data\nextracted from surveillance cameras. Unlike traditional sensor-dependent\nmethods, our approach uses AI-powered vision models to extract lane-specific\nfeatures, including vehicle count, occupancy, and truck percentage, without\nrelying on costly hardware or complex road modeling. We introduce a novel\ndataset containing 73,139 lane-wise samples, annotated with four classes of\nexpert-validated anomalies: three traffic-related anomalies (lane blockage and\nrecovery, foreign object intrusion, and sustained congestion) and one\nsensor-related anomaly (camera angle shift). Our multi-branch detection system\nintegrates deep learning, rule-based logic, and machine learning to improve\nrobustness and precision. Extensive experiments demonstrate that our framework\noutperforms state-of-the-art methods in precision, recall, and F1-score,\nproviding a cost-effective and scalable solution for real-world intelligent\ntransportation systems.\n", "date": "2025-05-05", "categories": []}
{"id": "2505.02626", "title": "Detect, Classify, Act: Categorizing Industrial Anomalies with\n  Multi-Modal Large Language Models", "abstract": "  Recent advances in visual industrial anomaly detection have demonstrated\nexceptional performance in identifying and segmenting anomalous regions while\nmaintaining fast inference speeds. However, anomaly\nclassification-distinguishing different types of anomalies-remains largely\nunexplored despite its critical importance in real-world inspection tasks. To\naddress this gap, we propose VELM, a novel LLM-based pipeline for anomaly\nclassification. Given the critical importance of inference speed, we first\napply an unsupervised anomaly detection method as a vision expert to assess the\nnormality of an observation. If an anomaly is detected, the LLM then classifies\nits type. A key challenge in developing and evaluating anomaly classification\nmodels is the lack of precise annotations of anomaly classes in existing\ndatasets. To address this limitation, we introduce MVTec-AC and VisA-AC,\nrefined versions of the widely used MVTec-AD and VisA datasets, which include\naccurate anomaly class labels for rigorous evaluation. Our approach achieves a\nstate-of-the-art anomaly classification accuracy of 80.4% on MVTec-AD,\nexceeding the prior baselines by 5%, and 84% on MVTec-AC, demonstrating the\neffectiveness of VELM in understanding and categorizing anomalies. We hope our\nmethodology and benchmark inspire further research in anomaly classification,\nhelping bridge the gap between detection and comprehensive anomaly\ncharacterization.\n", "date": "2025-05-05", "categories": []}
{"id": "2504.14204", "title": "DConAD: A Differencing-based Contrastive Representation Learning\n  Framework for Time Series Anomaly Detection", "abstract": "  Time series anomaly detection holds notable importance for risk\nidentification and fault detection across diverse application domains.\nUnsupervised learning methods have become popular because they have no\nrequirement for labels. However, due to the challenges posed by the\nmultiplicity of abnormal patterns, the sparsity of anomalies, and the growth of\ndata scale and complexity, these methods often fail to capture robust and\nrepresentative dependencies within the time series for identifying anomalies.\nTo enhance the ability of models to capture normal patterns of time series and\navoid the retrogression of modeling ability triggered by the dependencies on\nhigh-quality prior knowledge, we propose a differencing-based contrastive\nrepresentation learning framework for time series anomaly detection (DConAD).\nSpecifically, DConAD generates differential data to provide additional\ninformation about time series and utilizes transformer-based architecture to\ncapture spatiotemporal dependencies, which enhances the robustness of unbiased\nrepresentation learning ability. Furthermore, DConAD implements a novel KL\ndivergence-based contrastive learning paradigm that only uses positive samples\nto avoid deviation from reconstruction and deploys the stop-gradient strategy\nto compel convergence. Extensive experiments on five public datasets show the\nsuperiority and effectiveness of DConAD compared with nine baselines. The code\nis available at https://github.com/shaieesss/DConAD.\n", "date": "2025-04-19", "categories": []}
{"id": "2505.00876", "title": "Car Sensors Health Monitoring by Verification Based on Autoencoder and\n  Random Forest Regression", "abstract": "  Driver assistance systems provide a wide range of crucial services, including\nclosely monitoring the condition of vehicles. This paper showcases a\ngroundbreaking sensor health monitoring system designed for the automotive\nindustry. The ingenious system leverages cutting-edge techniques to process\ndata collected from various vehicle sensors. It compares their outputs within\nthe Electronic Control Unit (ECU) to evaluate the health of each sensor. To\nunravel the intricate correlations between sensor data, an extensive\nexploration of machine learning and deep learning methodologies was conducted.\nThrough meticulous analysis, the most correlated sensor data were identified.\nThese valuable insights were then utilized to provide accurate estimations of\nsensor values. Among the diverse learning methods examined, the combination of\nautoencoders for detecting sensor failures and random forest regression for\nestimating sensor values proved to yield the most impressive outcomes. A\nstatistical model using the normal distribution has been developed to identify\npossible sensor failures proactively. By comparing the actual values of the\nsensors with their estimated values based on correlated sensors, faulty sensors\ncan be detected early. When a defective sensor is detected, both the driver and\nthe maintenance department are promptly alerted. Additionally, the system\nreplaces the value of the faulty sensor with the estimated value obtained\nthrough analysis. This proactive approach was evaluated using data from twenty\nessential sensors in the Saipa's Quick vehicle's ECU, resulting in an\nimpressive accuracy rate of 99\\%.\n", "date": "2025-05-01", "categories": []}
{"id": "2505.00415", "title": "CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and\n  Adaptation in Multivariate Time Series", "abstract": "  Unsupervised Time series anomaly detection plays a crucial role in\napplications across industries. However, existing methods face significant\nchallenges due to data distributional shifts across different domains, which\nare exacerbated by the non-stationarity of time series over time. Existing\nmodels fail to generalize under multiple heterogeneous source domains and\nemerging unseen new target domains. To fill the research gap, we introduce\nCICADA (Cross-domain Interpretable Coding for Anomaly Detection and\nAdaptation), with four key innovations: (1) a mixture of experts (MOE)\nframework that captures domain-agnostic anomaly features with high flexibility\nand interpretability; (2) a novel selective meta-learning mechanism to prevent\nnegative transfer between dissimilar domains, (3) an adaptive expansion\nalgorithm for emerging heterogeneous domain expansion, and (4) a hierarchical\nattention structure that quantifies expert contributions during fusion to\nenhance interpretability further.Extensive experiments on synthetic and\nreal-world industrial datasets demonstrate that CICADA outperforms\nstate-of-the-art methods in both cross-domain detection performance and\ninterpretability.\n", "date": "2025-05-01", "categories": []}
{"id": "2302.00058", "title": "Graph Anomaly Detection in Time Series: A Survey", "abstract": "  With the recent advances in technology, a wide range of systems continue to\ncollect a large amount of data over time and thus generate time series.\nTime-Series Anomaly Detection (TSAD) is an important task in various\ntime-series applications such as e-commerce, cybersecurity, vehicle\nmaintenance, and healthcare monitoring. However, this task is very challenging\nas it requires considering both the intra-variable dependency (relationships\nwithin a variable over time) and the inter-variable dependency (relationships\nbetween multiple variables) existing in time-series data. Recent graph-based\napproaches have made impressive progress in tackling the challenges of this\nfield. In this survey, we conduct a comprehensive and up-to-date review of TSAD\nusing graphs, referred to as G-TSAD. First, we explore the significant\npotential of graph representation for time-series data and and its\ncontributions to facilitating anomaly detection. Then, we review\nstate-of-the-art graph anomaly detection techniques, mostly leveraging deep\nlearning architectures, in the context of time series. For each method, we\ndiscuss its strengths, limitations, and the specific applications where it\nexcels. Finally, we address both the technical and application challenges\ncurrently facing the field, and suggest potential future directions for\nadvancing research and improving practical outcomes.\n", "date": "2023-01-31", "categories": []}
{"id": "2504.21008", "title": "Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on\n  MindSpore", "abstract": "  With the widespread adoption of the Internet of Things (IoT) and Industrial\nIoT (IIoT) technologies, network architectures have become increasingly\ncomplex, and the volume of traffic has grown substantially. This evolution\nposes significant challenges to traditional security mechanisms, particularly\nin detecting high-frequency, diverse, and highly covert network attacks. To\naddress these challenges, this study proposes a novel network traffic anomaly\ndetection model that integrates a Convolutional Neural Network (CNN) with a\nBidirectional Long Short-Term Memory (BiLSTM) network, implemented on the\nMindSpore framework. Comprehensive experiments were conducted using the\nNF-BoT-IoT dataset. The results demonstrate that the proposed model achieves\n99% across accuracy, precision, recall, and F1-score, indicating its strong\nperformance and robustness in network intrusion detection tasks.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.21294", "title": "Learning Multi-view Multi-class Anomaly Detection", "abstract": "  The latest trend in anomaly detection is to train a unified model instead of\ntraining a separate model for each category. However, existing multi-class\nanomaly detection (MCAD) models perform poorly in multi-view scenarios because\nthey often fail to effectively model the relationships and complementary\ninformation among different views. In this paper, we introduce a Multi-View\nMulti-Class Anomaly Detection model (MVMCAD), which integrates information from\nmultiple views to accurately identify anomalies. Specifically, we propose a\nsemi-frozen encoder, where a pre-encoder prior enhancement mechanism is added\nbefore the frozen encoder, enabling stable cross-view feature modeling and\nefficient adaptation for improved anomaly detection. Furthermore, we propose an\nAnomaly Amplification Module (AAM) that models global token interactions and\nsuppresses normal regions to enhance anomaly signals, leading to improved\ndetection performance in multi-view settings. Finally, we propose a\nCross-Feature Loss that aligns shallow encoder features with deep decoder\nfeatures and vice versa, enhancing the model's sensitivity to anomalies at\ndifferent semantic levels under multi-view scenarios. Extensive experiments on\nthe Real-IAD dataset for multi-view multi-class anomaly detection validate the\neffectiveness of our approach, achieving state-of-the-art performance of\n91.0/88.6/82.1 and 99.1/43.9/48.2/95.2 for image-level and the pixel-level,\nrespectively.\n", "date": "2025-04-30", "categories": []}
{"id": "2408.13509", "title": "Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation", "abstract": "  The performance of anomaly inspection in industrial manufacturing is\nconstrained by the scarcity of anomaly data. To overcome this challenge,\nresearchers have started employing anomaly generation approaches to augment the\nanomaly dataset. However, existing anomaly generation methods suffer from\nlimited diversity in the generated anomalies and struggle to achieve a seamless\nblending of this anomaly with the original image. Moreover, the generated mask\nis usually not aligned with the generated anomaly. In this paper, we overcome\nthese challenges from a new perspective, simultaneously generating a pair of\nthe overall image and the corresponding anomaly part. We propose DualAnoDiff, a\nnovel diffusion-based few-shot anomaly image generation model, which can\ngenerate diverse and realistic anomaly images by using a dual-interrelated\ndiffusion model, where one of them is employed to generate the whole image\nwhile the other one generates the anomaly part. Moreover, we extract background\nand shape information to mitigate the distortion and blurriness phenomenon in\nfew-shot image generation. Extensive experiments demonstrate the superiority of\nour proposed model over state-of-the-art methods in terms of diversity, realism\nand the accuracy of mask. Overall, our approach significantly improves the\nperformance of downstream anomaly inspection tasks, including anomaly\ndetection, anomaly localization, and anomaly classification tasks.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.15268", "title": "Anomaly Detection in Time Series of EDFA Pump Currents to Monitor\n  Degeneration Processes using Fuzzy Clustering", "abstract": "  This article proposes a novel fuzzy clustering based anomaly detection method\nfor pump current time series of EDFA systems. The proposed change detection\nframework (CDF) strategically combines the advantages of entropy analysis (EA)\nand principle component analysis (PCA) with fuzzy clustering procedures. In the\nframework, EA is applied for dynamic selection of features for reduction of the\nfeature space and increase of computational performance. Furthermore, PCA is\nutilized to extract features from the raw feature space to enable\ngeneralization capability of the subsequent fuzzy clustering procedures. Three\ndifferent fuzzy clustering methods, more precisely the fuzzy clustering\nalgorithm, a probabilistic clustering algorithm and a possibilistic clustering\nalgorithm are evaluated for performance and generalization. Hence, the proposed\nframework has the innovative feature to detect changes in pump current time\nseries at an early stage for arbitrary points of operation, compared to\nstate-of-the-art predefined alarms in commercially used EDFAs. Moreover, the\napproach is implemented and tested using experimental data. In addition, the\nproposed framework enables further approaches of applying decentralized\npredictive maintenance for optical fiber networks.\n", "date": "2024-08-12", "categories": []}
{"id": "2504.20862", "title": "Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private\n  Data", "abstract": "  The remarkable success of Deep Learning approaches is often based and\ndemonstrated on large public datasets. However, when applying such approaches\nto internal, private datasets, one frequently faces challenges arising from\nstructural differences in the datasets, domain shift, and the lack of labels.\nIn this work, we introduce Tabular Data Adapters (TDA), a novel method for\ngenerating soft labels for unlabeled tabular data in outlier detection tasks.\nBy identifying statistically similar public datasets and transforming private\ndata (based on a shared autoencoder) into a format compatible with\nstate-of-the-art public models, our approach enables the generation of weak\nlabels. It thereby can help to mitigate the cold start problem of labeling by\nbasing on existing outlier detection models for public datasets. In experiments\non 50 tabular datasets across different domains, we demonstrate that our method\nis able to provide more accurate annotations than baseline approaches while\nreducing computational time. Our approach offers a scalable, efficient, and\ncost-effective solution, to bridge the gap between public research models and\nreal-world industrial applications.\n", "date": "2025-04-29", "categories": []}
{"id": "2305.15956", "title": "Anomaly Detection with Conditioned Denoising Diffusion Models", "abstract": "  Traditional reconstruction-based methods have struggled to achieve\ncompetitive performance in anomaly detection. In this paper, we introduce\nDenoising Diffusion Anomaly Detection (DDAD), a novel denoising process for\nimage reconstruction conditioned on a target image. This ensures a coherent\nrestoration that closely resembles the target image. Our anomaly detection\nframework employs the conditioning mechanism, where the target image is set as\nthe input image to guide the denoising process, leading to a defectless\nreconstruction while maintaining nominal patterns. Anomalies are then localised\nvia a pixel-wise and feature-wise comparison of the input and reconstructed\nimage. Finally, to enhance the effectiveness of the feature-wise comparison, we\nintroduce a domain adaptation method that utilises nearly identical generated\nexamples from our conditioned denoising process to fine-tune the pretrained\nfeature extractor. The veracity of DDAD is demonstrated on various datasets\nincluding MVTec and VisA benchmarks, achieving state-of-the-art results of\n\\(99.8 \\%\\) and \\(98.9 \\%\\) image-level AUROC respectively.\n", "date": "2023-05-25", "categories": []}
{"id": "2310.19545", "title": "MENTOR: Human Perception-Guided Pretraining for Increased Generalization", "abstract": "  Leveraging human perception into training of convolutional neural networks\n(CNN) has boosted generalization capabilities of such models in open-set\nrecognition tasks. One of the active research questions is where (in the model\narchitecture or training pipeline) and how to efficiently incorporate always\nlimited human perceptual data into training strategies of models. In this\npaper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased\ngeneRalization), which addresses this question through two unique rounds of\ntraining CNNs tasked with open-set anomaly detection. First, we train an\nautoencoder to learn human saliency maps given an input image, without any\nclass labels. The autoencoder is thus tasked with discovering domain-specific\nsalient features which mimic human perception. Second, we remove the decoder\npart, add a classification layer on top of the encoder, and train this new\nmodel conventionally, now using class labels. We show that MENTOR successfully\nraises the generalization performance across three different CNN backbones in a\nvariety of anomaly detection tasks (demonstrated for detection of unknown iris\npresentation attacks, synthetically-generated faces, and anomalies in chest\nX-ray images) compared to traditional pretraining methods (e.g., sourcing the\nweights from ImageNet), and as well as state-of-the-art methods that\nincorporate human perception guidance into training. In addition, we\ndemonstrate that MENTOR can be flexibly applied to existing human\nperception-guided methods and subsequently increasing their generalization with\nno architectural modifications.\n", "date": "2023-10-30", "categories": []}
{"id": "2406.06423", "title": "Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous\n  Driving", "abstract": "  In autonomous driving, the most challenging scenarios can only be detected\nwithin their temporal context. Most video anomaly detection approaches focus\neither on surveillance or traffic accidents, which are only a subfield of\nautonomous driving. We present HF$^2$-VAD$_{AD}$, a variation of the HF$^2$-VAD\nsurveillance video anomaly detection method for autonomous driving. We learn a\nrepresentation of normality from a vehicle's ego perspective and evaluate\npixel-wise anomaly detections in rare and critical scenarios.\n", "date": "2024-06-10", "categories": []}
{"id": "2501.02921", "title": "Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging\n  and Variational Autoencoders", "abstract": "  Tomato anomalies/damages pose a significant challenge in greenhouse farming.\nWhile this method of cultivation benefits from efficient resource utilization,\nanomalies can significantly degrade the quality of farm produce. A common\nanomaly associated with tomatoes is splitting, characterized by the development\nof cracks on the tomato skin, which degrades its quality. Detecting this type\nof anomaly is challenging due to dynamic variations in appearance and sizes,\ncompounded by dataset scarcity. We address this problem in an unsupervised\nmanner by utilizing a tailored variational autoencoder (VAE) with hyperspectral\ninput. Preliminary analysis of the dataset enabled us to select the optimal\nrange of wavelengths for detecting this anomaly. Our findings indicate that the\n530nm - 550nm range is suitable for identifying tomato dry splits. The proposed\nVAE model achieved a 97% detection accuracy for tomato split anomalies in the\ntest data. The analysis on reconstruction loss allow us to not only detect the\nanomalies but also to some degree estimate the anomalous regions.\n", "date": "2025-01-06", "categories": []}
{"id": "2504.00058", "title": "GAL-MAD: Towards Explainable Anomaly Detection in Microservice\n  Applications Using Graph Attention Networks", "abstract": "  The transition to microservices has revolutionized software architectures,\noffering enhanced scalability and modularity. However, the distributed and\ndynamic nature of microservices introduces complexities in ensuring system\nreliability, making anomaly detection crucial for maintaining performance and\nfunctionality. Anomalies stemming from network and performance issues must be\nswiftly identified and addressed. Existing anomaly detection techniques often\nrely on statistical models or machine learning methods that struggle with the\nhigh-dimensional, interdependent data inherent in microservice applications.\nCurrent techniques and available datasets predominantly focus on system traces\nand logs, limiting their ability to support advanced detection models. This\npaper addresses these gaps by introducing the RS-Anomic dataset generated using\nthe open-source RobotShop microservice application. The dataset captures\nmultivariate performance metrics and response times under normal and anomalous\nconditions, encompassing ten types of anomalies. We propose a novel anomaly\ndetection model called Graph Attention and LSTM-based Microservice Anomaly\nDetection (GAL-MAD), leveraging Graph Attention and Long Short-Term Memory\narchitectures to capture spatial and temporal dependencies in microservices. We\nutilize SHAP values to localize anomalous services and identify root causes to\nenhance explainability. Experimental results demonstrate that GAL-MAD\noutperforms state-of-the-art models on the RS-Anomic dataset, achieving higher\naccuracy and recall across varying anomaly rates. The explanations provide\nactionable insights into service anomalies, which benefits system\nadministrators.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.18633", "title": "Statistical Inference for Clustering-based Anomaly Detection", "abstract": "  Unsupervised anomaly detection (AD) is a fundamental problem in machine\nlearning and statistics. A popular approach to unsupervised AD is\nclustering-based detection. However, this method lacks the ability to guarantee\nthe reliability of the detected anomalies. In this paper, we propose SI-CLAD\n(Statistical Inference for CLustering-based Anomaly Detection), a novel\nstatistical framework for testing the clustering-based AD results. The key\nstrength of SI-CLAD lies in its ability to rigorously control the probability\nof falsely identifying anomalies, maintaining it below a pre-specified\nsignificance level $\\alpha$ (e.g., $\\alpha = 0.05$). By analyzing the selection\nmechanism inherent in clustering-based AD and leveraging the Selective\nInference (SI) framework, we prove that false detection control is attainable.\nMoreover, we introduce a strategy to boost the true detection rate, enhancing\nthe overall performance of SI-CLAD. Extensive experiments on synthetic and\nreal-world datasets provide strong empirical support for our theoretical\nfindings, showcasing the superior performance of the proposed method.\n", "date": "2025-04-25", "categories": []}
{"id": "2504.18771", "title": "Performance of Machine Learning Classifiers for Anomaly Detection in\n  Cyber Security Applications", "abstract": "  This work empirically evaluates machine learning models on two imbalanced\npublic datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data\npreparation, model training, and evaluation, using an 80/20 (train/test) split.\nModels tested include eXtreme Gradient Boosting (XGB), Multi Layer Perceptron\n(MLP), Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and\nMultiple-Objective Generative Adversarial Active Learning (MO-GAAL), with XGB\nand MLP further combined with Random-Over-Sampling (ROS) and\nSelf-Paced-Ensemble (SPE). Evaluation involves 5-fold cross-validation and\nimputation techniques (mean, median, and IterativeImputer) with 10, 20, 30, and\n50 % missing data. Findings show XGB and MLP outperform generative models.\nIterativeImputer results are comparable to mean and median, but not recommended\nfor large datasets due to increased complexity and execution time. The code\nused is publicly available on GitHub (github.com/markushaug/acr-25).\n", "date": "2025-04-26", "categories": []}
{"id": "2504.18802", "title": "Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis", "abstract": "  Urban roads and infrastructure, vital to city operations, face growing\nthreats from subsurface anomalies like cracks and cavities. Ground Penetrating\nRadar (GPR) effectively visualizes underground conditions employing\nelectromagnetic (EM) waves; however, accurate anomaly detection via GPR remains\nchallenging due to limited labeled data, varying subsurface conditions, and\nindistinct target boundaries. Although visually image-like, GPR data\nfundamentally represent EM waves, with variations within and between waves\ncritical for identifying anomalies. Addressing these, we propose the\nReservoir-enhanced Segment Anything Model (Res-SAM), an innovative framework\nexploiting both visual discernibility and wave-changing properties of GPR data.\nRes-SAM initially identifies apparent candidate anomaly regions given minimal\nprompts, and further refines them by analyzing anomaly-induced changing\ninformation within and between EM waves in local GPR data, enabling precise and\ncomplete anomaly region extraction and category determination. Real-world\nexperiments demonstrate that Res-SAM achieves high detection accuracy (>85%)\nand outperforms state-of-the-art. Notably, Res-SAM requires only minimal\naccessible non-target data, avoids intensive training, and incorporates simple\nhuman interaction to enhance reliability. Our research provides a scalable,\nresource-efficient solution for rapid subsurface anomaly detection across\ndiverse environments, improving urban safety monitoring while reducing manual\neffort and computational cost.\n", "date": "2025-04-26", "categories": []}
{"id": "2504.19524", "title": "LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning", "abstract": "  Industrial Anomaly Detection (IAD) is critical for ensuring product quality\nby identifying defects. Traditional methods such as feature embedding and\nreconstruction-based approaches require large datasets and struggle with\nscalability. Existing vision-language models (VLMs) and Multimodal Large\nLanguage Models (MLLMs) address some limitations but rely on mask annotations,\nleading to high implementation costs and false positives. Additionally,\nindustrial datasets like MVTec-AD and VisA suffer from severe class imbalance,\nwith defect samples constituting only 23.8% and 11.1% of total data\nrespectively. To address these challenges, we propose a reward function that\ndynamically prioritizes rare defect patterns during training to handle class\nimbalance. We also introduce a mask-free reasoning framework using Chain of\nThought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms,\nenabling anomaly detection directly from raw images without annotated masks.\nThis approach generates interpretable step-by-step explanations for defect\nlocalization. Our method achieves state-of-the-art performance, outperforming\nprior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating\nmask dependency and reducing costs while providing explainable outputs, this\nwork advances industrial anomaly detection and supports scalable quality\ncontrol in manufacturing. Code to reproduce the experiment is available at\nhttps://github.com/LilaKen/LR-IAD.\n", "date": "2025-04-28", "categories": []}
{"id": "2504.19706", "title": "Open-set Anomaly Segmentation in Complex Scenarios", "abstract": "  Precise segmentation of out-of-distribution (OoD) objects, herein referred to\nas anomalies, is crucial for the reliable deployment of semantic segmentation\nmodels in open-set, safety-critical applications, such as autonomous driving.\nCurrent anomalous segmentation benchmarks predominantly focus on favorable\nweather conditions, resulting in untrustworthy evaluations that overlook the\nrisks posed by diverse meteorological conditions in open-set environments, such\nas low illumination, dense fog, and heavy rain. To bridge this gap, this paper\nintroduces the ComsAmy, a challenging benchmark specifically designed for\nopen-set anomaly segmentation in complex scenarios. ComsAmy encompasses a wide\nspectrum of adverse weather conditions, dynamic driving environments, and\ndiverse anomaly types to comprehensively evaluate the model performance in\nrealistic open-world scenarios. Our extensive evaluation of several\nstate-of-the-art anomalous segmentation models reveals that existing methods\ndemonstrate significant deficiencies in such challenging scenarios,\nhighlighting their serious safety risks for real-world deployment. To solve\nthat, we propose a novel energy-entropy learning (EEL) strategy that integrates\nthe complementary information from energy and entropy to bolster the robustness\nof anomaly segmentation under complex open-world environments. Additionally, a\ndiffusion-based anomalous training data synthesizer is proposed to generate\ndiverse and high-quality anomalous images to enhance the existing copy-paste\ntraining data synthesizer. Extensive experimental results on both public and\nComsAmy benchmarks demonstrate that our proposed diffusion-based synthesizer\nwith energy and entropy learning (DiffEEL) serves as an effective and\ngeneralizable plug-and-play method to enhance existing models, yielding an\naverage improvement of around 4.96% in $\\rm{AUPRC}$ and 9.87% in\n$\\rm{FPR}_{95}$.\n", "date": "2025-04-28", "categories": []}
{"id": "2504.06643", "title": "AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series\n  Anomaly Detection", "abstract": "  Unsupervised multivariate time series anomaly detection (UMTSAD) plays a\ncritical role in various domains, including finance, networks, and sensor\nsystems. In recent years, due to the outstanding performance of deep learning\nin general sequential tasks, many models have been specialized for deep UMTSAD\ntasks and have achieved impressive results, particularly those based on the\nTransformer and self-attention mechanisms. However, the sequence anomaly\nassociation assumptions underlying these models are often limited to specific\npredefined patterns and scenarios, such as concentrated or peak anomaly\npatterns. These limitations hinder their ability to generalize to diverse\nanomaly situations, especially where the lack of labels poses significant\nchallenges. To address these issues, we propose AMAD, which integrates\n\\textbf{A}uto\\textbf{M}asked Attention for UMTS\\textbf{AD} scenarios. AMAD\nintroduces a novel structure based on the AutoMask mechanism and an attention\nmixup module, forming a simple yet generalized anomaly association\nrepresentation framework. This framework is further enhanced by a Max-Min\ntraining strategy and a Local-Global contrastive learning approach. By\ncombining multi-scale feature extraction with automatic relative association\nmodeling, AMAD provides a robust and adaptable solution to UMTSAD challenges.\nExtensive experimental results demonstrate that the proposed model achieving\ncompetitive performance results compared to SOTA benchmarks across a variety of\ndatasets.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.17807", "title": "Research on Cloud Platform Network Traffic Monitoring and Anomaly\n  Detection System based on Large Language Models", "abstract": "  The rapidly evolving cloud platforms and the escalating complexity of network\ntraffic demand proper network traffic monitoring and anomaly detection to\nensure network security and performance. This paper introduces a large language\nmodel (LLM)-based network traffic monitoring and anomaly detection system. In\naddition to existing models such as autoencoders and decision trees, we harness\nthe power of large language models for processing sequence data from network\ntraffic, which allows us a better capture of underlying complex patterns, as\nwell as slight fluctuations in the dataset. We show for a given detection task,\nthe need for a hybrid model that incorporates the attention mechanism of the\ntransformer architecture into a supervised learning framework in order to\nachieve better accuracy. A pre-trained large language model analyzes and\npredicts the probable network traffic, and an anomaly detection layer that\nconsiders temporality and context is added. Moreover, we present a novel\ntransfer learning-based methodology to enhance the model's effectiveness to\nquickly adapt to unknown network structures and adversarial conditions without\nrequiring extensive labeled datasets. Actual results show that the designed\nmodel outperforms traditional methods in detection accuracy and computational\nefficiency, effectively identify various network anomalies such as zero-day\nattacks and traffic congestion pattern, and significantly reduce the false\npositive rate.\n", "date": "2025-04-22", "categories": []}
{"id": "2504.18231", "title": "Time and Frequency Domain-based Anomaly Detection in Smart Meter Data\n  for Distribution Network Studies", "abstract": "  The widespread integration of new technologies in low-voltage distribution\nnetworks on the consumer side creates the need for distribution system\noperators to perform advanced real-time calculations to estimate network\nconditions. In recent years, data-driven models based on machine learning and\nbig data analysis have emerged for calculation purposes, leveraging the\ninformation available in large datasets obtained from smart meters and other\nadvanced measurement infrastructure. However, existing data-driven algorithms\ndo not take into account the quality of data collected from smart meters. They\nlack built-in anomaly detection mechanisms and fail to differentiate anomalies\nbased on whether the value or context of anomalous data instances deviates from\nthe norm. This paper focuses on methods for detecting and mitigating the impact\nof anomalies on the consumption of active and reactive power datasets. It\nproposes an anomaly detection framework based on the Isolation Forest machine\nlearning algorithm and Fast Fourier Transform filtering that works in both the\ntime and frequency domain and is unaffected by point anomalies or contextual\nanomalies of the power consumption data. The importance of integrating anomaly\ndetection methods is demonstrated in the analysis important for distribution\nnetworks with a high share of smart meters.\n", "date": "2025-04-25", "categories": []}
{"id": "2412.10892", "title": "Know Unreported Roadway Incidents in Real-time: Early Traffic Anomaly\n  Detection", "abstract": "  This research aims to know traffic anomalies as early as possible. A traffic\nanomaly refers to a generic incident on the road that influences traffic flow\nand calls for urgent traffic management measures. `Knowing'' the occurrence of\na traffic anomaly is twofold: the ability to detect this anomaly before it is\nreported anywhere, or it may be such that an anomaly can be predicted before it\nactually occurs on the road (e.g., non-recurrent traffic breakdown). In either\nway, the objective is to inform traffic operators of unreported incidents in\nreal time and as early as possible. The key is to stay ahead of the curve. Time\nis of the essence.\n  Conventional automatic incident detection (AID) methods often struggle with\nearly detection due to their limited consideration of spatial effects and\nearly-stage characteristics. Therefore, we propose a deep learning framework\nutilizing prior domain knowledge and model-designing strategies. This allows\nthe model to detect a broader range of anomalies, not only incidents that\nsignificantly influence traffic flow but also early characteristics of\nincidents along with historically unreported anomalies. We specially design the\nmodel to target the early-stage detection/prediction of an incident.\nAdditionally, unlike most conventional AID studies, our method is highly\nscalable and generalizable, as it is fully automated with no manual selection\nof historical reports required, relies solely on widely available low-cost\ndata, and requires no additional detectors. The experimental results across\nnumerous road segments on different maps demonstrate that our model leads to\nmore effective and early anomaly detection.\n", "date": "2024-12-14", "categories": []}
{"id": "2504.15491", "title": "Application of Deep Generative Models for Anomaly Detection in Complex\n  Financial Transactions", "abstract": "  This study proposes an algorithm for detecting suspicious behaviors in large\npayment flows based on deep generative models. By combining Generative\nAdversarial Networks (GAN) and Variational Autoencoders (VAE), the algorithm is\ndesigned to detect abnormal behaviors in financial transactions. First, the GAN\nis used to generate simulated data that approximates normal payment flows. The\ndiscriminator identifies anomalous patterns in transactions, enabling the\ndetection of potential fraud and money laundering behaviors. Second, a VAE is\nintroduced to model the latent distribution of payment flows, ensuring that the\ngenerated data more closely resembles real transaction features, thus improving\nthe model's detection accuracy. The method optimizes the generative\ncapabilities of both GAN and VAE, ensuring that the model can effectively\ncapture suspicious behaviors even in sparse data conditions. Experimental\nresults show that the proposed method significantly outperforms traditional\nmachine learning algorithms and other deep learning models across various\nevaluation metrics, especially in detecting rare fraudulent behaviors.\nFurthermore, this study provides a detailed comparison of performance in\nrecognizing different transaction patterns (such as normal, money laundering,\nand fraud) in large payment flows, validating the advantages of generative\nmodels in handling complex financial data.\n", "date": "2025-04-21", "categories": []}
{"id": "2504.15562", "title": "Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware\n  Approach for Brain 2 MRI Analysis", "abstract": "  In medical imaging, anomaly detection is a vital element of healthcare\ndiagnostics, especially for neurological conditions which can be\nlife-threatening. Conventional deterministic methods often fall short when it\ncomes to capturing the inherent uncertainty of anomaly detection tasks. This\npaper introduces a Bayesian Variational Autoencoder (VAE) equipped with\nmulti-head attention mechanisms for detecting anomalies in brain magnetic\nresonance imaging (MRI). For the purpose of improving anomaly detection\nperformance, we incorporate both epistemic and aleatoric uncertainty estimation\nthrough Bayesian inference. The model was tested on the BraTS2020 dataset, and\nthe findings were a 0.83 ROC AUC and a 0.83 PR AUC. The data in our paper\nsuggests that modeling uncertainty is an essential component of anomaly\ndetection, enhancing both performance and interpretability and providing\nconfidence estimates, as well as anomaly predictions, for clinicians to\nleverage in making medical decisions.\n", "date": "2025-04-22", "categories": []}
{"id": "2504.16075", "title": "Explainable Unsupervised Anomaly Detection with Random Forest", "abstract": "  We describe the use of an unsupervised Random Forest for similarity learning\nand improved unsupervised anomaly detection. By training a Random Forest to\ndiscriminate between real data and synthetic data sampled from a uniform\ndistribution over the real data bounds, a distance measure is obtained that\nanisometrically transforms the data, expanding distances at the boundary of the\ndata manifold. We show that using distances recovered from this transformation\nimproves the accuracy of unsupervised anomaly detection, compared to other\ncommonly used detectors, demonstrated over a large number of benchmark\ndatasets. As well as improved performance, this method has advantages over\nother unsupervised anomaly detection methods, including minimal requirements\nfor data preprocessing, native handling of missing data, and potential for\nvisualizations. By relating outlier scores to partitions of the Random Forest,\nwe develop a method for locally explainable anomaly predictions in terms of\nfeature importance.\n", "date": "2025-04-22", "categories": []}
{"id": "2504.14206", "title": "Decomposition-based multi-scale transformer framework for time series\n  anomaly detection", "abstract": "  Time series anomaly detection is crucial for maintaining stable systems.\nExisting methods face two main challenges. First, it is difficult to directly\nmodel the dependencies of diverse and complex patterns within the sequences.\nSecond, many methods that optimize parameters using mean squared error struggle\nwith noise in the time series, leading to performance deterioration. To address\nthese challenges, we propose a transformer-based framework built on\ndecomposition (TransDe) for multivariate time series anomaly detection. The key\nidea is to combine the strengths of time series decomposition and transformers\nto effectively learn the complex patterns in normal time series data. A\nmulti-scale patch-based transformer architecture is proposed to exploit the\nrepresentative dependencies of each decomposed component of the time series.\nFurthermore, a contrastive learn paradigm based on patch operation is proposed,\nwhich leverages KL divergence to align the positive pairs, namely the pure\nrepresentations of normal patterns between different patch-level views. A novel\nasynchronous loss function with a stop-gradient strategy is further introduced\nto enhance the performance of TransDe effectively. It can avoid time-consuming\nand labor-intensive computation costs in the optimization process. Extensive\nexperiments on five public datasets are conducted and TransDe shows superiority\ncompared with twelve baselines in terms of F1 score. Our code is available at\nhttps://github.com/shaieesss/TransDe.\n", "date": "2025-04-19", "categories": []}
{"id": "2504.14221", "title": "Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly\n  Detection", "abstract": "  The increasing complexity of industrial anomaly detection (IAD) has\npositioned multimodal detection methods as a focal area of machine vision\nresearch. However, dedicated multimodal datasets specifically tailored for IAD\nremain limited. Pioneering datasets like MVTec 3D have laid essential\ngroundwork in multimodal IAD by incorporating RGB+3D data, but still face\nchallenges in bridging the gap with real industrial environments due to\nlimitations in scale and resolution. To address these challenges, we introduce\nReal-IAD D3, a high-precision multimodal dataset that uniquely incorporates an\nadditional pseudo3D modality generated through photometric stereo, alongside\nhigh-resolution RGB images and micrometer-level 3D point clouds. Real-IAD D3\nfeatures finer defects, diverse anomalies, and greater scale across 20\ncategories, providing a challenging benchmark for multimodal IAD Additionally,\nwe introduce an effective approach that integrates RGB, point cloud, and\npseudo-3D depth information to leverage the complementary strengths of each\nmodality, enhancing detection performance. Our experiments highlight the\nimportance of these modalities in boosting detection robustness and overall IAD\nperformance. The dataset and code are publicly accessible for research purposes\nat https://realiad4ad.github.io/Real-IAD D3\n", "date": "2025-04-19", "categories": []}
{"id": "2504.14686", "title": "Uncovering Issues in the Radio Access Network by Looking at the\n  Neighbors", "abstract": "  Mobile network operators (MNOs) manage Radio Access Networks (RANs) with\nmassive amounts of cells over multiple radio generations (2G-5G). To handle\nsuch complexity, operations teams rely on monitoring systems, including anomaly\ndetection tools that identify unexpected behaviors. In this paper, we present\nc-ANEMON, a Contextual ANomaly dEtection MONitor for the RAN based on Graph\nNeural Networks (GNNs). Our solution captures spatio-temporal variations by\nanalyzing the behavior of individual cells in relation to their local\nneighborhoods, enabling the detection of anomalies that are independent of\nexternal mobility factors. This, in turn, allows focusing on anomalies\nassociated with network issues (e.g., misconfigurations, equipment failures).\nWe evaluate c-ANEMON using real-world data from a large European metropolitan\narea (7,890 cells; 3 months). First, we show that the GNN model within our\nsolution generalizes effectively to cells from previously unseen areas,\nsuggesting the possibility of using a single model across extensive deployment\nregions. Then, we analyze the anomalies detected by c-ANEMON through manual\ninspection and define several categories of long-lasting anomalies (6+ hours).\nNotably, 45.95% of these anomalies fall into a category that is more likely to\nrequire intervention by operations teams.\n", "date": "2025-04-20", "categories": []}
{"id": "2504.14753", "title": "Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for\n  Enhanced Single- and Multi-Task Approaches", "abstract": "  Despite the prevailing transition from single-task to multi-task approaches\nin video anomaly detection, we observe that many adopt sub-optimal frameworks\nfor individual proxy tasks. Motivated by this, we contend that optimizing\nsingle-task frameworks can advance both single- and multi-task approaches.\nAccordingly, we leverage middle-frame prediction as the primary proxy task, and\nintroduce an effective hybrid framework designed to generate accurate\npredictions for normal frames and flawed predictions for abnormal frames. This\nhybrid framework is built upon a bi-directional structure that seamlessly\nintegrates both vision transformers and ConvLSTMs. Specifically, we utilize\nthis bi-directional structure to fully analyze the temporal dimension by\npredicting frames in both forward and backward directions, significantly\nboosting the detection stability. Given the transformer's capacity to model\nlong-range contextual dependencies, we develop a convolutional temporal\ntransformer that efficiently associates feature maps from all context frames to\ngenerate attention-based predictions for target frames. Furthermore, we devise\na layer-interactive ConvLSTM bridge that facilitates the smooth flow of\nlow-level features across layers and time-steps, thereby strengthening\npredictions with fine details. Anomalies are eventually identified by\nscrutinizing the discrepancies between target frames and their corresponding\npredictions. Several experiments conducted on public benchmarks affirm the\nefficacy of our hybrid framework, whether used as a standalone single-task\napproach or integrated as a branch in a multi-task approach. These experiments\nalso underscore the advantages of merging vision transformers and ConvLSTMs for\nvideo anomaly detection.\n", "date": "2025-04-20", "categories": []}
{"id": "2504.14884", "title": "Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised\n  Anomaly Detection", "abstract": "  Recent advances in unsupervised anomaly detection (UAD) have shifted from\nsingle-class to multi-class scenarios. In such complex contexts, the increasing\npattern diversity has brought two challenges to reconstruction-based\napproaches: (1) over-generalization: anomalies that are subtle or share\ncompositional similarities with normal patterns may be reconstructed with high\nfidelity, making them difficult to distinguish from normal instances; and (2)\ninsufficient normality reconstruction: complex normal features, such as\nintricate textures or fine-grained structures, may not be faithfully\nreconstructed due to the model's limited representational capacity, resulting\nin false positives. Existing methods typically focus on addressing the former,\nwhich unintentionally exacerbate the latter, resulting in inadequate\nrepresentation of intricate normal patterns. To concurrently address these two\nchallenges, we propose a Memory-augmented Dual-Decoder Networks (MDD-Net). This\nnetwork includes two critical components: a Dual-Decoder Reverse Distillation\nNetwork (DRD-Net) and a Class-aware Memory Module (CMM). Specifically, the\nDRD-Net incorporates a restoration decoder designed to recover normal features\nfrom synthetic abnormal inputs and an identity decoder to reconstruct features\nthat maintain the anomalous semantics. By exploiting the discrepancy between\nfeatures produced by two decoders, our approach refines anomaly scores beyond\nthe conventional encoder-decoder comparison paradigm, effectively reducing\nfalse positives and enhancing localization accuracy. Furthermore, the CMM\nexplicitly encodes and preserves class-specific normal prototypes, actively\nsteering the network away from anomaly reconstruction. Comprehensive\nexperimental results across several benchmarks demonstrate the superior\nperformance of our MDD-Net framework over current SoTA approaches in\nmulti-class UAD tasks.\n", "date": "2025-04-21", "categories": []}
{"id": "2504.15225", "title": "M$^2$AD: Multi-Sensor Multi-System Anomaly Detection through Global\n  Scoring and Calibrated Thresholding", "abstract": "  With the widespread availability of sensor data across industrial and\noperational systems, we frequently encounter heterogeneous time series from\nmultiple systems. Anomaly detection is crucial for such systems to facilitate\npredictive maintenance. However, most existing anomaly detection methods are\ndesigned for either univariate or single-system multivariate data, making them\ninsufficient for these complex scenarios. To address this, we introduce\nM$^2$AD, a framework for unsupervised anomaly detection in multivariate time\nseries data from multiple systems. M$^2$AD employs deep models to capture\nexpected behavior under normal conditions, using the residuals as indicators of\npotential anomalies. These residuals are then aggregated into a global anomaly\nscore through a Gaussian Mixture Model and Gamma calibration. We theoretically\ndemonstrate that this framework can effectively address heterogeneity and\ndependencies across sensors and systems. Empirically, M$^2$AD outperforms\nexisting methods in extensive evaluations by 21% on average, and its\neffectiveness is demonstrated on a large-scale real-world case study on 130\nassets in Amazon Fulfillment Centers. Our code and results are available at\nhttps://github.com/sarahmish/M2AD.\n", "date": "2025-04-21", "categories": []}
{"id": "2504.08115", "title": "Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly\n  Detection (SARIAD) Algorithms", "abstract": "  Anomaly detection is a key research challenge in computer vision and machine\nlearning with applications in many fields from quality control to radar\nimaging. In radar imaging, specifically synthetic aperture radar (SAR), anomaly\ndetection can be used for the classification, detection, and segmentation of\nobjects of interest. However, there is no method for developing and\nbenchmarking these methods on SAR imagery. To address this issue, we introduce\nSAR imagery anomaly detection (SARIAD). In conjunction with Anomalib, a\ndeep-learning library for anomaly detection, SARIAD provides a comprehensive\nsuite of algorithms and datasets for assessing and developing anomaly detection\napproaches on SAR imagery. SARIAD specifically integrates multiple SAR datasets\nalong with tools to effectively apply various anomaly detection algorithms to\nSAR imagery. Several anomaly detection metrics and visualizations are\navailable. Overall, SARIAD acts as a central package for benchmarking SAR\nmodels and datasets to allow for reproducible research in the field of anomaly\ndetection in SAR imagery. This package is publicly available:\nhttps://github.com/Advanced-Vision-and-Learning-Lab/SARIAD.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.13560", "title": "Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt\n  Generation", "abstract": "  Anomaly segmentation is essential for industrial quality, maintenance, and\nstability. Existing text-guided zero-shot anomaly segmentation models are\neffective but rely on fixed prompts, limiting adaptability in diverse\nindustrial scenarios. This highlights the need for flexible, context-aware\nprompting strategies. We propose Image-Aware Prompt Anomaly Segmentation\n(IAP-AS), which enhances anomaly segmentation by generating dynamic,\ncontext-aware prompts using an image tagging model and a large language model\n(LLM). IAP-AS extracts object attributes from images to generate context-aware\nprompts, improving adaptability and generalization in dynamic and unstructured\nindustrial environments. In our experiments, IAP-AS improves the F1-max metric\nby up to 10%, demonstrating superior adaptability and generalization. It\nprovides a scalable solution for anomaly segmentation across industries\n", "date": "2025-04-18", "categories": []}
{"id": "2504.12689", "title": "HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset", "abstract": "  Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving\nincreasing attention due to their relatively low deployment costs and improved\ntraining efficiency. However, the real-world effectiveness of MUAD methods is\nquestioned due to limitations in current Industrial Anomaly Detection (IAD)\ndatasets. These datasets contain numerous classes that are unlikely to be\nproduced by the same factory and fail to cover multiple structures or\nappearances. Additionally, the defects do not reflect real-world\ncharacteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial\nAnomaly Detection (HSS-IAD) dataset, which contains 8,580 images of\nmetallic-like industrial parts and precise anomaly annotations. These parts\nexhibit variations in structure and appearance, with subtle defects that\nclosely resemble the base materials. We also provide foreground images for\nsynthetic anomaly generation. Finally, we evaluate popular IAD methods on this\ndataset under multi-class and class-separated settings, demonstrating its\npotential to bridge the gap between existing datasets and real factory\nconditions. The dataset is available at\nhttps://github.com/Qiqigeww/HSS-IAD-Dataset.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.12749", "title": "LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical\n  Anomaly Detection", "abstract": "  Recent advances in industrial anomaly detection have highlighted the need for\ndeeper logical anomaly analysis, where unexpected relationships among objects,\ncounts, and spatial configurations must be identified and explained. Existing\napproaches often rely on large-scale external reasoning modules or elaborate\npipeline designs, hindering practical deployment and interpretability. To\naddress these limitations, we introduce a new task, Reasoning Logical Anomaly\nDetection (RLAD), which extends traditional anomaly detection by incorporating\nlogical reasoning. We propose a new framework, LAD-Reasoner, a customized tiny\nmultimodal language model built on Qwen2.5-VL 3B. Our approach leverages a\ntwo-stage training paradigm that first employs Supervised Fine-Tuning (SFT) for\nfine-grained visual understanding, followed by Group Relative Policy\nOptimization (GRPO) to refine logical anomaly detection and enforce coherent,\nhuman-readable reasoning. Crucially, reward signals are derived from both the\ndetection accuracy and the structural quality of the outputs, obviating the\nneed for building chain of thought (CoT) reasoning data. Experiments on the\nMVTec LOCO AD dataset show that LAD-Reasoner, though significantly smaller,\nmatches the performance of Qwen2.5-VL-72B in accuracy and F1 score, and further\nexcels in producing concise and interpretable rationales. This unified design\nreduces reliance on large models and complex pipelines, while offering\ntransparent and interpretable insights into logical anomaly detection. Code and\ndata will be released.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.12856", "title": "3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise", "abstract": "  Large pretrained vision foundation models have shown significant potential in\nvarious vision tasks. However, for industrial anomaly detection, the scarcity\nof real defect samples poses a critical challenge in leveraging these models.\nWhile 2D anomaly generation has significantly advanced with established\ngenerative models, the adoption of 3D sensors in industrial manufacturing has\nmade leveraging 3D data for surface quality inspection an emerging trend. In\ncontrast to 2D techniques, 3D anomaly generation remains largely unexplored,\nlimiting the potential of 3D data in industrial quality inspection. To address\nthis gap, we propose a novel yet simple 3D anomaly generation method, 3D-PNAS,\nbased on Perlin noise and surface parameterization. Our method generates\nrealistic 3D surface anomalies by projecting the point cloud onto a 2D plane,\nsampling multi-scale noise values from a Perlin noise field, and perturbing the\npoint cloud along its normal direction. Through comprehensive visualization\nexperiments, we demonstrate how key parameters - including noise scale,\nperturbation strength, and octaves, provide fine-grained control over the\ngenerated anomalies, enabling the creation of diverse defect patterns from\npronounced deformations to subtle surface variations. Additionally, our\ncross-category experiments show that the method produces consistent yet\ngeometrically plausible anomalies across different object types, adapting to\ntheir specific surface characteristics. We also provide a comprehensive\ncodebase and visualization toolkit to facilitate future research.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.12918", "title": "Sliced-Wasserstein Distance-based Data Selection", "abstract": "  We propose a new unsupervised anomaly detection method based on the\nsliced-Wasserstein distance for training data selection in machine learning\napproaches. Our filtering technique is interesting for decision-making\npipelines deploying machine learning models in critical sectors, e.g., power\nsystems, as it offers a conservative data selection and an optimal transport\ninterpretation. To ensure the scalability of our method, we provide two\nefficient approximations. The first approximation processes reduced-cardinality\nrepresentations of the datasets concurrently. The second makes use of a\ncomputationally light Euclidian distance approximation. Additionally, we open\nthe first dataset showcasing localized critical peak rebate demand response in\na northern climate. We present the filtering patterns of our method on\nsynthetic datasets and numerically benchmark our method for training data\nselection. Finally, we employ our method as part of a first forecasting\nbenchmark for our open-source dataset.\n", "date": "2025-04-17", "categories": []}
{"id": "2412.17619", "title": "Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection", "abstract": "  Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with\nthe guidance of very few normal support images from the same class. Existing\nFSAD methods usually find anomalies by directly designing complex text prompts\nto align them with visual features under the prevailing large vision-language\nmodel paradigm. However, these methods, almost always, neglect intrinsic\ncontextual information in visual features, e.g., the interaction relationships\nbetween different vision layers, which is an important clue for detecting\nanomalies comprehensively. To this end, we propose a kernel-aware graph prompt\nlearning framework, termed as KAG-prompt, by reasoning the cross-layer\nrelations among visual features for FSAD. Specifically, a kernel-aware\nhierarchical graph is built by taking the different layer features focusing on\nanomalous regions of different sizes as nodes, meanwhile, the relationships\nbetween arbitrary pairs of nodes stand for the edges of the graph. By message\npassing over this graph, KAG-prompt can capture cross-layer contextual\ninformation, thus leading to more accurate anomaly prediction. Moreover, to\nintegrate the information of multiple important anomaly signals in the\nprediction map, we propose a novel image-level scoring method based on\nmulti-level information fusion. Extensive experiments on MVTecAD and VisA\ndatasets show that KAG-prompt achieves state-of-the-art FSAD results for\nimage-level/pixel-level anomaly detection. Code is available at\nhttps://github.com/CVL-hub/KAG-prompt.git.\n", "date": "2024-12-23", "categories": []}
{"id": "2504.11520", "title": "Strengthening Anomaly Awareness", "abstract": "  We present a refined version of the Anomaly Awareness framework for enhancing\nunsupervised anomaly detection. Our approach introduces minimal supervision\ninto Variational Autoencoders (VAEs) through a two-stage training strategy: the\nmodel is first trained in an unsupervised manner on background data, and then\nfine-tuned using a small sample of labeled anomalies to encourage larger\nreconstruction errors for anomalous samples.\n  We validate the method across diverse domains, including the MNIST dataset\nwith synthetic anomalies, network intrusion data from the CICIDS benchmark,\ncollider physics data from the LHCO2020 dataset, and simulated events from the\nStandard Model Effective Field Theory (SMEFT). The latter provides a realistic\nexample of subtle kinematic deviations in Higgs boson production. In all cases,\nthe model demonstrates improved sensitivity to unseen anomalies, achieving\nbetter separation between normal and anomalous samples. These results indicate\nthat even limited anomaly information, when incorporated through targeted\nfine-tuning, can substantially improve the generalization and performance of\nunsupervised models for anomaly detection.\n", "date": "2025-04-15", "categories": []}
{"id": "2504.11623", "title": "Possibility for Proactive Anomaly Detection", "abstract": "  Time-series anomaly detection, which detects errors and failures in a\nworkflow, is one of the most important topics in real-world applications. The\npurpose of time-series anomaly detection is to reduce potential damages or\nlosses. However, existing anomaly detection models detect anomalies through the\nerror between the model output and the ground truth (observed) value, which\nmakes them impractical. In this work, we present a \\textit{proactive} approach\nfor time-series anomaly detection based on a time-series forecasting model\nspecialized for anomaly detection and a data-driven anomaly detection model.\nOur proactive approach establishes an anomaly threshold from training data with\na data-driven anomaly detection model, and anomalies are subsequently detected\nby identifying predicted values that exceed the anomaly threshold. In addition,\nwe extensively evaluated the model using four anomaly detection benchmarks and\nanalyzed both predictable and unpredictable anomalies. We attached the source\ncode as supplementary material.\n", "date": "2025-04-15", "categories": []}
{"id": "2504.11914", "title": "AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection", "abstract": "  Industrial Anomaly Detection (IAD) poses a formidable challenge due to the\nscarcity of defective samples, making it imperative to deploy models capable of\nrobust generalization to detect unseen anomalies effectively. Traditional\napproaches, often constrained by hand-crafted features or domain-specific\nexpert models, struggle to address this limitation, underscoring the need for a\nparadigm shift. We introduce AnomalyR1, a pioneering framework that leverages\nVLM-R1, a Multimodal Large Language Model (MLLM) renowned for its exceptional\ngeneralization and interpretability, to revolutionize IAD. By integrating MLLM\nwith Group Relative Policy Optimization (GRPO), enhanced by our novel Reasoned\nOutcome Alignment Metric (ROAM), AnomalyR1 achieves a fully end-to-end solution\nthat autonomously processes inputs of image and domain knowledge, reasons\nthrough analysis, and generates precise anomaly localizations and masks. Based\non the latest multimodal IAD benchmark, our compact 3-billion-parameter model\noutperforms existing methods, establishing state-of-the-art results. As MLLM\ncapabilities continue to advance, this study is the first to deliver an\nend-to-end VLM-based IAD solution that demonstrates the transformative\npotential of ROAM-enhanced GRPO, positioning our framework as a forward-looking\ncornerstone for next-generation intelligent anomaly detection systems in\nindustrial applications with limited defective data.\n", "date": "2025-04-16", "categories": []}
{"id": "2403.10802", "title": "Anomaly Detection Based on Isolation Mechanisms: A Survey", "abstract": "  Anomaly detection is a longstanding and active research area that has many\napplications in domains such as finance, security, and manufacturing. However,\nthe efficiency and performance of anomaly detection algorithms are challenged\nby the large-scale, high-dimensional, and heterogeneous data that are prevalent\nin the era of big data. Isolation-based unsupervised anomaly detection is a\nnovel and effective approach for identifying anomalies in data. It relies on\nthe idea that anomalies are few and different from normal instances, and thus\ncan be easily isolated by random partitioning. Isolation-based methods have\nseveral advantages over existing methods, such as low computational complexity,\nlow memory usage, high scalability, robustness to noise and irrelevant\nfeatures, and no need for prior knowledge or heavy parameter tuning. In this\nsurvey, we review the state-of-the-art isolation-based anomaly detection\nmethods, including their data partitioning strategies, anomaly score functions,\nand algorithmic details. We also discuss some extensions and applications of\nisolation-based methods in different scenarios, such as detecting anomalies in\nstreaming data, time series, trajectory, and image datasets. Finally, we\nidentify some open challenges and future directions for isolation-based anomaly\ndetection research.\n", "date": "2024-03-16", "categories": []}
{"id": "2411.08561", "title": "LogLLM: Log-based Anomaly Detection Using Large Language Models", "abstract": "  Software systems often record important runtime information in logs to help\nwith troubleshooting. Log-based anomaly detection has become a key research\narea that aims to identify system issues through log data, ultimately enhancing\nthe reliability of software systems. Traditional deep learning methods often\nstruggle to capture the semantic information embedded in log data, which is\ntypically organized in natural language. In this paper, we propose LogLLM, a\nlog-based anomaly detection framework that leverages large language models\n(LLMs). LogLLM employs BERT for extracting semantic vectors from log messages,\nwhile utilizing Llama, a transformer decoder-based model, for classifying log\nsequences. Additionally, we introduce a projector to align the vector\nrepresentation spaces of BERT and Llama, ensuring a cohesive understanding of\nlog semantics. Unlike conventional methods that require log parsers to extract\ntemplates, LogLLM preprocesses log messages with regular expressions,\nstreamlining the entire process. Our framework is trained through a novel\nthree-stage procedure designed to enhance performance and adaptability.\nExperimental results across four public datasets demonstrate that LogLLM\noutperforms state-of-the-art methods. Even when handling unstable logs, it\neffectively captures the semantic meaning of log messages and detects anomalies\naccurately.\n", "date": "2024-11-13", "categories": []}
{"id": "2504.08812", "title": "Mechanistic Anomaly Detection for \"Quirky\" Language Models", "abstract": "  As LLMs grow in capability, the task of supervising LLMs becomes more\nchallenging. Supervision failures can occur if LLMs are sensitive to factors\nthat supervisors are unaware of. We investigate Mechanistic Anomaly Detection\n(MAD) as a technique to augment supervision of capable models; we use internal\nmodel features to identify anomalous training signals so they can be\ninvestigated or discarded. We train detectors to flag points from the test\nenvironment that differ substantially from the training environment, and\nexperiment with a large variety of detector features and scoring rules to\ndetect anomalies in a set of ``quirky'' language models. We find that detectors\ncan achieve high discrimination on some tasks, but no detector is effective\nacross all models and tasks. MAD techniques may be effective in low-stakes\napplications, but advances in both detection and evaluation are likely needed\nif they are to be used in high stakes settings.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.09504", "title": "MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs", "abstract": "  When applying pre-trained large language models (LLMs) to address anomaly\ndetection tasks, the multivariate time series (MTS) modality of anomaly\ndetection does not align with the text modality of LLMs. Existing methods\nsimply transform the MTS data into multiple univariate time series sequences,\nwhich can cause many problems. This paper introduces MADLLM, a novel\nmultivariate anomaly detection method via pre-trained LLMs. We design a new\ntriple encoding technique to align the MTS modality with the text modality of\nLLMs. Specifically, this technique integrates the traditional patch embedding\nmethod with two novel embedding approaches: Skip Embedding, which alters the\norder of patch processing in traditional methods to help LLMs retain knowledge\nof previous features, and Feature Embedding, which leverages contrastive\nlearning to allow the model to better understand the correlations between\ndifferent features. Experimental results demonstrate that our method\noutperforms state-of-the-art methods in various public anomaly detection\ndatasets.\n", "date": "2025-04-13", "categories": []}
{"id": "2504.10320", "title": "SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and\n  RAG-Enhanced Vision-Language Model", "abstract": "  Video anomaly detection (VAD) aims to identify unexpected events in videos\nand has wide applications in safety-critical domains. While semi-supervised\nmethods trained on only normal samples have gained traction, they often suffer\nfrom high false alarm rates and poor interpretability. Recently,\nvision-language models (VLMs) have demonstrated strong multimodal reasoning\ncapabilities, offering new opportunities for explainable anomaly detection.\nHowever, their high computational cost and lack of domain adaptation hinder\nreal-time deployment and reliability. Inspired by dual complementary pathways\nin human visual perception, we propose SlowFastVAD, a hybrid framework that\nintegrates a fast anomaly detector with a slow anomaly detector (namely a\nretrieval augmented generation (RAG) enhanced VLM), to address these\nlimitations. Specifically, the fast detector first provides coarse anomaly\nconfidence scores, and only a small subset of ambiguous segments, rather than\nthe entire video, is further analyzed by the slower yet more interpretable VLM\nfor elaborate detection and reasoning. Furthermore, to adapt VLMs to\ndomain-specific VAD scenarios, we construct a knowledge base including normal\npatterns based on few normal samples and abnormal patterns inferred by VLMs.\nDuring inference, relevant patterns are retrieved and used to augment prompts\nfor anomaly reasoning. Finally, we smoothly fuse the anomaly confidence of fast\nand slow detectors to enhance robustness of anomaly detection. Extensive\nexperiments on four benchmarks demonstrate that SlowFastVAD effectively\ncombines the strengths of both fast and slow detectors, and achieves remarkable\ndetection accuracy and interpretability with significantly reduced\ncomputational overhead, making it well-suited for real-world VAD applications\nwith high reliability requirements.\n", "date": "2025-04-14", "categories": []}
{"id": "2406.11835", "title": "OoDIS: Anomaly Instance Segmentation and Detection Benchmark", "abstract": "  Safe navigation of self-driving cars and robots requires a precise\nunderstanding of their environment. Training data for perception systems cannot\ncover the wide variety of objects that may appear during deployment. Thus,\nreliable identification of unknown objects, such as wild animals and untypical\nobstacles, is critical due to their potential to cause serious accidents.\nSignificant progress in semantic segmentation of anomalies has been facilitated\nby the availability of out-of-distribution (OOD) benchmarks. However, a\ncomprehensive understanding of scene dynamics requires the segmentation of\nindividual objects, and thus the segmentation of instances is essential.\nDevelopment in this area has been lagging, largely due to the lack of dedicated\nbenchmarks. The situation is similar in object detection. While there is\ninterest in detecting and potentially tracking every anomalous object, the\navailability of dedicated benchmarks is clearly limited. To address this gap,\nthis work extends some commonly used anomaly segmentation benchmarks to include\nthe instance segmentation and object detection tasks. Our evaluation of anomaly\ninstance segmentation and object detection methods shows that both of these\nchallenges remain unsolved problems. We provide a competition and benchmark\nwebsite under https://vision.rwth-aachen.de/oodis\n", "date": "2024-06-17", "categories": []}
{"id": "2504.07522", "title": "Adversarial Subspace Generation for Outlier Detection in\n  High-Dimensional Data", "abstract": "  Outlier detection in high-dimensional tabular data is challenging since data\nis often distributed across multiple lower-dimensional subspaces -- a\nphenomenon known as the Multiple Views effect (MV). This effect led to a large\nbody of research focused on mining such subspaces, known as subspace selection.\nHowever, as the precise nature of the MV effect was not well understood,\ntraditional methods had to rely on heuristic-driven search schemes that\nstruggle to accurately capture the true structure of the data. Properly\nidentifying these subspaces is critical for unsupervised tasks such as outlier\ndetection or clustering, where misrepresenting the underlying data structure\ncan hinder the performance. We introduce Myopic Subspace Theory (MST), a new\ntheoretical framework that mathematically formulates the Multiple Views effect\nand writes subspace selection as a stochastic optimization problem. Based on\nMST, we introduce V-GAN, a generative method trained to solve such an\noptimization problem. This approach avoids any exhaustive search over the\nfeature space while ensuring that the intrinsic data structure is preserved.\nExperiments on 42 real-world datasets show that using V-GAN subspaces to build\nensemble methods leads to a significant increase in one-class classification\nperformance -- compared to existing subspace selection, feature selection, and\nembedding methods. Further experiments on synthetic data show that V-GAN\nidentifies subspaces more accurately while scaling better than other relevant\nsubspace selection methods. These results confirm the theoretical guarantees of\nour approach and also highlight its practical viability in high-dimensional\nsettings.\n", "date": "2025-04-10", "categories": []}
{"id": "2504.05962", "title": "Autoencoder-Based Detection of Anomalous Stokes V Spectra in the\n  Flare-Producing Active Region 13663 Using Hinode/SP Observations", "abstract": "  Detecting unusual signals in observational solar spectra is crucial for\nunderstanding the features associated with impactful solar events, such as\nsolar flares. However, existing spectral analysis techniques face challenges,\nparticularly when relying on pre-defined, physics-based calculations to process\nlarge volumes of noisy and complex observational data. To address these\nlimitations, we applied deep learning to detect anomalies in the Stokes V\nspectra from the Hinode/SP instrument. Specifically, we developed an\nautoencoder model for spectral compression, which serves as an anomaly\ndetection method. Our model effectively identifies anomalous spectra within\nspectro-polarimetric maps captured prior to the onset of the X1.3 flare on May\n5, 2024, in NOAA AR 13663. These atypical spectral points exhibit highly\ncomplex profiles and spatially align with polarity inversion lines in\nmagnetogram images, indicating their potential as sites of magnetic energy\nstorage and possible triggers for flares. Notably, the detected anomalies are\nhighly localized, making them particularly challenging to identify in\nmagnetogram images using current manual methods.\n", "date": "2025-04-08", "categories": []}
{"id": "2410.20537", "title": "SIGMA: Single Interpolated Generative Model for Anomalies", "abstract": "  A key step in any resonant anomaly detection search is accurate modeling of\nthe background distribution in each signal region. Data-driven methods like\nCATHODE accomplish this by training separate generative models on the\ncomplement of each signal region, and interpolating them into their\ncorresponding signal regions. Having to re-train the generative model on\nessentially the entire dataset for each signal region is a major computational\ncost in a typical sliding window search with many signal regions. Here, we\npresent SIGMA, a new, fully data-driven, computationally-efficient method for\nestimating background distributions. The idea is to train a single generative\nmodel on all of the data and interpolate its parameters in sideband regions in\norder to obtain a model for the background in the signal region. The SIGMA\nmethod significantly reduces the computational cost compared to previous\napproaches, while retaining a similar high quality of background modeling and\nsensitivity to anomalous signals.\n", "date": "2024-10-27", "categories": []}
{"id": "2504.03649", "title": "Diagnostic Method for Hydropower Plant Condition-based Maintenance\n  combining Autoencoder with Clustering Algorithms", "abstract": "  The French company EDF uses supervisory control and data acquisition systems\nin conjunction with a data management platform to monitor hydropower plant,\nallowing engineers and technicians to analyse the time-series collected.\nDepending on the strategic importance of the monitored hydropower plant, the\nnumber of time-series collected can vary greatly making it difficult to\ngenerate valuable information from the extracted data. In an attempt to provide\nan answer to this particular problem, a condition detection and diagnosis\nmethod combining clustering algorithms and autoencoder neural networks for\npattern recognition has been developed and is presented in this paper. First, a\ndimension reduction algorithm is used to create a 2-or 3-dimensional projection\nthat allows the users to identify unsuspected relationships between datapoints.\nThen, a collection of clustering algorithms regroups the datapoints into\nclusters. For each identified cluster, an autoencoder neural network is trained\non the corresponding dataset. The aim is to measure the reconstruction error\nbetween each autoencoder model and the measured values, thus creating a\nproximity index for each state discovered during the clustering stage.\n", "date": "2025-02-24", "categories": []}
{"id": "2504.04115", "title": "Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral\n  Anomaly Detection", "abstract": "  The surge of deep learning has catalyzed considerable progress in\nself-supervised Hyperspectral Anomaly Detection (HAD). The core premise for\nself-supervised HAD is that anomalous pixels are inherently more challenging to\nreconstruct, resulting in larger errors compared to the background. However,\nowing to the powerful nonlinear fitting capabilities of neural networks,\nself-supervised models often suffer from the Identity Mapping Problem (IMP).\nThe IMP manifests as a tendency for the model to overfit to the entire image,\nparticularly with increasing network complexity or prolonged training\niterations. Consequently, the whole image can be precisely reconstructed, and\neven the anomalous pixels exhibit imperceptible errors, making them difficult\nto detect. Despite the proposal of several models aimed at addressing the\nIMP-related issues, a unified descriptive framework and validation of solutions\nfor IMP remain lacking. In this paper, we conduct an in-depth exploration to\nIMP, and summarize a unified framework that describes IMP from the perspective\nof network optimization, which encompasses three aspects: perturbation,\nreconstruction, and regularization. Correspondingly, we introduce three\nsolutions: superpixel pooling and uppooling for perturbation, error-adaptive\nconvolution for reconstruction, and online background pixel mining for\nregularization. With extensive experiments being conducted to validate the\neffectiveness, it is hoped that our work will provide valuable insights and\ninspire further research for self-supervised HAD. Code:\n\\url{https://github.com/yc-cui/Super-AD}.\n", "date": "2025-04-05", "categories": []}
{"id": "2504.04340", "title": "AnomalyHybrid: A Domain-agnostic Generative Framework for General\n  Anomaly Detection", "abstract": "  Anomaly generation is an effective way to mitigate data scarcity for anomaly\ndetection task. Most existing works shine at industrial anomaly generation with\nmultiple specialists or large generative models, rarely generalizing to\nanomalies in other applications. In this paper, we present AnomalyHybrid, a\ndomain-agnostic framework designed to generate authentic and diverse anomalies\nsimply by combining the reference and target images. AnomalyHybrid is a\nGenerative Adversarial Network(GAN)-based framework having two decoders that\nintegrate the appearance of reference image into the depth and edge structures\nof target image respectively. With the help of depth decoders, AnomalyHybrid\nachieves authentic generation especially for the anomalies with depth values\nchanging, such a s protrusion and dent. More, it relaxes the fine granularity\nstructural control of the edge decoder and brings more diversity. Without using\nannotations, AnomalyHybrid is easily trained with sets of color, depth and edge\nof same images having different augmentations. Extensive experiments carried on\nHeliconiusButterfly, MVTecAD and MVTec3D datasets demonstrate that\nAnomalyHybrid surpasses the GAN-based state-of-the-art on anomaly generation\nand its downstream anomaly classification, detection and segmentation tasks. On\nMVTecAD dataset, AnomalyHybrid achieves 2.06/0.32 IS/LPIPS for anomaly\ngeneration, 52.6 Acc for anomaly classification with ResNet34, 97.3/72.9 AP for\nimage/pixel-level anomaly detection with a simple UNet.\n", "date": "2025-04-06", "categories": []}
{"id": "2504.04374", "title": "iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical\n  Systems via Incremental Meta-learning", "abstract": "  Anomaly detection for cyber-physical systems (ADCPS) is crucial in\nidentifying faults and potential attacks by analyzing the time series of sensor\nmeasurements and actuator states. However, current methods lack adaptation to\ndata distribution shifts in both temporal and spatial dimensions as\ncyber-physical systems evolve. To tackle this issue, we propose an incremental\nmeta-learning-based approach, namely iADCPS, which can continuously update the\nmodel through limited evolving normal samples to reconcile the distribution gap\nbetween evolving and historical time series. Specifically, We first introduce a\ntemporal mixup strategy to align data for data-level generalization which is\nthen combined with the one-class meta-learning approach for model-level\ngeneralization. Furthermore, we develop a non-parametric dynamic threshold to\nadaptively adjust the threshold based on the probability density of the\nabnormal scores without any anomaly supervision. We empirically evaluate the\neffectiveness of the iADCPS using three publicly available datasets PUMP, SWaT,\nand WADI. The experimental results demonstrate that our method achieves 99.0%,\n93.1%, and 78.7% F1-Score, respectively, which outperforms the state-of-the-art\n(SOTA) ADCPS method, especially in the context of the evolving CPSs.\n", "date": "2025-04-06", "categories": []}
{"id": "2305.09907", "title": "Incremental Outlier Detection Modelling Using Streaming Analytics in\n  Finance & Health Care", "abstract": "  In the era of real-time data, traditional methods often struggle to keep pace\nwith the dynamic nature of streaming environments. In this paper, we proposed a\nhybrid framework where in (i) stage-I follows a traditional approach where the\nmodel is built once and evaluated in a real-time environment, and (ii) stage-II\nemploys an incremental learning approach where the model is continuously\nretrained as new data arrives, enabling it to adapt and stay up to date. To\nimplement these frameworks, we employed 8 distinct state-of-the-art outlier\ndetection models, including one-class support vector machine (OCSVM), isolation\nforest adaptive sliding window approach (IForest ASD), exact storm (ES),\nangle-based outlier detection (ABOD), local outlier factor (LOF), Kitsunes\nonline algorithm (KitNet), and K-nearest neighbour conformal density and\ndistance based (KNN CAD). We evaluated the performance of these models across\nseven financial and healthcare prediction tasks, including credit card fraud\ndetection, churn prediction, Ethereum fraud detection, heart stroke prediction,\nand diabetes prediction. The results indicate that our proposed incremental\nlearning framework significantly improves performance, particularly on highly\nimbalanced datasets. Among all models, the IForest ASD model consistently\nranked among the top three best-performing models, demonstrating superior\neffectiveness across various datasets.\n", "date": "2023-05-17", "categories": []}
{"id": "2312.10040", "title": "Robust Errant Beam Prognostics with Conditional Modeling for Particle\n  Accelerators", "abstract": "  Particle accelerators are complex and comprise thousands of components, with\nmany pieces of equipment running at their peak power. Consequently, particle\naccelerators can fault and abort operations for numerous reasons. These faults\nimpact the availability of particle accelerators during scheduled run-time and\nhamper the efficiency and the overall science output. To avoid these faults, we\napply anomaly detection techniques to predict any unusual behavior and perform\npreemptive actions to improve the total availability of particle accelerators.\nSemi-supervised Machine Learning (ML) based anomaly detection approaches such\nas autoencoders and variational autoencoders are often used for such tasks.\nHowever, supervised ML techniques such as Siamese Neural Network (SNN) models\ncan outperform unsupervised or semi-supervised approaches for anomaly detection\nby leveraging the label information. One of the challenges specific to anomaly\ndetection for particle accelerators is the data's variability due to system\nconfiguration changes. To address this challenge, we employ Conditional Siamese\nNeural Network (CSNN) models and Conditional Variational Auto Encoder (CVAE)\nmodels to predict errant beam pulses at the Spallation Neutron Source (SNS)\nunder different system configuration conditions and compare their performance.\nWe demonstrate that CSNN outperforms CVAE in our application.\n", "date": "2023-11-22", "categories": []}
{"id": "2406.15523", "title": "Unifying Unsupervised Graph-Level Anomaly Detection and\n  Out-of-Distribution Detection: A Benchmark", "abstract": "  To build safe and reliable graph machine learning systems, unsupervised\ngraph-level anomaly detection (GLAD) and unsupervised graph-level\nout-of-distribution (OOD) detection (GLOD) have received significant attention\nin recent years. Though those two lines of research indeed share the same\nobjective, they have been studied independently in the community due to\ndistinct evaluation setups, creating a gap that hinders the application and\nevaluation of methods from one to the other. To bridge the gap, in this work,\nwe present a \\underline{\\textbf{U}}nified \\underline{\\textbf{B}}enchmark for\nunsupervised \\underline{\\textbf{G}}raph-level \\underline{\\textbf{O}}OD and\nanoma\\underline{\\textbf{L}}y \\underline{\\textbf{D}}etection (\\ourmethod), a\ncomprehensive evaluation framework that unifies GLAD and GLOD under the concept\nof generalized graph-level OOD detection. Our benchmark encompasses 35 datasets\nspanning four practical anomaly and OOD detection scenarios, facilitating the\ncomparison of 18 representative GLAD/GLOD methods. We conduct multi-dimensional\nanalyses to explore the effectiveness, OOD sensitivity spectrum, robustness,\nand efficiency of existing methods, shedding light on their strengths and\nlimitations. Furthermore, we provide an open-source codebase\n(https://github.com/UB-GOLD/UB-GOLD) of \\ourmethod to foster reproducible\nresearch and outline potential directions for future investigations based on\nour insights.\n", "date": "2024-06-21", "categories": []}
{"id": "2409.11223", "title": "Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised\n  Anomaly Violence Detection", "abstract": "  Weakly supervised video anomaly detection (WS-VAD) is a crucial area in\ncomputer vision for developing intelligent surveillance systems. This system\nuses three feature streams: RGB video, optical flow, and audio signals, where\neach stream extracts complementary spatial and temporal features using an\nenhanced attention module to improve detection accuracy and robustness. In the\nfirst stream, we employed an attention-based, multi-stage feature enhancement\napproach to improve spatial and temporal features from the RGB video where the\nfirst stage consists of a ViT-based CLIP module, with top-k features\nconcatenated in parallel with I3D and Temporal Contextual Aggregation (TCA)\nbased rich spatiotemporal features. The second stage effectively captures\ntemporal dependencies using the Uncertainty-Regulated Dual Memory Units\n(UR-DMU) model, which learns representations of normal and abnormal data\nsimultaneously, and the third stage is employed to select the most relevant\nspatiotemporal features. The second stream extracted enhanced attention-based\nspatiotemporal features from the flow data modality-based feature by taking\nadvantage of the integration of the deep learning and attention module. The\naudio stream captures auditory cues using an attention module integrated with\nthe VGGish model, aiming to detect anomalies based on sound patterns. These\nstreams enrich the model by incorporating motion and audio signals often\nindicative of abnormal events undetectable through visual analysis alone. The\nconcatenation of the multimodal fusion leverages the strengths of each\nmodality, resulting in a comprehensive feature set that significantly improves\nanomaly detection accuracy and robustness across three datasets. The extensive\nexperiment and high performance with the three benchmark datasets proved the\neffectiveness of the proposed system over the existing state-of-the-art system.\n", "date": "2024-09-17", "categories": []}
{"id": "2504.02994", "title": "Improving log-based anomaly detection through learned adaptive filter", "abstract": "  Log messages record important system runtime information and are useful for\ndetecting anomalous behaviors and managing modern software systems. Many\nsupervised and unsupervised learning methods have been proposed recently for\nlog-based anomaly detection. State-of-the-art unsupervised methods predict the\nnext log event given a log sequence and apply fixed configurations that use the\nsame filter condition (i.e. k, the top k predicted log events will be regarded\nas normal next events) which leads to inferior performance in the detection\nstage because it sets one fixed k for all log sequences, which ignores the\ndynamic nature and variance in different log sequences. Recently, deep\nreinforcement learning (DRL) are widely applied to make intelligent decisions\nin a dynamic environment. In this work, we contend that it is necessary to\napply adaptive filters for different log sequences. To achieve this, we propose\na novel approach based on DRL to construct a learned adaptive filter and apply\ndifferent normal/abnormal filter thresholds for different log sequences. We\ndefine the Markov Decision Process (MDP) and formulate the learned adaptive\nfilter as a problem that can be solved by DRL. We evaluate the learned adaptive\nfilter on two state-of-the-art log-based anomaly detection unsupervised\napproaches DeepLog and LogAnomaly in two datasets HDFS and BGL. Extensive\nexperiments show that our approach outperforms the fixed configurations and\nachieves significantly better performance in log-based anomaly detection.\n", "date": "2025-04-03", "categories": []}
{"id": "2504.02999", "title": "Anomaly Detection in Time Series Data Using Reinforcement Learning,\n  Variational Autoencoder, and Active Learning", "abstract": "  A novel approach to detecting anomalies in time series data is presented in\nthis paper. This approach is pivotal in domains such as data centers, sensor\nnetworks, and finance. Traditional methods often struggle with manual parameter\ntuning and cannot adapt to new anomaly types. Our method overcomes these\nlimitations by integrating Deep Reinforcement Learning (DRL) with a Variational\nAutoencoder (VAE) and Active Learning. By incorporating a Long Short-Term\nMemory (LSTM) network, our approach models sequential data and its dependencies\neffectively, allowing for the detection of new anomaly classes with minimal\nlabeled data. Our innovative DRL- VAE and Active Learning combination\nsignificantly improves existing methods, as shown by our evaluations on\nreal-world datasets, enhancing anomaly detection techniques and advancing time\nseries analysis.\n", "date": "2025-04-03", "categories": []}
{"id": "2504.03306", "title": "Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly\n  Detection", "abstract": "  With more well-performing anomaly detection methods proposed, many of the\nsingle-view tasks have been solved to a relatively good degree. However,\nreal-world production scenarios often involve complex industrial products,\nwhose properties may not be fully captured by one single image. While\nnormalizing flow based approaches already work well in single-camera scenarios,\nthey currently do not make use of the priors in multi-view data. We aim to\nbridge this gap by using these flow-based models as a strong foundation and\npropose Multi-Flow, a novel multi-view anomaly detection method. Multi-Flow\nmakes use of a novel multi-view architecture, whose exact likelihood estimation\nis enhanced by fusing information across different views. For this, we propose\na new cross-view message-passing scheme, letting information flow between\nneighboring views. We empirically validate it on the real-world multi-view data\nset Real-IAD and reach a new state-of-the-art, surpassing current baselines in\nboth image-wise and sample-wise anomaly detection tasks.\n", "date": "2025-04-04", "categories": []}
{"id": "2504.03442", "title": "Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection", "abstract": "  Recent advances in convolutional neural networks (CNNs) and transformer-based\nmethods have improved anomaly detection and localization, but challenges\npersist in precisely localizing small anomalies. While CNNs face limitations in\ncapturing long-range dependencies, transformer architectures often suffer from\nsubstantial computational overheads. We introduce a state space model\n(SSM)-based Pyramidal Scanning Strategy (PSS) for multi-class anomaly detection\nand localization--a novel approach designed to address the challenge of small\nanomaly localization. Our method captures fine-grained details at multiple\nscales by integrating the PSS with a pre-trained encoder for multi-scale\nfeature extraction and a feature-level synthetic anomaly generator. An\nimprovement of $+1\\%$ AP for multi-class anomaly localization and a +$1\\%$\nincrease in AU-PRO on MVTec benchmark demonstrate our method's superiority in\nprecise anomaly localization across diverse industrial scenarios. The code is\navailable at https://github.com/iqbalmlpuniud/Pyramid Mamba.\n", "date": "2025-04-04", "categories": []}
{"id": "2405.10347", "title": "Networking Systems for Video Anomaly Detection: A Tutorial and Survey", "abstract": "  The increasing utilization of surveillance cameras in smart cities, coupled\nwith the surge of online video applications, has heightened concerns regarding\npublic security and privacy protection, which propelled automated Video Anomaly\nDetection (VAD) into a fundamental research task within the Artificial\nIntelligence (AI) community. With the advancements in deep learning and edge\ncomputing, VAD has made significant progress and advances synergized with\nemerging applications in smart cities and video internet, which has moved\nbeyond the conventional research scope of algorithm engineering to deployable\nNetworking Systems for VAD (NSVAD), a practical hotspot for intersection\nexploration in the AI, IoVT, and computing fields. In this article, we\ndelineate the foundational assumptions, learning frameworks, and applicable\nscenarios of various deep learning-driven VAD routes, offering an exhaustive\ntutorial for novices in NSVAD. In addition, this article elucidates core\nconcepts by reviewing recent advances and typical solutions and aggregating\navailable research resources accessible at https://github.com/fdjingliu/NSVAD.\nLastly, this article projects future development trends and discusses how the\nintegration of AI and computing technologies can address existing research\nchallenges and promote open opportunities, serving as an insightful guide for\nprospective researchers and engineers.\n", "date": "2024-05-16", "categories": []}
{"id": "2504.02248", "title": "CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection", "abstract": "  Graph Anomaly Detection (GAD) is critical in security-sensitive domains, yet\nfaces reliability challenges: miscalibrated confidence estimation\n(underconfidence in normal nodes, overconfidence in anomalies), adversarial\nvulnerability of derived confidence score under structural perturbations, and\nlimited efficacy of conventional calibration methods for sparse anomaly\npatterns. Thus we propose CRC-SGAD, a framework integrating statistical risk\ncontrol into GAD via two innovations: (1) A Dual-Threshold Conformal Risk\nControl mechanism that provides theoretically guaranteed bounds for both False\nNegative Rate (FNR) and False Positive Rate (FPR) through providing prediction\nsets; (2) A Subgraph-aware Spectral Graph Neural Calibrator (SSGNC) that\noptimizes node representations through adaptive spectral filtering while\nreducing the size of prediction sets via hybrid loss optimization. Experiments\non four datasets and five GAD models demonstrate statistically significant\nimprovements in FNR and FPR control and prediction set size. CRC-SGAD\nestablishes a paradigm for statistically rigorous anomaly detection in\ngraph-structured security applications.\n", "date": "2025-04-03", "categories": []}
{"id": "2504.02498", "title": "VISTA: Unsupervised 2D Temporal Dependency Representations for Time\n  Series Anomaly Detection", "abstract": "  Time Series Anomaly Detection (TSAD) is essential for uncovering rare and\npotentially harmful events in unlabeled time series data. Existing methods are\nhighly dependent on clean, high-quality inputs, making them susceptible to\nnoise and real-world imperfections. Additionally, intricate temporal\nrelationships in time series data are often inadequately captured in\ntraditional 1D representations, leading to suboptimal modeling of dependencies.\nWe introduce VISTA, a training-free, unsupervised TSAD algorithm designed to\novercome these challenges. VISTA features three core modules: 1) Time Series\nDecomposition using Seasonal and Trend Decomposition via Loess (STL) to\ndecompose noisy time series into trend, seasonal, and residual components; 2)\nTemporal Self-Attention, which transforms 1D time series into 2D temporal\ncorrelation matrices for richer dependency modeling and anomaly detection; and\n3) Multivariate Temporal Aggregation, which uses a pretrained feature extractor\nto integrate cross-variable information into a unified, memory-efficient\nrepresentation. VISTA's training-free approach enables rapid deployment and\neasy hyperparameter tuning, making it suitable for industrial applications. It\nachieves state-of-the-art performance on five multivariate TSAD benchmarks.\n", "date": "2025-04-03", "categories": []}
{"id": "2208.03486", "title": "HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly\n  Detection and Localization", "abstract": "  Unsupervised anomaly detection and localization is a crucial task as it is\nimpossible to collect and label all possible anomalies. Many studies have\nemphasized the importance of integrating local and global information to\nachieve accurate segmentation of anomalies. To this end, there has been a\ngrowing interest in Transformer, which allows modeling long-range content\ninteractions. However, global interactions through self attention are generally\ntoo expensive for most image scales. In this study, we introduce HaloAE, the\nfirst auto-encoder based on a local 2D version of Transformer with HaloNet.\nWith HaloAE, we have created a hybrid model that combines convolution and local\n2D block-wise self-attention layers and jointly performs anomaly detection and\nsegmentation through a single model. We achieved competitive results on the\nMVTec dataset, suggesting that vision models incorporating Transformer could\nbenefit from a local computation of the self-attention operation, and pave the\nway for other applications.\n", "date": "2022-08-06", "categories": []}
{"id": "2405.14325", "title": "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised\n  Anomaly Detection", "abstract": "  Recent studies highlighted a practical setting of unsupervised anomaly\ndetection (UAD) that builds a unified model for multi-class images. Despite\nvarious advancements addressing this challenging task, the detection\nperformance under the multi-class setting still lags far behind\nstate-of-the-art class-separated models. Our research aims to bridge this\nsubstantial performance gap. In this paper, we introduce a minimalistic\nreconstruction-based anomaly detection framework, namely Dinomaly, which\nleverages pure Transformer architectures without relying on complex designs,\nadditional modules, or specialized tricks. Given this powerful framework\nconsisted of only Attentions and MLPs, we found four simple components that are\nessential to multi-class anomaly detection: (1) Foundation Transformers that\nextracts universal and discriminative features, (2) Noisy Bottleneck where\npre-existing Dropouts do all the noise injection tricks, (3) Linear Attention\nthat naturally cannot focus, and (4) Loose Reconstruction that does not force\nlayer-to-layer and point-by-point reconstruction. Extensive experiments are\nconducted across popular anomaly detection benchmarks including MVTec-AD, VisA,\nand Real-IAD. Our proposed Dinomaly achieves impressive image-level AUROC of\n99.6%, 98.7%, and 89.3% on the three datasets respectively, which is not only\nsuperior to state-of-the-art multi-class UAD methods, but also achieves the\nmost advanced class-separated UAD records.\n", "date": "2024-05-23", "categories": []}
{"id": "2502.11570", "title": "Towards a Trustworthy Anomaly Detection for Critical Applications\n  through Approximated Partial AUC Loss", "abstract": "  Anomaly Detection is a crucial step for critical applications such in the\nindustrial, medical or cybersecurity domains. These sectors share the same\nrequirement of handling differently the different types of classification\nerrors. Indeed, even if false positives are acceptable, false negatives are\nnot, because it would reflect a missed detection of a quality issue, a disease\nor a cyber threat. To fulfill this requirement, we propose a method that\ndynamically applies a trustworthy approximated partial AUC ROC loss (tapAUC). A\nbinary classifier is trained to optimize the specific range of the AUC ROC\ncurve that prevents the True Positive Rate (TPR) to reach 100% while minimizing\nthe False Positive Rate (FPR). The optimal threshold that does not trigger any\nfalse negative is then kept and used at the test step. The results show a TPR\nof 92.52% at a 20.43% FPR for an average across 6 datasets, representing a TPR\nimprovement of 4.3% for a FPR cost of 12.2% against other state-of-the-art\nmethods. The code is available at https://github.com/ArnaudBougaham/tapAUC.\n", "date": "2025-02-17", "categories": []}
{"id": "2504.01671", "title": "Anomaly Detection for Hybrid Butterfly Subspecies via Probability\n  Filtering", "abstract": "  Detecting butterfly hybrids requires knowledge of the parent subspecies, and\nthe process can be tedious when encountering a new subspecies. This study\nfocuses on a specific scenario where a model trained to recognize hybrid\nspecies A can generalize to species B when B biologically mimics A. Since\nspecies A and B share similar patterns, we leverage BioCLIP as our feature\nextractor to capture features based on their taxonomy. Consequently, the\nalgorithm designed for species A can be transferred to B, as their hybrid and\nnon-hybrid patterns exhibit similar relationships. To determine whether a\nbutterfly is a hybrid, we adopt proposed probability filtering and color\njittering to augment and simulate the mimicry. With these approaches, we\nachieve second place in the official development phase. Our code is publicly\navailable at https://github.com/Justin900429/NSF-HDR-Challenge.\n", "date": "2025-04-02", "categories": []}
{"id": "2412.01095", "title": "VERA: Explainable Video Anomaly Detection via Verbalized Learning of\n  Vision-Language Models", "abstract": "  The rapid advancement of vision-language models (VLMs) has established a new\nparadigm in video anomaly detection (VAD): leveraging VLMs to simultaneously\ndetect anomalies and provide comprehendible explanations for the decisions.\nExisting work in this direction often assumes the complex reasoning required\nfor VAD exceeds the capabilities of pretrained VLMs. Consequently, these\napproaches either incorporate specialized reasoning modules during inference or\nrely on instruction tuning datasets through additional training to adapt VLMs\nfor VAD. However, such strategies often incur substantial computational costs\nor data annotation overhead. To address these challenges in explainable VAD, we\nintroduce a verbalized learning framework named VERA that enables VLMs to\nperform VAD without model parameter modifications. Specifically, VERA\nautomatically decomposes the complex reasoning required for VAD into\nreflections on simpler, more focused guiding questions capturing distinct\nabnormal patterns. It treats these reflective questions as learnable parameters\nand optimizes them through data-driven verbal interactions between learner and\noptimizer VLMs, using coarsely labeled training data. During inference, VERA\nembeds the learned questions into model prompts to guide VLMs in generating\nsegment-level anomaly scores, which are then refined into frame-level scores\nvia the fusion of scene and temporal contexts. Experimental results on\nchallenging benchmarks demonstrate that the learned questions of VERA are\nhighly adaptable, significantly improving both detection performance and\nexplainability of VLMs for VAD.\n", "date": "2024-12-02", "categories": []}
{"id": "2504.00287", "title": "A Deep Learning Approach to Anomaly Detection in High-Frequency Trading\n  Data", "abstract": "  This paper proposes an algorithm based on a staged sliding window Transformer\narchitecture to detect abnormal behaviors in the microstructure of the foreign\nexchange market, focusing on high-frequency EUR/USD trading data. The method\ncaptures multi-scale temporal features through a staged sliding window,\nextracts global and local dependencies by combining the self-attention\nmechanism and weighted attention mechanism of the Transformer, and uses a\nclassifier to identify abnormal events. Experimental results on a real\nhigh-frequency dataset containing order book depth, spread, and trading volume\nshow that the proposed method significantly outperforms traditional machine\nlearning (such as decision trees and random forests) and deep learning methods\n(such as MLP, CNN, RNN, LSTM) in terms of accuracy (0.93), F1-Score (0.91), and\nAUC-ROC (0.95). Ablation experiments verify the contribution of each component,\nand the visualization of order book depth and anomaly detection further reveals\nthe effectiveness of the model under complex market dynamics. Despite the false\npositive problem, the model still provides important support for market\nsupervision. In the future, noise processing can be optimized and extended to\nother markets to improve generalization and real-time performance.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.00609", "title": "Bi-Grid Reconstruction for Image Anomaly Detection", "abstract": "  In image anomaly detection, significant advancements have been made using un-\nand self-supervised methods with datasets containing only normal samples.\nHowever, these approaches often struggle with fine-grained anomalies. This\npaper introduces \\textbf{GRAD}: Bi-\\textbf{G}rid \\textbf{R}econstruction for\nImage \\textbf{A}nomaly \\textbf{D}etection, which employs two continuous grids\nto enhance anomaly detection from both normal and abnormal perspectives. In\nthis work: 1) Grids as feature repositories that improve generalization and\nmitigate the Identical Shortcut (IS) issue; 2) An abnormal feature grid that\nrefines normal feature boundaries, boosting detection of fine-grained defects;\n3) The Feature Block Paste (FBP) module, which synthesizes various anomalies at\nthe feature level for quick abnormal grid deployment. GRAD's robust\nrepresentation capabilities also allow it to handle multiple classes with a\nsingle model. Evaluations on datasets like MVTecAD, VisA, and GoodsAD show\nsignificant performance improvements in fine-grained anomaly detection. GRAD\nexcels in overall accuracy and in discerning subtle differences, demonstrating\nits superiority over existing methods.\n", "date": "2025-04-01", "categories": []}
{"id": "2308.00187", "title": "Detecting the Anomalies in LiDAR Pointcloud", "abstract": "  LiDAR sensors play an important role in the perception stack of modern\nautonomous driving systems. Adverse weather conditions such as rain, fog and\ndust, as well as some (occasional) LiDAR hardware fault may cause the LiDAR to\nproduce pointcloud with abnormal patterns such as scattered noise points and\nuncommon intensity values. In this paper, we propose a novel approach to detect\nwhether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud\ncharacteristics. Specifically, we develop a pointcloud quality metric based on\nthe LiDAR points' spatial and intensity distribution to characterize the noise\nlevel of the pointcloud, which relies on pure mathematical analysis and does\nnot require any labeling or training as learning-based methods do. Therefore,\nthe method is scalable and can be quickly deployed either online to improve the\nautonomy safety by monitoring anomalies in the LiDAR data or offline to perform\nin-depth study of the LiDAR behavior over large amount of data. The proposed\napproach is studied with extensive real public road data collected by LiDARs\nwith different scanning mechanisms and laser spectrums, and is proven to be\nable to effectively handle various known and unknown sources of pointcloud\nanomaly.\n", "date": "2023-07-31", "categories": []}
{"id": "2412.05580", "title": "Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection\n  on 3D Cortical Surfaces", "abstract": "  Unsupervised anomaly detection in brain imaging is challenging. In this\npaper, we propose self-supervised masked mesh learning for unsupervised anomaly\ndetection on 3D cortical surfaces. Our framework leverages the intrinsic\ngeometry of the cortical surface to learn a self-supervised representation that\ncaptures the underlying structure of the brain. We introduce a masked mesh\nconvolutional neural network (MMN) that learns to predict masked regions of the\ncortical surface. By training the MMN on a large dataset of healthy subjects,\nwe learn a representation that captures the normal variation in the cortical\nsurface. We then use this representation to detect anomalies in unseen\nindividuals by calculating anomaly scores based on the reconstruction error of\nthe MMN. We evaluated our framework by training on population-scale dataset UKB\nand HCP-Aging and testing on two datasets of Alzheimer's disease patients ADNI\nand OASIS3. Our results show that our framework can detect anomalies in\ncortical thickness, cortical volume, and cortical sulcus characteristics, which\nare known to be biomarkers of Alzheimer's disease. Our proposed framework\nprovides a promising approach for unsupervised anomaly detection based on\nnormative variation of cortical features.\n", "date": "2024-12-07", "categories": []}
{"id": "2503.23060", "title": "Unsupervised Anomaly Detection in Multivariate Time Series across\n  Heterogeneous Domains", "abstract": "  The widespread adoption of digital services, along with the scale and\ncomplexity at which they operate, has made incidents in IT operations\nincreasingly more likely, diverse, and impactful. This has led to the rapid\ndevelopment of a central aspect of \"Artificial Intelligence for IT Operations\"\n(AIOps), focusing on detecting anomalies in vast amounts of multivariate time\nseries data generated by service entities. In this paper, we begin by\nintroducing a unifying framework for benchmarking unsupervised anomaly\ndetection (AD) methods, and highlight the problem of shifts in normal behaviors\nthat can occur in practical AIOps scenarios. To tackle anomaly detection under\ndomain shift, we then cast the problem in the framework of domain\ngeneralization and propose a novel approach, Domain-Invariant VAE for Anomaly\nDetection (DIVAD), to learn domain-invariant representations for unsupervised\nanomaly detection. Our evaluation results using the Exathlon benchmark show\nthat the two main DIVAD variants significantly outperform the best unsupervised\nAD method in maximum performance, with 20% and 15% improvements in maximum peak\nF1-scores, respectively. Evaluation using the Application Server Dataset\nfurther demonstrates the broader applicability of our domain generalization\nmethods.\n", "date": "2025-03-29", "categories": []}
{"id": "2503.23451", "title": "Beyond Academic Benchmarks: Critical Analysis and Best Practices for\n  Visual Industrial Anomaly Detection", "abstract": "  Anomaly detection (AD) is essential for automating visual inspection in\nmanufacturing. This field of computer vision is rapidly evolving, with\nincreasing attention towards real-world applications. Meanwhile, popular\ndatasets are typically produced in controlled lab environments with\nartificially created defects, unable to capture the diversity of real\nproduction conditions. New methods often fail in production settings, showing\nsignificant performance degradation or requiring impractical computational\nresources. This disconnect between academic results and industrial viability\nthreatens to misdirect visual anomaly detection research. This paper makes\nthree key contributions: (1) we demonstrate the importance of real-world\ndatasets and establish benchmarks using actual production data, (2) we provide\na fair comparison of existing SOTA methods across diverse tasks by utilizing\nmetrics that are valuable for practical applications, and (3) we present a\ncomprehensive analysis of recent advancements in this field by discussing\nimportant challenges and new perspectives for bridging the academia-industry\ngap. The code is publicly available at\nhttps://github.com/abc-125/viad-benchmark\n", "date": "2025-03-30", "categories": []}
{"id": "2503.21125", "title": "Omni-AD: Learning to Reconstruct Global and Local Features for\n  Multi-class Anomaly Detection", "abstract": "  In multi-class unsupervised anomaly detection(MUAD), reconstruction-based\nmethods learn to map input images to normal patterns to identify anomalous\npixels. However, this strategy easily falls into the well-known \"learning\nshortcut\" issue when decoders fail to capture normal patterns and reconstruct\nboth normal and abnormal samples naively. To address that, we propose to learn\nthe input features in global and local manners, forcing the network to memorize\nthe normal patterns more comprehensively. Specifically, we design a two-branch\ndecoder block, named Omni-block. One branch corresponds to global feature\nlearning, where we serialize two self-attention blocks but replace the query\nand (key, value) with learnable tokens, respectively, thus capturing global\nfeatures of normal patterns concisely and thoroughly. The local branch\ncomprises depth-separable convolutions, whose locality enables effective and\nefficient learning of local features for normal patterns. By stacking\nOmni-blocks, we build a framework, Omni-AD, to learn normal patterns of\ndifferent granularity and reconstruct them progressively. Comprehensive\nexperiments on public anomaly detection benchmarks show that our method\noutperforms state-of-the-art approaches in MUAD. Code is available at\nhttps://github.com/easyoo/Omni-AD.git\n", "date": "2025-03-27", "categories": []}
{"id": "2503.21833", "title": "Refining Time Series Anomaly Detectors using Large Language Models", "abstract": "  Time series anomaly detection (TSAD) is of widespread interest across many\nindustries, including finance, healthcare, and manufacturing. Despite the\ndevelopment of numerous automatic methods for detecting anomalies, human\noversight remains necessary to review and act upon detected anomalies, as well\nas verify their accuracy. We study the use of multimodal large language models\n(LLMs) to partially automate this process. We find that LLMs can effectively\nidentify false alarms by integrating visual inspection of time series plots\nwith text descriptions of the data-generating process. By leveraging the\ncapabilities of LLMs, we aim to reduce the reliance on human effort required to\nmaintain a TSAD system\n", "date": "2025-03-26", "categories": []}
{"id": "2503.21904", "title": "AssistPDA: An Online Video Surveillance Assistant for Video Anomaly\n  Prediction, Detection, and Analysis", "abstract": "  The rapid advancements in large language models (LLMs) have spurred growing\ninterest in LLM-based video anomaly detection (VAD). However, existing\napproaches predominantly focus on video-level anomaly question answering or\noffline detection, ignoring the real-time nature essential for practical VAD\napplications. To bridge this gap and facilitate the practical deployment of\nLLM-based VAD, we introduce AssistPDA, the first online video anomaly\nsurveillance assistant that unifies video anomaly prediction, detection, and\nanalysis (VAPDA) within a single framework. AssistPDA enables real-time\ninference on streaming videos while supporting interactive user engagement.\nNotably, we introduce a novel event-level anomaly prediction task, enabling\nproactive anomaly forecasting before anomalies fully unfold. To enhance the\nability to model intricate spatiotemporal relationships in anomaly events, we\npropose a Spatio-Temporal Relation Distillation (STRD) module. STRD transfers\nthe long-term spatiotemporal modeling capabilities of vision-language models\n(VLMs) from offline settings to real-time scenarios. Thus it equips AssistPDA\nwith a robust understanding of complex temporal dependencies and long-sequence\nmemory. Additionally, we construct VAPDA-127K, the first large-scale benchmark\ndesigned for VLM-based online VAPDA. Extensive experiments demonstrate that\nAssistPDA outperforms existing offline VLM-based approaches, setting a new\nstate-of-the-art for real-time VAPDA. Our dataset and code will be open-sourced\nto facilitate further research in the community.\n", "date": "2025-03-27", "categories": []}
{"id": "2503.22309", "title": "A Dataset for Semantic Segmentation in the Presence of Unknowns", "abstract": "  Before deployment in the real-world deep neural networks require thorough\nevaluation of how they handle both knowns, inputs represented in the training\ndata, and unknowns (anomalies). This is especially important for scene\nunderstanding tasks with safety critical applications, such as in autonomous\ndriving. Existing datasets allow evaluation of only knowns or unknowns - but\nnot both, which is required to establish \"in the wild\" suitability of deep\nneural network models. To bridge this gap, we propose a novel anomaly\nsegmentation dataset, ISSU, that features a diverse set of anomaly inputs from\ncluttered real-world environments. The dataset is twice larger than existing\nanomaly segmentation datasets, and provides a training, validation and test set\nfor controlled in-domain evaluation. The test set consists of a static and\ntemporal part, with the latter comprised of videos. The dataset provides\nannotations for both closed-set (knowns) and anomalies, enabling closed-set and\nopen-set evaluation. The dataset covers diverse conditions, such as domain and\ncross-sensor shift, illumination variation and allows ablation of anomaly\ndetection methods with respect to these variations. Evaluation results of\ncurrent state-of-the-art methods confirm the need for improvements especially\nin domain-generalization, small and large object segmentation.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.19588", "title": "Video Anomaly Detection with Contours -- A Study", "abstract": "  In Pose-based Video Anomaly Detection prior art is rooted on the assumption\nthat abnormal events can be mostly regarded as a result of uncommon human\nbehavior. Opposed to utilizing skeleton representations of humans, however, we\ninvestigate the potential of learning recurrent motion patterns of normal human\nbehavior using 2D contours. Keeping all advantages of pose-based methods, such\nas increased object anonymization, the shift from human skeletons to contours\nis hypothesized to leave the opportunity to cover more object categories open\nfor future research. We propose formulating the problem as a regression and a\nclassification task, and additionally explore two distinct data representation\ntechniques for contours. To further reduce the computational complexity of\nPose-based Video Anomaly Detection solutions, all methods in this study are\nbased on shallow Neural Networks from the field of Deep Learning, and evaluated\non the three most prominent benchmark datasets within Video Anomaly Detection\nand their human-related counterparts, totaling six datasets. Our results\nindicate that this novel perspective on Pose-based Video Anomaly Detection\nmarks a promising direction for future research.\n", "date": "2025-03-25", "categories": []}
{"id": "2410.14379", "title": "AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial\n  Scenarios", "abstract": "  Recently, multi-class anomaly classification has garnered increasing\nattention. Previous methods directly cluster anomalies but often struggle due\nto the lack of anomaly-prior knowledge. Acquiring this knowledge faces two\nissues: the non-prominent and weak-semantics anomalies. In this paper, we\npropose AnomalyNCD, a multi-class anomaly classification network compatible\nwith different anomaly detection methods. To address the non-prominence of\nanomalies, we design main element binarization (MEBin) to obtain\nanomaly-centered images, ensuring anomalies are learned while avoiding the\nimpact of incorrect detections. Next, to learn anomalies with weak semantics,\nwe design mask-guided representation learning, which focuses on isolated\nanomalies guided by masks and reduces confusion from erroneous inputs through\ncorrected pseudo labels. Finally, to enable flexible classification at both\nregion and image levels, we develop a region merging strategy that determines\nthe overall image category based on the classified anomaly regions. Our method\noutperforms the state-of-the-art works on the MVTec AD and MTD datasets.\nCompared with the current methods, AnomalyNCD combined with zero-shot anomaly\ndetection method achieves a 10.8% $F_1$ gain, 8.8% NMI gain, and 9.5% ARI gain\non MVTec AD, and 12.8% $F_1$ gain, 5.7% NMI gain, and 10.8% ARI gain on MTD.\nCode is available at https://github.com/HUST-SLOW/AnomalyNCD.\n", "date": "2024-10-18", "categories": []}
{"id": "2412.03044", "title": "Frequency-Guided Diffusion Model with Perturbation Training for\n  Skeleton-Based Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) is a vital yet complex open-set task in\ncomputer vision, commonly tackled through reconstruction-based methods.\nHowever, these methods struggle with two key limitations: (1) insufficient\nrobustness in open-set scenarios, where unseen normal motions are frequently\nmisclassified as anomalies, and (2) an overemphasis on, but restricted capacity\nfor, local motion reconstruction, which are inherently difficult to capture\naccurately due to their diversity. To overcome these challenges, we introduce a\nnovel frequency-guided diffusion model with perturbation training. First, we\nenhance robustness by training a generator to produce perturbed samples, which\nare similar to normal samples and target the weakness of the reconstruction\nmodel. This training paradigm expands the reconstruction domain of the model,\nimproving its generalization to unseen normal motions. Second, to address the\noveremphasis on motion details, we employ the 2D Discrete Cosine Transform\n(DCT) to separate high-frequency (local) and low-frequency (global) motion\ncomponents. By guiding the diffusion model with observed high-frequency\ninformation, we prioritize the reconstruction of low-frequency components,\nenabling more accurate and robust anomaly detection. Extensive experiments on\nfive widely used VAD datasets demonstrate that our approach surpasses\nstate-of-the-art methods, underscoring its effectiveness in open-set scenarios\nand diverse motion contexts. Our project website is\nhttps://xiaofeng-tan.github.io/projects/FG-Diff/index.html.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.03907", "title": "ONER: Online Experience Replay for Incremental Anomaly Detection", "abstract": "  Incremental anomaly detection aims to sequentially identify defects in\nindustrial product lines but suffers from catastrophic forgetting, primarily\ndue to knowledge overwriting during parameter updates and feature conflicts\nbetween tasks. In this work, We propose ONER (ONline Experience Replay), an\nend-to-end framework that addresses these issues by synergistically integrating\ntwo types of experience: (1) decomposed prompts, which dynamically generate\nimage-conditioned prompts from reusable modules to retain prior knowledge thus\nprevent knowledge overwriting, and (2) semantic prototypes, which enforce\nseparability in latent feature spaces at pixel and image levels to mitigate\ncross-task feature conflicts. Extensive experiments demonstrate the superiority\nof ONER, achieving state-of-the-art performance with +4.4% Pixel AUROC and\n+28.3% Pixel AUPR improvements on the MVTec AD dataset over prior methods.\nRemarkably, ONER achieves this with only 0.019M parameters and 5 training\nepochs per task, confirming its efficiency and stability for real-world\nindustrial deployment.\n", "date": "2024-12-05", "categories": []}
{"id": "2503.03562", "title": "Towards Visual Discrimination and Reasoning of Real-World Physical\n  Dynamics: Physics-Grounded Anomaly Detection", "abstract": "  Humans detect real-world object anomalies by perceiving, interacting, and\nreasoning based on object-conditioned physical knowledge. The long-term goal of\nIndustrial Anomaly Detection (IAD) is to enable machines to autonomously\nreplicate this skill. However, current IAD algorithms are largely developed and\ntested on static, semantically simple datasets, which diverge from real-world\nscenarios where physical understanding and reasoning are essential. To bridge\nthis gap, we introduce the Physics Anomaly Detection (Phys-AD) dataset, the\nfirst large-scale, real-world, physics-grounded video dataset for industrial\nanomaly detection. Collected using a real robot arm and motor, Phys-AD provides\na diverse set of dynamic, semantically rich scenarios. The dataset includes\nmore than 6400 videos across 22 real-world object categories, interacting with\nrobot arms and motors, and exhibits 47 types of anomalies. Anomaly detection in\nPhys-AD requires visual reasoning, combining both physical knowledge and video\ncontent to determine object abnormality. We benchmark state-of-the-art anomaly\ndetection methods under three settings: unsupervised AD, weakly-supervised AD,\nand video-understanding AD, highlighting their limitations in handling\nphysics-grounded anomalies. Additionally, we introduce the Physics Anomaly\nExplanation (PAEval) metric, designed to assess the ability of visual-language\nfoundation models to not only detect anomalies but also provide accurate\nexplanations for their underlying physical causes. Our project is available at\nhttps://guyao2023.github.io/Phys-AD/.\n", "date": "2025-03-05", "categories": []}
{"id": "2503.19357", "title": "Correcting Deviations from Normality: A Reformulated Diffusion Model for\n  Multi-Class Unsupervised Anomaly Detection", "abstract": "  Recent advances in diffusion models have spurred research into their\napplication for Reconstruction-based unsupervised anomaly detection. However,\nthese methods may struggle with maintaining structural integrity and recovering\nthe anomaly-free content of abnormal regions, especially in multi-class\nscenarios. Furthermore, diffusion models are inherently designed to generate\nimages from pure noise and struggle to selectively alter anomalous regions of\nan image while preserving normal ones. This leads to potential degradation of\nnormal regions during reconstruction, hampering the effectiveness of anomaly\ndetection. This paper introduces a reformulation of the standard diffusion\nmodel geared toward selective region alteration, allowing the accurate\nidentification of anomalies. By modeling anomalies as noise in the latent\nspace, our proposed Deviation correction diffusion (DeCo-Diff) model preserves\nthe normal regions and encourages transformations exclusively on anomalous\nareas. This selective approach enhances the reconstruction quality,\nfacilitating effective unsupervised detection and localization of anomaly\nregions. Comprehensive evaluations demonstrate the superiority of our method in\naccurately identifying and localizing anomalies in complex images, with\npixel-level AUPRC improvements of 11-14% over state-of-the-art models on well\nknown anomaly detection datasets. The code is available at\nhttps://github.com/farzad-bz/DeCo-Diff\n", "date": "2025-03-25", "categories": []}
{"id": "2503.20355", "title": "CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for\n  Emergency Rescue", "abstract": "  The unmanned aerial vehicle (UAV) network has gained significant attentions\nin recent years due to its various applications. However, the traffic security\nbecomes the key threatening public safety issue in an emergency rescue system\ndue to the increasing vulnerability of UAVs to cyber attacks in environments\nwith high heterogeneities. Hence, in this paper, we propose a novel anomaly\ntraffic detection architecture for UAV networks based on the software-defined\nnetworking (SDN) framework and blockchain technology. Specifically, SDN\nseparates the control and data plane to enhance the network manageability and\nsecurity. Meanwhile, the blockchain provides decentralized identity\nauthentication and data security records. Beisdes, a complete security\narchitecture requires an effective mechanism to detect the time-series based\nabnormal traffic. Thus, an integrated algorithm combining convolutional neural\nnetworks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection\nis developed, which is called CTranATD. Finally, the simulation results show\nthat the proposed CTranATD algorithm is effective and outperforms the\nindividual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.\n", "date": "2025-03-26", "categories": []}
{"id": "2207.08486", "title": "Using Anomaly Detection to Detect Poisoning Attacks in Federated\n  Learning Applications", "abstract": "  Adversarial attacks such as poisoning attacks have attracted the attention of\nmany machine learning researchers. Traditionally, poisoning attacks attempt to\ninject adversarial training data in order to manipulate the trained model. In\nfederated learning (FL), data poisoning attacks can be generalized to model\npoisoning attacks, which cannot be detected by simpler methods due to the lack\nof access to local training data by the detector. State-of-the-art poisoning\nattack detection methods for FL have various weaknesses, e.g., the number of\nattackers has to be known or not high enough, working with i.i.d. data only,\nand high computational complexity. To overcome above weaknesses, we propose a\nnovel framework for detecting poisoning attacks in FL, which employs a\nreference model based on a public dataset and an auditor model to detect\nmalicious updates. We implemented a detector based on the proposed framework\nand using a one-class support vector machine (OC-SVM), which reaches the lowest\npossible computational complexity O(K) where K is the number of clients. We\nevaluated our detector's performance against state-of-the-art (SOTA) poisoning\nattacks for two typical applications of FL: electrocardiograph (ECG)\nclassification and human activity recognition (HAR). Our experimental results\nvalidated the performance of our detector over other SOTA detection methods.\n", "date": "2022-07-18", "categories": []}
{"id": "2406.10326", "title": "VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs", "abstract": "  The recent developments in Large Multi-modal Video Models (Video-LMMs) have\nsignificantly enhanced our ability to interpret and analyze video data. Despite\ntheir impressive capabilities, current Video-LMMs have not been evaluated for\nanomaly detection tasks, which is critical to their deployment in practical\nscenarios e.g., towards identifying deepfakes, manipulated video content,\ntraffic accidents and crimes. In this paper, we introduce VANE-Bench, a\nbenchmark designed to assess the proficiency of Video-LMMs in detecting and\nlocalizing anomalies and inconsistencies in videos. Our dataset comprises an\narray of videos synthetically generated using existing state-of-the-art\ntext-to-video generation models, encompassing a variety of subtle anomalies and\ninconsistencies grouped into five categories: unnatural transformations,\nunnatural appearance, pass-through, disappearance and sudden appearance.\nAdditionally, our benchmark features real-world samples from existing anomaly\ndetection datasets, focusing on crime-related irregularities, atypical\npedestrian behavior, and unusual events. The task is structured as a visual\nquestion-answering challenge to gauge the models' ability to accurately detect\nand localize the anomalies within the videos. We evaluate nine existing\nVideo-LMMs, both open and closed sources, on this benchmarking task and find\nthat most of the models encounter difficulties in effectively identifying the\nsubtle anomalies. In conclusion, our research offers significant insights into\nthe current capabilities of Video-LMMs in the realm of anomaly detection,\nhighlighting the importance of our work in evaluating and improving these\nmodels for real-world applications. Our code and data is available at\nhttps://hananshafi.github.io/vane-benchmark/\n", "date": "2024-06-14", "categories": []}
{"id": "2503.06413", "title": "Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection\n  with Multiple Mamba Models", "abstract": "  Despite a plethora of anomaly detection models developed over the years,\ntheir ability to generalize to unseen anomalies remains an issue, particularly\nin critical systems. This paper aims to address this challenge by introducing\nSwift Hydra, a new framework for training an anomaly detection method based on\ngenerative AI and reinforcement learning (RL). Through featuring an RL policy\nthat operates on the latent variables of a generative model, the framework\nsynthesizes novel and diverse anomaly samples that are capable of bypassing a\ndetection model. These generated synthetic samples are, in turn, used to\naugment the detection model, further improving its ability to handle\nchallenging anomalies. Swift Hydra also incorporates Mamba models structured as\na Mixture of Experts (MoE) to enable scalable adaptation of the number of Mamba\nexperts based on data complexity, effectively capturing diverse feature\ndistributions without increasing the model's inference time. Empirical\nevaluations on ADBench benchmark demonstrate that Swift Hydra outperforms other\nstate-of-the-art anomaly detection models while maintaining a relatively short\ninference time. From these results, our research highlights a new and\nauspicious paradigm of integrating RL and generative AI for advancing anomaly\ndetection.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.19100", "title": "Anomaly Detection Using Computer Vision: A Comparative Analysis of Class\n  Distinction and Performance Metrics", "abstract": "  This paper showcases an experimental study on anomaly detection using\ncomputer vision. The study focuses on class distinction and performance\nevaluation, combining OpenCV with deep learning techniques while employing a\nTensorFlow-based convolutional neural network for real-time face recognition\nand classification. The system effectively distinguishes among three classes:\nauthorized personnel (admin), intruders, and non-human entities. A\nMobileNetV2-based deep learning model is utilized to optimize real-time\nperformance, ensuring high computational efficiency without compromising\naccuracy. Extensive dataset preprocessing, including image augmentation and\nnormalization, enhances the models generalization capabilities. Our analysis\ndemonstrates classification accuracies of 90.20% for admin, 98.60% for\nintruders, and 75.80% for non-human detection, while maintaining an average\nprocessing rate of 30 frames per second. The study leverages transfer learning,\nbatch normalization, and Adam optimization to achieve stable and robust\nlearning, and a comparative analysis of class differentiation strategies\nhighlights the impact of feature extraction techniques and training\nmethodologies. The results indicate that advanced feature selection and data\naugmentation significantly enhance detection performance, particularly in\ndistinguishing human from non-human scenes. As an experimental study, this\nresearch provides critical insights into optimizing deep learning-based\nsurveillance systems for high-security environments and improving the accuracy\nand efficiency of real-time anomaly detection.\n", "date": "2025-03-24", "categories": []}
{"id": "2503.19380", "title": "Social Network User Profiling for Anomaly Detection Based on Graph\n  Neural Networks", "abstract": "  This study proposes a risk pricing anomaly detection method for social\nnetwork user portraits based on graph neural networks (GNNs), aiming to improve\nthe ability to identify abnormal users in social network environments. In view\nof the limitations of traditional methods in social network data modeling, this\npaper combines graph autoencoders (GAEs) and graph attention networks (GATs) to\nachieve accurate detection of abnormal users through dynamic aggregation of\nneighbor features and reconstruction error evaluation. The Facebook Page-Page\nNetwork dataset is used in the experiment and compared with VAE, GNN,\nTransformer and GAE. The results show that the proposed method achieves the\nbest performance in AUC, F1-score, Precision and Recall, verifying its\neffectiveness. In addition, this paper explores the computational efficiency of\nthe model in large-scale data and looks forward to combining self-supervised\nlearning, federated learning, and other technologies in the future to improve\nthe robustness and privacy protection of risk assessment. The research results\ncan provide efficient anomaly detection solutions for financial risk control,\nsocial security management, and other fields.\n", "date": "2025-03-25", "categories": []}
{"id": "2503.19577", "title": "Post-Hoc Calibrated Anomaly Detection", "abstract": "  Deep unsupervised anomaly detection has seen improvements in a supervised\nbinary classification paradigm in which auxiliary external data is included in\nthe training set as anomalous data in a process referred to as outlier\nexposure, which opens the possibility of exploring the efficacy of post-hoc\ncalibration for anomaly detection and localization. Post-hoc Platt scaling and\nBeta calibration are found to improve results with gradient-based input\nperturbation, as well as post-hoc training with a strictly proper loss of a\nbase model initially trained on an unsupervised loss. Post-hoc calibration is\nalso found at times to be more effective using random synthesized spectral data\nas labeled anomalous data in the calibration set, suggesting that outlier\nexposure is superior only for initial training.\n", "date": "2025-03-25", "categories": []}
{"id": "2406.20099", "title": "Odd-One-Out: Anomaly Detection by Comparing with Neighbors", "abstract": "  This paper introduces a novel anomaly detection (AD) problem aimed at\nidentifying `odd-looking' objects within a scene by comparing them to other\nobjects present. Unlike traditional AD benchmarks with fixed anomaly criteria,\nour task detects anomalies specific to each scene by inferring a reference\ngroup of regular objects. To address occlusions, we use multiple views of each\nscene as input, construct 3D object-centric models for each instance from 2D\nviews, enhancing these models with geometrically consistent part-aware\nrepresentations. Anomalous objects are then detected through cross-instance\ncomparison. We also introduce two new benchmarks, ToysAD-8K and PartsAD-15K as\ntestbeds for future research in this task. We provide a comprehensive analysis\nof our method quantitatively and qualitatively on these benchmarks.\n", "date": "2024-06-28", "categories": []}
{"id": "2503.17712", "title": "Multi-modality Anomaly Segmentation on the Road", "abstract": "  Semantic segmentation allows autonomous driving cars to understand the\nsurroundings of the vehicle comprehensively. However, it is also crucial for\nthe model to detect obstacles that may jeopardize the safety of autonomous\ndriving systems. Based on our experiments, we find that current uni-modal\nanomaly segmentation frameworks tend to produce high anomaly scores for\nnon-anomalous regions in images. Motivated by this empirical finding, we\ndevelop a multi-modal uncertainty-based anomaly segmentation framework, named\nMMRAS+, for autonomous driving systems. MMRAS+ effectively reduces the high\nanomaly outputs of non-anomalous classes by introducing text-modal using the\nCLIP text encoder. Indeed, MMRAS+ is the first multi-modal anomaly segmentation\nsolution for autonomous driving. Moreover, we develop an ensemble module to\nfurther boost the anomaly segmentation performance. Experiments on RoadAnomaly,\nSMIYC, and Fishyscapes validation datasets demonstrate the superior performance\nof our method. The code is available in\nhttps://github.com/HengGao12/MMRAS_plus.\n", "date": "2025-03-22", "categories": []}
{"id": "2503.18094", "title": "Anomize: Better Open Vocabulary Video Anomaly Detection", "abstract": "  Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify\nboth base and novel anomalies. However, existing methods face two specific\nchallenges related to novel anomalies. The first challenge is detection\nambiguity, where the model struggles to assign accurate anomaly scores to\nunfamiliar anomalies. The second challenge is categorization confusion, where\nnovel anomalies are often misclassified as visually similar base instances. To\naddress these challenges, we explore supplementary information from multiple\nsources to mitigate detection ambiguity by leveraging multiple levels of visual\ndata alongside matching textual information. Furthermore, we propose\nincorporating label relations to guide the encoding of new labels, thereby\nimproving alignment between novel videos and their corresponding labels, which\nhelps reduce categorization confusion. The resulting Anomize framework\neffectively tackles these issues, achieving superior performance on UCF-Crime\nand XD-Violence datasets, demonstrating its effectiveness in OVVAD.\n", "date": "2025-03-23", "categories": []}
{"id": "2503.18325", "title": "Towards Training-free Anomaly Detection with Vision and Language\n  Foundation Models", "abstract": "  Anomaly detection is valuable for real-world applications, such as industrial\nquality inspection. However, most approaches focus on detecting local\nstructural anomalies while neglecting compositional anomalies incorporating\nlogical constraints. In this paper, we introduce LogSAD, a novel multi-modal\nframework that requires no training for both Logical and Structural Anomaly\nDetection. First, we propose a match-of-thought architecture that employs\nadvanced large multi-modal models (i.e. GPT-4V) to generate matching proposals,\nformulating interests and compositional rules of thought for anomaly detection.\nSecond, we elaborate on multi-granularity anomaly detection, consisting of\npatch tokens, sets of interests, and composition matching with vision and\nlanguage foundation models. Subsequently, we present a calibration module to\nalign anomaly scores from different detectors, followed by integration\nstrategies for the final decision. Consequently, our approach addresses both\nlogical and structural anomaly detection within a unified framework and\nachieves state-of-the-art results without the need for training, even when\ncompared to supervised approaches, highlighting its robustness and\neffectiveness. Code is available at https://github.com/zhang0jhon/LogSAD.\n", "date": "2025-03-24", "categories": []}
{"id": "2503.18385", "title": "RoCA: Robust Contrastive One-class Time Series Anomaly Detection with\n  Contaminated Data", "abstract": "  The accumulation of time-series signals and the absence of labels make\ntime-series Anomaly Detection (AD) a self-supervised task of deep learning.\nMethods based on normality assumptions face the following three limitations:\n(1) A single assumption could hardly characterize the whole normality or lead\nto some deviation. (2) Some assumptions may go against the principle of AD. (3)\nTheir basic assumption is that the training data is uncontaminated (free of\nanomalies), which is unrealistic in practice, leading to a decline in\nrobustness. This paper proposes a novel robust approach, RoCA, which is the\nfirst to address all of the above three challenges, as far as we are aware. It\nfuses the separated assumptions of one-class classification and contrastive\nlearning in a single training process to characterize a more complete so-called\nnormality. Additionally, it monitors the training data and computes a carefully\ndesigned anomaly score throughout the training process. This score helps\nidentify latent anomalies, which are then used to define the classification\nboundary, inspired by the concept of outlier exposure. The performance on AIOps\ndatasets improved by 6% compared to when contamination was not considered\n(COCA). On two large and high-dimensional multivariate datasets, the\nperformance increased by 5% to 10%. RoCA achieves the highest average\nperformance on both univariate and multivariate datasets. The source code is\navailable at https://github.com/ruiking04/RoCA.\n", "date": "2025-03-24", "categories": []}
{"id": "2503.18808", "title": "CRCL: Causal Representation Consistency Learning for Anomaly Detection\n  in Surveillance Videos", "abstract": "  Video Anomaly Detection (VAD) remains a fundamental yet formidable task in\nthe video understanding community, with promising applications in areas such as\ninformation forensics and public safety protection. Due to the rarity and\ndiversity of anomalies, existing methods only use easily collected regular\nevents to model the inherent normality of normal spatial-temporal patterns in\nan unsupervised manner. Previous studies have shown that existing unsupervised\nVAD models are incapable of label-independent data offsets (e.g., scene\nchanges) in real-world scenarios and may fail to respond to light anomalies due\nto the overgeneralization of deep neural networks. Inspired by causality\nlearning, we argue that there exist causal factors that can adequately\ngeneralize the prototypical patterns of regular events and present significant\ndeviations when anomalous instances occur. In this regard, we propose Causal\nRepresentation Consistency Learning (CRCL) to implicitly mine potential\nscene-robust causal variable in unsupervised video normality learning.\nSpecifically, building on the structural causal models, we propose\nscene-debiasing learning and causality-inspired normality learning to strip\naway entangled scene bias in deep representations and learn causal video\nnormality, respectively. Extensive experiments on benchmarks validate the\nsuperiority of our method over conventional deep representation learning.\nMoreover, ablation studies and extension validation show that the CRCL can cope\nwith label-independent biases in multi-scene settings and maintain stable\nperformance with only limited training data available.\n", "date": "2025-03-24", "categories": []}
{"id": "2410.01180", "title": "UAL-Bench: The First Comprehensive Unusual Activity Localization\n  Benchmark", "abstract": "  Localizing unusual activities, such as human errors or surveillance\nincidents, in videos holds practical significance. However, current video\nunderstanding models struggle with localizing these unusual events likely\nbecause of their insufficient representation in models' pretraining datasets.\nTo explore foundation models' capability in localizing unusual activity, we\nintroduce UAL-Bench, a comprehensive benchmark for unusual activity\nlocalization, featuring three video datasets: UAG-OOPS, UAG-SSBD, UAG-FunQA,\nand an instruction-tune dataset: OOPS-UAG-Instruct, to improve model\ncapabilities. UAL-Bench evaluates three approaches: Video-Language Models\n(Vid-LLMs), instruction-tuned Vid-LLMs, and a novel integration of\nVision-Language Models and Large Language Models (VLM-LLM). Our results show\nthe VLM-LLM approach excels in localizing short-span unusual events and\npredicting their onset (start time) more accurately than Vid-LLMs. We also\npropose a new metric, R@1, TD <= p, to address limitations in existing\nevaluation methods. Our findings highlight the challenges posed by\nlong-duration videos, particularly in autism diagnosis scenarios, and the need\nfor further advancements in localization techniques. Our work not only provides\na benchmark for unusual activity localization but also outlines the key\nchallenges for existing foundation models, suggesting future research\ndirections on this important task.\n", "date": "2024-10-02", "categories": []}
{"id": "2410.21982", "title": "A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised\n  Industrial Image Anomaly Detection", "abstract": "  In the advancement of industrial informatization, unsupervised anomaly\ndetection technology effectively overcomes the scarcity of abnormal samples and\nsignificantly enhances the automation and reliability of smart manufacturing.\nAs an important branch, industrial image anomaly detection focuses on\nautomatically identifying visual anomalies in industrial scenarios (such as\nproduct surface defects, assembly errors, and equipment appearance anomalies)\nthrough computer vision techniques. With the rapid development of Unsupervised\nindustrial Image Anomaly Detection (UIAD), excellent detection performance has\nbeen achieved not only in RGB setting but also in 3D and multimodal (RGB and\n3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB\nsetting, with little discussion in 3D and multimodal settings. To address this\ngap, this artical provides a comprehensive review of UIAD tasks in the three\nmodal settings. Specifically, we first introduce the task concept and process\nof UIAD. We then overview the research on UIAD in three modal settings (RGB,\n3D, and multimodal), including datasets and methods, and review multimodal\nfeature fusion strategies in multimodal setting. Finally, we summarize the main\nchallenges faced by UIAD tasks in the three modal settings, and offer insights\ninto future development directions, aiming to provide researchers with a\ncomprehensive reference and offer new perspectives for the advancement of\nindustrial informatization. Corresponding resources are available at\nhttps://github.com/Sunny5250/Awesome-Multi-Setting-UIAD.\n", "date": "2024-10-29", "categories": []}
{"id": "2408.14329", "title": "Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive\n  Framework and A New Benchmark", "abstract": "  Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors\nthat deviate from normal. At its core, human-centric VAD faces substantial\nchallenges, such as the complexity of diverse human behaviors, the rarity of\nanomalies, and ethical constraints. These challenges limit access to\nhigh-quality datasets and highlight the need for a dataset and framework\nsupporting continual learning. Moving towards adaptive human-centric VAD, we\nintroduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection)\ndataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework.\nUCAL enables incremental learning, allowing models to adapt over time, bridging\ntraditional training and real-world deployment. HuVAD prioritizes privacy by\nproviding de-identified annotations and includes seven indoor/outdoor scenes,\noffering over 5x more pose-annotated frames than previous datasets. Our\nstandard and continual benchmarks, utilize a comprehensive set of metrics,\ndemonstrating that UCAL-enhanced models achieve superior performance in 82.14%\nof cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at\nhttps://github.com/TeCSAR-UNCC/HuVAD.\n", "date": "2024-08-26", "categories": []}
{"id": "2409.04982", "title": "2DSig-Detect: a semi-supervised framework for anomaly detection on image\n  data using 2D-signatures", "abstract": "  The rapid advancement of machine learning technologies raises questions about\nthe security of machine learning models, with respect to both training-time\n(poisoning) and test-time (evasion, impersonation, and inversion) attacks.\nModels performing image-related tasks, e.g. detection, and classification, are\nvulnerable to adversarial attacks that can degrade their performance and\nproduce undesirable outcomes. This paper introduces a novel technique for\nanomaly detection in images called 2DSig-Detect, which uses a\n2D-signature-embedded semi-supervised framework rooted in rough path theory. We\ndemonstrate our method in adversarial settings for training-time and test-time\nattacks, and benchmark our framework against other state of the art methods.\nUsing 2DSig-Detect for anomaly detection, we show both superior performance and\na reduction in the computation time to detect the presence of adversarial\nperturbations in images.\n", "date": "2024-09-08", "categories": []}
{"id": "2503.12478", "title": "KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector\n  Learning Framework for Time Series Anomaly Detection", "abstract": "  Model selection has been raised as an essential problem in the area of time\nseries anomaly detection (TSAD), because there is no single best TSAD model for\nthe highly heterogeneous time series in real-world applications. However,\ndespite the success of existing model selection solutions that train a\nclassification model (especially neural network, NN) using historical data as a\nselector to predict the correct TSAD model for each series, the NN-based\nselector learning methods used by existing solutions do not make full use of\nthe knowledge in the historical data and require iterating over all training\nsamples, which limits the accuracy and training speed of the selector. To\naddress these limitations, we propose KDSelector, a novel knowledge-enhanced\nand data-efficient framework for learning the NN-based TSAD model selector, of\nwhich three key components are specifically designed to integrate available\nknowledge into the selector and dynamically prune less important and redundant\nsamples during the learning. We develop a TSAD model selection system with\nKDSelector as the internal, to demonstrate how users improve the accuracy and\ntraining speed of their selectors by using KDSelector as a plug-and-play\nmodule. Our demonstration video is hosted at https://youtu.be/2uqupDWvTF0.\n", "date": "2025-03-16", "categories": []}
{"id": "2503.15946", "title": "Multivariate Time Series Anomaly Detection in Industry 5.0", "abstract": "  Industry5.0 environments present a critical need for effective anomaly\ndetection methods that can indicate equipment malfunctions, process\ninefficiencies, or potential safety hazards. The ever-increasing sensorization\nof manufacturing lines makes processes more observable, but also poses the\nchallenge of continuously analyzing vast amounts of multivariate time series\ndata. These challenges include data quality since data may contain noise, be\nunlabeled or even mislabeled. A promising approach consists of combining an\nembedding model with other Machine Learning algorithms to enhance the overall\nperformance in detecting anomalies. Moreover, representing time series as\nvectors brings many advantages like higher flexibility and improved ability to\ncapture complex temporal dependencies. We tested our solution in a real\nindustrial use case, using data collected from a Bonfiglioli plant. The results\ndemonstrate that, unlike traditional reconstruction-based autoencoders, which\noften struggle in the presence of sporadic noise, our embedding-based framework\nmaintains high performance across various noise conditions.\n", "date": "2025-03-20", "categories": []}
{"id": "2311.14773", "title": "Set Features for Anomaly Detection", "abstract": "  This paper proposes to use set features for detecting anomalies in samples\nthat consist of unusual combinations of normal elements. Many leading methods\ndiscover anomalies by detecting an unusual part of a sample. For example,\nstate-of-the-art segmentation-based approaches, first classify each element of\nthe sample (e.g., image patch) as normal or anomalous and then classify the\nentire sample as anomalous if it contains anomalous elements. However, such\napproaches do not extend well to scenarios where the anomalies are expressed by\nan unusual combination of normal elements. In this paper, we overcome this\nlimitation by proposing set features that model each sample by the distribution\nof its elements. We compute the anomaly score of each sample using a simple\ndensity estimation method, using fixed features. Our approach outperforms the\nprevious state-of-the-art in image-level logical anomaly detection and\nsequence-level time series anomaly detection.\n", "date": "2023-11-24", "categories": []}
{"id": "2503.14849", "title": "LogLLaMA: Transformer-based log anomaly detection with LLaMA", "abstract": "  Log anomaly detection refers to the task that distinguishes the anomalous log\nmessages from normal log messages. Transformer-based large language models\n(LLMs) are becoming popular for log anomaly detection because of their superb\nability to understand complex and long language patterns. In this paper, we\npropose LogLLaMA, a novel framework that leverages LLaMA2. LogLLaMA is first\nfinetuned on normal log messages from three large-scale datasets to learn their\npatterns. After finetuning, the model is capable of generating successive log\nmessages given previous log messages. Our generative model is further trained\nto identify anomalous log messages using reinforcement learning (RL). The\nexperimental results show that LogLLaMA outperforms the state-of-the-art\napproaches for anomaly detection on BGL, Thunderbird, and HDFS datasets.\n", "date": "2025-03-19", "categories": []}
{"id": "2503.14910", "title": "Robust Distribution Alignment for Industrial Anomaly Detection under\n  Distribution Shift", "abstract": "  Anomaly detection plays a crucial role in quality control for industrial\napplications. However, ensuring robustness under unseen domain shifts such as\nlighting variations or sensor drift remains a significant challenge. Existing\nmethods attempt to address domain shifts by training generalizable models but\noften rely on prior knowledge of target distributions and can hardly generalise\nto backbones designed for other data modalities. To overcome these limitations,\nwe build upon memory-bank-based anomaly detection methods, optimizing a robust\nSinkhorn distance on limited target training data to enhance generalization to\nunseen target domains. We evaluate the effectiveness on both 2D and 3D anomaly\ndetection benchmarks with simulated distribution shifts. Our proposed method\ndemonstrates superior results compared with state-of-the-art anomaly detection\nand domain adaptation methods.\n", "date": "2025-03-19", "categories": []}
{"id": "2312.04610", "title": "Data-Driven Semi-Supervised Machine Learning with Safety Indicators for\n  Abnormal Driving Behavior Detection", "abstract": "  Detecting abnormal driving behavior is critical for road traffic safety and\nthe evaluation of drivers' behavior. With the advancement of machine learning\n(ML) algorithms and the accumulation of naturalistic driving data, many ML\nmodels have been adopted for abnormal driving behavior detection (also referred\nto in this paper as \"anomalies\"). Most existing ML-based detectors rely on\n(fully) supervised ML methods, which require substantial labeled data. However,\nground truth labels are not always available in the real world, and labeling\nlarge amounts of data is tedious. Thus, there is a need to explore unsupervised\nor semi-supervised methods to make the anomaly detection process more feasible\nand efficient. To fill this research gap, this study analyzes large-scale\nreal-world data revealing several abnormal driving behaviors (e.g., sudden\nacceleration, rapid lane-changing) and develops a hierarchical extreme learning\nmachine (HELM)-based semi-supervised ML method using partly labeled data to\ndetect the identified abnormal driving behaviors. Moreover, previous ML-based\napproaches predominantly utilized basic vehicle motion features (such as\nvelocity and acceleration) to label and detect abnormal driving behaviors,\nwhile this study seeks to introduce event-level safety indicators as input\nfeatures for ML models to improve detection performance. Results from extensive\nexperiments demonstrate the effectiveness of the proposed semi-supervised ML\nmodel with the introduced safety indicators serving as important features. The\nproposed semi-supervised ML method outperforms other baseline semi-supervised\nor unsupervised methods: for example, it delivers the best accuracy at 99.58%\nand the best F1-score at 0.9913. The ablation study further highlights the\nsignificance of safety indicators for advancing the detection performance of\nabnormal driving behaviors.\n", "date": "2023-12-07", "categories": []}
{"id": "2503.13828", "title": "Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical\n  Anomaly Detection", "abstract": "  Unsupervised anomaly detection using deep learning has garnered significant\nresearch attention due to its broad applicability, particularly in medical\nimaging where labeled anomalous data are scarce. While earlier approaches\nleverage generative models like autoencoders and generative adversarial\nnetworks (GANs), they often fall short due to overgeneralization. Recent\nmethods explore various strategies, including memory banks, normalizing flows,\nself-supervised learning, and knowledge distillation, to enhance\ndiscrimination. Among these, knowledge distillation, particularly reverse\ndistillation, has shown promise. Following this paradigm, we propose a novel\nscale-aware contrastive reverse distillation model that addresses two key\nlimitations of existing reverse distillation methods: insufficient feature\ndiscriminability and inability to handle anomaly scale variations.\nSpecifically, we introduce a contrastive student-teacher learning approach to\nderive more discriminative representations by generating and exploring\nout-of-normal distributions. Further, we design a scale adaptation mechanism to\nsoftly weight contrastive distillation losses at different scales to account\nfor the scale variation issue. Extensive experiments on benchmark datasets\ndemonstrate state-of-the-art performance, validating the efficacy of the\nproposed method. Code is available at https://github.com/MedAITech/SCRD4AD.\n", "date": "2025-03-18", "categories": []}
{"id": "2408.15185", "title": "Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose\n  Tokenization and Transformer", "abstract": "  Video Anomaly Detection (VAD) presents a significant challenge in computer\nvision, particularly due to the unpredictable and infrequent nature of\nanomalous events, coupled with the diverse and dynamic environments in which\nthey occur. Human-centric VAD, a specialized area within this domain, faces\nadditional complexities, including variations in human behavior, potential\nbiases in data, and substantial privacy concerns related to human subjects.\nThese issues complicate the development of models that are both robust and\ngeneralizable. To address these challenges, recent advancements have focused on\npose-based VAD, which leverages human pose as a high-level feature to mitigate\nprivacy concerns, reduce appearance biases, and minimize background\ninterference. In this paper, we introduce SPARTA, a novel transformer-based\narchitecture designed specifically for human-centric pose-based VAD. SPARTA\nintroduces an innovative Spatio-Temporal Pose and Relative Pose (ST-PRP)\ntokenization method that produces an enriched representation of human motion\nover time. This approach ensures that the transformer's attention mechanism\ncaptures both spatial and temporal patterns simultaneously, rather than\nfocusing on only one aspect. The addition of the relative pose further\nemphasizes subtle deviations from normal human movements. The architecture's\ncore, a novel Unified Encoder Twin Decoders (UETD) transformer, significantly\nimproves the detection of anomalous behaviors in video data. Extensive\nevaluations across multiple benchmark datasets demonstrate that SPARTA\nconsistently outperforms existing methods, establishing a new state-of-the-art\nin pose-based VAD.\n", "date": "2024-08-27", "categories": []}
{"id": "2501.15035", "title": "Semi-supervised Anomaly Detection with Extremely Limited Labels in\n  Dynamic Graphs", "abstract": "  Semi-supervised graph anomaly detection (GAD) has recently received\nincreasing attention, which aims to distinguish anomalous patterns from graphs\nunder the guidance of a moderate amount of labeled data and a large volume of\nunlabeled data. Although these proposed semi-supervised GAD methods have\nachieved great success, their superior performance will be seriously degraded\nwhen the provided labels are extremely limited due to some unpredictable\nfactors. Besides, the existing methods primarily focus on anomaly detection in\nstatic graphs, and little effort was paid to consider the continuous evolution\ncharacteristic of graphs over time (dynamic graphs). To address these\nchallenges, we propose a novel GAD framework (EL$^{2}$-DGAD) to tackle anomaly\ndetection problem in dynamic graphs with extremely limited labels.\nSpecifically, a transformer-based graph encoder model is designed to more\neffectively preserve evolving graph structures beyond the local neighborhood.\nThen, we incorporate an ego-context hypersphere classification loss to classify\ntemporal interactions according to their structure and temporal neighborhoods\nwhile ensuring the normal samples are mapped compactly against anomalous data.\nFinally, the above loss is further augmented with an ego-context contrasting\nmodule which utilizes unlabeled data to enhance model generalization. Extensive\nexperiments on four datasets and three label rates demonstrate the\neffectiveness of the proposed method in comparison to the existing GAD methods.\n", "date": "2025-01-25", "categories": []}
{"id": "2502.07601", "title": "Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large\n  Language Models", "abstract": "  Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the\ntraditional unsupervised AD setting that requires a large number of normal\nsamples to train a model, ZSAD is more practical for handling data-restricted\nreal-world scenarios. Recently, Multimodal Large Language Models (MLLMs) have\nshown revolutionary reasoning capabilities in various vision tasks. However,\nthe reasoning of image abnormalities remains underexplored due to the lack of\ncorresponding datasets and benchmarks. To facilitate research in AD &\nreasoning, we establish the first visual instruction tuning dataset,\nAnomaly-Instruct-125k, and the evaluation benchmark, VisA-D&R. Through\ninvestigation with our benchmark, we reveal that current MLLMs like GPT-4o\ncannot accurately detect and describe fine-grained anomalous details in images.\nTo address this, we propose Anomaly-OneVision (Anomaly-OV), the first\nspecialist visual assistant for ZSAD and reasoning. Inspired by human behavior\nin visual inspection, Anomaly-OV leverages a Look-Twice Feature Matching (LTFM)\nmechanism to adaptively select and emphasize abnormal visual tokens. Extensive\nexperiments demonstrate that Anomaly-OV achieves significant improvements over\nadvanced generalist models in both detection and reasoning. Extensions to\nmedical and 3D AD are provided for future study. The link to our project page:\nhttps://xujiacong.github.io/Anomaly-OV/\n", "date": "2025-02-11", "categories": []}
{"id": "2503.12037", "title": "Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic\n  Graph Learning", "abstract": "  Graph Anomaly Detection (GAD) plays a vital role in various data mining\napplications such as e-commerce fraud prevention and malicious user detection.\nRecently, Graph Neural Network (GNN) based approach has demonstrated great\neffectiveness in GAD by first encoding graph data into low-dimensional\nrepresentations and then identifying anomalies under the guidance of supervised\nor unsupervised signals. However, existing GNN-based approaches implicitly\nfollow the homophily principle (i.e., the \"like attracts like\" phenomenon) and\nfail to learn discriminative embedding for anomalies that connect vast normal\nnodes. Moreover, such approaches identify anomalies in a unified global\nperspective but overlook diversified abnormal patterns conditioned on local\ngraph context, leading to suboptimal performance. To overcome the\naforementioned limitations, in this paper, we propose a Multi-hypersphere\nHeterophilic Graph Learning (MHetGL) framework for unsupervised GAD.\nSpecifically, we first devise a Heterophilic Graph Encoding (HGE) module to\nlearn distinguishable representations for potential anomalies by purifying and\naugmenting their neighborhood in a fully unsupervised manner. Then, we propose\na Multi-Hypersphere Learning (MHL) module to enhance the detection capability\nfor context-dependent anomalies by jointly incorporating critical patterns from\nboth global and local perspectives. Extensive experiments on ten real-world\ndatasets show that MHetGL outperforms 14 baselines. Our code is publicly\navailable at https://github.com/KennyNH/MHetGL.\n", "date": "2025-03-15", "categories": []}
{"id": "2503.12125", "title": "Robust Isolation Forest using Soft Sparse Random Projection and Valley\n  Emphasis Method", "abstract": "  Isolation Forest (iForest) is an unsupervised anomaly detection algorithm\ndesigned to effectively detect anomalies under the assumption that anomalies\nare ``few and different.\" Various studies have aimed to enhance iForest, but\nthe resulting algorithms often exhibited significant performance disparities\nacross datasets. Additionally, the challenge of isolating rare and widely\ndistributed anomalies persisted in research focused on improving splits. To\naddress these challenges, we introduce Robust iForest (RiForest). RiForest\nleverages both existing features and random hyperplanes obtained through soft\nsparse random projection to identify superior split features for anomaly\ndetection, independent of datasets. It utilizes the underutilized valley\nemphasis method for optimal split point determination and incorporates sparsity\nrandomization in soft sparse random projection for enhanced anomaly detection\nrobustness. Across 24 benchmark datasets, experiments demonstrate RiForest's\nconsistent outperformance of existing algorithms in anomaly detection,\nemphasizing stability and robustness to noise variables.\n", "date": "2025-03-15", "categories": []}
{"id": "2503.12534", "title": "Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection\n  in Industrial Equipment", "abstract": "  With the advancement of Industry 4.0, intelligent manufacturing extensively\nemploys sensors for real-time multidimensional data collection, playing a\ncrucial role in equipment monitoring, process optimisation, and efficiency\nenhancement. Industrial data exhibit characteristics such as multi-source\nheterogeneity, nonlinearity, strong coupling, and temporal interactions, while\nalso being affected by noise interference. These complexities make it\nchallenging for traditional anomaly detection methods to extract key features,\nimpacting detection accuracy and stability. Traditional machine learning\napproaches often struggle with such complex data due to limitations in\nprocessing capacity and generalisation ability, making them inadequate for\npractical applications. While deep learning feature extraction modules have\ndemonstrated remarkable performance in image and text processing, they remain\nineffective when applied to multi-source heterogeneous industrial data lacking\nexplicit correlations. Moreover, existing multi-source heterogeneous data\nprocessing techniques still rely on dimensionality reduction and feature\nselection, which can lead to information loss and difficulty in capturing\nhigh-order interactions. To address these challenges, this study applies the\nEAPCR and Time-EAPCR models proposed in previous research and introduces a new\nmodel, Time-EAPCR-T, where Transformer replaces the LSTM module in the\ntime-series processing component of Time-EAPCR. This modification effectively\naddresses multi-source data heterogeneity, facilitates efficient multi-source\nfeature fusion, and enhances the temporal feature extraction capabilities of\nmulti-source industrial data.Experimental results demonstrate that the proposed\nmethod outperforms existing approaches across four industrial datasets,\nhighlighting its broad application potential.\n", "date": "2025-03-16", "categories": []}
{"id": "2503.12905", "title": "UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection\n  with Spiking Neural Networks", "abstract": "  Video anomaly detection plays a significant role in intelligent surveillance\nsystems. To enhance model's anomaly recognition ability, previous works have\ntypically involved RGB, optical flow, and text features. Recently, dynamic\nvision sensors (DVS) have emerged as a promising technology, which capture\nvisual information as discrete events with a very high dynamic range and\ntemporal resolution. It reduces data redundancy and enhances the capture\ncapacity of moving objects compared to conventional camera. To introduce this\nrich dynamic information into the surveillance field, we created the first DVS\nvideo anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this\nnew data modality, a multi-scale spiking fusion network (MSF) is designed based\non spiking neural networks (SNNs). This work explores the potential application\nof dynamic information from event data in video anomaly detection. Our\nexperiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and\nits superior performance compared to other models, establishing a new baseline\nfor SNN-based weakly supervised video anomaly detection.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.13195", "title": "Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey", "abstract": "  The rapid expansion of data from diverse sources has made anomaly detection\n(AD) increasingly essential for identifying unexpected observations that may\nsignal system failures, security breaches, or fraud. As datasets become more\ncomplex and high-dimensional, traditional detection methods struggle to\neffectively capture intricate patterns. Advances in deep learning have made AD\nmethods more powerful and adaptable, improving their ability to handle\nhigh-dimensional and unstructured data. This survey provides a comprehensive\nreview of over 180 recent studies, focusing on deep learning-based AD\ntechniques. We categorize and analyze these methods into reconstruction-based\nand prediction-based approaches, highlighting their effectiveness in modeling\ncomplex data distributions. Additionally, we explore the integration of\ntraditional and deep learning methods, highlighting how hybrid approaches\ncombine the interpretability of traditional techniques with the flexibility of\ndeep learning to enhance detection accuracy and model transparency. Finally, we\nidentify open issues and propose future research directions to advance the\nfield of AD. This review bridges gaps in existing literature and serves as a\nvaluable resource for researchers and practitioners seeking to enhance AD\ntechniques using deep learning.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.13400", "title": "U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for\n  Detecting T2 Hyperintensity in MRI Spinal Cord", "abstract": "  T2 hyperintensities in spinal cord MR images are crucial biomarkers for\nconditions such as degenerative cervical myelopathy. However, current clinical\ndiagnoses primarily rely on manual evaluation. Deep learning methods have shown\npromise in lesion detection, but most supervised approaches are heavily\ndependent on large, annotated datasets. Unsupervised anomaly detection (UAD)\noffers a compelling alternative by eliminating the need for abnormal data\nannotations. However, existing UAD methods rely on curated normal datasets and\ntheir performance frequently deteriorates when applied to clinical datasets due\nto domain shifts. We propose an Uncertainty-based Unsupervised Anomaly\nDetection framework, termed U2AD, to address these limitations. Unlike\ntraditional methods, U2AD is designed to be trained and tested within the same\nclinical dataset, following a \"mask-and-reconstruction\" paradigm built on a\nVision Transformer-based architecture. We introduce an uncertainty-guided\nmasking strategy to resolve task conflicts between normal reconstruction and\nanomaly detection to achieve an optimal balance. Specifically, we employ a\nMonte-Carlo sampling technique to estimate reconstruction uncertainty mappings\nduring training. By iteratively optimizing reconstruction training under the\nguidance of both epistemic and aleatoric uncertainty, U2AD reduces overall\nreconstruction variance while emphasizing regions. Experimental results\ndemonstrate that U2AD outperforms existing supervised and unsupervised methods\nin patient-level identification and segment-level localization tasks. This\nframework establishes a new benchmark for incorporating uncertainty guidance\ninto UAD, highlighting its clinical utility in addressing domain shifts and\ntask conflicts in medical image anomaly detection. Our code is available:\nhttps://github.com/zhibaishouheilab/U2AD\n", "date": "2025-03-17", "categories": []}
{"id": "2210.14485", "title": "Reconstruction from edge image combined with color and gradient\n  difference for industrial surface anomaly detection", "abstract": "  Reconstruction-based methods are widely explored in industrial visual anomaly\ndetection. Such methods commonly require the model to well reconstruct the\nnormal patterns but fail in the anomalies, and thus the anomalies can be\ndetected by evaluating the reconstruction errors. However, in practice, it's\nusually difficult to control the generalization boundary of the model. The\nmodel with an overly strong generalization capability can even well reconstruct\nthe abnormal regions, making them less distinguishable, while the model with a\npoor generalization capability can not reconstruct those changeable\nhigh-frequency components in the normal regions, which ultimately leads to\nfalse positives. To tackle the above issue, we propose a new reconstruction\nnetwork where we reconstruct the original RGB image from its gray value edges\n(EdgRec). Specifically, this is achieved by an UNet-type denoising autoencoder\nwith skip connections. The input edge and skip connections can well preserve\nthe high-frequency information in the original image. Meanwhile, the proposed\nrestoration task can force the network to memorize the normal low-frequency and\ncolor information. Besides, the denoising design can prevent the model from\ndirectly copying the original high-frequent components. To evaluate the\nanomalies, we further propose a new interpretable hand-crafted evaluation\nfunction that considers both the color and gradient differences. Our method\nachieves competitive results on the challenging benchmark MVTec AD (97.8\\% for\ndetection and 97.7\\% for localization, AUROC). In addition, we conduct\nexperiments on the MVTec 3D-AD dataset and show convincing results using RGB\nimages only. Our code will be available at\nhttps://github.com/liutongkun/EdgRec.\n", "date": "2022-10-26", "categories": []}
{"id": "2309.07068", "title": "FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly\n  Detection", "abstract": "  Image reconstruction-based anomaly detection models are widely explored in\nindustrial visual inspection. However, existing models usually suffer from the\ntrade-off between normal reconstruction fidelity and abnormal reconstruction\ndistinguishability, which damages the performance. In this paper, we find that\nthe above trade-off can be better mitigated by leveraging the distinct\nfrequency biases between normal and abnormal reconstruction errors. To this\nend, we propose Frequency-aware Image Restoration (FAIR), a novel\nself-supervised image restoration task that restores images from their\nhigh-frequency components. It enables precise reconstruction of normal patterns\nwhile mitigating unfavorable generalization to anomalies. Using only a simple\nvanilla UNet, FAIR achieves state-of-the-art performance with higher efficiency\non various defect detection datasets. Code: https://github.com/liutongkun/FAIR.\n", "date": "2023-09-13", "categories": []}
{"id": "2503.10653", "title": "Video Anomaly Detection with Structured Keywords", "abstract": "  This paper focuses on detecting anomalies in surveillance video using\nkeywords by leveraging foundational models' feature representation\ngeneralization capabilities. We present a novel, lightweight pipeline for\nanomaly classification using keyword weights. Our pipeline employs a two-stage\nprocess: induction followed by deduction. In induction, descriptions are\ngenerated from normal and anomalous frames to identify and assign weights to\nrelevant keywords. In deduction, inference frame descriptions are converted\ninto keyword encodings using induction-derived weights for input into our\nneural network for anomaly classification. We achieved comparable performance\non the three benchmarks UCSD Ped2, Shanghai Tech, and CUHK Avenue, with ROC AUC\nscores of 0.865, 0.745, and 0.742, respectively. These results are achieved\nwithout temporal context, making such a system viable for real-time\napplications. Our model improves implementation setup, interpretability, and\ninference speed for surveillance devices on the edge, introducing a performance\ntrade-off against other video anomaly detection systems. As the generalization\ncapabilities of open-source foundational models improve, our model demonstrates\nthat the exclusive use of text for feature representations is a promising\ndirection for efficient real-time interpretable video anomaly detection.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.11088", "title": "Multi-View Industrial Anomaly Detection with Epipolar Constrained\n  Cross-View Fusion", "abstract": "  Multi-camera systems provide richer contextual information for industrial\nanomaly detection. However, traditional methods process each view\nindependently, disregarding the complementary information across viewpoints.\nExisting multi-view anomaly detection approaches typically employ data-driven\ncross-view attention for feature fusion but fail to leverage the unique\ngeometric properties of multi-camera setups. In this work, we introduce an\nepipolar geometry-constrained attention module to guide cross-view fusion,\nensuring more effective information aggregation. To further enhance the\npotential of cross-view attention, we propose a pretraining strategy inspired\nby memory bank-based anomaly detection. This approach encourages normal feature\nrepresentations to form multiple local clusters and incorporate multi-view\naware negative sample synthesis to regularize pretraining. We demonstrate that\nour epipolar guided multi-view anomaly detection framework outperforms existing\nmethods on the state-of-the-art multi-view anomaly detection dataset.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.11255", "title": "Federated Koopman-Reservoir Learning for Large-Scale Multivariate\n  Time-Series Anomaly Detection", "abstract": "  The proliferation of edge devices has dramatically increased the generation\nof multivariate time-series (MVTS) data, essential for applications from\nhealthcare to smart cities. Such data streams, however, are vulnerable to\nanomalies that signal crucial problems like system failures or security\nincidents. Traditional MVTS anomaly detection methods, encompassing statistical\nand centralized machine learning approaches, struggle with the heterogeneity,\nvariability, and privacy concerns of large-scale, distributed environments. In\nresponse, we introduce FedKO, a novel unsupervised Federated Learning framework\nthat leverages the linear predictive capabilities of Koopman operator theory\nalong with the dynamic adaptability of Reservoir Computing. This enables\neffective spatiotemporal processing and privacy preservation for MVTS data.\nFedKO is formulated as a bi-level optimization problem, utilizing a specific\nfederated algorithm to explore a shared Reservoir-Koopman model across diverse\ndatasets. Such a model is then deployable on edge devices for efficient\ndetection of anomalies in local MVTS streams. Experimental results across\nvarious datasets showcase FedKO's superior performance against state-of-the-art\nmethods in MVTS anomaly detection. Moreover, FedKO reduces up to 8x\ncommunication size and 2x memory usage, making it highly suitable for\nlarge-scale systems.\n", "date": "2025-03-14", "categories": []}
{"id": "2405.14529", "title": "AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2", "abstract": "  Recent advances in multimodal foundation models have set new standards in\nfew-shot anomaly detection. This paper explores whether high-quality visual\nfeatures alone are sufficient to rival existing state-of-the-art\nvision-language models. We affirm this by adapting DINOv2 for one-shot and\nfew-shot anomaly detection, with a focus on industrial applications. We show\nthat this approach does not only rival existing techniques but can even\noutmatch them in many settings. Our proposed vision-only approach, AnomalyDINO,\nfollows the well-established patch-level deep nearest neighbor paradigm, and\nenables both image-level anomaly prediction and pixel-level anomaly\nsegmentation. The approach is methodologically simple and training-free and,\nthus, does not require any additional data for fine-tuning or meta-learning.\nThe approach is methodologically simple and training-free and, thus, does not\nrequire any additional data for fine-tuning or meta-learning. Despite its\nsimplicity, AnomalyDINO achieves state-of-the-art results in one- and few-shot\nanomaly detection (e.g., pushing the one-shot performance on MVTec-AD from an\nAUROC of 93.1% to 96.6%). The reduced overhead, coupled with its outstanding\nfew-shot performance, makes AnomalyDINO a strong candidate for fast deployment,\ne.g., in industrial contexts.\n", "date": "2024-05-23", "categories": []}
{"id": "2410.05440", "title": "Can LLMs Understand Time Series Anomalies?", "abstract": "  Large Language Models (LLMs) have gained popularity in time series\nforecasting, but their potential for anomaly detection remains largely\nunexplored. Our study investigates whether LLMs can understand and detect\nanomalies in time series data, focusing on zero-shot and few-shot scenarios.\nInspired by conjectures about LLMs' behavior from time series forecasting\nresearch, we formulate key hypotheses about LLMs' capabilities in time series\nanomaly detection. We design and conduct principled experiments to test each of\nthese hypotheses. Our investigation reveals several surprising findings about\nLLMs for time series: (1) LLMs understand time series better as images rather\nthan as text, (2) LLMs do not demonstrate enhanced performance when prompted to\nengage in explicit reasoning about time series analysis. (3) Contrary to common\nbeliefs, LLMs' understanding of time series does not stem from their repetition\nbiases or arithmetic abilities. (4) LLMs' behaviors and performance in time\nseries analysis vary significantly across different models. This study provides\nthe first comprehensive analysis of contemporary LLM capabilities in time\nseries anomaly detection. Our results suggest that while LLMs can understand\ntrivial time series anomalies, we have no evidence that they can understand\nmore subtle real-world anomalies. Many common conjectures based on their\nreasoning capabilities do not hold. All synthetic dataset generators, final\nprompts, and evaluation scripts have been made available in\nhttps://github.com/rose-stl-lab/anomllm.\n", "date": "2024-10-07", "categories": []}
{"id": "2503.09200", "title": "Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection\n  Applied to the Environmental Field", "abstract": "  As human activities intensify, environmental systems such as aquatic\necosystems and water treatment systems face increasingly complex pressures,\nimpacting ecological balance, public health, and sustainable development,\nmaking intelligent anomaly monitoring essential. However, traditional\nmonitoring methods suffer from delayed responses, insufficient data processing\ncapabilities, and weak generalisation, making them unsuitable for complex\nenvironmental monitoring needs.In recent years, machine learning has been\nwidely applied to anomaly detection, but the multi-dimensional features and\nspatiotemporal dynamics of environmental ecological data, especially the\nlong-term dependencies and strong variability in the time dimension, limit the\neffectiveness of traditional methods.Deep learning, with its ability to\nautomatically learn features, captures complex nonlinear relationships,\nimproving detection performance. However, its application in environmental\nmonitoring is still in its early stages and requires further exploration.This\npaper introduces a new deep learning method, Time-EAPCR\n(Time-Embedding-Attention-Permutated CNN-Residual), and applies it to\nenvironmental science. The method uncovers feature correlations, captures\ntemporal evolution patterns, and enables precise anomaly detection in\nenvironmental systems.We validated Time-EAPCR's high accuracy and robustness\nacross four publicly available environmental datasets. Experimental results\nshow that the method efficiently handles multi-source data, improves detection\naccuracy, and excels across various scenarios with strong adaptability and\ngeneralisation. Additionally, a real-world river monitoring dataset confirmed\nthe feasibility of its deployment, providing reliable technical support for\nenvironmental monitoring.\n", "date": "2025-03-12", "categories": []}
{"id": "2409.20503", "title": "What Information Contributes to Log-based Anomaly Detection? Insights\n  from a Configurable Transformer-Based Approach", "abstract": "  Log data are generated from logging statements in the source code, providing\ninsights into the execution processes of software applications and systems.\nState-of-the-art log-based anomaly detection approaches typically leverage deep\nlearning models to capture the semantic or sequential information in the log\ndata and detect anomalous runtime behaviors. However, the impacts of these\ndifferent types of information are not clear. In addition, most existing\napproaches ignore the timestamps in log data, which can potentially provide\nfine-grained sequential and temporal information. In this work, we propose a\nconfigurable Transformer-based anomaly detection model that can capture the\nsemantic, sequential, and temporal information in the log data and allows us to\nconfigure the different types of information as the model's features.\nAdditionally, we train and evaluate the proposed model using log sequences of\ndifferent lengths, thus overcoming the constraint of existing methods that rely\non fixed-length or time-windowed log sequences as inputs. With the proposed\nmodel, we conduct a series of experiments with different combinations of input\nfeatures to evaluate the roles of different types of information in anomaly\ndetection. The model can attain competitive and consistently stable performance\ncompared to the baselines when presented with log sequences of varying lengths.\nThe results indicate that the event occurrence information plays a key role in\nidentifying anomalies, while the impact of the sequential and temporal\ninformation is not significant for anomaly detection on the studied public\ndatasets. On the other hand, the findings also reveal the simplicity of the\nstudied public datasets and highlight the importance of constructing new\ndatasets that contain different types of anomalies to better evaluate the\nperformance of anomaly detection models.\n", "date": "2024-09-30", "categories": []}
{"id": "2503.02800", "title": "RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration", "abstract": "  Anomaly detection in complex industrial environments poses unique challenges,\nparticularly in contexts characterized by data sparsity and evolving\noperational conditions. Predictive maintenance (PdM) in such settings demands\nmethodologies that are adaptive, transferable, and capable of integrating\ndomain-specific knowledge. In this paper, we present RAAD-LLM, a novel\nframework for adaptive anomaly detection, leveraging large language models\n(LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach\naddresses the aforementioned PdM challenges. By effectively utilizing\ndomain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time\nseries data without requiring fine-tuning on specific datasets. The framework's\nadaptability mechanism enables it to adjust its understanding of normal\noperating conditions dynamically, thus increasing detection accuracy. We\nvalidate this methodology through a real-world application for a plastics\nmanufacturing plant and the Skoltech Anomaly Benchmark (SKAB). Results show\nsignificant improvements over our previous model with an accuracy increase from\n70.7% to 88.6% on the real-world dataset. By allowing for the enriching of\ninput series data with semantics, RAAD-LLM incorporates multimodal capabilities\nthat facilitate more collaborative decision-making between the model and plant\noperators. Overall, our findings support RAAD-LLM's ability to revolutionize\nanomaly detection methodologies in PdM, potentially leading to a paradigm shift\nin how anomaly detection is implemented across various industries.\n", "date": "2025-03-04", "categories": []}
{"id": "2503.07799", "title": "Self-supervised Normality Learning and Divergence Vector-guided Model\n  Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound\n  Videos", "abstract": "  Congenital Heart Disease (CHD) is one of the leading causes of fetal\nmortality, yet the scarcity of labeled CHD data and strict privacy regulations\nsurrounding fetal ultrasound (US) imaging present significant challenges for\nthe development of deep learning-based models for CHD detection. Centralised\ncollection of large real-world datasets for rare conditions, such as CHD, from\nlarge populations requires significant co-ordination and resource. In addition,\ndata governance rules increasingly prevent data sharing between sites. To\naddress these challenges, we introduce, for the first time, a novel\nprivacy-preserving, zero-shot CHD detection framework that formulates CHD\ndetection as a normality modeling problem integrated with model merging. In our\nframework dubbed Sparse Tube Ultrasound Distillation (STUD), each hospital site\nfirst trains a sparse video tube-based self-supervised video anomaly detection\n(VAD) model on normal fetal heart US clips with self-distillation loss. This\nenables site-specific models to independently learn the distribution of healthy\ncases. To aggregate knowledge across the decentralized models while maintaining\nprivacy, we propose a Divergence Vector-Guided Model Merging approach,\nDivMerge, that combines site-specific models into a single VAD model without\ndata exchange. Our approach preserves domain-agnostic rich spatio-temporal\nrepresentations, ensuring generalization to unseen CHD cases. We evaluated our\napproach on real-world fetal US data collected from 5 hospital sites. Our\nmerged model outperformed site-specific models by 23.77% and 30.13% in accuracy\nand F1-score respectively on external test sets.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.07942", "title": "STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute\n  Sensitive Applications", "abstract": "  This paper presents a new method for anomaly detection in automated systems\nwith time and compute sensitive requirements, such as autonomous driving, with\nunparalleled efficiency. As systems like autonomous driving become increasingly\npopular, ensuring their safety has become more important than ever. Therefore,\nthis paper focuses on how to quickly and effectively detect various anomalies\nin the aforementioned systems, with the goal of making them safer and more\neffective. Many detection systems have been developed with great success under\nspatial contexts; however, there is still significant room for improvement when\nit comes to temporal context. While there is substantial work regarding this\ntask, there is minimal work done regarding the efficiency of models and their\nability to be applied to scenarios that require real-time inference, i.e.,\nautonomous driving where anomalies need to be detected the moment they are\nwithin view. To address this gap, we propose STEAD (Spatio-Temporal Efficient\nAnomaly Detection), whose backbone is developed using (2+1)D Convolutions and\nPerformer Linear Attention, which ensures computational efficiency without\nsacrificing performance. When tested on the UCF-Crime benchmark, our base model\nachieves an AUC of 91.34%, outperforming the previous state-of-the-art, and our\nfast version achieves an AUC of 88.87%, while having 99.70% less parameters and\noutperforming the previous state-of-the-art as well. The code and pretrained\nmodels are made publicly available at https://github.com/agao8/STEAD\n", "date": "2025-03-11", "categories": []}
{"id": "2503.08045", "title": "Adapting Large Language Models for Parameter-Efficient Log Anomaly\n  Detection", "abstract": "  Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data\nthat are crucial to assessing the security and condition of systems. Although\nLarge Language Models (LLMs) have shown tremendous success in various fields,\nthe use of LLMs in enabling the detection of log anomalies is largely\nunexplored. This work aims to fill this gap. Due to the prohibitive costs\ninvolved in fully fine-tuning LLMs, we explore the use of parameter-efficient\nfine-tuning techniques (PEFTs) for adapting LLMs to LAD. To have an in-depth\nexploration of the potential of LLM-driven LAD, we present a comprehensive\ninvestigation of leveraging two of the most popular PEFTs -- Low-Rank\nAdaptation (LoRA) and Representation Fine-tuning (ReFT) -- to tap into three\nprominent LLMs of varying size, including RoBERTa, GPT-2, and Llama-3, for\nparameter-efficient LAD. Comprehensive experiments on four public log datasets\nare performed to reveal important insights into effective LLM-driven LAD in\nseveral key perspectives, including the efficacy of these PEFT-based LLM-driven\nLAD methods, their stability, sample efficiency, robustness w.r.t. unstable\nlogs, and cross-dataset generalization. Code is available at\nhttps://github.com/mala-lab/LogADReft.\n", "date": "2025-03-11", "categories": []}
{"id": "2404.06564", "title": "MambaAD: Exploring State Space Models for Multi-class Unsupervised\n  Anomaly Detection", "abstract": "  Recent advancements in anomaly detection have seen the efficacy of CNN- and\ntransformer-based approaches. However, CNNs struggle with long-range\ndependencies, while transformers are burdened by quadratic computational\ncomplexity. Mamba-based models, with their superior long-range modeling and\nlinear efficiency, have garnered substantial attention. This study pioneers the\napplication of Mamba to multi-class unsupervised anomaly detection, presenting\nMambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring\n(Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS\nmodule, integrating parallel cascaded (Hybrid State Space) HSS blocks and\nmulti-kernel convolutions operations, effectively captures both long-range and\nlocal information. The HSS block, utilizing (Hybrid Scanning) HS encoders,\nencodes feature maps into five scanning methods and eight directions, thereby\nstrengthening global connections through the (State Space Model) SSM. The use\nof Hilbert scanning and eight directions significantly improves feature\nsequence modeling. Comprehensive experiments on six diverse anomaly detection\ndatasets and seven metrics demonstrate state-of-the-art performance,\nsubstantiating the method's effectiveness. The code and models are available at\nhttps://lewandofskee.github.io/projects/MambaAD.\n", "date": "2024-04-09", "categories": []}
{"id": "2412.03342", "title": "UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly\n  Detection", "abstract": "  Visual Anomaly Detection (VAD) aims to identify abnormal samples in images\nthat deviate from normal patterns, covering multiple domains, including\nindustrial, logical, and medical fields. Due to the domain gaps between these\nfields, existing VAD methods are typically tailored to each domain, with\nspecialized detection techniques and model architectures that are difficult to\ngeneralize across different domains. Moreover, even within the same domain,\ncurrent VAD approaches often follow a \"one-category-one-model\" paradigm,\nrequiring large amounts of normal samples to train class-specific models,\nresulting in poor generalizability and hindering unified evaluation across\ndomains. To address this issue, we propose a generalized few-shot VAD method,\nUniVAD, capable of detecting anomalies across various domains, such as\nindustrial, logical, and medical anomalies, with a training-free unified model.\nUniVAD only needs few normal samples as references during testing to detect\nanomalies in previously unseen objects, without training on the specific\ndomain. Specifically, UniVAD employs a Contextual Component Clustering ($C^3$)\nmodule based on clustering and vision foundation models to segment components\nwithin the image accurately, and leverages Component-Aware Patch Matching\n(CAPM) and Graph-Enhanced Component Modeling (GECM) modules to detect anomalies\nat different semantic levels, which are aggregated to produce the final\ndetection result. We conduct experiments on nine datasets spanning industrial,\nlogical, and medical fields, and the results demonstrate that UniVAD achieves\nstate-of-the-art performance in few-shot anomaly detection tasks across\nmultiple domains, outperforming domain-specific anomaly detection models. Code\nis available at https://github.com/FantasticGNU/UniVAD.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.03442", "title": "State Frequency Estimation for Anomaly Detection", "abstract": "  Many works have studied the efficacy of state machines for detecting\nanomalies within NetFlows. These works typically learn a model from unlabeled\ndata and compute anomaly scores for arbitrary traces based on their likelihood\nof occurrence or how well they fit within the model. However, these methods do\nnot dynamically adapt their scores based on the traces seen at test time. This\nbecomes a problem when an adversary produces seemingly common traces in their\nattack, causing the model to miss the detection by assigning low anomaly\nscores. We propose SEQUENT, a new unsupervised approach that uses the state\nvisit frequency of a state machine to adapt its scoring dynamically for anomaly\ndetection. SEQUENT subsequently uses the scores to generate root causes for\nanomalies. These allow the grouping of alarms and simplify the analysis of\nanomalies. We evaluate SEQUENT's effectiveness in detecting network anomalies\non three publicly available NetFlow datasets and compare its performance\nagainst various existing unsupervised anomaly detection methods. Our evaluation\nshows promising results for using the state visit frequency of a state machine\nto detect network anomalies.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.13461", "title": "Look Inside for More: Internal Spatial Modality Perception for 3D\n  Anomaly Detection", "abstract": "  3D anomaly detection has recently become a significant focus in computer\nvision. Several advanced methods have achieved satisfying anomaly detection\nperformance. However, they typically concentrate on the external structure of\n3D samples and struggle to leverage the internal information embedded within\nsamples. Inspired by the basic intuition of why not look inside for more, we\nintroduce a straightforward method named Internal Spatial Modality\nPerception~(ISMP) to explore the feature representation from internal views\nfully. Specifically, our proposed ISMP consists of a critical perception\nmodule, Spatial Insight Engine~(SIE), which abstracts complex internal\ninformation of point clouds into essential global features. Besides, to better\nalign structural information with point data, we propose an enhanced key point\nfeature extraction module for amplifying spatial structure feature\nrepresentation. Simultaneously, a novel feature filtering module is\nincorporated to reduce noise and redundant features for further aligning\nprecise spatial structure. Extensive experiments validate the effectiveness of\nour proposed method, achieving object-level and pixel-level AUROC improvements\nof 3.2\\% and 13.1\\%, respectively, on the Real3D-AD benchmarks. Note that the\nstrong generalization ability of SIE has been theoretically proven and is\nverified in both classification and segmentation tasks.\n", "date": "2024-12-18", "categories": []}
{"id": "2412.17210", "title": "Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection", "abstract": "  Video Anomaly Detection (VAD) is essential for computer vision research.\nExisting VAD methods utilize either reconstruction-based or prediction-based\nframeworks. The former excels at detecting irregular patterns or structures,\nwhereas the latter is capable of spotting abnormal deviations or trends. We\naddress pose-based video anomaly detection and introduce a novel framework\ncalled Dual Conditioned Motion Diffusion (DCMD), which enjoys the advantages of\nboth approaches. The DCMD integrates conditioned motion and conditioned\nembedding to comprehensively utilize the pose characteristics and latent\nsemantics of observed movements, respectively. In the reverse diffusion\nprocess, a motion transformer is proposed to capture potential correlations\nfrom multi-layered characteristics within the spectrum space of human motion.\nTo enhance the discriminability between normal and abnormal instances, we\ndesign a novel United Association Discrepancy (UAD) regularization that\nprimarily relies on a Gaussian kernel-based time association and a\nself-attention-based global association. Finally, a mask completion strategy is\nintroduced during the inference stage of the reverse diffusion process to\nenhance the utilization of conditioned motion for the prediction branch of\nanomaly detection. Extensive experiments on four datasets demonstrate that our\nmethod dramatically outperforms state-of-the-art methods and exhibits superior\ngeneralization performance.\n", "date": "2024-12-23", "categories": []}
{"id": "2501.12295", "title": "Towards Accurate Unified Anomaly Segmentation", "abstract": "  Unsupervised anomaly detection (UAD) from images strives to model normal data\ndistributions, creating discriminative representations to distinguish and\nprecisely localize anomalies. Despite recent advancements in the efficient and\nunified one-for-all scheme, challenges persist in accurately segmenting\nanomalies for further monitoring. Moreover, this problem is obscured by the\nwidely-used AUROC metric under imbalanced UAD settings. This motivates us to\nemphasize the significance of precise segmentation of anomaly pixels using pAP\nand DSC as metrics. To address the unsolved segmentation task, we introduce the\nUnified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid\npipeline that progressively enhances normal information from coarse to fine,\nincorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer\nlayers to explicitly aggregate local details from different granularities.\nUniAS achieves state-of-the-art anomaly segmentation performance, attaining\n65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets,\nrespectively, surpassing previous methods significantly. The codes are shared\nat https://github.com/Mwxinnn/UniAS.\n", "date": "2025-01-21", "categories": []}
{"id": "2501.15211", "title": "\"Stones from Other Hills can Polish Jade\": Zero-shot Anomaly Image\n  Synthesis via Cross-domain Anomaly Injection", "abstract": "  Industrial image anomaly detection (IAD) is a pivotal topic with huge value.\nDue to anomaly's nature, real anomalies in a specific modern industrial domain\n(i.e. domain-specific anomalies) are usually too rare to collect, which\nseverely hinders IAD. Thus, zero-shot anomaly synthesis (ZSAS), which\nsynthesizes pseudo anomaly images without any domain-specific anomaly, emerges\nas a vital technique for IAD. However, existing solutions are either unable to\nsynthesize authentic pseudo anomalies, or require cumbersome training. Thus, we\nfocus on ZSAS and propose a brand-new paradigm that can realize both authentic\nand training-free ZSAS. It is based on a chronically-ignored fact: Although\ndomain-specific anomalies are rare, real anomalies from other domains (i.e.\ncross-domain anomalies) are actually abundant and directly applicable to ZSAS.\nSpecifically, our new ZSAS paradigm makes three-fold contributions: First, we\npropose a novel method named Cross-domain Anomaly Injection (CAI), which\ndirectly exploits cross-domain anomalies to enable highly authentic ZSAS in a\ntraining-free manner. Second, to supply CAI with sufficient cross-domain\nanomalies, we build the first Domain-agnostic Anomaly Dataset within our best\nknowledge, which provides ZSAS with abundant real anomaly patterns. Third, we\npropose a CAI-guided Diffusion Mechanism, which further breaks the quantity\nlimit of real anomalies and enable unlimited anomaly synthesis. Our\nhead-to-head comparison with existing ZSAS solutions justifies our paradigm's\nsuperior performance for IAD and demonstrates it as an effective and pragmatic\nZSAS solution.\n", "date": "2025-01-25", "categories": []}
{"id": "2503.06661", "title": "AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP", "abstract": "  Anomaly detection (AD) identifies outliers for applications like defect and\nlesion detection. While CLIP shows promise for zero-shot AD tasks due to its\nstrong generalization capabilities, its inherent Anomaly-Unawareness leads to\nlimited discrimination between normal and abnormal features. To address this\nproblem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly\ndiscrimination ability in both text and visual spaces while preserving its\ngeneralization capability. AA-CLIP is achieved through a straightforward yet\neffective two-stage approach: it first creates anomaly-aware text anchors to\ndifferentiate normal and abnormal semantics clearly, then aligns patch-level\nvisual features with these anchors for precise anomaly localization. This\ntwo-stage strategy, with the help of residual adapters, gradually adapts CLIP\nin a controlled manner, achieving effective AD while maintaining CLIP's class\nknowledge. Extensive experiments validate AA-CLIP as a resource-efficient\nsolution for zero-shot AD tasks, achieving state-of-the-art results in\nindustrial and medical applications. The code is available at\nhttps://github.com/Mwxinnn/AA-CLIP.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.07401", "title": "ECNN: A Low-complex, Adjustable CNN for Industrial Pump Monitoring Using\n  Vibration Data", "abstract": "  Industrial pumps are essential components in various sectors, such as\nmanufacturing, energy production, and water treatment, where their failures can\ncause significant financial and safety risks. Anomaly detection can be used to\nreduce those risks and increase reliability. In this work, we propose a novel\nenhanced convolutional neural network (ECNN) to predict the failure of an\nindustrial pump based on the vibration data captured by an acceleration sensor.\nThe convolutional neural network (CNN) is designed with a focus on low\ncomplexity to enable its implementation on edge devices with limited\ncomputational resources. Therefore, a detailed design space exploration is\nperformed to find a topology satisfying the trade-off between complexity and\naccuracy. Moreover, to allow for adaptation to unknown pumps, our algorithm\nfeatures a pump-specific parameter that can be determined by a small set of\nnormal data samples. Finally, we combine the ECNN with a threshold approach to\nfurther increase the performance and satisfy the application requirements. As a\nresult, our combined approach significantly outperforms a traditional\nstatistical approach and a classical CNN in terms of accuracy. To summarize,\nthis work provides a novel, low-complex, CNN-based algorithm that is enhanced\nby classical methods to offer high accuracy for anomaly detection of industrial\npumps.\n", "date": "2025-03-10", "categories": []}
{"id": "2503.05183", "title": "Spectral-Spatial Extraction through Layered Tensor Decomposition for\n  Hyperspectral Anomaly Detection", "abstract": "  Low rank tensor representation (LRTR) methods are very useful for\nhyperspectral anomaly detection (HAD). To overcome the limitations that they\noften overlook spectral anomaly and rely on large-scale matrix singular value\ndecomposition, we first apply non-negative matrix factorization (NMF) to\nalleviate spectral dimensionality redundancy and extract spectral anomaly and\nthen employ LRTR to extract spatial anomaly while mitigating spatial\nredundancy, yielding a highly efffcient layered tensor decomposition (LTD)\nframework for HAD. An iterative algorithm based on proximal alternating\nminimization is developed to solve the proposed LTD model, with convergence\nguarantees provided. Moreover, we introduce a rank reduction strategy with\nvalidation mechanism that adaptively reduces data size while preventing\nexcessive reduction. Theoretically, we rigorously establish the equivalence\nbetween the tensor tubal rank and tensor group sparsity regularization (TGSR)\nand, under mild conditions, demonstrate that the relaxed formulation of TGSR\nshares the same global minimizers and optimal values as its original\ncounterpart. Experimental results on the Airport-Beach-Urban and MVTec datasets\ndemonstrate that our approach outperforms state-of-the-art methods in the HAD\ntask.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.05520", "title": "Removing Geometric Bias in One-Class Anomaly Detection with Adaptive\n  Feature Perturbation", "abstract": "  One-class anomaly detection aims to detect objects that do not belong to a\npredefined normal class. In practice training data lack those anomalous\nsamples; hence state-of-the-art methods are trained to discriminate between\nnormal and synthetically-generated pseudo-anomalous data. Most methods use data\naugmentation techniques on normal images to simulate anomalies. However the\nbest-performing ones implicitly leverage a geometric bias present in the\nbenchmarking datasets. This limits their usability in more general conditions.\nOthers are relying on basic noising schemes that may be suboptimal in capturing\nthe underlying structure of normal data. In addition most still favour the\nimage domain to generate pseudo-anomalies training models end-to-end from only\nthe normal class and overlooking richer representations of the information. To\novercome these limitations we consider frozen yet rich feature spaces given by\npretrained models and create pseudo-anomalous features with a novel adaptive\nlinear feature perturbation technique. It adapts the noise distribution to each\nsample applies decaying linear perturbations to feature vectors and further\nguides the classification process using a contrastive learning objective.\nExperimental evaluation conducted on both standard and geometric bias-free\ndatasets demonstrates the superiority of our approach with respect to\ncomparable baselines. The codebase is accessible via our public repository.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.03729", "title": "Graph-Augmented LSTM for Forecasting Sparse Anomalies in\n  Graph-Structured Time Series", "abstract": "  Detecting anomalies in time series data is a critical task across many\ndomains. The challenge intensifies when anomalies are sparse and the data are\nmultivariate with relational dependencies across sensors or nodes. Traditional\nunivariate anomaly detectors struggle to capture such cross-node dependencies,\nparticularly in sparse anomaly settings. To address this, we propose a\ngraph-augmented time series forecasting approach that explicitly integrates the\ngraph of relationships among time series into an LSTM forecasting model. This\nenables the model to detect rare anomalies that might otherwise go unnoticed in\npurely univariate approaches. We evaluate the approach on two benchmark\ndatasets - the Yahoo Webscope S5 anomaly dataset and the METR-LA traffic sensor\nnetwork - and compare the performance of the Graph-Augmented LSTM against\nLSTM-only, ARIMA, and Prophet baselines. Results demonstrate that the\ngraph-augmented model achieves significantly higher precision and recall,\nimproving F1-score by up to 10% over the best baseline\n", "date": "2025-03-05", "categories": []}
{"id": "2502.09926", "title": "Robust Anomaly Detection via Tensor Pseudoskeleton Decomposition", "abstract": "  Anomaly detection plays a critical role in modern data-driven applications,\nfrom identifying fraudulent transactions and safeguarding network\ninfrastructure to monitoring sensor systems for irregular patterns. Traditional\napproaches, such as distance, density, or cluster-based methods, face\nsignificant challenges when applied to high dimensional tensor data, where\ncomplex interdependencies across dimensions amplify noise and computational\ncomplexity. To address these limitations, this paper leverages Tensor Chidori\npseudoskeleton decomposition within a tensor-robust principal component\nanalysis framework to extract low Tucker rank structure while isolating sparse\nanomalies, ensuring robustness to anomaly detection. We establish theoretical\nresults regarding convergence, and estimation error, demonstrating the\nstability and accuracy of the proposed approach. Numerical experiments on\nreal-world spatiotemporal data from New York City taxi trip records validate\nthe effectiveness of the proposed method in detecting anomalous urban events\ncompared to existing benchmark methods. Our results suggest that tensor\npseudoskeleton decomposition may offer potential for enhancing anomaly\ndetection in large-scale, high-dimensional data.\n", "date": "2025-02-14", "categories": []}
{"id": "2503.01100", "title": "Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in\n  Preprocessing Phase for 3D Anomaly Detection", "abstract": "  3D anomaly detection (AD) is prominent but difficult due to lacking a unified\ntheoretical foundation for preprocessing design. We establish the Fence\nTheorem, formalizing preprocessing as a dual-objective semantic isolator: (1)\nmitigating cross-semantic interference to the greatest extent feasible and (2)\nconfining anomaly judgments to aligned semantic spaces wherever viable, thereby\nestablishing intra-semantic comparability. Any preprocessing approach achieves\nthis goal through a two-stage process of Emantic-Division and\nSpatial-Constraints stage. Through systematic deconstruction, we theoretically\nand experimentally subsume existing preprocessing methods under this theorem\nvia tripartite evidence: qualitative analyses, quantitative studies, and\nmathematical proofs. Guided by the Fence Theorem, we implement Patch3D,\nconsisting of Patch-Cutting and Patch-Matching modules, to segment semantic\nspaces and consolidate similar ones while independently modeling normal\nfeatures within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with\ndifferent settings demonstrate that progressively finer-grained semantic\nalignment in preprocessing directly enhances point-level AD accuracy, providing\ninverse validation of the theorem's causal logic.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.02234", "title": "Anomaly detection in non-stationary videos using time-recursive\n  differencing network based prediction", "abstract": "  Most videos, including those captured through aerial remote sensing, are\nusually non-stationary in nature having time-varying feature statistics.\nAlthough, sophisticated reconstruction and prediction models exist for video\nanomaly detection, effective handling of non-stationarity has seldom been\nconsidered explicitly. In this paper, we propose to perform prediction using a\ntime-recursive differencing network followed by autoregressive moving average\nestimation for video anomaly detection. The differencing network is employed to\neffectively handle non-stationarity in video data during the anomaly detection.\nFocusing on the prediction process, the effectiveness of the proposed approach\nis demonstrated considering a simple optical flow based video feature, and by\ngenerating qualitative and quantitative results on three aerial video datasets\nand two standard anomaly detection video datasets. EER, AUC and ROC curve based\ncomparison with several existing methods including the state-of-the-art reveal\nthe superiority of the proposed approach.\n", "date": "2025-03-04", "categories": []}
{"id": "2503.02691", "title": "Memory Efficient Continual Learning for Edge-Based Visual Anomaly\n  Detection", "abstract": "  Visual Anomaly Detection (VAD) is a critical task in computer vision with\nnumerous real-world applications. However, deploying these models on edge\ndevices presents significant challenges, such as constrained computational and\nmemory resources. Additionally, dynamic data distributions in real-world\nsettings necessitate continuous model adaptation, further complicating\ndeployment under limited resources. To address these challenges, we present a\nnovel investigation into the problem of Continual Learning for Visual Anomaly\nDetection (CLAD) on edge devices. We evaluate the STFPM approach, given its low\nmemory footprint on edge devices, which demonstrates good performance when\ncombined with the Replay approach. Furthermore, we propose to study the\nbehavior of a recently proposed approach, PaSTe, specifically designed for the\nedge but not yet explored in the Continual Learning context. Our results show\nthat PaSTe is not only a lighter version of STPFM, but it also achieves\nsuperior anomaly detection performance, improving the f1 pixel performance by\n10% with the Replay technique. In particular, the structure of PaSTe allows us\nto test it using a series of Compressed Replay techniques, reducing memory\noverhead by a maximum of 91.5% compared to the traditional Replay for STFPM.\nOur study proves the feasibility of deploying VAD models that adapt and learn\nincrementally on CLAD scenarios on resource-constrained edge devices.\n", "date": "2025-03-04", "categories": []}
{"id": "2405.15273", "title": "Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks\n  and Dual Adversarial Decoders", "abstract": "  Time series anomaly detection plays a vital role in a wide range of\napplications. Existing methods require training one specific model for each\ndataset, which exhibits limited generalization capability across different\ntarget datasets, hindering anomaly detection performance in various scenarios\nwith scarce training data. Aiming at this problem, we propose constructing a\ngeneral time series anomaly detection model, which is pre-trained on extensive\nmulti-domain datasets and can subsequently apply to a multitude of downstream\nscenarios. The significant divergence of time series data across different\ndomains presents two primary challenges in building such a general model: (1)\nmeeting the diverse requirements of appropriate information bottlenecks\ntailored to different datasets in one unified model, and (2) enabling\ndistinguishment between multiple normal and abnormal patterns, both are crucial\nfor effective anomaly detection in various target scenarios. To tackle these\ntwo challenges, we propose a General time series anomaly Detector with Adaptive\nBottlenecks and Dual Adversarial Decoders (DADA), which enables flexible\nselection of bottlenecks based on different data and explicitly enhances clear\ndifferentiation between normal and abnormal series. We conduct extensive\nexperiments on nine target datasets from different domains. After pre-training\non multi-domain data, DADA, serving as a zero-shot anomaly detector for these\ndatasets, still achieves competitive or even superior results compared to those\nmodels tailored to each specific dataset. The code is made available at\nhttps://github.com/decisionintelligence/DADA.\n", "date": "2024-05-24", "categories": []}
{"id": "2406.00987", "title": "Enhancing Fairness in Unsupervised Graph Anomaly Detection through\n  Disentanglement", "abstract": "  Graph anomaly detection (GAD) is increasingly crucial in various\napplications, ranging from financial fraud detection to fake news detection.\nHowever, current GAD methods largely overlook the fairness problem, which might\nresult in discriminatory decisions skewed toward certain demographic groups\ndefined on sensitive attributes (e.g., gender, religion, ethnicity, etc.). This\ngreatly limits the applicability of these methods in real-world scenarios in\nlight of societal and ethical restrictions. To address this critical gap, we\nmake the first attempt to integrate fairness with utility in GAD\ndecision-making. Specifically, we devise a novel DisEntangle-based\nFairnEss-aware aNomaly Detection framework on the attributed graph, named\nDEFEND. DEFEND first introduces disentanglement in GNNs to capture informative\nyet sensitive-irrelevant node representations, effectively reducing societal\nbias inherent in graph representation learning. Besides, to alleviate\ndiscriminatory bias in evaluating anomalous nodes, DEFEND adopts a\nreconstruction-based anomaly detection, which concentrates solely on node\nattributes without incorporating any graph structure. Additionally, given the\ninherent association between input and sensitive attributes, DEFEND constrains\nthe correlation between the reconstruction error and the predicted sensitive\nattributes. Our empirical evaluations on real-world datasets reveal that DEFEND\nperforms effectively in GAD and significantly enhances fairness compared to\nstate-of-the-art baselines. To foster reproducibility, our code is available at\nhttps://github.com/AhaChang/DEFEND.\n", "date": "2024-06-03", "categories": []}
{"id": "2408.11561", "title": "Self-Supervised Iterative Refinement for Anomaly Detection in Industrial\n  Quality Control", "abstract": "  This study introduces the Iterative Refinement Process (IRP), a robust\nanomaly detection methodology designed for high-stakes industrial quality\ncontrol. The IRP enhances defect detection accuracy through a cyclic data\nrefinement strategy, iteratively removing misleading data points to improve\nmodel performance and robustness. We validate the IRP's effectiveness using two\nbenchmark datasets, Kolektor SDD2 (KSDD2) and MVTec AD, covering a wide range\nof industrial products and defect types. Our experimental results demonstrate\nthat the IRP consistently outperforms traditional anomaly detection models,\nparticularly in environments with high noise levels. This study highlights the\nIRP's potential to significantly enhance anomaly detection processes in\nindustrial settings, effectively managing the challenges of sparse and noisy\ndata.\n", "date": "2024-08-21", "categories": []}
{"id": "2503.00036", "title": "A Novel Spatiotemporal Correlation Anomaly Detection Method Based on\n  Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in\n  Wireless Sensor Network", "abstract": "  Attention-based transformers have played an important role in wireless sensor\nnetwork (WSN) timing anomaly detection due to their ability to capture\nlong-term dependencies. However, there are several issues that must be\naddressed, such as the fact that their ability to capture long-term\ndependencies is not completely reliable, their computational complexity levels\nare high, and the spatiotemporal features of WSN timing data are not\nsufficiently extracted for detecting the correlation anomalies of multinode WSN\ntiming data. To address these limitations, this paper proposes a WSN anomaly\ndetection method that integrates frequency-domain features with dynamic graph\nneural networks (GNN) under a designed self-encoder reconstruction framework.\nFirst, the discrete wavelet transform effectively decomposes trend and seasonal\ncomponents of time series to solve the poor long-term reliability of\ntransformers. Second, a frequency-domain attention mechanism is designed to\nmake full use of the difference between the amplitude distributions of normal\ndata and anomalous data in this domain. Finally, a multimodal fusion-based\ndynamic graph convolutional network (MFDGCN) is designed by combining an\nattention mechanism and a graph convolutional network (GCN) to adaptively\nextract spatial correlation features. A series of experiments conducted on\npublic datasets and their results demonstrate that the anomaly detection method\ndesigned in this paper exhibits superior precision and recall than the existing\nmethods do, with an F1 score of 93.5%, representing an improvement of 2.9% over\nthat of the existing models.\n", "date": "2025-02-25", "categories": []}
{"id": "2503.00348", "title": "SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and\n  Mapping", "abstract": "  The increasing frequency of environmental hazards due to climate change\nunderscores the urgent need for effective monitoring systems. Current\napproaches either rely on expensive labelled datasets, struggle with seasonal\nvariations, or require multiple observations for confirmation (which delays\ndetection). To address these challenges, this work presents SHAZAM -\nSelf-Supervised Change Monitoring for Hazard Detection and Mapping. SHAZAM uses\na lightweight conditional UNet to generate expected images of a region of\ninterest (ROI) for any day of the year, allowing for the direct modelling of\nnormal seasonal changes and the ability to distinguish potential hazards. A\nmodified structural similarity measure compares the generated images with\nactual satellite observations to compute region-level anomaly scores and\npixel-level hazard maps. Additionally, a theoretically grounded seasonal\nthreshold eliminates the need for dataset-specific optimisation. Evaluated on\nfour diverse datasets that contain bushfires (wildfires), burned regions,\nextreme and out-of-season snowfall, floods, droughts, algal blooms, and\ndeforestation, SHAZAM achieved F1 score improvements of between 0.066 and 0.234\nover existing methods. This was achieved primarily through more effective\nhazard detection (higher recall) while using only 473K parameters. SHAZAM\ndemonstrated superior mapping capabilities through higher spatial resolution\nand improved ability to suppress background features while accentuating both\nimmediate and gradual hazards. SHAZAM has been established as an effective and\ngeneralisable solution for hazard detection and mapping across different\ngeographical regions and a diverse range of hazards. The Python code is\navailable at: https://github.com/WiseGamgee/SHAZAM\n", "date": "2025-03-01", "categories": []}
{"id": "2503.00670", "title": "Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly\n  Detection in Videos", "abstract": "  Anomaly detection in videos is a challenging task as anomalies in different\nvideos are of different kinds. Therefore, a promising way to approach video\nanomaly detection is by learning the non-anomalous nature of the video at hand.\nTo this end, we propose a one-class few-shot learning driven transformer based\napproach for anomaly detection in videos that is self-context aware. Features\nfrom the first few consecutive non-anomalous frames in a video are used to\ntrain the transformer in predicting the non-anomalous feature of the subsequent\nframe. This takes place under the attention of a self-context learned from the\ninput features themselves. After the learning, given a few previous frames, the\nvideo-specific transformer is used to infer if a frame is anomalous or not by\ncomparing the feature predicted by it with the actual. The effectiveness of the\nproposed method with respect to the state-of-the-art is demonstrated through\nqualitative and quantitative results on different standard datasets. We also\nstudy the positive effect of the self-context used in our approach.\n", "date": "2025-03-02", "categories": []}
{"id": "2503.00871", "title": "CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection\n  in Cybersecurity Systems", "abstract": "  Cybersecurity systems are continuously producing a huge number of\ntime-stamped events in the form of high-order tensors, such as {count; time,\nport, flow duration, packet size, . . . }, and so how can we detect\nanomalies/intrusions in real time? How can we identify multiple types of\nintrusions and capture their characteristic behaviors? The tensor data consists\nof categorical and continuous attributes and the data distributions of\ncontinuous attributes typically exhibit skew. These data properties require\nhandling skewed infinite and finite dimensional spaces simultaneously. In this\npaper, we propose a novel streaming method, namely CyberCScope. The method\neffectively decomposes incoming tensors into major trends while explicitly\ndistinguishing between categorical and skewed continuous attributes. To our\nknowledge, it is the first to compute hybrid skewed infinite and finite\ndimensional decomposition. Based on this decomposition, it streamingly finds\ndistinct time-evolving patterns, enabling the detection of multiple types of\nanomalies. Extensive experiments on large-scale real datasets demonstrate that\nCyberCScope detects various intrusions with higher accuracy than\nstate-of-the-art baselines while providing meaningful summaries for the\nintrusions that occur in practice.\n", "date": "2025-03-02", "categories": []}
{"id": "2503.01184", "title": "Language-Assisted Feature Transformation for Anomaly Detection", "abstract": "  This paper introduces LAFT, a novel feature transformation method designed to\nincorporate user knowledge and preferences into anomaly detection using natural\nlanguage. Accurately modeling the boundary of normality is crucial for\ndistinguishing abnormal data, but this is often challenging due to limited data\nor the presence of nuisance attributes. While unsupervised methods that rely\nsolely on data without user guidance are common, they may fail to detect\nanomalies of specific interest. To address this limitation, we propose\nLanguage-Assisted Feature Transformation (LAFT), which leverages the shared\nimage-text embedding space of vision-language models to transform visual\nfeatures according to user-defined requirements. Combined with anomaly\ndetection methods, LAFT effectively aligns visual features with user\npreferences, allowing anomalies of interest to be detected. Extensive\nexperiments on both toy and real-world datasets validate the effectiveness of\nour method.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.01292", "title": "PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly\n  Awareness", "abstract": "  In industrial anomaly detection (IAD), accurately identifying defects amidst\ndiverse anomalies and under varying imaging conditions remains a significant\nchallenge. Traditional approaches often struggle with high false-positive\nrates, frequently misclassifying normal shadows and surface deformations as\ndefects, an issue that becomes particularly pronounced in products with complex\nand intricate surface features. To address these challenges, we introduce\nPA-CLIP, a zero-shot anomaly detection method that reduces background noise and\nenhances defect detection through a pseudo-anomaly-based framework. The\nproposed method integrates a multiscale feature aggregation strategy for\ncapturing detailed global and local information, two memory banks for\ndistinguishing background information, including normal patterns and\npseudo-anomalies, from true anomaly features, and a decision-making module\ndesigned to minimize false positives caused by environmental variations while\nmaintaining high defect sensitivity. Demonstrated on the MVTec AD and VisA\ndatasets, PA-CLIP outperforms existing zero-shot methods, providing a robust\nsolution for industrial defect detection.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.01569", "title": "Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection\n  in Industrial Inspection", "abstract": "  This study investigates the performance of robust anomaly detection models in\nindustrial inspection, focusing particularly on their ability to handle noisy\ndata. We propose to leverage the adaptation ability of meta learning approaches\nto identify and reject noisy training data to improve the learning process. In\nour model, we employ Model Agnostic Meta Learning (MAML) and an iterative\nrefinement process through an Inter-Quartile Range rejection scheme to enhance\ntheir adaptability and robustness. This approach significantly improves the\nmodels capability to distinguish between normal and defective conditions. Our\nresults of experiments conducted on well known MVTec and KSDD2 datasets\ndemonstrate that the proposed method not only excels in environments with\nsubstantial noise but can also contribute in case of a clear training set,\nisolating those samples that are relatively out of distribution, thus offering\nsignificant improvements over traditional models.\n", "date": "2025-03-03", "categories": []}
{"id": "2411.16767", "title": "Background-Aware Defect Generation for Robust Industrial Anomaly\n  Detection", "abstract": "  Detecting anomalies in industrial settings is challenging due to the scarcity\nof labeled anomalous data. Generative models can mitigate this issue by\nsynthesizing realistic defect samples, but existing approaches often fail to\nmodel the crucial interplay between defects and their background. This\noversight leads to unrealistic anomalies, especially in scenarios where\ncontextual consistency is essential (i.e., logical anomaly). To address this,\nwe propose a novel background-aware defect generation framework, where the\nbackground influences defect denoising without affecting the background itself\nby ensuring realistic synthesis while preserving structural integrity. Our\nmethod leverages a disentanglement loss to separate the background' s denoising\nprocess from the defect, enabling controlled defect synthesis through DDIM\nInversion. We theoretically demonstrate that our approach maintains background\nfidelity while generating contextually accurate defects. Extensive experiments\non MVTec AD and MVTec Loco benchmarks validate our mehtod's superiority over\nexisting techniques in both defect generation quality and anomaly detection\nperformance.\n", "date": "2024-11-25", "categories": []}
{"id": "2502.21051", "title": "Detection of anomalies in cow activity using wavelet transform based\n  features", "abstract": "  In Precision Livestock Farming, detecting deviations from optimal or baseline\nvalues - i.e. anomalies in time series - is essential to allow undertaking\ncorrective actions rapidly. Here we aim at detecting anomalies in 24h time\nseries of cow activity, with a view to detect cases of disease or oestrus.\nDeviations must be distinguished from noise which can be very high in case of\nbiological data. It is also important to detect the anomaly early, e.g. before\na farmer would notice it visually. Here, we investigate the benefit of using\nwavelet transforms to denoise data and we assess the performance of an anomaly\ndetection algorithm considering the timing of the detection. We developed\nfeatures based on the comparisons between the wavelet transforms of the mean of\nthe time series and the wavelet transforms of individual time series instances.\nWe hypothesized that these features contribute to the detection of anomalies in\nperiodic time series using a feature-based algorithm. We tested this hypothesis\nwith two datasets representing cow activity, which typically follows a daily\npattern but can deviate due to specific physiological or pathological\nconditions. We applied features derived from wavelet transform as well as\nstatistical features in an Isolation Forest algorithm. We measured the distance\nof detection between the days annotated abnormal by animal caretakers days and\nthe days predicted abnormal by the algorithm. The results show that\nwavelet-based features are among the features most contributing to anomaly\ndetection. They also show that detections are close to the annotated days, and\noften precede it. In conclusion, using wavelet transforms on time series of cow\nactivity data helps to detect anomalies related to specific cow states. The\ndetection is often obtained on days that precede the day annotated by\ncaretakers, which offer possibility to take corrective actions at an early\nstage.\n", "date": "2025-02-28", "categories": []}
{"id": "2501.11430", "title": "A Survey on Diffusion Models for Anomaly Detection", "abstract": "  Diffusion models (DMs) have emerged as a powerful class of generative AI\nmodels, showing remarkable potential in anomaly detection (AD) tasks across\nvarious domains, such as cybersecurity, fraud detection, healthcare, and\nmanufacturing. The intersection of these two fields, termed diffusion models\nfor anomaly detection (DMAD), offers promising solutions for identifying\ndeviations in increasingly complex and high-dimensional data. In this survey,\nwe review recent advances in DMAD research. We begin by presenting the\nfundamental concepts of AD and DMs, followed by a comprehensive analysis of\nclassic DM architectures including DDPMs, DDIMs, and Score SDEs. We further\ncategorize existing DMAD methods into reconstruction-based, density-based, and\nhybrid approaches, providing detailed examinations of their methodological\ninnovations. We also explore the diverse tasks across different data\nmodalities, encompassing image, time series, video, and multimodal data\nanalysis. Furthermore, we discuss critical challenges and emerging research\ndirections, including computational efficiency, model interpretability,\nrobustness enhancement, edge-cloud collaboration, and integration with large\nlanguage models. The collection of DMAD research papers and resources is\navailable at https://github.com/fdjingliu/DMAD.\n", "date": "2025-01-20", "categories": []}
{"id": "2502.19534", "title": "Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment\n  Without Retraining", "abstract": "  We propose a novel mechanism for real-time (human-in-the-loop) feedback\nfocused on false positive reduction to enhance anomaly detection models. It was\ndesigned for the lightweight deployment of a behavioral network anomaly\ndetection model. This methodology is easily integrable to similar domains that\nrequire a premium on throughput while maintaining high precision. In this\npaper, we introduce Retrieval Augmented Anomaly Detection, a novel method\ntaking inspiration from Retrieval Augmented Generation. Human annotated\nexamples are sent to a vector store, which can modify model outputs on the very\nnext processed batch for model inference. To demonstrate the generalization of\nthis technique, we benchmarked several different model architectures and\nmultiple data modalities, including images, text, and graph-based data.\n", "date": "2025-02-26", "categories": []}
{"id": "2502.18863", "title": "Sherlock: Towards Multi-scene Video Abnormal Event Extraction and\n  Localization via a Global-local Spatial-sensitive LLM", "abstract": "  Prior studies on Video Anomaly Detection (VAD) mainly focus on detecting\nwhether each video frame is abnormal or not in the video, which largely ignore\nthe structured video semantic information (i.e., what, when, and where does the\nabnormal event happen). With this in mind, we propose a new chat-paradigm\n\\textbf{M}ulti-scene Video Abnormal Event Extraction and Localization (M-VAE)\ntask, aiming to extract the abnormal event quadruples (i.e., subject, event\ntype, object, scene) and localize such event. Further, this paper believes that\nthis new task faces two key challenges, i.e., global-local spatial modeling and\nglobal-local spatial balancing. To this end, this paper proposes a Global-local\nSpatial-sensitive Large Language Model (LLM) named Sherlock, i.e., acting like\nSherlock Holmes to track down the criminal events, for this M-VAE task.\nSpecifically, this model designs a Global-local Spatial-enhanced MoE (GSM)\nmodule and a Spatial Imbalance Regulator (SIR) to address the two challenges\nrespectively. Extensive experiments on our M-VAE instruction dataset show the\nsignificant advantages of Sherlock over several advanced Video-LLMs. This\njustifies the importance of global-local spatial information for the M-VAE task\nand the effectiveness of Sherlock in capturing such information.\n", "date": "2025-02-26", "categories": []}
{"id": "2403.04405", "title": "Signature Isolation Forest", "abstract": "  Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly\nDetection (AD) algorithm designed for functional data. It relies on a tree\npartition procedure where an abnormality score is computed by projecting each\ncurve observation on a drawn dictionary through a linear inner product. Such\nlinear inner product and the dictionary are a priori choices that highly\ninfluence the algorithm's performances and might lead to unreliable results,\nparticularly with complex datasets. This work addresses these challenges by\nintroducing \\textit{Signature Isolation Forest}, a novel AD algorithm class\nleveraging the rough path theory's signature transform. Our objective is to\nremove the constraints imposed by FIF through the proposition of two algorithms\nwhich specifically target the linearity of the FIF inner product and the choice\nof the dictionary. We provide several numerical experiments, including a\nreal-world applications benchmark showing the relevance of our methods.\n", "date": "2024-03-07", "categories": []}
{"id": "2410.06549", "title": "DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector", "abstract": "  Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities\nwithin networks, garnering significant attention across various fields.\nTraditional unsupervised methods, which decode encoded latent representations\nof unlabeled data with a reconstruction focus, often fail to capture critical\ndiscriminative content, leading to suboptimal anomaly detection. To address\nthese challenges, we present a Diffusion-based Graph Anomaly Detector\n(DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm,\nmeticulously designed to enhance its proficiency by guiding it with\ndiscriminative content. This innovative approach leverages diffusion sampling\nto infuse the latent space with discriminative content and introduces a\ncontent-preservation mechanism that retains valuable information across\ndifferent scales, significantly improving its adeptness at identifying\nanomalies with limited time and space complexity. Our comprehensive evaluation\nof DiffGAD, conducted on six real-world and large-scale datasets with various\nmetrics, demonstrated its exceptional performance.\n", "date": "2024-10-09", "categories": []}
{"id": "2502.17762", "title": "A digital eye-fixation biomarker using a deep anomaly scheme to classify\n  Parkisonian patterns", "abstract": "  Oculomotor alterations constitute a promising biomarker to detect and\ncharacterize Parkinson's disease (PD), even in prodromal stages. Currently,\nonly global and simplified eye movement trajectories are employed to\napproximate the complex and hidden kinematic relationships of the oculomotor\nfunction. Recent advances on machine learning and video analysis have\nencouraged novel characterizations of eye movement patterns to quantify PD.\nThese schemes enable the identification of spatiotemporal segments primarily\nassociated with PD. However, they rely on discriminative models that require\nlarge training datasets and depend on balanced class distributions. This work\nintroduces a novel video analysis scheme to quantify Parkinsonian eye fixation\npatterns with an anomaly detection framework. Contrary to classical deep\ndiscriminative schemes that learn differences among labeled classes, the\nproposed approach is focused on one-class learning, avoiding the necessity of a\nsignificant amount of data. The proposed approach focuses only on Parkinson's\nrepresentation, considering any other class sample as an anomaly of the\ndistribution. This approach was evaluated for an ocular fixation task, in a\ntotal of 13 control subjects and 13 patients on different stages of the\ndisease. The proposed digital biomarker achieved an average sensitivity and\nspecificity of 0.97 and 0.63, respectively, yielding an AUC-ROC of 0.95. A\nstatistical test shows significant differences (p < 0.05) among predicted\nclasses, evidencing a discrimination between patients and control subjects.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.17961", "title": "Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment", "abstract": "  The normal operation of power equipment plays a critical role in the power\nsystem, making anomaly detection for power equipment highly significant. This\npaper proposes an improved YOLOv7x-based anomaly detection algorithm for power\nequipment. First, the ACmix convolutional mixed attention mechanism module is\nintroduced to effectively suppress background noise and irrelevant features,\nthereby enhancing the network's feature extraction capability. Second, the\nBiformer attention mechanism is added to the network to strengthen the focus on\nkey features, improving the network's ability to flexibly recognize feature\nimages. Finally, to more comprehensively evaluate the relationship between\npredicted and ground truth bounding boxes, the original loss function is\nreplaced with the MPDIoU function, addressing the issue of mismatched predicted\nbounding boxes. The improved algorithm enhances detection accuracy, achieving a\nmAP@0.5/% of 93.5% for all target categories, a precision of 97.1%, and a\nrecall of 97%.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.18328", "title": "From Vision to Sound: Advancing Audio Anomaly Detection with\n  Vision-Based Algorithms", "abstract": "  Recent advances in Visual Anomaly Detection (VAD) have introduced\nsophisticated algorithms leveraging embeddings generated by pre-trained feature\nextractors. Inspired by these developments, we investigate the adaptation of\nsuch algorithms to the audio domain to address the problem of Audio Anomaly\nDetection (AAD). Unlike most existing AAD methods, which primarily classify\nanomalous samples, our approach introduces fine-grained temporal-frequency\nlocalization of anomalies within the spectrogram, significantly improving\nexplainability. This capability enables a more precise understanding of where\nand when anomalies occur, making the results more actionable for end users. We\nevaluate our approach on industrial and environmental benchmarks, demonstrating\nthe effectiveness of VAD techniques in detecting anomalies in audio signals.\nMoreover, they improve explainability by enabling localized anomaly\nidentification, making audio anomaly detection systems more interpretable and\npractical.\n", "date": "2025-02-25", "categories": []}
{"id": "2502.16389", "title": "An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and\n  Behaviors in Autonomous Driving", "abstract": "  As automated vehicles enter public roads, safety in a near-infinite number of\ndriving scenarios becomes one of the major concerns for the widespread adoption\nof fully autonomous driving. The ability to detect anomalous situations outside\nof the operational design domain is a key component in self-driving cars,\nenabling us to mitigate the impact of abnormal ego behaviors and to realize\ntrustworthy driving systems. On-road anomaly detection in egocentric videos\nremains a challenging problem due to the difficulties introduced by complex and\ninteractive scenarios. We conduct a holistic analysis of common on-road anomaly\npatterns, from which we propose three unsupervised anomaly detection experts: a\nscene expert that focuses on frame-level appearances to detect abnormal scenes\nand unexpected scene motions; an interaction expert that models normal relative\nmotions between two road participants and raises alarms whenever anomalous\ninteractions emerge; and a behavior expert which monitors abnormal behaviors of\nindividual objects by future trajectory prediction. To combine the strengths of\nall the modules, we propose an expert ensemble (Xen) using a Kalman filter, in\nwhich the final anomaly score is absorbed as one of the states and the\nobservations are generated by the experts. Our experiments employ a novel\nevaluation protocol for realistic model performance, demonstrate superior\nanomaly detection performance than previous methods, and show that our\nframework has potential in classifying anomaly types using unsupervised\nlearning on a large-scale on-road anomaly dataset.\n", "date": "2025-02-23", "categories": []}
{"id": "2502.14880", "title": "KKA: Improving Vision Anomaly Detection through Anomaly-related\n  Knowledge from Large Language Models", "abstract": "  Vision anomaly detection, particularly in unsupervised settings, often\nstruggles to distinguish between normal samples and anomalies due to the wide\nvariability in anomalies. Recently, an increasing number of studies have\nfocused on generating anomalies to help detectors learn more effective\nboundaries between normal samples and anomalies. However, as the generated\nanomalies are often derived from random factors, they frequently lack realism.\nAdditionally, randomly generated anomalies typically offer limited support in\nconstructing effective boundaries, as most differ substantially from normal\nsamples and lie far from the boundary. To address these challenges, we propose\nKey Knowledge Augmentation (KKA), a method that extracts anomaly-related\nknowledge from large language models (LLMs). More specifically, KKA leverages\nthe extensive prior knowledge of LLMs to generate meaningful anomalies based on\nnormal samples. Then, KKA classifies the generated anomalies as easy anomalies\nand hard anomalies according to their similarity to normal samples. Easy\nanomalies exhibit significant differences from normal samples, whereas hard\nanomalies closely resemble normal samples. KKA iteratively updates the\ngenerated anomalies, and gradually increasing the proportion of hard anomalies\nto enable the detector to learn a more effective boundary. Experimental results\nshow that the proposed method significantly improves the performance of various\nvision anomaly detectors while maintaining low generation costs. The code for\nCMG can be found at https://github.com/Anfeather/KKA.\n", "date": "2025-02-14", "categories": []}
{"id": "2502.14197", "title": "Adaptive Sparsified Graph Learning Framework for Vessel Behavior\n  Anomalies", "abstract": "  Graph neural networks have emerged as a powerful tool for learning\nspatiotemporal interactions. However, conventional approaches often rely on\npredefined graphs, which may obscure the precise relationships being modeled.\nAdditionally, existing methods typically define nodes based on fixed spatial\nlocations, a strategy that is ill-suited for dynamic environments like maritime\nenvironments. Our method introduces an innovative graph representation where\ntimestamps are modeled as distinct nodes, allowing temporal dependencies to be\nexplicitly captured through graph edges. This setup is extended to construct a\nmulti-ship graph that effectively captures spatial interactions while\npreserving graph sparsity. The graph is processed using Graph Convolutional\nNetwork layers to capture spatiotemporal patterns, with a forecasting layer for\nfeature prediction and a Variational Graph Autoencoder for reconstruction,\nenabling robust anomaly detection.\n", "date": "2025-02-20", "categories": []}
{"id": "2502.14381", "title": "dtaianomaly: A Python library for time series anomaly detection", "abstract": "  dtaianomaly is an open-source Python library for time series anomaly\ndetection, designed to bridge the gap between academic research and real-world\napplications. Our goal is to (1) accelerate the development of novel\nstate-of-the-art anomaly detection techniques through simple extensibility; (2)\noffer functionality for large-scale experimental validation; and thereby (3)\nbring cutting-edge research to business and industry through a standardized\nAPI, similar to scikit-learn to lower the entry barrier for both new and\nexperienced users. Besides these key features, dtaianomaly offers (1) a broad\nrange of built-in anomaly detectors, (2) support for time series preprocessing,\n(3) tools for visual analysis, (4) confidence prediction of anomaly scores, (5)\nruntime and memory profiling, (6) comprehensive documentation, and (7)\ncross-platform unit testing.\n  The source code of dtaianomaly, documentation, code examples and installation\nguides are publicly available at https://github.com/ML-KULeuven/dtaianomaly.\n", "date": "2025-02-20", "categories": []}
{"id": "2404.04518", "title": "MedIAnomaly: A comparative study of anomaly detection in medical images", "abstract": "  Anomaly detection (AD) aims at detecting abnormal samples that deviate from\nthe expected normal patterns. Generally, it can be trained merely on normal\ndata, without a requirement for abnormal samples, and thereby plays an\nimportant role in rare disease recognition and health screening in the medical\ndomain. Despite the emergence of numerous methods for medical AD, the lack of a\nfair and comprehensive evaluation causes ambiguous conclusions and hinders the\ndevelopment of this field. To address this problem, this paper builds a\nbenchmark with unified comparison. Seven medical datasets with five image\nmodalities, including chest X-rays, brain MRIs, retinal fundus images,\ndermatoscopic images, and histopathology images, are curated for extensive\nevaluation. Thirty typical AD methods, including reconstruction and\nself-supervised learning-based methods, are involved in comparison of\nimage-level anomaly classification and pixel-level anomaly segmentation.\nFurthermore, for the first time, we systematically investigate the effect of\nkey components in existing methods, revealing unresolved challenges and\npotential future directions. The datasets and code are available at\nhttps://github.com/caiyu6666/MedIAnomaly.\n", "date": "2024-04-06", "categories": []}
{"id": "2409.09770", "title": "Cluster Aware Graph Anomaly Detection", "abstract": "  Graph anomaly detection has gained significant attention across various\ndomains, particularly in critical applications like fraud detection in\ne-commerce platforms and insider threat detection in cybersecurity. Usually,\nthese data are composed of multiple types (e.g., user information and\ntransaction records for financial data), thus exhibiting view heterogeneity.\nHowever, in the era of big data, the heterogeneity of views and the lack of\nlabel information pose substantial challenges to traditional approaches.\nExisting unsupervised graph anomaly detection methods often struggle with\nhigh-dimensionality issues, rely on strong assumptions about graph structures\nor fail to handle complex multi-view graphs. To address these challenges, we\npropose a cluster aware multi-view graph anomaly detection method, called CARE.\nOur approach captures both local and global node affinities by augmenting the\ngraph's adjacency matrix with the pseudo-label (i.e., soft membership\nassignments) without any strong assumption about the graph. To mitigate\npotential biases from the pseudo-label, we introduce a similarity-guided loss.\nTheoretically, we show that the proposed similarity-guided loss is a variant of\ncontrastive learning loss, and we present how this loss alleviates the bias\nintroduced by pseudo-label with the connection to graph spectral clustering.\nExperimental results on several datasets demonstrate the effectiveness and\nefficiency of our proposed framework. Specifically, CARE outperforms the\nsecond-best competitors by more than 39% on the Amazon dataset with respect to\nAUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our\nmethod is available at the GitHub link:\nhttps://github.com/zhenglecheng/CARE-demo.\n", "date": "2024-09-15", "categories": []}
{"id": "2502.13318", "title": "VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly\n  Detection", "abstract": "  Anomaly detection (AD) is a fundamental task for time-series analytics with\nimportant implications for the downstream performance of many applications. In\ncontrast to other domains where AD mainly focuses on point-based anomalies\n(i.e., outliers in standalone observations), AD for time series is also\nconcerned with range-based anomalies (i.e., outliers spanning multiple\nobservations). Nevertheless, it is common to use traditional point-based\ninformation retrieval measures, such as Precision, Recall, and F-score, to\nassess the quality of methods by thresholding the anomaly score to mark each\npoint as an anomaly or not. However, mapping discrete labels into continuous\ndata introduces unavoidable shortcomings, complicating the evaluation of\nrange-based anomalies. Notably, the choice of evaluation measure may\nsignificantly bias the experimental outcome. Despite over six decades of\nattention, there has never been a large-scale systematic quantitative and\nqualitative analysis of time-series AD evaluation measures. This paper\nextensively evaluates quality measures for time-series AD to assess their\nrobustness under noise, misalignments, and different anomaly cardinality\nratios. Our results indicate that measures producing quality values\nindependently of a threshold (i.e., AUC-ROC and AUC-PR) are more suitable for\ntime-series AD. Motivated by this observation, we first extend the AUC-based\nmeasures to account for range-based anomalies. Then, we introduce a new family\nof parameter-free and threshold-independent measures, Volume Under the Surface\n(VUS), to evaluate methods while varying parameters. We also introduce two\noptimized implementations for VUS that reduce significantly the execution time\nof the initial implementation. Our findings demonstrate that our four measures\nare significantly more robust in assessing the quality of time-series AD\nmethods.\n", "date": "2025-02-18", "categories": []}
{"id": "2502.05761", "title": "3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised\n  Anomaly", "abstract": "  Industrial anomaly detection achieves progress thanks to datasets such as\nMVTec-AD and VisA. However, they suffer from limitations in terms of the number\nof defect samples, types of defects, and availability of real-world scenes.\nThese constraints inhibit researchers from further exploring the performance of\nindustrial detection with higher accuracy. To this end, we propose a new\nlarge-scale anomaly detection dataset called 3CAD, which is derived from real\n3C production lines. Specifically, the proposed 3CAD includes eight different\ntypes of manufactured parts, totaling 27,039 high-resolution images labeled\nwith pixel-level anomalies. The key features of 3CAD are that it covers\nanomalous regions of different sizes, multiple anomaly types, and the\npossibility of multiple anomalous regions and multiple anomaly types per\nanomaly image. This is the largest and first anomaly detection dataset\ndedicated to 3C product quality control for community exploration and\ndevelopment. Meanwhile, we introduce a simple yet effective framework for\nunsupervised anomaly detection: a Coarse-to-Fine detection paradigm with\nRecovery Guidance (CFRG). To detect small defect anomalies, the proposed CFRG\nutilizes a coarse-to-fine detection paradigm. Specifically, we utilize a\nheterogeneous distillation model for coarse localization and then fine\nlocalization through a segmentation model. In addition, to better capture\nnormal patterns, we introduce recovery features as guidance. Finally, we report\nthe results of our CFRG framework and popular anomaly detection methods on the\n3CAD dataset, demonstrating strong competitiveness and providing a highly\nchallenging benchmark to promote the development of the anomaly detection\nfield. Data and code are available: https://github.com/EnquanYang2022/3CAD.\n", "date": "2025-02-09", "categories": []}
{"id": "2502.09001", "title": "Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection:\n  Balancing Security and Data Protection", "abstract": "  Privacy-preserving network anomaly detection has become an essential area of\nresearch due to growing concerns over the protection of sensitive data.\nTraditional anomaly detection models often prioritize accuracy while neglecting\nthe critical aspect of privacy. In this work, we propose a hybrid ensemble\nmodel that incorporates privacy-preserving techniques to address both detection\naccuracy and data protection. Our model combines the strengths of several\nmachine learning algorithms, including K-Nearest Neighbors (KNN), Support\nVector Machines (SVM), XGBoost, and Artificial Neural Networks (ANN), to create\na robust system capable of identifying network anomalies while ensuring\nprivacy. The proposed approach integrates advanced preprocessing techniques\nthat enhance data quality and address the challenges of small sample sizes and\nimbalanced datasets. By embedding privacy measures into the model design, our\nsolution offers a significant advancement over existing methods, ensuring both\nenhanced detection performance and strong privacy safeguards.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.11307", "title": "Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly\n  Detection", "abstract": "  Anomaly detection (AD) in 3D point clouds is crucial in a wide range of\nindustrial applications, especially in various forms of precision\nmanufacturing. Considering the industrial demand for reliable 3D AD, several\nmethods have been developed. However, most of these approaches typically\nrequire training separate models for each category, which is memory-intensive\nand lacks flexibility. In this paper, we propose a novel Point-Language model\nwith dual-prompts for 3D ANomaly dEtection (PLANE). The approach leverages\nmulti-modal prompts to extend the strong generalization capabilities of\npre-trained Point-Language Models (PLMs) to the domain of 3D point cloud AD,\nachieving impressive detection performance across multiple categories using a\nsingle model. Specifically, we propose a dual-prompt learning method,\nincorporating both text and point cloud prompts. The method utilizes a dynamic\nprompt creator module (DPCM) to produce sample-specific dynamic prompts, which\nare then integrated with class-specific static prompts for each modality,\neffectively driving the PLMs. Additionally, based on the characteristics of\npoint cloud data, we propose a pseudo 3D anomaly generation method (Ano3D) to\nimprove the model's detection capabilities in an unsupervised setting.\nExperimental results demonstrate that the proposed method, which is under the\nmulti-class-one-model paradigm, achieves a +8.7%/+17% gain on anomaly detection\nand localization performance as compared to the state-of-the-art\none-class-one-model methods for the Anomaly-ShapeNet dataset, and obtains\n+4.3%/+4.1% gain for the Real3D-AD dataset. Code will be available upon\npublication.\n", "date": "2025-02-16", "categories": []}
{"id": "2502.11669", "title": "Deep Subspace Learning for Surface Anomaly Classification Based on 3D\n  Point Cloud Data", "abstract": "  Surface anomaly classification is critical for manufacturing system fault\ndiagnosis and quality control. However, the following challenges always hinder\naccurate anomaly classification in practice: (i) Anomaly patterns exhibit\nintra-class variation and inter-class similarity, presenting challenges in the\naccurate classification of each sample. (ii) Despite the predefined classes,\nnew types of anomalies can occur during production that require to be detected\naccurately. (iii) Anomalous data is rare in manufacturing processes, leading to\nlimited data for model learning. To tackle the above challenges simultaneously,\nthis paper proposes a novel deep subspace learning-based 3D anomaly\nclassification model. Specifically, starting from a lightweight encoder to\nextract the latent representations, we model each class as a subspace to\naccount for the intra-class variation, while promoting distinct subspaces of\ndifferent classes to tackle the inter-class similarity. Moreover, the explicit\nmodeling of subspaces offers the capability to detect out-of-distribution\nsamples, i.e., new types of anomalies, and the regularization effect with much\nfewer learnable parameters of our proposed subspace classifier, compared to the\npopular Multi-Layer Perceptions (MLPs). Extensive numerical experiments\ndemonstrate our method achieves better anomaly classification results than\nbenchmark methods, and can effectively identify the new types of anomalies.\n", "date": "2025-02-17", "categories": []}
{"id": "2502.11712", "title": "Component-aware Unsupervised Logical Anomaly Generation for Industrial\n  Anomaly Detection", "abstract": "  Anomaly detection is critical in industrial manufacturing for ensuring\nproduct quality and improving efficiency in automated processes. The scarcity\nof anomalous samples limits traditional detection methods, making anomaly\ngeneration essential for expanding the data repository. However, recent\ngenerative models often produce unrealistic anomalies increasing false\npositives, or require real-world anomaly samples for training. In this work, we\ntreat anomaly generation as a compositional problem and propose ComGEN, a\ncomponent-aware and unsupervised framework that addresses the gap in logical\nanomaly generation. Our method comprises a multi-component learning strategy to\ndisentangle visual components, followed by subsequent generation editing\nprocedures. Disentangled text-to-component pairs, revealing intrinsic logical\nconstraints, conduct attention-guided residual mapping and model training with\niteratively matched references across multiple scales. Experiments on the\nMVTecLOCO dataset confirm the efficacy of ComGEN, achieving the best AUROC\nscore of 91.2%. Additional experiments on the real-world scenario of Diesel\nEngine and widely-used MVTecAD dataset demonstrate significant performance\nimprovements when integrating simulated anomalies generated by ComGEN into\nautomated production workflows.\n", "date": "2025-02-17", "categories": []}
{"id": "2502.11854", "title": "Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on\n  the CICIoMT2024 Dataset", "abstract": "  The rapid proliferation of Internet of Medical Things (IoMT) devices in\nhealthcare has introduced unique cybersecurity challenges, primarily due to the\ndiverse communication protocols and critical nature of these devices This\nresearch aims to develop an advanced, real-time anomaly detection framework\ntailored for IoMT network traffic, leveraging AI/ML models and the CICIoMT2024\ndataset By integrating multi-protocol (MQTT, WiFi), attack-specific (DoS,\nDDoS), time-series (active/idle states), and device-specific (Bluetooth) data,\nour study captures a comprehensive range of IoMT interactions As part of our\ndata analysis, various machine learning techniques are employed which include\nan ensemble model using XGBoost for improved performance against specific\nattack types, sequential models comprised of LSTM and CNN-LSTM that leverage\ntime dependencies, and unsupervised models such as Autoencoders and Isolation\nForest that are good in general anomaly detection The results of the experiment\nprove with an ensemble model lowers false positive rates and reduced\ndetections.\n", "date": "2025-02-17", "categories": []}
{"id": "2303.13194", "title": "Complementary Pseudo Multimodal Feature for Point Cloud Anomaly\n  Detection", "abstract": "  Point cloud (PCD) anomaly detection steadily emerges as a promising research\narea. This study aims to improve PCD anomaly detection performance by combining\nhandcrafted PCD descriptions with powerful pre-trained 2D neural networks. To\nthis end, this study proposes Complementary Pseudo Multimodal Feature (CPMF)\nthat incorporates local geometrical information in 3D modality using\nhandcrafted PCD descriptors and global semantic information in the generated\npseudo 2D modality using pre-trained 2D neural networks. For global semantics\nextraction, CPMF projects the origin PCD into a pseudo 2D modality containing\nmulti-view images. These images are delivered to pre-trained 2D neural networks\nfor informative 2D modality feature extraction. The 3D and 2D modality features\nare aggregated to obtain the CPMF for PCD anomaly detection. Extensive\nexperiments demonstrate the complementary capacity between 2D and 3D modality\nfeatures and the effectiveness of CPMF, with 95.15% image-level AU-ROC and\n92.93% pixel-level PRO on the MVTec3D benchmark. Code is available on\nhttps://github.com/caoyunkang/CPMF.\n", "date": "2023-03-23", "categories": []}
{"id": "2305.10724", "title": "Segment Any Anomaly without Training via Hybrid Prompt Regularization", "abstract": "  We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for\nzero-shot anomaly segmentation with hybrid prompt regularization to improve the\nadaptability of modern foundation models. Existing anomaly segmentation models\ntypically rely on domain-specific fine-tuning, limiting their generalization\nacross countless anomaly patterns. In this work, inspired by the great\nzero-shot generalization ability of foundation models like Segment Anything, we\nfirst explore their assembly to leverage diverse multi-modal prior knowledge\nfor anomaly localization. For non-parameter foundation model adaptation to\nanomaly segmentation, we further introduce hybrid prompts derived from domain\nexpert knowledge and target image context as regularization. Our proposed SAA+\nmodel achieves state-of-the-art performance on several anomaly segmentation\nbenchmarks, including VisA, MVTec-AD, MTD, and KSDD2, in the zero-shot setting.\nWe will release the code at\n\\href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}.\n", "date": "2023-05-18", "categories": []}
{"id": "2406.00625", "title": "SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection", "abstract": "  Visual anomaly detection is vital in real-world applications, such as\nindustrial defect detection and medical diagnosis. However, most existing\nmethods focus on local structural anomalies and fail to detect higher-level\nfunctional anomalies under logical conditions. Although recent studies have\nexplored logical anomaly detection, they can only address simple anomalies like\nmissing or addition and show poor generalizability due to being heavily\ndata-driven. To fill this gap, we propose SAM-LAD, a zero-shot, plug-and-play\nframework for logical anomaly detection in any scene. First, we obtain a query\nimage's feature map using a pre-trained backbone. Simultaneously, we retrieve\nthe reference images and their corresponding feature maps via the nearest\nneighbor search of the query image. Then, we introduce the Segment Anything\nModel (SAM) to obtain object masks of the query and reference images. Each\nobject mask is multiplied with the entire image's feature map to obtain object\nfeature maps. Next, an Object Matching Model (OMM) is proposed to match objects\nin the query and reference images. To facilitate object matching, we further\npropose a Dynamic Channel Graph Attention (DCGA) module, treating each object\nas a keypoint and converting its feature maps into feature vectors. Finally,\nbased on the object matching relations, an Anomaly Measurement Model (AMM) is\nproposed to detect objects with logical anomalies. Structural anomalies in the\nobjects can also be detected. We validate our proposed SAM-LAD using various\nbenchmarks, including industrial datasets (MVTec Loco AD, MVTec AD), and the\nlogical dataset (DigitAnatomy). Extensive experimental results demonstrate that\nSAM-LAD outperforms existing SoTA methods, particularly in detecting logical\nanomalies.\n", "date": "2024-06-02", "categories": []}
{"id": "2407.15795", "title": "AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot\n  Anomaly Detection", "abstract": "  Zero-shot anomaly detection (ZSAD) targets the identification of anomalies\nwithin images from arbitrary novel categories. This study introduces AdaCLIP\nfor the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP.\nAdaCLIP incorporates learnable prompts into CLIP and optimizes them through\ntraining on auxiliary annotated anomaly detection data. Two types of learnable\nprompts are proposed: static and dynamic. Static prompts are shared across all\nimages, serving to preliminarily adapt CLIP for ZSAD. In contrast, dynamic\nprompts are generated for each test image, providing CLIP with dynamic\nadaptation capabilities. The combination of static and dynamic prompts is\nreferred to as hybrid prompts, and yields enhanced ZSAD performance. Extensive\nexperiments conducted across 14 real-world anomaly detection datasets from\nindustrial and medical domains indicate that AdaCLIP outperforms other ZSAD\nmethods and can generalize better to different categories and even domains.\nFinally, our analysis highlights the importance of diverse auxiliary data and\noptimized prompts for enhanced generalization capacity. Code is available at\nhttps://github.com/caoyunkang/AdaCLIP.\n", "date": "2024-07-22", "categories": []}
{"id": "2409.01980", "title": "Large Language Models for Anomaly and Out-of-Distribution Detection: A\n  Survey", "abstract": "  Detecting anomalies or out-of-distribution (OOD) samples is critical for\nmaintaining the reliability and trustworthiness of machine learning systems.\nRecently, Large Language Models (LLMs) have demonstrated their effectiveness\nnot only in natural language processing but also in broader applications due to\ntheir advanced comprehension and generative capabilities. The integration of\nLLMs into anomaly and OOD detection marks a significant shift from the\ntraditional paradigm in the field. This survey focuses on the problem of\nanomaly and OOD detection under the context of LLMs. We propose a new taxonomy\nto categorize existing approaches into two classes based on the role played by\nLLMs. Following our proposed taxonomy, we further discuss the related work\nunder each of the categories and finally discuss potential challenges and\ndirections for future research in this field. We also provide an up-to-date\nreading list of relevant papers.\n", "date": "2024-09-03", "categories": []}
{"id": "2412.17263", "title": "VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual\n  Autoregressive Modeling", "abstract": "  This paper addresses a practical task: High-Resolution Image Anomaly\nDetection (HRIAD). In comparison to conventional image anomaly detection for\nlow-resolution images, HRIAD imposes a heavier computational burden and\nnecessitates superior global information capture capacity. To tackle HRIAD,\nthis paper translates image anomaly detection into visual token prediction and\nproposes VarAD based on visual autoregressive modeling for token prediction.\nSpecifically, VarAD first extracts multi-hierarchy and multi-directional visual\ntoken sequences, and then employs an advanced model, Mamba, for visual\nautoregressive modeling and token prediction. During the prediction process,\nVarAD effectively exploits information from all preceding tokens to predict the\ntarget token. Finally, the discrepancies between predicted tokens and original\ntokens are utilized to score anomalies. Comprehensive experiments on four\npublicly available datasets and a real-world button inspection dataset\ndemonstrate that the proposed VarAD achieves superior high-resolution image\nanomaly detection performance while maintaining lightweight, rendering VarAD a\nviable solution for HRIAD. Code is available at\n\\href{https://github.com/caoyunkang/VarAD}{\\url{https://github.com/caoyunkang/VarAD}}.\n", "date": "2024-12-23", "categories": []}
{"id": "2502.05679", "title": "Federated Learning with Reservoir State Analysis for Time Series Anomaly\n  Detection", "abstract": "  With a growing data privacy concern, federated learning has emerged as a\npromising framework to train machine learning models without sharing locally\ndistributed data. In federated learning, local model training by multiple\nclients and model integration by a server are repeated only through model\nparameter sharing. Most existing federated learning methods assume training\ndeep learning models, which are often computationally demanding. To deal with\nthis issue, we propose federated learning methods with reservoir state analysis\nto seek computational efficiency and data privacy protection simultaneously.\nSpecifically, our method relies on Mahalanobis Distance of Reservoir States\n(MD-RS) method targeting time series anomaly detection, which learns a\ndistribution of reservoir states for normal inputs and detects anomalies based\non a deviation from the learned distribution. Iterative updating of statistical\nparameters in the MD-RS enables incremental federated learning (IncFed MD-RS).\nWe evaluate the performance of IncFed MD-RS using benchmark datasets for time\nseries anomaly detection. The results show that IncFed MD-RS outperforms other\nfederated learning methods with deep learning and reservoir computing models\nparticularly when clients' data are relatively short and heterogeneous. We\ndemonstrate that IncFed MD-RS is robust against reduced sample data compared to\nother methods. We also show that the computational cost of IncFed MD-RS can be\nreduced by subsampling from the reservoir states without performance\ndegradation. The proposed method is beneficial especially in anomaly detection\napplications where computational efficiency, algorithm simplicity, and low\ncommunication cost are required.\n", "date": "2025-02-08", "categories": []}
{"id": "2502.10163", "title": "Enhancing anomaly detection with topology-aware autoencoders", "abstract": "  Anomaly detection in high-energy physics is essential for identifying new\nphysics beyond the Standard Model. Autoencoders provide a signal-agnostic\napproach but are limited by the topology of their latent space. This work\nexplores topology-aware autoencoders, embedding phase-space distributions onto\ncompact manifolds that reflect energy-momentum conservation. We construct\nautoencoders with spherical ($S^n$), product ($S^2 \\otimes S^2$), and\nprojective ($\\mathbb{RP}^2$) latent spaces and compare their anomaly detection\nperformance against conventional Euclidean embeddings. Our results show that\nautoencoders with topological priors significantly improve anomaly separation\nby preserving the global structure of the data manifold and reducing spurious\nreconstruction errors. Applying our approach to simulated hadronic top-quark\ndecays, we show that latent spaces with appropriate topological constraints\nenhance sensitivity and robustness in detecting anomalous events. This study\nestablishes topology-aware autoencoders as a powerful tool for unsupervised\nsearches for new physics in particle-collision data.\n", "date": "2025-02-14", "categories": []}
{"id": "2502.09088", "title": "Unsupervised Anomaly Detection on Implicit Shape representations for\n  Sarcopenia Detection", "abstract": "  Sarcopenia is an age-related progressive loss of muscle mass and strength\nthat significantly impacts daily life. A commonly studied criterion for\ncharacterizing the muscle mass has been the combination of 3D imaging and\nmanual segmentations. In this paper, we instead study the muscles' shape. We\nrely on an implicit neural representation (INR) to model normal muscle shapes.\nWe then introduce an unsupervised anomaly detection method to identify\nsarcopenic muscles based on the reconstruction error of the implicit model.\nRelying on a conditional INR with an auto-decoding strategy, we also learn a\nlatent representation of the muscles that clearly separates normal from\nabnormal muscles in an unsupervised fashion. Experimental results on a dataset\nof 103 segmented volumes indicate that our double anomaly detection strategy\neffectively discriminates sarcopenic and non-sarcopenic muscles.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.09325", "title": "A Benchmark for Crime Surveillance Video Analysis with Large Models", "abstract": "  Anomaly analysis in surveillance videos is a crucial topic in computer\nvision. In recent years, multimodal large language models (MLLMs) have\noutperformed task-specific models in various domains. Although MLLMs are\nparticularly versatile, their abilities to understand anomalous concepts and\ndetails are insufficiently studied because of the outdated benchmarks of this\nfield not providing MLLM-style QAs and efficient algorithms to assess the\nmodel's open-ended text responses. To fill this gap, we propose a benchmark for\ncrime surveillance video analysis with large models denoted as UCVL, including\n1,829 videos and reorganized annotations from the UCF-Crime and UCF-Crime\nAnnotation datasets. We design six types of questions and generate diverse QA\npairs. Then we develop detailed instructions and use OpenAI's GPT-4o for\naccurate assessment. We benchmark eight prevailing MLLMs ranging from 0.5B to\n40B parameters, and the results demonstrate the reliability of this bench.\nMoreover, we finetune LLaVA-OneVision on UCVL's training set. The improvement\nvalidates our data's high quality for video anomaly analysis.\n", "date": "2025-02-13", "categories": []}
{"id": "2404.07664", "title": "Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of\n  Out-of-Distribution Objects Using Prototypes", "abstract": "  Detecting and localising unknown or out-of-distribution (OOD) objects in any\nscene can be a challenging task in vision, particularly in safety-critical\ncases involving autonomous systems like automated vehicles or trains.\nSupervised anomaly segmentation or open-world object detection models depend on\ntraining on exhaustively annotated datasets for every domain and still struggle\nin distinguishing between background and OOD objects. In this work, we present\na plug-and-play framework - PRototype-based OOD detection Without Labels\n(PROWL). It is an inference-based method that does not require training on the\ndomain dataset and relies on extracting relevant features from self-supervised\npre-trained models. PROWL can be easily adapted to detect in-domain objects in\nany operational design domain (ODD) in a zero-shot manner by specifying a list\nof known classes from this domain. PROWL, as a first zero-shot unsupervised\nmethod, achieves state-of-the-art results on the RoadAnomaly and RoadObstacle\ndatasets provided in road driving benchmarks - SegmentMeIfYouCan (SMIYC) and\nFishyscapes, as well as comparable performance against existing supervised\nmethods trained without auxiliary OOD data. We also demonstrate its\ngeneralisability to other domains such as rail and maritime.\n", "date": "2024-04-11", "categories": []}
{"id": "2502.06918", "title": "Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business\n  Processes", "abstract": "  This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the\nLarge Language Models (LLM) from OpenAI, in detecting business process\nanomalies, with a focus on rework anomalies. In our study, we developed a\nGPT-4o-based tool capable of transforming event logs into a structured format\nand identifying reworked activities within business event logs. The analysis\nwas performed on a synthetic dataset designed to contain rework anomalies but\nfree of loops. To evaluate the anomaly detection capabilities of GPT\n4o-2024-08-06, we used three prompting techniques: zero-shot, one-shot, and\nfew-shot. These techniques were tested on different anomaly distributions,\nnamely normal, uniform, and exponential, to identify the most effective\napproach for each case. The results demonstrate the strong performance of\nGPT-4o-2024-08-06. On our dataset, the model achieved 96.14% accuracy with\none-shot prompting for the normal distribution, 97.94% accuracy with few-shot\nprompting for the uniform distribution, and 74.21% accuracy with few-shot\nprompting for the exponential distribution. These results highlight the model's\npotential as a reliable tool for detecting rework anomalies in event logs and\nhow anomaly distribution and prompting strategy influence the model's\nperformance.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.07389", "title": "FADE: Forecasting for Anomaly Detection on ECG", "abstract": "  Cardiovascular diseases, a leading cause of noncommunicable disease-related\ndeaths, require early and accurate detection to improve patient outcomes.\nTaking advantage of advances in machine learning and deep learning, multiple\napproaches have been proposed in the literature to address the challenge of\ndetecting ECG anomalies. Typically, these methods are based on the manual\ninterpretation of ECG signals, which is time consuming and depends on the\nexpertise of healthcare professionals. The objective of this work is to propose\na deep learning system, FADE, designed for normal ECG forecasting and anomaly\ndetection, which reduces the need for extensive labeled datasets and manual\ninterpretation. FADE has been trained in a self-supervised manner with a novel\nmorphological inspired loss function. Unlike conventional models that learn\nfrom labeled anomalous ECG waveforms, our approach predicts the future of\nnormal ECG signals, thus avoiding the need for extensive labeled datasets.\nUsing a novel distance function to compare forecasted ECG signals with actual\nsensor data, our method effectively identifies cardiac anomalies. Additionally,\nthis approach can be adapted to new contexts through domain adaptation\ntechniques. To evaluate our proposal, we performed a set of experiments using\ntwo publicly available datasets: MIT-BIH NSR and MIT-BIH Arrythmia. The results\ndemonstrate that our system achieves an average accuracy of 83.84% in anomaly\ndetection, while correctly classifying normal ECG signals with an accuracy of\n85.46%. Our proposed approach exhibited superior performance in the early\ndetection of cardiac anomalies in ECG signals, surpassing previous methods that\npredominantly identify a limited range of anomalies. FADE effectively detects\nboth abnormal heartbeats and arrhythmias, offering significant advantages in\nhealthcare through cost reduction or processing of large-scale ECG data.\n", "date": "2025-02-11", "categories": []}
{"id": "2405.18929", "title": "Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled\n  Data", "abstract": "  Semi-supervised anomaly detection, which aims to improve the anomaly\ndetection performance by using a small amount of labeled anomaly data in\naddition to unlabeled data, has attracted attention. Existing semi-supervised\napproaches assume that most unlabeled data are normal, and train anomaly\ndetectors by minimizing the anomaly scores for the unlabeled data while\nmaximizing those for the labeled anomaly data. However, in practice, the\nunlabeled data are often contaminated with anomalies. This weakens the effect\nof maximizing the anomaly scores for anomalies, and prevents us from improving\nthe detection performance. To solve this problem, we propose the deep\npositive-unlabeled anomaly detection framework, which integrates\npositive-unlabeled learning with deep anomaly detection models such as\nautoencoders and deep support vector data descriptions. Our approach enables\nthe approximation of anomaly scores for normal data using the unlabeled data\nand the labeled anomaly data. Therefore, without labeled normal data, our\napproach can train anomaly detectors by minimizing the anomaly scores for\nnormal data while maximizing those for the labeled anomaly data. Experiments on\nvarious datasets show that our approach achieves better detection performance\nthan existing approaches.\n", "date": "2024-05-29", "categories": []}
{"id": "2502.03245", "title": "Calibrated Unsupervised Anomaly Detection in Multivariate Time-series\n  using Reinforcement Learning", "abstract": "  This paper investigates unsupervised anomaly detection in multivariate\ntime-series data using reinforcement learning (RL) in the latent space of an\nautoencoder. A significant challenge is the limited availability of anomalous\ndata, often leading to misclassifying anomalies as normal events, thus raising\nfalse negatives. RL can help overcome this limitation by promoting exploration\nand balancing exploitation during training, effectively preventing overfitting.\nWavelet analysis is also utilized to enhance anomaly detection, enabling\ntime-series data decomposition into both time and frequency domains. This\napproach captures anomalies at multiple resolutions, with wavelet coefficients\nextracted to detect both sudden and subtle shifts in the data, thereby refining\nthe anomaly detection process. We calibrate the decision boundary by generating\nsynthetic anomalies and embedding a supervised framework within the model. This\nsupervised element aids the unsupervised learning process by fine-tuning the\ndecision boundary and increasing the model's capacity to distinguish between\nnormal and anomalous patterns effectively.\n", "date": "2025-02-05", "categories": []}
{"id": "2502.05392", "title": "Open Challenges in Time Series Anomaly Detection: An Industry\n  Perspective", "abstract": "  Current research in time-series anomaly detection is using definitions that\nmiss critical aspects of how anomaly detection is commonly used in practice. We\nlist several areas that are of practical relevance and that we believe are\neither under-investigated or missing entirely from the current discourse. Based\non an investigation of systems deployed in a cloud environment, we motivate the\nareas of streaming algorithms, human-in-the-loop scenarios, point processes,\nconditional anomalies and populations analysis of time series. This paper\nserves as a motivation and call for action, including opportunities for\ntheoretical and applied research, as well as for building new dataset and\nbenchmarks.\n", "date": "2025-02-08", "categories": []}
{"id": "2502.05494", "title": "Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection", "abstract": "  Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing\ncardiovascular conditions, yet anomaly detection in ECG signals remains\nchallenging due to their inherent complexity and variability. We propose\nMulti-scale Masked Autoencoder for ECG anomaly detection (MMAE-ECG), a novel\nend-to-end framework that effectively captures both global and local\ndependencies in ECG data. Unlike state-of-the-art methods that rely on\nheartbeat segmentation or R-peak detection, MMAE-ECG eliminates the need for\nsuch pre-processing steps, enhancing its suitability for clinical deployment.\nMMAE-ECG partitions ECG signals into non-overlapping segments, with each\nsegment assigned learnable positional embeddings. A novel multi-scale masking\nstrategy and multi-scale attention mechanism, along with distinct positional\nembeddings, enable a lightweight Transformer encoder to effectively capture\nboth local and global dependencies. The masked segments are then reconstructed\nusing a single-layer Transformer block, with an aggregation strategy employed\nduring inference to refine the outputs. Experimental results demonstrate that\nour method achieves performance comparable to state-of-the-art approaches while\nsignificantly reducing computational complexity-approximately 1/78 of the\nfloating-point operations (FLOPs) required for inference. Ablation studies\nfurther validate the effectiveness of each component, highlighting the\npotential of multi-scale masked autoencoders for anomaly detection.\n", "date": "2025-02-08", "categories": []}
{"id": "2502.05496", "title": "Feature Explosion: a generic optimization strategy for outlier detection\n  algorithms", "abstract": "  Outlier detection tasks aim at discovering potential issues or opportunities\nand are widely used in cybersecurity, financial security, industrial\ninspection, etc. To date, thousands of outlier detection algorithms have been\nproposed. Clearly, in real-world scenarios, such a large number of algorithms\nis unnecessary. In other words, a large number of outlier detection algorithms\nare redundant. We believe the root cause of this redundancy lies in the current\nhighly customized (i.e., non-generic) optimization strategies. Specifically,\nwhen researchers seek to improve the performance of existing outlier detection\nalgorithms, they have to design separate optimized versions tailored to the\nprinciples of each algorithm, leading to an ever-growing number of outlier\ndetection algorithms. To address this issue, in this paper, we introduce the\nexplosion from physics into the outlier detection task and propose a generic\noptimization strategy based on feature explosion, called OSD (Optimization\nStrategy for outlier Detection algorithms). In the future, when improving the\nperformance of existing outlier detection algorithms, it will be sufficient to\ninvoke the OSD plugin without the need to design customized optimized versions\nfor them. We compared the performances of 14 outlier detection algorithms on 24\ndatasets before and after invoking the OSD plugin. The experimental results\nshow that the performances of all outlier detection algorithms are improved on\nalmost all datasets. In terms of average accuracy, OSD make these outlier\ndetection algorithms improve by 15% (AUC), 63.7% (AP).\n", "date": "2025-02-08", "categories": []}
{"id": "2502.06194", "title": "Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting\n  in Anomaly Detection", "abstract": "  Unsupervised Continuous Anomaly Detection (UCAD) faces significant challenges\nin multi-task representation learning, with existing methods suffering from\nincomplete representation and catastrophic forgetting. Unlike supervised\nmodels, unsupervised scenarios lack prior information, making it difficult to\neffectively distinguish redundant and complementary multimodal features. To\naddress this, we propose the Multimodal Task Representation Memory Bank (MTRMB)\nmethod through two key technical innovations: A Key-Prompt-Multimodal Knowledge\n(KPMK) mechanism that uses concise key prompts to guide cross-modal feature\ninteraction between BERT and ViT. Refined Structure-based Contrastive Learning\n(RSCL) leveraging Grounding DINO and SAM to generate precise segmentation\nmasks, pulling features of the same structural region closer while pushing\ndifferent structural regions apart. Experiments on MVtec AD and VisA datasets\ndemonstrate MTRMB's superiority, achieving an average detection accuracy of\n0.921 at the lowest forgetting rate, significantly outperforming\nstate-of-the-art methods. We plan to open source on GitHub.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.04310", "title": "Finding Pegasus: Enhancing Unsupervised Anomaly Detection in\n  High-Dimensional Data using a Manifold-Based Approach", "abstract": "  Unsupervised machine learning methods are well suited to searching for\nanomalies at scale but can struggle with the high-dimensional representation of\nmany modern datasets, hence dimensionality reduction (DR) is often performed\nfirst. In this paper we analyse unsupervised anomaly detection (AD) from the\nperspective of the manifold created in DR. We present an idealised\nillustration, \"Finding Pegasus\", and a novel formal framework with which we\ncategorise AD methods and their results into \"on manifold\" and \"off manifold\".\nWe define these terms and show how they differ. We then use this insight to\ndevelop an approach of combining AD methods which significantly boosts AD\nrecall without sacrificing precision in situations employing high DR. When\ntested on MNIST data, our approach of combining AD methods improves recall by\nas much as 16 percent compared with simply combining with the best standalone\nAD method (Isolation Forest), a result which shows great promise for its\napplication to real-world data.\n", "date": "2025-02-06", "categories": []}
{"id": "2502.04219", "title": "NLP-Based .NET CLR Event Logs Analyzer", "abstract": "  In this paper, we present a tool for analyzing .NET CLR event logs based on a\nnovel method inspired by Natural Language Processing (NLP) approach. Our\nresearch addresses the growing need for effective monitoring and optimization\nof software systems through detailed event log analysis. We utilize a\nBERT-based architecture with an enhanced tokenization process customized to\nevent logs. The tool, developed using Python, its libraries, and an SQLite\ndatabase, allows both conducting experiments for academic purposes and\nefficiently solving industry-emerging tasks. Our experiments demonstrate the\nefficacy of our approach in compressing event sequences, detecting recurring\npatterns, and identifying anomalies. The trained model shows promising results,\nwith a high accuracy rate in anomaly detection, which demonstrates the\npotential of NLP methods to improve the reliability and stability of software\nsystems.\n", "date": "2025-02-06", "categories": []}
{"id": "2501.16718", "title": "Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution\n  Detection", "abstract": "  Out-of-distribution (OOD) detection is crucial for developing trustworthy and\nreliable machine learning systems. Recent advances in training with auxiliary\nOOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless,\nthese methods heavily rely on acquiring a large pool of high-quality natural\noutliers. Some prior methods try to alleviate this problem by synthesizing\nvirtual outliers but suffer from either poor quality or high cost due to the\nmonotonous sampling strategy and the heavy-parameterized generative models. In\nthis paper, we overcome all these problems by proposing the Hamiltonian Monte\nCarlo Outlier Synthesis (HamOS) framework, which views the synthesis process as\nsampling from Markov chains. Based solely on the in-distribution data, the\nMarkov chains can extensively traverse the feature space and generate diverse\nand representative outliers, hence exposing the model to miscellaneous\npotential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance\nrate almost close to 1 also makes our framework enjoy great efficiency. By\nempirically competing with SOTA baselines on both standard and large-scale\nbenchmarks, we verify the efficacy and efficiency of our proposed HamOS.\n", "date": "2025-01-28", "categories": []}
{"id": "2502.03201", "title": "SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection\n  with Extremely Limited Labels", "abstract": "  Node Anomaly Detection (NAD) has gained significant attention in the deep\nlearning community due to its diverse applications in real-world scenarios.\nExisting NAD methods primarily embed graphs within a single Euclidean space,\nwhile overlooking the potential of non-Euclidean spaces. Besides, to address\nthe prevalent issue of limited supervision in real NAD tasks, previous methods\ntend to leverage synthetic data to collect auxiliary information, which is not\nan effective solution as shown in our experiments. To overcome these\nchallenges, we introduce a novel SpaceGNN model designed for NAD tasks with\nextremely limited labels. Specifically, we provide deeper insights into a\ntask-relevant framework by empirically analyzing the benefits of different\nspaces for node representations, based on which, we design a Learnable Space\nProjection function that effectively encodes nodes into suitable spaces.\nBesides, we introduce the concept of weighted homogeneity, which we empirically\nand theoretically validate as an effective coefficient during information\npropagation. This concept inspires the design of the Distance Aware Propagation\nmodule. Furthermore, we propose the Multiple Space Ensemble module, which\nextracts comprehensive information for NAD under conditions of extremely\nlimited supervision. Our findings indicate that this module is more beneficial\nthan data augmentation techniques for NAD. Extensive experiments conducted on 9\nreal datasets confirm the superiority of SpaceGNN, which outperforms the best\nrival by an average of 8.55% in AUC and 4.31% in F1 scores. Our code is\navailable at https://github.com/xydong127/SpaceGNN.\n", "date": "2025-02-05", "categories": []}
{"id": "2210.16334", "title": "Learning to Detect Interesting Anomalies", "abstract": "  Anomaly detection algorithms are typically applied to static, unchanging,\ndata features hand-crafted by the user. But how does a user systematically\ncraft good features for anomalies that have never been seen? Here we couple\ndeep learning with active learning -- in which an Oracle iteratively labels\nsmall amounts of data selected algorithmically over a series of rounds -- to\nautomatically and dynamically improve the data features for efficient outlier\ndetection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10,\nand Galaxy-DESI data, significantly outperforming both standard anomaly\ndetection and active learning algorithms with static feature spaces. Beyond\nimproved performance, AHUNT also allows the number of anomaly classes to grow\norganically in response to Oracle's evaluations. Extensive ablation studies\nexplore the impact of Oracle question selection strategy and loss function on\nperformance. We illustrate how the dynamic anomaly class taxonomy represents\nanother step towards fully personalized rankings of different anomaly classes\nthat reflect a user's interests, allowing the algorithm to learn to ignore\nstatistically significant but uninteresting outliers (e.g., noise). This should\nprove useful in the era of massive astronomical datasets serving diverse sets\nof users who can only review a tiny subset of the incoming data.\n", "date": "2022-10-28", "categories": []}
{"id": "2409.01713", "title": "Interpreting Outliers in Time Series Data through Decoding Autoencoder", "abstract": "  Outlier detection is a crucial analytical tool in various fields. In critical\nsystems like manufacturing, malfunctioning outlier detection can be costly and\nsafety-critical. Therefore, there is a significant need for explainable\nartificial intelligence (XAI) when deploying opaque models in such\nenvironments. This study focuses on manufacturing time series data from a\nGerman automotive supply industry. We utilize autoencoders to compress the\nentire time series and then apply anomaly detection techniques to its latent\nfeatures. For outlier interpretation, we (i) adopt widely used XAI techniques\nto the autoencoder's encoder. Additionally, (ii) we propose AEE, Aggregated\nExplanatory Ensemble, a novel approach that fuses explanations of multiple XAI\ntechniques into a single, more expressive interpretation. For evaluation of\nexplanations, (iii) we propose a technique to measure the quality of encoder\nexplanations quantitatively. Furthermore, we qualitatively assess the\neffectiveness of outlier explanations with domain expertise.\n", "date": "2024-09-03", "categories": []}
{"id": "2501.17976", "title": "KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated\n  Recurrent Units", "abstract": "  Anomaly detection in real-world time-series data is a challenging task due to\nthe complex and nonlinear temporal dynamics involved. This paper introduces\nKoopAGRU, a new deep learning model designed to tackle this problem by\ncombining Fast Fourier Transform (FFT), Deep Dynamic Mode Decomposition\n(DeepDMD), and Koopman theory. FFT allows KoopAGRU to decompose temporal data\ninto time-variant and time-invariant components providing precise modeling of\ncomplex patterns. To better control these two components, KoopAGRU utilizes\nGate Recurrent Unit (GRU) encoders to learn Koopman observables, enhancing the\ndetection capability across multiple temporal scales. KoopAGRU is trained in a\nsingle process and offers fast inference times. Extensive tests on various\nbenchmark datasets show that KoopAGRU outperforms other leading methods,\nachieving a new average F1-score of 90.88\\% on the well-known anomalies\ndetection task of times series datasets, and proves to be efficient and\nreliable in detecting anomalies in real-world scenarios.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.18417", "title": "Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)", "abstract": "  Anomaly detection is essential for identifying rare and significant events\nacross diverse domains such as finance, cybersecurity, and network monitoring.\nThis paper presents Synthetic Anomaly Monitoring (SAM), an innovative approach\nthat applies synthetic control methods from causal inference to improve both\nthe accuracy and interpretability of anomaly detection processes. By modeling\nnormal behavior through the treatment of each feature as a control unit, SAM\nidentifies anomalies as deviations within this causal framework. We conducted\nextensive experiments comparing SAM with established benchmark models,\nincluding Isolation Forest, Local Outlier Factor (LOF), k-Nearest Neighbors\n(kNN), and One-Class Support Vector Machine (SVM), across five diverse\ndatasets, including Credit Card Fraud, HTTP Dataset CSIC 2010, and KDD Cup\n1999, among others. Our results demonstrate that SAM consistently delivers\nrobust performance, highlighting its potential as a powerful tool for real-time\nanomaly detection in dynamic and complex environments.\n", "date": "2025-01-30", "categories": []}
{"id": "2502.00413", "title": "Predictive modeling and anomaly detection in large-scale web portals\n  through the CAWAL framework", "abstract": "  This study presents an approach that uses session and page view data\ncollected through the CAWAL framework, enriched through specialized processes,\nfor advanced predictive modeling and anomaly detection in web usage mining\n(WUM) applications. Traditional WUM methods often rely on web server logs,\nwhich limit data diversity and quality. Integrating application logs with web\nanalytics, the CAWAL framework creates comprehensive session and page view\ndatasets, providing a more detailed view of user interactions and effectively\naddressing these limitations. This integration enhances data diversity and\nquality while eliminating the preprocessing stage required in conventional WUM,\nleading to greater process efficiency. The enriched datasets, created by\ncross-integrating session and page view data, were applied to advanced machine\nlearning models, such as Gradient Boosting and Random Forest, which are known\nfor their effectiveness in capturing complex patterns and modeling non-linear\nrelationships. These models achieved over 92% accuracy in predicting user\nbehavior and significantly improved anomaly detection capabilities. The results\nshow that this approach offers detailed insights into user behavior and system\nperformance metrics, making it a reliable solution for improving large-scale\nweb portals' efficiency, reliability, and scalability.\n", "date": "2025-02-01", "categories": []}
{"id": "2502.01201", "title": "One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection", "abstract": "  Traditional Anomaly Detection (AD) methods have predominantly relied on\nunsupervised learning from extensive normal data. Recent AD methods have\nevolved with the advent of large pre-trained vision-language models, enhancing\nfew-shot anomaly detection capabilities. However, these latest AD methods still\nexhibit limitations in accuracy improvement. One contributing factor is their\ndirect comparison of a query image's features with those of few-shot normal\nimages. This direct comparison often leads to a loss of precision and\ncomplicates the extension of these techniques to more complex domains--an area\nthat remains underexplored in a more refined and comprehensive manner. To\naddress these limitations, we introduce the anomaly personalization method,\nwhich performs a personalized one-to-normal transformation of query images\nusing an anomaly-free customized generation model, ensuring close alignment\nwith the normal manifold. Moreover, to further enhance the stability and\nrobustness of prediction results, we propose a triplet contrastive anomaly\ninference strategy, which incorporates a comprehensive comparison between the\nquery and generated anomaly-free data pool and prompt information. Extensive\nevaluations across eleven datasets in three domains demonstrate our model's\neffectiveness compared to the latest AD methods. Additionally, our method has\nbeen proven to transfer flexibly to other AD methods, with the generated image\ndata effectively improving the performance of other AD methods.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.01543", "title": "Unsupervised anomaly detection in large-scale estuarine acoustic\n  telemetry data", "abstract": "  Acoustic telemetry data plays a vital role in understanding the behaviour and\nmovement of aquatic animals. However, these datasets, which often consist of\nmillions of individual data points, frequently contain anomalous movements that\npose significant challenges. Traditionally, anomalous movements are identified\neither manually or through basic statistical methods, approaches that are\ntime-consuming and prone to high rates of unidentified anomalies in large\ndatasets. This study focuses on the development of automated classifiers for a\nlarge telemetry dataset comprising detections from fifty acoustically tagged\ndusky kob monitored in the Breede Estuary, South Africa. Using an array of 16\nacoustic receivers deployed throughout the estuary between 2016 and 2021, we\ncollected over three million individual data points. We present detailed\nguidelines for data pre-processing, resampling strategies, labelling process,\nfeature engineering, data splitting methodologies, and the selection and\ninterpretation of machine learning and deep learning models for anomaly\ndetection. Among the evaluated models, neural networks autoencoder (NN-AE)\ndemonstrated superior performance, aided by our proposed threshold-finding\nalgorithm. NN-AE achieved a high recall with no false normal (i.e., no\nmisclassifications of anomalous movements as normal patterns), a critical\nfactor in ensuring that no true anomalies are overlooked. In contrast, other\nmodels exhibited false normal fractions exceeding 0.9, indicating they failed\nto detect the majority of true anomalies; a significant limitation for\ntelemetry studies where undetected anomalies can distort interpretations of\nmovement patterns. While the NN-AE's performance highlights its reliability and\nrobustness in detecting anomalies, it faced challenges in accurately learning\nnormal movement patterns when these patterns gradually deviated from anomalous\nones.\n", "date": "2025-02-03", "categories": []}
{"id": "2310.03738", "title": "Robust Novelty Detection through Style-Conscious Feature Ranking", "abstract": "  Novelty detection seeks to identify samples deviating from a known\ndistribution, yet data shifts in a multitude of ways, and only a few consist of\nrelevant changes. Aligned with out-of-distribution generalization literature,\nwe advocate for a formal distinction between task-relevant semantic or content\nchanges and irrelevant style changes. This distinction forms the basis for\nrobust novelty detection, emphasizing the identification of semantic changes\nresilient to style distributional shifts. To this end, we introduce Stylist, a\nmethod that utilizes pretrained large-scale model representations to\nselectively discard environment-biased features. By computing per-feature\nscores based on feature distribution distances between environments, Stylist\neffectively eliminates features responsible for spurious correlations,\nenhancing novelty detection performance. Evaluations on adapted domain\ngeneralization datasets and a synthetic dataset demonstrate Stylist's efficacy\nin improving novelty detection across diverse datasets with stylistic and\ncontent shifts. The code is available at https://github.com/bit-ml/Stylist.\n", "date": "2023-10-05", "categories": []}
{"id": "2410.05687", "title": "Extreme Value Modelling of Feature Residuals for Anomaly Detection in\n  Dynamic Graphs", "abstract": "  Detecting anomalies in a temporal sequence of graphs can be applied is areas\nsuch as the detection of accidents in transport networks and cyber attacks in\ncomputer networks. Existing methods for detecting abnormal graphs can suffer\nfrom multiple limitations, such as high false positive rates as well as\ndifficulties with handling variable-sized graphs and non-trivial temporal\ndynamics. To address this, we propose a technique where temporal dependencies\nare explicitly modelled via time series analysis of a large set of pertinent\ngraph features, followed by using residuals to remove the dependencies. Extreme\nValue Theory is then used to robustly model and classify any remaining\nextremes, aiming to produce low false positives rates. Comparative evaluations\non a multitude of graph instances show that the proposed approach obtains\nconsiderably better accuracy than TensorSplat and Laplacian Anomaly Detection.\n", "date": "2024-10-08", "categories": []}
{"id": "2501.17906", "title": "Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained\n  Novelty Detection in Medical Imaging", "abstract": "  Detecting novel anomalies in medical imaging is challenging due to the\nlimited availability of labeled data for rare abnormalities, which often\ndisplay high variability and subtlety. This challenge is further compounded\nwhen small abnormal regions are embedded within larger normal areas, as\nwhole-image predictions frequently overlook these subtle deviations. To address\nthese issues, we propose an unsupervised Patch-GAN framework designed to detect\nand localize anomalies by capturing both local detail and global structure. Our\nframework first reconstructs masked images to learn fine-grained,\nnormal-specific features, allowing for enhanced sensitivity to minor deviations\nfrom normality. By dividing these reconstructed images into patches and\nassessing the authenticity of each patch, our approach identifies anomalies at\na more granular level, overcoming the limitations of whole-image evaluation.\nAdditionally, a patch-ranking mechanism prioritizes regions with higher\nabnormal scores, reinforcing the alignment between local patch discrepancies\nand the global image context. Experimental results on the ISIC 2016 skin lesion\nand BraTS 2019 brain tumor datasets validate our framework's effectiveness,\nachieving AUCs of 95.79% and 96.05%, respectively, and outperforming three\nstate-of-the-art baselines.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.17289", "title": "A Contrastive Teacher-Student Framework for Novelty Detection under\n  Style Shifts", "abstract": "  There have been several efforts to improve Novelty Detection (ND)\nperformance. However, ND methods often suffer significant performance drops\nunder minor distribution shifts caused by changes in the environment, known as\nstyle shifts. This challenge arises from the ND setup, where the absence of\nout-of-distribution (OOD) samples during training causes the detector to be\nbiased toward the dominant style features in the in-distribution (ID) data. As\na result, the model mistakenly learns to correlate style with core features,\nusing this shortcut for detection. Robust ND is crucial for real-world\napplications like autonomous driving and medical imaging, where test samples\nmay have different styles than the training data. Motivated by this, we propose\na robust ND method that crafts an auxiliary OOD set with style features similar\nto the ID set but with different core features. Then, a task-based knowledge\ndistillation strategy is utilized to distinguish core features from style\nfeatures and help our model rely on core features for discriminating crafted\nOOD and ID sets. We verified the effectiveness of our method through extensive\nexperimental evaluations on several datasets, including synthetic and\nreal-world benchmarks, against nine different ND methods.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.17329", "title": "Anomaly Detection in Cooperative Vehicle Perception Systems under\n  Imperfect Communication", "abstract": "  Anomaly detection is a critical requirement for ensuring safety in autonomous\ndriving. In this work, we leverage Cooperative Perception to share information\nacross nearby vehicles, enabling more accurate identification and consensus of\nanomalous behaviors in complex traffic scenarios. To account for the real-world\nchallenge of imperfect communication, we propose a cooperative-perception-based\nanomaly detection framework (CPAD), which is a robust architecture that remains\neffective under communication interruptions, thereby facilitating reliable\nperformance even in low-bandwidth settings. Since no multi-agent anomaly\ndetection dataset exists for vehicle trajectories, we introduce 15,000\ndifferent scenarios with a 90,000 trajectories benchmark dataset generated\nthrough rule-based vehicle dynamics analysis. Empirical results demonstrate\nthat our approach outperforms standard anomaly classification methods in\nF1-score, AUC and showcase strong robustness to agent connection interruptions.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.17787", "title": "Detecting Anomalies Using Rotated Isolation Forest", "abstract": "  The Isolation Forest (iForest), proposed by Liu, Ting, and Zhou at TKDE 2012,\nhas become a prominent tool for unsupervised anomaly detection. However, recent\nresearch by Hariri, Kind, and Brunner, published in TKDE 2021, has revealed\nissues with iForest. They identified the presence of axis-aligned ghost\nclusters that can be misidentified as normal clusters, leading to biased\nanomaly scores and inaccurate predictions. In response, they developed the\nExtended Isolation Forest (EIF), which effectively solves these issues by\neliminating the ghost clusters introduced by iForest. This enhancement results\nin improved consistency of anomaly scores and superior performance. We reveal a\npreviously overlooked problem in the Extended Isolation Forest (EIF), showing\nthat it is vulnerable to ghost inter-clusters between normal clusters of data\npoints. In this paper, we introduce the Rotated Isolation Forest (RIF)\nalgorithm which effectively addresses both the axis-aligned ghost clusters\nobserved in iForest and the ghost inter-clusters seen in EIF. RIF accomplishes\nthis by randomly rotating the dataset (using random rotation matrices and QR\ndecomposition) before feeding it into the iForest construction, thereby\nincreasing dataset variation and eliminating ghost clusters. Our experiments\nconclusively demonstrate that the RIF algorithm outperforms iForest and EIF, as\nevidenced by the results obtained from both synthetic datasets and real-world\ndatasets.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.16364", "title": "Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained\n  Intra- and Inter-Variate Dependencies", "abstract": "  Multivariate time series anomaly detection is essential for failure\nmanagement in web application operations, as it directly influences the\neffectiveness and timeliness of implementing remedial or preventive measures.\nThis task is often framed as a semi-supervised learning problem, where only\nnormal data are available for model training, primarily due to the\nlabor-intensive nature of data labeling and the scarcity of anomalous data.\nExisting semi-supervised methods often detect anomalies by capturing\nintra-variate temporal dependencies and/or inter-variate relationships to learn\nnormal patterns, flagging timestamps that deviate from these patterns as\nanomalies. However, these approaches often fail to capture salient\nintra-variate temporal and inter-variate dependencies in time series due to\ntheir focus on excessively fine granularity, leading to suboptimal performance.\nIn this study, we introduce MtsCID, a novel semi-supervised multivariate time\nseries anomaly detection method. MtsCID employs a dual network architecture:\none network operates on the attention maps of multi-scale intra-variate patches\nfor coarse-grained temporal dependency learning, while the other works on\nvariates to capture coarse-grained inter-variate relationships through\nconvolution and interaction with sinusoidal prototypes. This design enhances\nthe ability to capture the patterns from both intra-variate temporal\ndependencies and inter-variate relationships, resulting in improved\nperformance. Extensive experiments across seven widely used datasets\ndemonstrate that MtsCID achieves performance comparable or superior to\nstate-of-the-art benchmark methods.\n", "date": "2025-01-22", "categories": []}
{"id": "2501.16744", "title": "LLM Assisted Anomaly Detection Service for Site Reliability Engineers:\n  Enhancing Cloud Infrastructure Resilience", "abstract": "  This paper introduces a scalable Anomaly Detection Service with a\ngeneralizable API tailored for industrial time-series data, designed to assist\nSite Reliability Engineers (SREs) in managing cloud infrastructure. The service\nenables efficient anomaly detection in complex data streams, supporting\nproactive identification and resolution of issues. Furthermore, it presents an\ninnovative approach to anomaly modeling in cloud infrastructure by utilizing\nLarge Language Models (LLMs) to understand key components, their failure modes,\nand behaviors. A suite of algorithms for detecting anomalies is offered in\nunivariate and multivariate time series data, including regression-based,\nmixture-model-based, and semi-supervised approaches. We provide insights into\nthe usage patterns of the service, with over 500 users and 200,000 API calls in\na year. The service has been successfully applied in various industrial\nsettings, including IoT-based AI applications. We have also evaluated our\nsystem on public anomaly benchmarks to show its effectiveness. By leveraging\nit, SREs can proactively identify potential issues before they escalate,\nreducing downtime and improving response times to incidents, ultimately\nenhancing the overall customer experience. We plan to extend the system to\ninclude time series foundation models, enabling zero-shot anomaly detection\ncapabilities.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.16875", "title": "Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal\n  Association and Frequency Domain Analysis", "abstract": "  Anomaly detection is crucial for ensuring the stability and reliability of\nweb service systems. Logs and metrics contain multiple information that can\nreflect the system's operational state and potential anomalies. Thus, existing\nanomaly detection methods use logs and metrics to detect web service systems'\nanomalies through data fusion approaches. They associate logs and metrics using\ncoarse-grained time window alignment and capture the normal patterns of system\noperation through reconstruction. However, these methods have two issues that\nlimit their performance in anomaly detection. First, due to asynchrony between\nlogs and metrics, coarse-grained time window alignment cannot achieve a precise\nassociation between the two modalities. Second, reconstruction-based methods\nsuffer from severe overgeneralization problems, resulting in anomalies being\naccurately reconstructed. In this paper, we propose a novel anomaly detection\nmethod named FFAD to address these two issues. On the one hand, FFAD employs\ngraph-based alignment to mine and extract associations between the modalities\nfrom the constructed log-metric relation graph, achieving precise associations\nbetween logs and metrics. On the other hand, we improve the model's fit to\nnormal data distributions through Fourier Frequency Focus, thereby enhancing\nthe effectiveness of anomaly detection. We validated the effectiveness of our\nmodel on two real-world industrial datasets and one open-source dataset. The\nresults show that our method achieves an average anomaly detection F1-score of\n93.6%, representing an 8.8% improvement over previous state-of-the-art methods.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.16971", "title": "RODEO: Robust Outlier Detection via Exposing Adaptive\n  Out-of-Distribution Samples", "abstract": "  In recent years, there have been significant improvements in various forms of\nimage outlier detection. However, outlier detection performance under\nadversarial settings lags far behind that in standard settings. This is due to\nthe lack of effective exposure to adversarial scenarios during training,\nespecially on unseen outliers, leading to detection models failing to learn\nrobust features. To bridge this gap, we introduce RODEO, a data-centric\napproach that generates effective outliers for robust outlier detection. More\nspecifically, we show that incorporating outlier exposure (OE) and adversarial\ntraining can be an effective strategy for this purpose, as long as the exposed\ntraining outliers meet certain characteristics, including diversity, and both\nconceptual differentiability and analogy to the inlier samples. We leverage a\ntext-to-image model to achieve this goal. We demonstrate both quantitatively\nand qualitatively that our adaptive OE method effectively generates ``diverse''\nand ``near-distribution'' outliers, leveraging information from both text and\nimage domains. Moreover, our experimental results show that utilizing our\nsynthesized outliers significantly enhances the performance of the outlier\ndetector, particularly in adversarial settings.\n", "date": "2025-01-28", "categories": []}
{"id": "2212.00789", "title": "An Attribute-based Method for Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) identifies suspicious events in videos, which\nis critical for crime prevention and homeland security. In this paper, we\npropose a simple but highly effective VAD method that relies on attribute-based\nrepresentations. The base version of our method represents every object by its\nvelocity and pose, and computes anomaly scores by density estimation.\nSurprisingly, this simple representation is sufficient to achieve\nstate-of-the-art performance in ShanghaiTech, the most commonly used VAD\ndataset. Combining our attribute-based representations with an off-the-shelf,\npretrained deep representation yields state-of-the-art performance with a\n$99.1\\%, 93.7\\%$, and $85.9\\%$ AUROC on Ped2, Avenue, and ShanghaiTech,\nrespectively.\n", "date": "2022-12-01", "categories": []}
{"id": "2304.07898", "title": "Harnessing Contrastive Learning and Neural Transformation for Time\n  Series Anomaly Detection", "abstract": "  Time series anomaly detection (TSAD) plays a vital role in many industrial\napplications. While contrastive learning has gained momentum in the time series\ndomain for its prowess in extracting meaningful representations from unlabeled\ndata, its straightforward application to anomaly detection is not without\nhurdles. Firstly, contrastive learning typically requires negative sampling to\navoid the representation collapse issue, where the encoder converges to a\nconstant solution. However, drawing from the same dataset for dissimilar\nsamples is ill-suited for TSAD as most samples are ``normal'' in the training\ndataset. Secondly, conventional contrastive learning focuses on instance\ndiscrimination, which may overlook anomalies that are detectable when compared\nto their temporal context. In this study, we propose a novel approach, CNT,\nthat incorporates a window-based contrastive learning strategy fortified with\nlearnable transformations. This dual configuration focuses on capturing\ntemporal anomalies in local regions while simultaneously mitigating the\nrepresentation collapse issue. Our theoretical analysis validates the\neffectiveness of CNT in circumventing constant encoder solutions. Through\nextensive experiments on diverse real-world industrial datasets, we show the\nsuperiority of our framework by outperforming various baselines and model\nvariants.\n", "date": "2023-04-16", "categories": []}
{"id": "2306.01951", "title": "GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction", "abstract": "  Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes\nwithin graphs, finding applications in network security, fraud detection,\nsocial media spam detection, and various other domains. A common method for GAD\nis Graph Auto-Encoders (GAEs), which encode graph data into node\nrepresentations and identify anomalies by assessing the reconstruction quality\nof the graphs based on these representations. However, existing GAE models are\nprimarily optimized for direct link reconstruction, resulting in nodes\nconnected in the graph being clustered in the latent space. As a result, they\nexcel at detecting cluster-type structural anomalies but struggle with more\ncomplex structural anomalies that do not conform to clusters. To address this\nlimitation, we propose a novel solution called GAD-NR, a new variant of GAE\nthat incorporates neighborhood reconstruction for graph anomaly detection.\nGAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the\nlocal structure, self-attributes, and neighbor attributes, based on the\ncorresponding node representation. By comparing the neighborhood reconstruction\nloss between anomalous nodes and normal nodes, GAD-NR can effectively detect\nany anomalies. Extensive experimentation conducted on six real-world datasets\nvalidates the effectiveness of GAD-NR, showcasing significant improvements (by\nup to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR\nis openly available. Importantly, the comparative analysis reveals that the\nexisting methods perform well only in detecting one or two types of anomalies\nout of the three types studied. In contrast, GAD-NR excels at detecting all\nthree types of anomalies across the datasets, demonstrating its comprehensive\nanomaly detection capabilities.\n", "date": "2023-06-02", "categories": []}
{"id": "2501.15053", "title": "Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based\n  Contextual Anomaly Detector", "abstract": "  The exponential growth in the usage of Internet of Things in daily life has\ncaused immense increase in the generation of time series data. Smart homes is\none such domain where bulk of data is being generated and anomaly detection is\none of the many challenges addressed by researchers in recent years. Contextual\nanomaly is a kind of anomaly that may show deviation from the normal pattern\nlike point or sequence anomalies, but it also requires prior knowledge about\nthe data domain and the actions that caused the deviation. Recent studies based\non Recurrent Neural Networks (RNN) have demonstrated strong performance in\nanomaly detection. This study explores the impact of automatically tuned\nhyperparamteres on Unsupervised Online Contextual Anomaly Detection (UoCAD)\napproach by proposing UoCAD with Optimised Hyperparamnters (UoCAD-OH). UoCAD-OH\nconducts hyperparameter optimisation on Bi-LSTM model in an offline phase and\nuses the fine-tuned hyperparameters to detect anomalies during the online\nphase. The experiments involve evaluating the proposed framework on two smart\nhome air quality datasets containing contextual anomalies. The evaluation\nmetrics used are Precision, Recall, and F1 score.\n", "date": "2025-01-25", "categories": []}
{"id": "2501.15265", "title": "Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes", "abstract": "  We present a novel approach to anomaly detection by integrating Generalized\nHyperbolic (GH) processes into kernel-based methods. The GH distribution, known\nfor its flexibility in modeling skewness, heavy tails, and kurtosis, helps to\ncapture complex patterns in data that deviate from Gaussian assumptions. We\npropose a GH-based kernel function and utilize it within Kernel Density\nEstimation (KDE) and One-Class Support Vector Machines (OCSVM) to develop\nanomaly detection frameworks. Theoretical results confirmed the positive\nsemi-definiteness and consistency of the GH-based kernel, ensuring its\nsuitability for machine learning applications. Empirical evaluation on\nsynthetic and real-world datasets showed that our method improves detection\nperformance in scenarios involving heavy-tailed and asymmetric or imbalanced\ndistributions. https://github.com/paulinebourigault/GHKernelAnomalyDetect\n", "date": "2025-01-25", "categories": []}
{"id": "2501.15434", "title": "Mitigating Spurious Negative Pairs for Robust Industrial Anomaly\n  Detection", "abstract": "  Despite significant progress in Anomaly Detection (AD), the robustness of\nexisting detection methods against adversarial attacks remains a challenge,\ncompromising their reliability in critical real-world applications such as\nautonomous driving. This issue primarily arises from the AD setup, which\nassumes that training data is limited to a group of unlabeled normal samples,\nmaking the detectors vulnerable to adversarial anomaly samples during testing.\nAdditionally, implementing adversarial training as a safeguard encounters\ndifficulties, such as formulating an effective objective function without\naccess to labels. An ideal objective function for adversarial training in AD\nshould promote strong perturbations both within and between the normal and\nanomaly groups to maximize margin between normal and anomaly distribution. To\naddress these issues, we first propose crafting a pseudo-anomaly group derived\nfrom normal group samples. Then, we demonstrate that adversarial training with\ncontrastive loss could serve as an ideal objective function, as it creates both\ninter- and intra-group perturbations. However, we notice that spurious negative\npairs compromise the conventional contrastive loss to achieve robust AD.\nSpurious negative pairs are those that should be closely mapped but are\nerroneously separated. These pairs introduce noise and misguide the direction\nof inter-group adversarial perturbations. To overcome the effect of spurious\nnegative pairs, we define opposite pairs and adversarially pull them apart to\nstrengthen inter-group perturbations. Experimental results demonstrate our\nsuperior performance in both clean and adversarial scenarios, with a 26.1%\nimprovement in robust detection across various challenging benchmark datasets.\nThe implementation of our work is available at:\nhttps://github.com/rohban-lab/COBRA.\n", "date": "2025-01-26", "categories": []}
{"id": "2501.15795", "title": "Can Multimodal Large Language Models be Guided to Improve Industrial\n  Anomaly Detection?", "abstract": "  In industrial settings, the accurate detection of anomalies is essential for\nmaintaining product quality and ensuring operational safety. Traditional\nindustrial anomaly detection (IAD) models often struggle with flexibility and\nadaptability, especially in dynamic production environments where new defect\ntypes and operational changes frequently arise. Recent advancements in\nMultimodal Large Language Models (MLLMs) hold promise for overcoming these\nlimitations by combining visual and textual information processing\ncapabilities. MLLMs excel in general visual understanding due to their training\non large, diverse datasets, but they lack domain-specific knowledge, such as\nindustry-specific defect tolerance levels, which limits their effectiveness in\nIAD tasks. To address these challenges, we propose Echo, a novel multi-expert\nframework designed to enhance MLLM performance for IAD. Echo integrates four\nexpert modules: Reference Extractor which provides a contextual baseline by\nretrieving similar normal images, Knowledge Guide which supplies\ndomain-specific insights, Reasoning Expert which enables structured, stepwise\nreasoning for complex queries, and Decision Maker which synthesizes information\nfrom all modules to deliver precise, context-aware responses. Evaluated on the\nMMAD benchmark, Echo demonstrates significant improvements in adaptability,\nprecision, and robustness, moving closer to meeting the demands of real-world\nindustrial anomaly detection.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.14170", "title": "Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule\n  Generation via Large Language Models", "abstract": "  Observability in cloud infrastructure is critical for service providers,\ndriving the widespread adoption of anomaly detection systems for monitoring\nmetrics. However, existing systems often struggle to simultaneously achieve\nexplainability, reproducibility, and autonomy, which are three indispensable\nproperties for production use. We introduce Argos, an agentic system for\ndetecting time-series anomalies in cloud infrastructure by leveraging large\nlanguage models (LLMs). Argos proposes to use explainable and reproducible\nanomaly rules as intermediate representation and employs LLMs to autonomously\ngenerate such rules. The system will efficiently train error-free and\naccuracy-guaranteed anomaly rules through multiple collaborative agents and\ndeploy the trained rules for low-cost online anomaly detection. Through\nevaluation results, we demonstrate that Argos outperforms state-of-the-art\nmethods, increasing $F_1$ scores by up to $9.5\\%$ and $28.3\\%$ on public\nanomaly detection datasets and an internal dataset collected from Microsoft,\nrespectively.\n", "date": "2025-01-24", "categories": []}
{"id": "2501.14197", "title": "Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual\n  Focus on Homogeneity and Heterogeneity", "abstract": "  Graph anomaly detection (GAD) aims to identify nodes from a graph that are\nsignificantly different from normal patterns. Most previous studies are\nmodel-driven, focusing on enhancing the detection effect by improving the model\nstructure. However, these approaches often treat all nodes equally, neglecting\nthe different contributions of various nodes to the training. Therefore, we\nintroduce graph curriculum learning as a simple and effective plug-and-play\nmodule to optimize GAD methods. The existing graph curriculum learning mainly\nfocuses on the homogeneity of graphs and treats nodes with high homogeneity as\neasy nodes. In fact, GAD models can handle not only graph homogeneity but also\nheterogeneity, which leads to the unsuitability of these existing methods. To\naddress this problem, we propose an innovative Bi-directional Curriculum\nLearning strategy (BCL), which considers nodes with higher and lower similarity\nto neighbor nodes as simple nodes in the direction of focusing on homogeneity\nand focusing on heterogeneity, respectively, and prioritizes their training.\nExtensive experiments show that BCL can be quickly integrated into existing\ndetection processes and significantly improves the performance of ten GAD\nanomaly detection models on seven commonly used datasets.\n", "date": "2025-01-24", "categories": []}
{"id": "2312.04215", "title": "Guided Reconstruction with Conditioned Diffusion Models for Unsupervised\n  Anomaly Detection in Brain MRIs", "abstract": "  The application of supervised models to clinical screening tasks is\nchallenging due to the need for annotated data for each considered pathology.\nUnsupervised Anomaly Detection (UAD) is an alternative approach that aims to\nidentify any anomaly as an outlier from a healthy training distribution. A\nprevalent strategy for UAD in brain MRI involves using generative models to\nlearn the reconstruction of healthy brain anatomy for a given input image. As\nthese models should fail to reconstruct unhealthy structures, the\nreconstruction errors indicate anomalies. However, a significant challenge is\nto balance the accurate reconstruction of healthy anatomy and the undesired\nreplication of abnormal structures. While diffusion models have shown promising\nresults with detailed and accurate reconstructions, they face challenges in\npreserving intensity characteristics, resulting in false positives. We propose\nconditioning the denoising process of diffusion models with additional\ninformation derived from a latent representation of the input image. We\ndemonstrate that this conditioning allows for accurate and local adaptation to\nthe general input intensity distribution while avoiding the replication of\nunhealthy structures. We compare the novel approach to different\nstate-of-the-art methods and for different data sets. Our results show\nsubstantial improvements in the segmentation performance, with the Dice score\nimproved by 11.9%, 20.0%, and 44.6%, for the BraTS, ATLAS and MSLUB data sets,\nrespectively, while maintaining competitive performance on the WMH data set.\nFurthermore, our results indicate effective domain adaptation across different\nMRI acquisitions and simulated contrasts, an important attribute for general\nanomaly detection methods. The code for our work is available at\nhttps://github.com/FinnBehrendt/Conditioned-Diffusion-Models-UAD\n", "date": "2023-12-07", "categories": []}
{"id": "2501.13493", "title": "GCAD: Anomaly Detection in Multivariate Time Series from the Perspective\n  of Granger Causality", "abstract": "  Multivariate time series anomaly detection has numerous real-world\napplications and is being extensively studied. Modeling pairwise correlations\nbetween variables is crucial. Existing methods employ learnable graph\nstructures and graph neural networks to explicitly model the spatial\ndependencies between variables. However, these methods are primarily based on\nprediction or reconstruction tasks, which can only learn similarity\nrelationships between sequence embeddings and lack interpretability in how\ngraph structures affect time series evolution. In this paper, we designed a\nframework that models spatial dependencies using interpretable causal\nrelationships and detects anomalies through changes in causal patterns.\nSpecifically, we propose a method to dynamically discover Granger causality\nusing gradients in nonlinear deep predictors and employ a simple sparsification\nstrategy to obtain a Granger causality graph, detecting anomalies from a causal\nperspective. Experiments on real-world datasets demonstrate that the proposed\nmodel achieves more accurate anomaly detection compared to baseline methods.\n", "date": "2025-01-23", "categories": []}
{"id": "2501.13864", "title": "Autoencoders for Anomaly Detection are Unreliable", "abstract": "  Autoencoders are frequently used for anomaly detection, both in the\nunsupervised and semi-supervised settings. They rely on the assumption that\nwhen trained using the reconstruction loss, they will be able to reconstruct\nnormal data more accurately than anomalous data. Some recent works have posited\nthat this assumption may not always hold, but little has been done to study the\nvalidity of the assumption in theory. In this work we show that this assumption\nindeed does not hold, and illustrate that anomalies, lying far away from normal\ndata, can be perfectly reconstructed in practice. We revisit the theory of\nfailure of linear autoencoders for anomaly detection by showing how they can\nperfectly reconstruct out of bounds, or extrapolate undesirably, and note how\nthis can be dangerous in safety critical applications. We connect this to\nnon-linear autoencoders through experiments on both tabular data and real-world\nimage data, the two primary application areas of autoencoders for anomaly\ndetection.\n", "date": "2025-01-23", "categories": []}
{"id": "2406.02831", "title": "Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly\n  Detection", "abstract": "  Video anomaly detection aims to develop automated models capable of\nidentifying abnormal events in surveillance videos. The benchmark setup for\nthis task is extremely challenging due to: i) the limited size of the training\nsets, ii) weak supervision provided in terms of video-level labels, and iii)\nintrinsic class imbalance induced by the scarcity of abnormal events. In this\nwork, we show that distilling knowledge from aggregated representations of\nmultiple backbones into a single-backbone Student model achieves\nstate-of-the-art performance. In particular, we develop a bi-level distillation\napproach along with a novel disentangled cross-attention-based feature\naggregation network. Our proposed approach, DAKD (Distilling Aggregated\nKnowledge with Disentangled Attention), demonstrates superior performance\ncompared to existing methods across multiple benchmark datasets. Notably, we\nachieve significant improvements of 1.36%, 0.78%, and 7.02% on the UCF-Crime,\nShanghaiTech, and XD-Violence datasets, respectively.\n", "date": "2024-06-05", "categories": []}
{"id": "2412.16788", "title": "DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive\n  Learning Reconstruction", "abstract": "  Anomaly detection using a network-based approach is one of the most efficient\nways to identify abnormal events such as fraud, security breaches, and system\nfaults in a variety of applied domains. While most of the earlier works address\nthe complex nature of graph-structured data and predefined anomalies, the\nimpact of data attributes and emerging anomalies are often neglected. This\npaper introduces DCOR, a novel approach on attributed networks that integrates\nreconstruction-based anomaly detection with Contrastive Learning. Utilizing a\nGraph Neural Network (GNN) framework, DCOR contrasts the reconstructed\nadjacency and feature matrices from both the original and augmented graphs to\ndetect subtle anomalies. We employed comprehensive experimental studies on\nbenchmark datasets through standard evaluation measures. The results show that\nDCOR significantly outperforms state-of-the-art methods. Obtained results\ndemonstrate the efficacy of proposed approach in attributed networks with the\npotential of uncovering new patterns of anomalies.\n", "date": "2024-12-21", "categories": []}
{"id": "2501.07999", "title": "Unsupervised Feature Construction for Anomaly Detection in Time Series\n  -- An Evaluation", "abstract": "  To detect anomalies with precision and without prior knowledge in time\nseries, is it better to build a detector from the initial temporal\nrepresentation, or to compute a new (tabular) representation using an existing\nautomatic variable construction library? In this article, we address this\nquestion by conducting an in-depth experimental study for two popular detectors\n(Isolation Forest and Local Outlier Factor). The obtained results, for 5\ndifferent datasets, show that the new representation, computed using the\ntsfresh library, allows Isolation Forest to significantly improve its\nperformance.\n", "date": "2025-01-14", "categories": []}
{"id": "2501.11310", "title": "Anomaly Detection for Industrial Applications, Its Challenges,\n  Solutions, and Future Directions: A Review", "abstract": "  Anomaly detection from images captured using camera sensors is one of the\nmainstream applications at the industrial level. Particularly, it maintains the\nquality and optimizes the efficiency in production processes across diverse\nindustrial tasks, including advanced manufacturing and aerospace engineering.\nTraditional anomaly detection workflow is based on a manual inspection by human\noperators, which is a tedious task. Advances in intelligent automated\ninspection systems have revolutionized the Industrial Anomaly Detection (IAD)\nprocess. Recent vision-based approaches can automatically extract, process, and\ninterpret features using computer vision and align with the goals of automation\nin industrial operations. In light of the shift in inspection methodologies,\nthis survey reviews studies published since 2019, with a specific focus on\nvision-based anomaly detection. The components of an IAD pipeline that are\noverlooked in existing surveys are presented, including areas related to data\nacquisition, preprocessing, learning mechanisms, and evaluation. In addition to\nthe collected publications, several scientific and industry-related challenges\nand their perspective solutions are highlighted. Popular and relevant\nindustrial datasets are also summarized, providing further insight into\ninspection applications. Finally, future directions of vision-based IAD are\ndiscussed, offering researchers insight into the state-of-the-art of industrial\ninspection.\n", "date": "2025-01-20", "categories": []}
{"id": "2501.11959", "title": "Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak\n  Segment Labels", "abstract": "  Detecting anomalies in temporal data has gained significant attention across\nvarious real-world applications, aiming to identify unusual events and mitigate\npotential hazards. In practice, situations often involve a mix of segment-level\nlabels (detected abnormal events with segments of time points) and unlabeled\ndata (undetected events), while the ideal algorithmic outcome should be\npoint-level predictions. Therefore, the huge label information gap between\ntraining data and targets makes the task challenging. In this study, we\nformulate the above imperfect information as noisy labels and propose\nNRdetector, a noise-resilient framework that incorporates confidence-based\nsample selection, robust segment-level learning, and data-centric point-level\ndetection for multivariate time series anomaly detection. Particularly, to\nbridge the information gap between noisy segment-level labels and missing\npoint-level labels, we develop a novel loss function that can effectively\nmitigate the label noise and consider the temporal features. It encourages the\nsmoothness of consecutive points and the separability of points from segments\nwith different labels. Extensive experiments on real-world multivariate time\nseries datasets with 11 different evaluation metrics demonstrate that\nNRdetector consistently achieves robust results across multiple real-world\ndatasets, outperforming various baselines adapted to operate in our setting.\n", "date": "2025-01-21", "categories": []}
{"id": "2501.12166", "title": "Beyond Window-Based Detection: A Graph-Centric Framework for Discrete\n  Log Anomaly Detection", "abstract": "  Detecting anomalies in discrete event logs is critical for ensuring system\nreliability, security, and efficiency. Traditional window-based methods for log\nanomaly detection often suffer from context bias and fuzzy localization, which\nhinder their ability to precisely and efficiently identify anomalies. To\naddress these challenges, we propose a graph-centric framework, TempoLog, which\nleverages multi-scale temporal graph networks for discrete log anomaly\ndetection. Unlike conventional methods, TempoLog constructs continuous-time\ndynamic graphs directly from event logs, eliminating the need for fixed-size\nwindow grouping. By representing log templates as nodes and their temporal\nrelationships as edges, the framework dynamically captures both local and\nglobal dependencies across multiple temporal scales. Additionally, a\nsemantic-aware model enhances detection by incorporating rich contextual\ninformation. Extensive experiments on public datasets demonstrate that our\nmethod achieves state-of-the-art performance in event-level anomaly detection,\nsignificantly outperforming existing approaches in both accuracy and\nefficiency.\n", "date": "2025-01-21", "categories": []}
{"id": "2501.12204", "title": "Score Combining for Contrastive OOD Detection", "abstract": "  In out-of-distribution (OOD) detection, one is asked to classify whether a\ntest sample comes from a known inlier distribution or not. We focus on the case\nwhere the inlier distribution is defined by a training dataset and there exists\nno additional knowledge about the novelties that one is likely to encounter.\nThis problem is also referred to as novelty detection, one-class\nclassification, and unsupervised anomaly detection. The current literature\nsuggests that contrastive learning techniques are state-of-the-art for OOD\ndetection. We aim to improve on those techniques by combining/ensembling their\nscores using the framework of null hypothesis testing and, in particular, a\nnovel generalized likelihood ratio test (GLRT). We demonstrate that our\nproposed GLRT-based technique outperforms the state-of-the-art CSI and SupCSI\ntechniques from Tack et al. 2020 in dataset-vs-dataset experiments with\nCIFAR-10, SVHN, LSUN, ImageNet, and CIFAR-100, as well as leave-one-class-out\nexperiments with CIFAR-10. We also demonstrate that our GLRT outperforms the\nscore-combining methods of Fisher, Bonferroni, Simes, Benjamini-Hochwald, and\nStouffer in our application.\n", "date": "2025-01-21", "categories": []}
{"id": "2310.19070", "title": "Myriad: Large Multimodal Model by Applying Vision Experts for Industrial\n  Anomaly Detection", "abstract": "  Due to the training configuration, traditional industrial anomaly detection\n(IAD) methods have to train a specific model for each deployment scenario,\nwhich is insufficient to meet the requirements of modern design and\nmanufacturing. On the contrary, large multimodal models~(LMMs) have shown\neminent generalization ability on various vision tasks, and their perception\nand comprehension capabilities imply the potential of applying LMMs on IAD\ntasks. However, we observe that even though the LMMs have abundant knowledge\nabout industrial anomaly detection in the textual domain, the LMMs are unable\nto leverage the knowledge due to the modality gap between textual and visual\ndomains. To stimulate the relevant knowledge in LMMs and adapt the LMMs towards\nanomaly detection tasks, we introduce existing IAD methods as vision experts\nand present a novel large multimodal model applying vision experts for\nindustrial anomaly detection~(abbreviated to {Myriad}). Specifically, we\nutilize the anomaly map generated by the vision experts as guidance for LMMs,\nsuch that the vision model is guided to pay more attention to anomalous\nregions. Then, the visual features are modulated via an adapter to fit the\nanomaly detection tasks, which are fed into the language model together with\nthe vision expert guidance and human instructions to generate the final\noutputs. Extensive experiments are applied on MVTec-AD, VisA, and PCB Bank\nbenchmarks demonstrate that our proposed method not only performs favorably\nagainst state-of-the-art methods, but also inherits the flexibility and\ninstruction-following ability of LMMs in the field of IAD. Source code and\npre-trained models are publicly available at\n\\url{https://github.com/tzjtatata/Myriad}.\n", "date": "2023-10-29", "categories": []}
{"id": "2501.09187", "title": "Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual\n  Defect Detection", "abstract": "  Unsupervised visual defect detection is critical in industrial applications,\nrequiring a representation space that captures normal data features while\ndetecting deviations. Achieving a balance between expressiveness and\ncompactness is challenging; an overly expressive space risks inefficiency and\nmode collapse, impairing detection accuracy. We propose a novel approach using\nan enhanced VQ-VAE framework optimized for unsupervised defect detection. Our\nmodel introduces a patch-aware dynamic code assignment scheme, enabling\ncontext-sensitive code allocation to optimize spatial representation. This\nstrategy enhances normal-defect distinction and improves detection accuracy\nduring inference. Experiments on MVTecAD, BTAD, and MTSD datasets show our\nmethod achieves state-of-the-art performance.\n", "date": "2025-01-15", "categories": []}
{"id": "2501.09579", "title": "Sequential PatchCore: Anomaly Detection for Surface Inspection using\n  Synthetic Impurities", "abstract": "  The appearance of surface impurities (e.g., water stains, fingerprints,\nstickers) is an often-mentioned issue that causes degradation of automated\nvisual inspection systems. At the same time, synthetic data generation\ntechniques for visual surface inspection have focused primarily on generating\nperfect examples and defects, disregarding impurities. This study highlights\nthe importance of considering impurities when generating synthetic data. We\nintroduce a procedural method to include photorealistic water stains in\nsynthetic data. The synthetic datasets are generated to correspond to real\ndatasets and are further used to train an anomaly detection model and\ninvestigate the influence of water stains. The high-resolution images used for\nsurface inspection lead to memory bottlenecks during anomaly detection\ntraining. To address this, we introduce Sequential PatchCore - a method to\nbuild coresets sequentially and make training on large images using\nconsumer-grade hardware tractable. This allows us to perform transfer learning\nusing coresets pre-trained on different dataset versions. Our results show the\nbenefits of using synthetic data for pre-training an explicit coreset anomaly\nmodel and the extended performance benefits of finetuning the coreset using\nreal data. We observed how the impurities and labelling ambiguity lower the\nmodel performance and have additionally reported the defect-wise recall to\nprovide an industrially relevant perspective on model performance.\n", "date": "2025-01-16", "categories": []}
{"id": "2501.09733", "title": "ComplexVAD: Detecting Interaction Anomalies in Video", "abstract": "  Existing video anomaly detection datasets are inadequate for representing\ncomplex anomalies that occur due to the interactions between objects. The\nabsence of complex anomalies in previous video anomaly detection datasets\naffects research by shifting the focus onto simple anomalies. To address this\nproblem, we introduce a new large-scale dataset: ComplexVAD. In addition, we\npropose a novel method to detect complex anomalies via modeling the\ninteractions between objects using a scene graph with spatio-temporal\nattributes. With our proposed method and two other state-of-the-art video\nanomaly detection methods, we obtain baseline scores on ComplexVAD and\ndemonstrate that our new method outperforms existing works.\n", "date": "2025-01-16", "categories": []}
{"id": "2306.03492", "title": "Industrial Anomaly Detection and Localization Using Weakly-Supervised\n  Residual Transformers", "abstract": "  Recent advancements in industrial anomaly detection (AD) have demonstrated\nthat incorporating a small number of anomalous samples during training can\nsignificantly enhance accuracy. However, this improvement often comes at the\ncost of extensive annotation efforts, which are impractical for many real-world\napplications. In this paper, we introduce a novel framework, Weak}ly-supervised\nRESidual Transformer (WeakREST), designed to achieve high anomaly detection\naccuracy while minimizing the reliance on manual annotations. First, we\nreformulate the pixel-wise anomaly localization task into a block-wise\nclassification problem. Second, we introduce a residual-based feature\nrepresentation called Positional Fast Anomaly Residuals (PosFAR) which captures\nanomalous patterns more effectively. To leverage this feature, we adapt the\nSwin Transformer for enhanced anomaly detection and localization. Additionally,\nwe propose a weak annotation approach, utilizing bounding boxes and image tags\nto define anomalous regions. This approach establishes a semi-supervised\nlearning context that reduces the dependency on precise pixel-level labels. To\nfurther improve the learning process, we develop a novel ResMixMatch algorithm,\ncapable of handling the interplay between weak labels and residual-based\nrepresentations.\n  On the benchmark dataset MVTec-AD, our method achieves an Average Precision\n(AP) of $83.0\\%$, surpassing the previous best result of $82.7\\%$ in the\nunsupervised setting. In the supervised AD setting, WeakREST attains an AP of\n$87.6\\%$, outperforming the previous best of $86.0\\%$. Notably, even when using\nweaker annotations such as bounding boxes, WeakREST exceeds the performance of\nleading methods relying on pixel-wise supervision, achieving an AP of $87.1\\%$\ncompared to the prior best of $86.0\\%$ on MVTec-AD.\n", "date": "2023-06-06", "categories": []}
{"id": "2501.08470", "title": "Detecting Contextual Anomalies by Discovering Consistent Spatial Regions", "abstract": "  We describe a method for modeling spatial context to enable video anomaly\ndetection. The main idea is to discover regions that share similar object-level\nactivities by clustering joint object attributes using Gaussian mixture models.\nWe demonstrate that this straightforward approach, using orders of magnitude\nfewer parameters than competing models, achieves state-of-the-art performance\nin the challenging spatial-context-dependent Street Scene dataset. As a side\nbenefit, the high-resolution discovered regions learned by the model also\nprovide explainable normalcy maps for human operators without the need for any\npre-trained segmentation model.\n", "date": "2025-01-14", "categories": []}
{"id": "2501.08149", "title": "Multiple-Input Variational Auto-Encoder for Anomaly Detection in\n  Heterogeneous Data", "abstract": "  Anomaly detection (AD) plays a pivotal role in AI applications, e.g., in\nclassification, and intrusion/threat detection in cybersecurity. However, most\nexisting methods face challenges of heterogeneity amongst feature subsets posed\nby non-independent and identically distributed (non-IID) data. We propose a\nnovel neural network model called Multiple-Input Auto-Encoder for AD (MIAEAD)\nto address this. MIAEAD assigns an anomaly score to each feature subset of a\ndata sample to indicate its likelihood of being an anomaly. This is done by\nusing the reconstruction error of its sub-encoder as the anomaly score. All\nsub-encoders are then simultaneously trained using unsupervised learning to\ndetermine the anomaly scores of feature subsets. The final AUC of MIAEAD is\ncalculated for each sub-dataset, and the maximum AUC obtained among the\nsub-datasets is selected. To leverage the modelling of the distribution of\nnormal data to identify anomalies of the generative models, we develop a novel\nneural network architecture/model called Multiple-Input Variational\nAuto-Encoder (MIVAE). MIVAE can process feature subsets through its\nsub-encoders before learning distribution of normal data in the latent space.\nThis allows MIVAE to identify anomalies that deviate from the learned\ndistribution. We theoretically prove that the difference in the average anomaly\nscore between normal samples and anomalies obtained by the proposed MIVAE is\ngreater than that of the Variational Auto-Encoder (VAEAD), resulting in a\nhigher AUC for MIVAE. Extensive experiments on eight real-world anomaly\ndatasets demonstrate the superior performance of MIAEAD and MIVAE over\nconventional methods and the state-of-the-art unsupervised models, by up to 6%\nin terms of AUC score. Alternatively, MIAEAD and MIVAE have a high AUC when\napplied to feature subsets with low heterogeneity based on the coefficient of\nvariation (CV) score.\n", "date": "2025-01-14", "categories": []}
{"id": "2412.20870", "title": "SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation", "abstract": "  Although mainstream unsupervised anomaly detection (AD) (including\nimage-level classification and pixel-level segmentation)algorithms perform well\nin academic datasets, their performance is limited in practical application due\nto the ideal experimental setting of clean training data. Training with noisy\ndata is an inevitable problem in real-world anomaly detection but is seldom\ndiscussed. This paper is the first to consider fully unsupervised industrial\nanomaly detection (i.e., unsupervised AD with noisy data). To solve this\nproblem, we proposed memory-based unsupervised AD methods, SoftPatch and\nSoftPatch+, which efficiently denoise the data at the patch level. Noise\ndiscriminators are utilized to generate outlier scores for patch-level noise\nelimination before coreset construction. The scores are then stored in the\nmemory bank to soften the anomaly detection boundary. Compared with existing\nmethods, SoftPatch maintains a strong modeling ability of normal data and\nalleviates the overconfidence problem in coreset, and SoftPatch+ has more\nrobust performance which is articularly useful in real-world industrial\ninspection scenarios with high levels of noise (from 10% to 40%). Comprehensive\nexperiments conducted in diverse noise scenarios demonstrate that both\nSoftPatch and SoftPatch+ outperform the state-of-the-art AD methods on the\nMVTecAD, ViSA, and BTAD benchmarks. Furthermore, the performance of SoftPatch\nand SoftPatch+ is comparable to that of the noise-free methods in conventional\nunsupervised AD setting. The code of the proposed methods can be found at\nhttps://github.com/TencentYoutuResearch/AnomalyDetection-SoftPatch.\n", "date": "2024-12-30", "categories": []}
{"id": "2501.06571", "title": "Active Rule Mining for Multivariate Anomaly Detection in Radio Access\n  Networks", "abstract": "  Multivariate anomaly detection finds its importance in diverse applications.\nDespite the existence of many detectors to solve this problem, one cannot\nsimply define why an obtained anomaly inferred by the detector is anomalous.\nThis reasoning is required for network operators to understand the root cause\nof the anomaly and the remedial action that should be taken to counteract its\noccurrence. Existing solutions in explainable AI may give cues to features that\ninfluence an anomaly, but they do not formulate generalizable rules that can be\nassessed by a domain expert. Furthermore, not all outliers are anomalous in a\nbusiness sense. There is an unfulfilled need for a system that can interpret\nanomalies predicted by a multivariate anomaly detector and map these patterns\nto actionable rules. This paper aims to fulfill this need by proposing a\nsemi-autonomous anomaly rule miner. The proposed method is applicable to both\ndiscrete and time series data and is tailored for radio access network (RAN)\nanomaly detection use cases. The proposed method is demonstrated in this paper\nwith time series RAN data.\n", "date": "2025-01-11", "categories": []}
{"id": "2501.06591", "title": "Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World\n  Shoplifting Dataset and Benchmark", "abstract": "  Shoplifting poses a significant challenge for retailers, resulting in\nbillions of dollars in annual losses. Traditional security measures often fall\nshort, highlighting the need for intelligent solutions capable of detecting\nshoplifting behaviors in real time. This paper frames shoplifting detection as\nan anomaly detection problem, focusing on the identification of deviations from\ntypical shopping patterns. We introduce PoseLift, a privacy-preserving dataset\nspecifically designed for shoplifting detection, addressing challenges such as\ndata scarcity, privacy concerns, and model biases. PoseLift is built in\ncollaboration with a retail store and contains anonymized human pose data from\nreal-world scenarios. By preserving essential behavioral information while\nanonymizing identities, PoseLift balances privacy and utility. We benchmark\nstate-of-the-art pose-based anomaly detection models on this dataset,\nevaluating performance using a comprehensive set of metrics. Our results\ndemonstrate that pose-based approaches achieve high detection accuracy while\neffectively addressing privacy and bias concerns inherent in traditional\nmethods. As one of the first datasets capturing real-world shoplifting\nbehaviors, PoseLift offers researchers a valuable tool to advance computer\nvision ethically and will be publicly available to foster innovation and\ncollaboration. The dataset is available at\nhttps://github.com/TeCSAR-UNCC/PoseLift.\n", "date": "2025-01-11", "categories": []}
{"id": "2501.06685", "title": "Tab-Shapley: Identifying Top-k Tabular Data Quality Insights", "abstract": "  We present an unsupervised method for aggregating anomalies in tabular\ndatasets by identifying the top-k tabular data quality insights. Each insight\nconsists of a set of anomalous attributes and the corresponding subsets of\nrecords that serve as evidence to the user. The process of identifying these\ninsight blocks is challenging due to (i) the absence of labeled anomalies, (ii)\nthe exponential size of the subset search space, and (iii) the complex\ndependencies among attributes, which obscure the true sources of anomalies.\nSimple frequency-based methods fail to capture these dependencies, leading to\ninaccurate results. To address this, we introduce Tab-Shapley, a cooperative\ngame theory based framework that uses Shapley values to quantify the\ncontribution of each attribute to the data's anomalous nature. While\ncalculating Shapley values typically requires exponential time, we show that\nour game admits a closed-form solution, making the computation efficient. We\nvalidate the effectiveness of our approach through empirical analysis on\nreal-world tabular datasets with ground-truth anomaly labels.\n", "date": "2025-01-12", "categories": []}
{"id": "2501.07078", "title": "ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training", "abstract": "  In the current development of large language models (LLMs), it is important\nto ensure the accuracy and reliability of the underlying data sources. LLMs are\ncritical for various applications, but they often suffer from hallucinations\nand inaccuracies due to knowledge gaps in the training data. Knowledge graphs\n(KGs), as a powerful structural tool, could serve as a vital external\ninformation source to mitigate the aforementioned issues. By providing a\nstructured and comprehensive understanding of real-world data, KGs enhance the\nperformance and reliability of LLMs. However, it is common that errors exist in\nKGs while extracting triplets from unstructured data to construct KGs. This\ncould lead to degraded performance in downstream tasks such as\nquestion-answering and recommender systems. Therefore, anomaly detection in KGs\nis essential to identify and correct these errors. This paper presents an\nanomaly detection algorithm in knowledge graphs with dual-channel learning\n(ADKGD). ADKGD leverages a dual-channel learning approach to enhance\nrepresentation learning from both the entity-view and triplet-view\nperspectives. Furthermore, using a cross-layer approach, our framework\nintegrates internal information aggregation and context information\naggregation. We introduce a kullback-leibler (KL)-loss component to improve the\naccuracy of the scoring function between the dual channels. To evaluate ADKGD's\nperformance, we conduct empirical studies on three real-world KGs: WN18RR,\nFB15K, and NELL-995. Experimental results demonstrate that ADKGD outperforms\nthe state-of-the-art anomaly detection algorithms. The source code and datasets\nare publicly available at https://github.com/csjywu1/ADKGD.\n", "date": "2025-01-13", "categories": []}
{"id": "2501.07172", "title": "Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in\n  Correlated, Multivariate Time Series Data", "abstract": "  Detecting and classifying abnormal system states is critical for condition\nmonitoring, but supervised methods often fall short due to the rarity of\nanomalies and the lack of labeled data. Therefore, clustering is often used to\ngroup similar abnormal behavior. However, evaluating cluster quality without\nground truth is challenging, as existing measures such as the Silhouette Score\n(SSC) only evaluate the cohesion and separation of clusters and ignore possible\nprior knowledge about the data. To address this challenge, we introduce the\nSynchronized Anomaly Agreement Index (SAAI), which exploits the synchronicity\nof anomalies across multivariate time series to assess cluster quality. We\ndemonstrate the effectiveness of SAAI by showing that maximizing SAAI improves\naccuracy on the task of finding the true number of anomaly classes K in\ncorrelated time series by 0.23 compared to SSC and by 0.32 compared to X-Means.\nWe also show that clusters obtained by maximizing SAAI are easier to interpret\ncompared to SSC.\n", "date": "2025-01-13", "categories": []}
{"id": "2501.06099", "title": "Explaining Deep Learning-based Anomaly Detection in Energy Consumption\n  Data by Focusing on Contextually Relevant Data", "abstract": "  Detecting anomalies in energy consumption data is crucial for identifying\nenergy waste, equipment malfunction, and overall, for ensuring efficient energy\nmanagement. Machine learning, and specifically deep learning approaches, have\nbeen greatly successful in anomaly detection; however, they are black-box\napproaches that do not provide transparency or explanations. SHAP and its\nvariants have been proposed to explain these models, but they suffer from high\ncomputational complexity (SHAP) or instability and inconsistency (e.g., Kernel\nSHAP). To address these challenges, this paper proposes an explainability\napproach for anomalies in energy consumption data that focuses on\ncontext-relevant information. The proposed approach leverages existing\nexplainability techniques, focusing on SHAP variants, together with global\nfeature importance and weighted cosine similarity to select background dataset\nbased on the context of each anomaly point. By focusing on the context and most\nrelevant features, this approach mitigates the instability of explainability\nalgorithms. Experimental results across 10 different machine learning models,\nfive datasets, and five XAI techniques, demonstrate that our method reduces the\nvariability of explanations providing consistent explanations. Statistical\nanalyses confirm the robustness of our approach, showing an average reduction\nin variability of approximately 38% across multiple datasets.\n", "date": "2025-01-10", "categories": []}
{"id": "2407.15879", "title": "Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip\n  Approach", "abstract": "  The increasing security and privacy concerns in the Smart Grid sector have\nled to a significant demand for robust intrusion detection systems within\ncritical smart grid infrastructure. To address the challenges posed by privacy\npreservation and decentralized power system zones with distinct data ownership,\nFederated Learning (FL) has emerged as a promising privacy-preserving solution\nwhich facilitates collaborative training of attack detection models without\nnecessitating the sharing of raw data. However, FL presents several\nimplementation limitations in the power system domain due to its heavy reliance\non a centralized aggregator and the risks of privacy leakage during model\nupdate transmission. To overcome these technical bottlenecks, this paper\nintroduces a novel decentralized federated anomaly detection scheme based on\ntwo main gossip protocols namely Random Walk and Epidemic. Our findings\nindicate that the Random Walk protocol exhibits superior performance compared\nto the Epidemic protocol, highlighting its efficacy in decentralized federated\nlearning environments. Experimental validation of the proposed framework\nutilizing publicly available industrial control systems datasets demonstrates\nsuperior attack detection accuracy while safeguarding data confidentiality and\nmitigating the impact of communication latency and stragglers. Furthermore, our\napproach yields a notable 35% improvement in training time compared to\nconventional FL, underscoring the efficacy and robustness of our decentralized\nlearning method.\n", "date": "2024-07-20", "categories": []}
{"id": "2501.04819", "title": "Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly\n  Detection in Industrial Wood Planers", "abstract": "  In recent years, the wood product industry has been facing a skilled labor\nshortage. The result is more frequent sudden failures, resulting in additional\ncosts for these companies already operating in a very competitive market.\nMoreover, sawmills are challenging environments for machinery and sensors.\nGiven that experienced machine operators may be able to diagnose defects or\nmalfunctions, one possible way of assisting novice operators is through\nacoustic monitoring. As a step towards the automation of wood-processing\nequipment and decision support systems for machine operators, in this paper, we\nexplore using a deep convolutional autoencoder for acoustic anomaly detection\nof wood planers on a new real-life dataset. Specifically, our convolutional\nautoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer\noutperform the DCASE autoencoder baseline, one-class SVM, isolation forest and\na published convolutional autoencoder architecture, respectively obtaining an\narea under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer\nsounds. Moreover, we show that adding skip connections and attention mechanism\nunder the form of a transformer encoder-decoder helps to further improve the\nanomaly detection capabilities.\n", "date": "2025-01-08", "categories": []}
{"id": "2207.03890", "title": "ENCODE: Encoding NetFlows for Network Anomaly Detection", "abstract": "  NetFlow data is a popular network log format used by many network analysts\nand researchers. The advantages of using NetFlow over deep packet inspection\nare that it is easier to collect and process, and it is less privacy intrusive.\nMany works have used machine learning to detect network attacks using NetFlow\ndata. The first step for these machine learning pipelines is to pre-process the\ndata before it is given to the machine learning algorithm. Many approaches\nexist to pre-process NetFlow data; however, these simply apply existing methods\nto the data, not considering the specific properties of network data. We argue\nthat for data originating from software systems, such as NetFlow or software\nlogs, similarities in frequency and contexts of feature values are more\nimportant than similarities in the value itself. In this work, we propose an\nencoding algorithm that directly takes the frequency and the context of the\nfeature values into account when the data is being processed. Different types\nof network behaviours can be clustered using this encoding, thus aiding the\nprocess of detecting anomalies within the network. We train several machine\nlearning models for anomaly detection using the data that has been encoded with\nour encoding algorithm. We evaluate the effectiveness of our encoding on a new\ndataset that we created for network attacks on Kubernetes clusters and two\nwell-known public NetFlow datasets. We empirically demonstrate that the machine\nlearning models benefit from using our encoding for anomaly detection.\n", "date": "2022-07-08", "categories": []}
{"id": "2501.01767", "title": "LogicAD: Explainable Anomaly Detection via VLM-based Text Feature\n  Extraction", "abstract": "  Logical image understanding involves interpreting and reasoning about the\nrelationships and consistency within an image's visual content. This capability\nis essential in applications such as industrial inspection, where logical\nanomaly detection is critical for maintaining high-quality standards and\nminimizing costly recalls. Previous research in anomaly detection (AD) has\nrelied on prior knowledge for designing algorithms, which often requires\nextensive manual annotations, significant computing power, and large amounts of\ndata for training. Autoregressive, multimodal Vision Language Models (AVLMs)\noffer a promising alternative due to their exceptional performance in visual\nreasoning across various domains. Despite this, their application to logical AD\nremains unexplored. In this work, we investigate using AVLMs for logical AD and\ndemonstrate that they are well-suited to the task. Combining AVLMs with format\nembedding and a logic reasoner, we achieve SOTA performance on public\nbenchmarks, MVTec LOCO AD, with an AUROC of 86.0% and F1-max of 83.7%, along\nwith explanations of anomalies. This significantly outperforms the existing\nSOTA method by a large margin.\n", "date": "2025-01-03", "categories": []}
{"id": "2408.09791", "title": "ALTBI: Constructing Improved Outlier Detection Models via Optimization\n  of Inlier-Memorization Effect", "abstract": "  Outlier detection (OD) is the task of identifying unusual observations (or\noutliers) from a given or upcoming data by learning unique patterns of normal\nobservations (or inliers). Recently, a study introduced a powerful unsupervised\nOD (UOD) solver based on a new observation of deep generative models, called\ninlier-memorization (IM) effect, which suggests that generative models memorize\ninliers before outliers in early learning stages. In this study, we aim to\ndevelop a theoretically principled method to address UOD tasks by maximally\nutilizing the IM effect. We begin by observing that the IM effect is observed\nmore clearly when the given training data contain fewer outliers. This finding\nindicates a potential for enhancing the IM effect in UOD regimes if we can\neffectively exclude outliers from mini-batches when designing the loss\nfunction. To this end, we introduce two main techniques: 1) increasing the\nmini-batch size as the model training proceeds and 2) using an adaptive\nthreshold to calculate the truncated loss function. We theoretically show that\nthese two techniques effectively filter out outliers from the truncated loss\nfunction, allowing us to utilize the IM effect to the fullest. Coupled with an\nadditional ensemble strategy, we propose our method and term it Adaptive Loss\nTruncation with Batch Increment (ALTBI). We provide extensive experimental\nresults to demonstrate that ALTBI achieves state-of-the-art performance in\nidentifying outliers compared to other recent methods, even with significantly\nlower computation costs. Additionally, we show that our method yields robust\nperformances when combined with privacy-preserving algorithms.\n", "date": "2024-08-19", "categories": []}
{"id": "2501.03368", "title": "Detecting Defective Wafers Via Modular Networks", "abstract": "  The growing availability of sensors within semiconductor manufacturing\nprocesses makes it feasible to detect defective wafers with data-driven models.\nWithout directly measuring the quality of semiconductor devices, they capture\nthe modalities between diverse sensor readings and can be used to predict key\nquality indicators (KQI, \\textit{e.g.}, roughness, resistance) to detect faulty\nproducts, significantly reducing the capital and human cost in maintaining\nphysical metrology steps. Nevertheless, existing models pay little attention to\nthe correlations among different processes for diverse wafer products and\ncommonly struggle with generalizability issues. To enable generic fault\ndetection, in this work, we propose a modular network (MN) trained using time\nseries stage-wise datasets that embodies the structure of the manufacturing\nprocess. It decomposes KQI prediction as a combination of stage modules to\nsimulate compositional semiconductor manufacturing, universally enhancing\nfaulty wafer detection among different wafer types and manufacturing processes.\nExtensive experiments demonstrate the usefulness of our approach, and shed\nlight on how the compositional design provides an interpretable interface for\nmore practical applications.\n", "date": "2025-01-06", "categories": []}
{"id": "2501.03786", "title": "KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt\n  Learning and Enhanced Cross-Modal Integration", "abstract": "  Zero-shot anomaly detection (ZSAD) identifies anomalies without needing\ntraining samples from the target dataset, essential for scenarios with privacy\nconcerns or limited data. Vision-language models like CLIP show potential in\nZSAD but have limitations: relying on manually crafted fixed textual\ndescriptions or anomaly prompts is time-consuming and prone to semantic\nambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing\nmore on global semantics than local details. To address these limitations, We\nintroduce KAnoCLIP, a novel ZSAD framework that leverages vision-language\nmodels. KAnoCLIP combines general knowledge from a Large Language Model\n(GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question\nAnswering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL\nuses a knowledge-driven (KD) loss function to create learnable anomaly prompts,\nremoving the need for fixed text prompts and enhancing generalization. KAnoCLIP\nincludes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional\nCross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and\nConv-Adapter. These components preserve local visual semantics, improve local\ncross-modal fusion, and align global visual features with textual information,\nenhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art\nperformance in ZSAD across 12 industrial and medical datasets, demonstrating\nsuperior generalization compared to existing methods.\n", "date": "2025-01-07", "categories": []}
{"id": "2501.02069", "title": "Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly\n  Detection", "abstract": "  The complexity of modern electro-mechanical systems require the development\nof sophisticated diagnostic methods like anomaly detection capable of detecting\ndeviations. Conventional anomaly detection approaches like signal processing\nand statistical modelling often struggle to effectively handle the intricacies\nof complex systems, particularly when dealing with multi-variate signals. In\ncontrast, neural network-based anomaly detection methods, especially\nAuto-Encoders, have emerged as a compelling alternative, demonstrating\nremarkable performance. However, Auto-Encoders exhibit inherent opaqueness in\ntheir decision-making processes, hindering their practical implementation at\nscale. Addressing this opacity is essential for enhancing the interpretability\nand trustworthiness of anomaly detection models. In this work, we address this\nchallenge by employing a feature selector to select features and counterfactual\nexplanations to give a context to the model output. We tested this approach on\nthe SKAB benchmark dataset and an industrial time-series dataset. The gradient\nbased counterfactual explanation approach was evaluated via validity, sparsity\nand distance measures. Our experimental findings illustrate that our proposed\ncounterfactual approach can offer meaningful and valuable insights into the\nmodel decision-making process, by explaining fewer signals compared to\nconventional approaches. These insights enhance the trustworthiness and\ninterpretability of anomaly detection models.\n", "date": "2025-01-03", "categories": []}
{"id": "2501.02796", "title": "GraphDART: Graph Distillation for Efficient Advanced Persistent Threat\n  Detection", "abstract": "  Cyber-physical-social systems (CPSSs) have emerged in many applications over\nrecent decades, requiring increased attention to security concerns. The rise of\nsophisticated threats like Advanced Persistent Threats (APTs) makes ensuring\nsecurity in CPSSs particularly challenging. Provenance graph analysis has\nproven effective for tracing and detecting anomalies within systems, but the\nsheer size and complexity of these graphs hinder the efficiency of existing\nmethods, especially those relying on graph neural networks (GNNs). To address\nthese challenges, we present GraphDART, a modular framework designed to distill\nprovenance graphs into compact yet informative representations, enabling\nscalable and effective anomaly detection. GraphDART can take advantage of\ndiverse graph distillation techniques, including classic and modern graph\ndistillation methods, to condense large provenance graphs while preserving\nessential structural and contextual information. This approach significantly\nreduces computational overhead, allowing GNNs to learn from distilled graphs\nefficiently and enhance detection performance. Extensive evaluations on\nbenchmark datasets demonstrate the robustness of GraphDART in detecting\nmalicious activities across cyber-physical-social systems. By optimizing\ncomputational efficiency, GraphDART provides a scalable and practical solution\nto safeguard interconnected environments against APTs.\n", "date": "2025-01-06", "categories": []}
{"id": "2501.02975", "title": "Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular\n  Balls", "abstract": "  Outlier detection refers to the identification of anomalous samples that\ndeviate significantly from the distribution of normal data and has been\nextensively studied and used in a variety of practical tasks. However, most\nunsupervised outlier detection methods are carefully designed to detect\nspecified outliers, while real-world data may be entangled with different types\nof outliers. In this study, we propose a fuzzy rough sets-based multi-scale\noutlier detection method to identify various types of outliers. Specifically, a\nnovel fuzzy rough sets-based method that integrates relative fuzzy granule\ndensity is first introduced to improve the capability of detecting local\noutliers. Then, a multi-scale view generation method based on granular-ball\ncomputing is proposed to collaboratively identify group outliers at different\nlevels of granularity. Moreover, reliable outliers and inliers determined by\nthe three-way decision are used to train a weighted support vector machine to\nfurther improve the performance of outlier detection. The proposed method\ninnovatively transforms unsupervised outlier detection into a semi-supervised\nclassification problem and for the first time explores the fuzzy rough\nsets-based outlier detection from the perspective of multi-scale granular\nballs, allowing for high adaptability to different types of outliers. Extensive\nexperiments carried out on both artificial and UCI datasets demonstrate that\nthe proposed outlier detection method significantly outperforms the\nstate-of-the-art methods, improving the results by at least 8.48% in terms of\nthe Area Under the ROC Curve (AUROC) index. { The source codes are released at\n\\url{https://github.com/Xiaofeng-Tan/MGBOD}. }\n", "date": "2025-01-06", "categories": []}
{"id": "2309.11120", "title": "Ano-SuPs: Multi-size anomaly detection for manufactured products by\n  identifying suspected patches", "abstract": "  Image-based systems have gained popularity owing to their capacity to provide\nrich manufacturing status information, low implementation costs and high\nacquisition rates. However, the complexity of the image background and various\nanomaly patterns pose new challenges to existing matrix decomposition methods,\nwhich are inadequate for modeling requirements. Moreover, the uncertainty of\nthe anomaly can cause anomaly contamination problems, making the designed model\nand method highly susceptible to external disturbances. To address these\nchallenges, we propose a two-stage strategy anomaly detection method that\ndetects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we\npropose to detect the patches with anomalies by reconstructing the input image\ntwice: the first step is to obtain a set of normal patches by removing those\nsuspected patches, and the second step is to use those normal patches to refine\nthe identification of the patches with anomalies. To demonstrate its\neffectiveness, we evaluate the proposed method systematically through\nsimulation experiments and case studies. We further identified the key\nparameters and designed steps that impact the model's performance and\nefficiency.\n", "date": "2023-09-20", "categories": []}
{"id": "2410.14579", "title": "Towards Unsupervised Validation of Anomaly-Detection Models", "abstract": "  Unsupervised validation of anomaly-detection models is a highly challenging\ntask. While the common practices for model validation involve a labeled\nvalidation set, such validation sets cannot be constructed when the underlying\ndatasets are unlabeled. The lack of robust and efficient unsupervised\nmodel-validation techniques presents an acute challenge in the implementation\nof automated anomaly-detection pipelines, especially when there exists no prior\nknowledge of the model's performance on similar datasets. This work presents a\nnew paradigm to automated validation of anomaly-detection models, inspired by\nreal-world, collaborative decision-making mechanisms. We focus on two\ncommonly-used, unsupervised model-validation tasks -- model selection and model\nevaluation -- and provide extensive experimental results that demonstrate the\naccuracy and robustness of our approach on both tasks.\n", "date": "2024-10-18", "categories": []}
{"id": "2501.01591", "title": "Multivariate Time Series Anomaly Detection using DiffGAN Model", "abstract": "  In recent years, some researchers have applied diffusion models to\nmultivariate time series anomaly detection. The partial diffusion strategy,\nwhich depends on the diffusion steps, is commonly used for anomaly detection in\nthese models. However, different diffusion steps have an impact on the\nreconstruction of the original data, thereby impacting the effectiveness of\nanomaly detection. To address this issue, we propose a novel method named\nDiffGAN, which adds a generative adversarial network component to the denoiser\nof diffusion model. This addition allows for the simultaneous generation of\nnoisy data and prediction of diffusion steps. Compared to multiple\nstate-of-the-art reconstruction models, experimental results demonstrate that\nDiffGAN achieves superior performance in anomaly detection.\n", "date": "2025-01-03", "categories": []}
{"id": "2302.06815", "title": "Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly\n  Segmentation in Urban Scenes", "abstract": "  Robust autonomous driving requires agents to accurately identify unexpected\nareas (anomalies) in urban scenes. To this end, some critical issues remain\nopen: how to design advisable metric to measure anomalies, and how to properly\ngenerate training samples of anomaly data? Classical effort in anomaly\ndetection usually resorts to pixel-wise uncertainty or sample synthesis, which\nignores the contextual information and sometimes requires auxiliary data with\nfine-grained annotations. On the contrary, in this paper, we exploit the strong\ncontext-dependent nature of the segmentation task and design an energy-guided\nself-supervised framework for anomaly segmentation, which optimizes an anomaly\nhead by maximizing the likelihood of self-generated anomaly pixels. For this\npurpose, we design two estimators to model anomaly likelihood, one is a\ntask-agnostic binary estimator and the other depicts the likelihood as residual\nof task-oriented joint energy. Based on the proposed estimators, we devise an\nadaptive self-supervised training framework, which exploits the contextual\nreliance and estimated likelihood to refine mask annotations in anomaly areas.\nWe conduct extensive experiments on challenging Fishyscapes and Road Anomaly\nbenchmarks, demonstrating that without any auxiliary data or synthetic models,\nour method can still achieve comparable performance to supervised competitors.\nCode is available at https://github.com/yuanpengtu/SLEEG..\n", "date": "2023-02-14", "categories": []}
{"id": "2310.17091", "title": "Detecting subtle cyberattacks on adaptive cruise control vehicles: A\n  machine learning approach", "abstract": "  With the advent of vehicles equipped with advanced driver-assistance systems,\nsuch as adaptive cruise control (ACC) and other automated driving features, the\npotential for cyberattacks on these automated vehicles (AVs) has emerged. While\novert attacks that force vehicles to collide may be easily identified, more\ninsidious attacks, which only slightly alter driving behavior, can result in\nnetwork-wide increases in congestion, fuel consumption, and even crash risk\nwithout being easily detected. To address the detection of such attacks, we\nfirst present a traffic model framework for three types of potential\ncyberattacks: malicious manipulation of vehicle control commands, false data\ninjection attacks on sensor measurements, and denial-of-service (DoS) attacks.\nWe then investigate the impacts of these attacks at both the individual vehicle\n(micro) and traffic flow (macro) levels. A novel generative adversarial network\n(GAN)-based anomaly detection model is proposed for real-time identification of\nsuch attacks using vehicle trajectory data. We provide numerical evidence {to\ndemonstrate} the efficacy of our machine learning approach in detecting\ncyberattacks on ACC-equipped vehicles. The proposed method is compared against\nsome recently proposed neural network models and observed to have higher\naccuracy in identifying anomalous driving behaviors of ACC vehicles.\n", "date": "2023-10-26", "categories": []}
{"id": "2403.14742", "title": "A Classifier-Based Approach to Multi-Class Anomaly Detection for\n  Astronomical Transients", "abstract": "  Automating real-time anomaly detection is essential for identifying rare\ntransients, with modern survey telescopes generating tens of thousands of\nalerts per night, and future telescopes, such as the Vera C. Rubin Observatory,\nprojected to increase this number dramatically. Currently, most anomaly\ndetection algorithms for astronomical transients rely either on hand-crafted\nfeatures extracted from light curves or on features generated through\nunsupervised representation learning, coupled with standard anomaly detection\nalgorithms. In this work, we introduce an alternative approach: using the\npenultimate layer of a neural network classifier as the latent space for\nanomaly detection. We then propose a novel method, Multi-Class Isolation\nForests (\\texttt{MCIF}), which trains separate isolation forests for each class\nto derive an anomaly score for a light curve from its latent space\nrepresentation. This approach significantly outperforms a standard isolation\nforest. We also use a simpler input method for real-time transient classifiers\nwhich circumvents the need for interpolation and helps the neural network\nhandle irregular sampling and model inter-passband relationships. Our anomaly\ndetection pipeline identifies rare classes including kilonovae,\npair-instability supernovae, and intermediate luminosity transients shortly\nafter trigger on simulated Zwicky Transient Facility light curves. Using a\nsample of our simulations matching the population of anomalies expected in\nnature (54 anomalies and 12,040 common transients), our method discovered\n$41\\pm3$ anomalies (~75% recall) after following up the top 2000 (~15%) ranked\ntransients. Our novel method shows that classifiers can be effectively\nrepurposed for real-time anomaly detection.\n", "date": "2024-03-21", "categories": []}
{"id": "2501.00107", "title": "An Unsupervised Anomaly Detection in Electricity Consumption Using\n  Reinforcement Learning and Time Series Forest Based Framework", "abstract": "  Anomaly detection (AD) plays a crucial role in time series applications,\nprimarily because time series data is employed across real-world scenarios.\nDetecting anomalies poses significant challenges since anomalies take diverse\nforms making them hard to pinpoint accurately. Previous research has explored\ndifferent AD models, making specific assumptions with varying sensitivity\ntoward particular anomaly types. To address this issue, we propose a novel\nmodel selection for unsupervised AD using a combination of time series forest\n(TSF) and reinforcement learning (RL) approaches that dynamically chooses an AD\ntechnique. Our approach allows for effective AD without explicitly depending on\nground truth labels that are often scarce and expensive to obtain. Results from\nthe real-time series dataset demonstrate that the proposed model selection\napproach outperforms all other AD models in terms of the F1 score metric. For\nthe synthetic dataset, our proposed model surpasses all other AD models except\nfor KNN, with an impressive F1 score of 0.989. The proposed model selection\nframework also exceeded the performance of GPT-4 when prompted to act as an\nanomaly detector on the synthetic dataset. Exploring different reward functions\nrevealed that the original reward function in our proposed AD model selection\napproach yielded the best overall scores. We evaluated the performance of the\nsix AD models on an additional three datasets, having global, local, and\nclustered anomalies respectively, showing that each AD model exhibited distinct\nperformance depending on the type of anomalies. This emphasizes the\nsignificance of our proposed AD model selection framework, maintaining high\nperformance across all datasets, and showcasing superior performance across\ndifferent anomaly types.\n", "date": "2024-12-30", "categories": []}
{"id": "2501.00346", "title": "CNC: Cross-modal Normality Constraint for Unsupervised Multi-class\n  Anomaly Detection", "abstract": "  Existing unsupervised distillation-based methods rely on the differences\nbetween encoded and decoded features to locate abnormal regions in test images.\nHowever, the decoder trained only on normal samples still reconstructs abnormal\npatch features well, degrading performance. This issue is particularly\npronounced in unsupervised multi-class anomaly detection tasks. We attribute\nthis behavior to over-generalization(OG) of decoder: the significantly\nincreasing diversity of patch patterns in multi-class training enhances the\nmodel generalization on normal patches, but also inadvertently broadens its\ngeneralization to abnormal patches. To mitigate OG, we propose a novel approach\nthat leverages class-agnostic learnable prompts to capture common textual\nnormality across various visual patterns, and then apply them to guide the\ndecoded features towards a normal textual representation, suppressing\nover-generalization of the decoder on abnormal patterns. To further improve\nperformance, we also introduce a gated mixture-of-experts module to specialize\nin handling diverse patch patterns and reduce mutual interference between them\nin multi-class training. Our method achieves competitive performance on the\nMVTec AD and VisA datasets, demonstrating its effectiveness.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.00585", "title": "Sidewalk Hazard Detection Using Variational Autoencoder and One-Class\n  SVM", "abstract": "  The unpredictable nature of outdoor settings introduces numerous safety\nconcerns, making hazard detection crucial for safe navigation. This paper\nintroduces a novel system for sidewalk safety navigation utilizing a hybrid\napproach that combines a Variational Autoencoder (VAE) with a One-Class Support\nVector Machine (OCSVM). The system is designed to detect anomalies on sidewalks\nthat could potentially pose walking hazards. A dataset comprising over 15,000\ntraining frames and 5,000 testing frames was collected using video recordings,\ncapturing various sidewalk scenarios, including normal and hazardous\nconditions. During deployment, the VAE utilizes its reconstruction mechanism to\ndetect anomalies within a frame. Poor reconstruction by the VAE implies the\npresence of an anomaly, after which the OCSVM is used to confirm whether the\nanomaly is hazardous or non-hazardous. The proposed VAE model demonstrated\nstrong performance, with a high Area Under the Curve (AUC) of 0.94, effectively\ndistinguishing anomalies that could be potential hazards. The OCSVM is employed\nto reduce the detection of false hazard anomalies, such as manhole or water\nvalve covers. This approach achieves an accuracy of 91.4%, providing a highly\nreliable system for distinguishing between hazardous and non-hazardous\nscenarios. These results suggest that the proposed system offers a robust\nsolution for hazard detection in uncertain environments.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.01061", "title": "An Efficient Outlier Detection Algorithm for Data Streaming", "abstract": "  The nature of modern data is increasingly real-time, making outlier detection\ncrucial in any data-related field, such as finance for fraud detection and\nhealthcare for monitoring patient vitals. Traditional outlier detection\nmethods, such as the Local Outlier Factor (LOF) algorithm, struggle with\nreal-time data due to the need for extensive recalculations with each new data\npoint, limiting their application in real-time environments. While the\nIncremental LOF (ILOF) algorithm has been developed to tackle the challenges of\nonline anomaly detection, it remains computationally expensive when processing\nlarge streams of data points, and its detection performance may degrade after a\ncertain threshold of points have streamed in. In this paper, we propose a novel\napproach to enhance the efficiency of LOF algorithms for online anomaly\ndetection, named the Efficient Incremental LOF (EILOF) algorithm. The EILOF\nalgorithm only computes the LOF scores of new points without altering the LOF\nscores of existing data points. Although exact LOF scores have not yet been\ncomputed for the existing points in the new algorithm, datasets often contain\nnoise, and minor deviations in LOF score calculations do not necessarily\ndegrade detection performance. In fact, such deviations can sometimes enhance\noutlier detection. We systematically tested this approach on both simulated and\nreal-world datasets, demonstrating that EILOF outperforms ILOF as the volume of\nstreaming data increases across various scenarios. The EILOF algorithm not only\nsignificantly reduces computational costs, but also systematically improves\ndetection accuracy when the number of additional points increases compared to\nthe ILOF algorithm.\n", "date": "2025-01-02", "categories": []}
{"id": "2412.19108", "title": "Graph Mixture of Experts and Memory-augmented Routers for Multivariate\n  Time Series Anomaly Detection", "abstract": "  Multivariate time series (MTS) anomaly detection is a critical task that\ninvolves identifying abnormal patterns or events in data that consist of\nmultiple interrelated time series. In order to better model the complex\ninterdependence between entities and the various inherent characteristics of\neach entity, the GNN based methods are widely adopted by existing methods. In\neach layer of GNN, node features aggregate information from their neighboring\nnodes to update their information. In doing so, from shallow layer to deep\nlayer in GNN, original individual node features continue to be weakened and\nmore structural information,i.e., from short-distance neighborhood to\nlong-distance neighborhood, continues to be enhanced. However, research to date\nhas largely ignored the understanding of how hierarchical graph information is\nrepresented and their characteristics that can benefit anomaly detection.\nExisting methods simply leverage the output from the last layer of GNN for\nanomaly estimation while neglecting the essential information contained in the\nintermediate GNN layers. To address such limitations, in this paper, we propose\na Graph Mixture of Experts (Graph-MoE) network for multivariate time series\nanomaly detection, which incorporates the mixture of experts (MoE) module to\nadaptively represent and integrate hierarchical multi-layer graph information\ninto entity representations. It is worth noting that our Graph-MoE can be\nintegrated into any GNN-based MTS anomaly detection method in a plug-and-play\nmanner. In addition, the memory-augmented routers are proposed in this paper to\ncapture the correlation temporal information in terms of the global historical\nfeatures of MTS to adaptively weigh the obtained entity representations to\nachieve successful anomaly estimation. Extensive experiments on five\nchallenging datasets prove the superiority of our approach and each proposed\nmodule.\n", "date": "2024-12-26", "categories": []}
{"id": "2412.20084", "title": "STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video\n  Anomaly Detection", "abstract": "  Video anomaly detection (VAD) has been extensively researched due to its\npotential for intelligent video systems. However, most existing methods based\non CNNs and transformers still suffer from substantial computational burdens\nand have room for improvement in learning spatial-temporal normality. Recently,\nMamba has shown great potential for modeling long-range dependencies with\nlinear complexity, providing an effective solution to the above dilemma. To\nthis end, we propose a lightweight and effective Mamba-based network named\nSTNMamba, which incorporates carefully designed Mamba modules to enhance the\nlearning of spatial-temporal normality. Firstly, we develop a dual-encoder\narchitecture, where the spatial encoder equipped with Multi-Scale Vision Space\nState Blocks (MS-VSSB) extracts multi-scale appearance features, and the\ntemporal encoder employs Channel-Aware Vision Space State Blocks (CA-VSSB) to\ncapture significant motion patterns. Secondly, a Spatial-Temporal Interaction\nModule (STIM) is introduced to integrate spatial and temporal information\nacross multiple levels, enabling effective modeling of intrinsic\nspatial-temporal consistency. Within this module, the Spatial-Temporal Fusion\nBlock (STFB) is proposed to fuse the spatial and temporal features into a\nunified feature space, and the memory bank is utilized to store\nspatial-temporal prototypes of normal patterns, restricting the model's ability\nto represent anomalies. Extensive experiments on three benchmark datasets\ndemonstrate that our STNMamba achieves competitive performance with fewer\nparameters and lower computational costs than existing methods.\n", "date": "2024-12-28", "categories": []}
{"id": "2412.20363", "title": "Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in\n  Crowded Video Scenes", "abstract": "  Detecting anomalies in crowded video scenes is critical for public safety,\nenabling timely identification of potential threats. This study explores video\nanomaly detection within a Functional Data Analysis framework, focusing on the\napplication of the Magnitude-Shape (MS) Plot. Autoencoders are used to learn\nand reconstruct normal behavioral patterns from anomaly-free training data,\nresulting in low reconstruction errors for normal frames and higher errors for\nframes with potential anomalies. The reconstruction error matrix for each frame\nis treated as multivariate functional data, with the MS-Plot applied to analyze\nboth magnitude and shape deviations, enhancing the accuracy of anomaly\ndetection. Using its capacity to evaluate the magnitude and shape of\ndeviations, the MS-Plot offers a statistically principled and interpretable\nframework for anomaly detection. The proposed methodology is evaluated on two\nwidely used benchmark datasets, UCSD Ped2 and CUHK Avenue, demonstrating\npromising performance. It performs better than traditional univariate\nfunctional detectors (e.g., FBPlot, TVDMSS, Extremal Depth, and Outliergram)\nand several state-of-the-art methods. These results highlight the potential of\nthe MS-Plot-based framework for effective anomaly detection in crowded video\nscenes.\n", "date": "2024-12-29", "categories": []}
{"id": "2412.20455", "title": "Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video\n  Anomaly Detection", "abstract": "  Recently, weakly supervised video anomaly detection (WS-VAD) has emerged as a\ncontemporary research direction to identify anomaly events like violence and\nnudity in videos using only video-level labels. However, this task has\nsubstantial challenges, including addressing imbalanced modality information\nand consistently distinguishing between normal and abnormal features. In this\npaper, we address these challenges and propose a multi-modal WS-VAD framework\nto accurately detect anomalies such as violence and nudity. Within the proposed\nframework, we introduce a new fusion mechanism known as the Cross-modal Fusion\nAdapter (CFA), which dynamically selects and enhances highly relevant\naudio-visual features in relation to the visual modality. Additionally, we\nintroduce a Hyperbolic Lorentzian Graph Attention (HLGAtt) to effectively\ncapture the hierarchical relationships between normal and abnormal\nrepresentations, thereby enhancing feature separation accuracy. Through\nextensive experiments, we demonstrate that the proposed model achieves\nstate-of-the-art results on benchmark datasets of violence and nudity\ndetection.\n", "date": "2024-12-29", "categories": []}
{"id": "2412.20512", "title": "Dive into Time-Series Anomaly Detection: A Decade Review", "abstract": "  Recent advances in data collection technology, accompanied by the ever-rising\nvolume and velocity of streaming data, underscore the vital need for time\nseries analytics. In this regard, time-series anomaly detection has been an\nimportant activity, entailing various applications in fields such as cyber\nsecurity, financial markets, law enforcement, and health care. While\ntraditional literature on anomaly detection is centered on statistical\nmeasures, the increasing number of machine learning algorithms in recent years\ncall for a structured, general characterization of the research methods for\ntime-series anomaly detection. This survey groups and summarizes anomaly\ndetection existing solutions under a process-centric taxonomy in the time\nseries context. In addition to giving an original categorization of anomaly\ndetection methods, we also perform a meta-analysis of the literature and\noutline general trends in time-series anomaly detection research.\n", "date": "2024-12-29", "categories": []}
{"id": "2307.13069", "title": "General-Purpose Multi-Modal OOD Detection Framework", "abstract": "  Out-of-distribution (OOD) detection identifies test samples that differ from\nthe training data, which is critical to ensuring the safety and reliability of\nmachine learning (ML) systems. While a plethora of methods have been developed\nto detect uni-modal OOD samples, only a few have focused on multi-modal OOD\ndetection. Current contrastive learning-based methods primarily study\nmulti-modal OOD detection in a scenario where both a given image and its\ncorresponding textual description come from a new domain. However, real-world\ndeployments of ML systems may face more anomaly scenarios caused by multiple\nfactors like sensor faults, bad weather, and environmental changes. Hence, the\ngoal of this work is to simultaneously detect from multiple different OOD\nscenarios in a fine-grained manner. To reach this goal, we propose a\ngeneral-purpose weakly-supervised OOD detection framework, called WOOD, that\ncombines a binary classifier and a contrastive learning component to reap the\nbenefits of both. In order to better distinguish the latent representations of\nin-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain\ntheir similarity. Furthermore, we develop a new scoring metric to integrate the\nprediction results from both the binary classifier and contrastive learning for\nidentifying OOD samples. We evaluate the proposed WOOD model on multiple\nreal-world datasets, and the experimental results demonstrate that the WOOD\nmodel outperforms the state-of-the-art methods for multi-modal OOD detection.\nImportantly, our approach is able to achieve high accuracy in OOD detection in\nthree different OOD scenarios simultaneously. The source code will be made\npublicly available upon publication.\n", "date": "2023-07-24", "categories": []}
{"id": "2308.15068", "title": "A Comprehensive Augmentation Framework for Anomaly Detection", "abstract": "  Data augmentation methods are commonly integrated into the training of\nanomaly detection models. Previous approaches have primarily focused on\nreplicating real-world anomalies or enhancing diversity, without considering\nthat the standard of anomaly varies across different classes, potentially\nleading to a biased training distribution. This paper analyzes crucial traits\nof simulated anomalies that contribute to the training of reconstructive\nnetworks and condenses them into several methods, thus creating a comprehensive\nframework by selectively utilizing appropriate combinations. Furthermore, we\nintegrate this framework with a reconstruction-based approach and concurrently\npropose a split training strategy that alleviates the issue of overfitting\nwhile avoiding introducing interference to the reconstruction process. The\nevaluations conducted on the MVTec anomaly detection dataset demonstrate that\nour method outperforms the previous state-of-the-art approach, particularly in\nterms of object classes. To evaluate generalizability, we generate a simulated\ndataset comprising anomalies with diverse characteristics since the original\ntest samples only include specific types of anomalies and may lead to biased\nevaluations. Experimental results demonstrate that our approach exhibits\npromising potential for generalizing effectively to various unforeseen\nanomalies encountered in real-world scenarios.\n", "date": "2023-08-29", "categories": []}
{"id": "2411.17217", "title": "Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning", "abstract": "  Segment Anything Model (SAM) has made great progress in anomaly segmentation\ntasks due to its impressive generalization ability. However, existing methods\nthat directly apply SAM through prompting often overlook the domain shift\nissue, where SAM performs well on natural images but struggles in industrial\nscenarios. Parameter-Efficient Fine-Tuning (PEFT) offers a promising solution,\nbut it may yield suboptimal performance by not adequately addressing the\nperception challenges during adaptation to anomaly images. In this paper, we\npropose a novel \\textbf{S}elf-\\textbf{P}erceptinon \\textbf{T}uning\n(\\textbf{SPT}) method, aiming to enhance SAM's perception capability for\nanomaly segmentation. The SPT method incorporates a self-drafting tuning\nstrategy, which generates an initial coarse draft of the anomaly mask, followed\nby a refinement process. Additionally, a visual-relation-aware adapter is\nintroduced to improve the perception of discriminative relational information\nfor mask generation. Extensive experimental results on several benchmark\ndatasets demonstrate that our SPT method can significantly outperform baseline\nmethods, validating its effectiveness.\n", "date": "2024-11-26", "categories": []}
{"id": "2412.18820", "title": "CausalTAD: Causal Implicit Generative Model for Debiased Online\n  Trajectory Anomaly Detection", "abstract": "  Trajectory anomaly detection, aiming to estimate the anomaly risk of\ntrajectories given the Source-Destination (SD) pairs, has become a critical\nproblem for many real-world applications. Existing solutions directly train a\ngenerative model for observed trajectories and calculate the conditional\ngenerative probability $P({T}|{C})$ as the anomaly risk, where ${T}$ and ${C}$\nrepresent the trajectory and SD pair respectively. However, we argue that the\nobserved trajectories are confounded by road network preference which is a\ncommon cause of both SD distribution and trajectories. Existing methods ignore\nthis issue limiting their generalization ability on out-of-distribution\ntrajectories. In this paper, we define the debiased trajectory anomaly\ndetection problem and propose a causal implicit generative model, namely\nCausalTAD, to solve it. CausalTAD adopts do-calculus to eliminate the\nconfounding bias of road network preference and estimates $P({T}|do({C}))$ as\nthe anomaly criterion. Extensive experiments show that CausalTAD can not only\nachieve superior performance on trained trajectories but also generally improve\nthe performance of out-of-distribution data, with improvements of $2.1\\% \\sim\n5.7\\%$ and $10.6\\% \\sim 32.7\\%$ respectively.\n", "date": "2024-12-25", "categories": []}
{"id": "2412.19286", "title": "Time Series Foundational Models: Their Role in Anomaly Detection and\n  Prediction", "abstract": "  Time series foundational models (TSFM) have gained prominence in time series\nforecasting, promising state-of-the-art performance across various\napplications. However, their application in anomaly detection and prediction\nremains underexplored, with growing concerns regarding their black-box nature,\nlack of interpretability and applicability. This paper critically evaluates the\nefficacy of TSFM in anomaly detection and prediction tasks. We systematically\nanalyze TSFM across multiple datasets, including those characterized by the\nabsence of discernible patterns, trends and seasonality. Our analysis shows\nthat while TSFMs can be extended for anomaly detection and prediction,\ntraditional statistical and deep learning models often match or outperform TSFM\nin these tasks. Additionally, TSFMs require high computational resources but\nfail to capture sequential dependencies effectively or improve performance in\nfew-shot or zero-shot scenarios. \\noindent The preprocessed datasets, codes to\nreproduce the results and supplementary materials are available at\nhttps://github.com/smtmnfg/TSFM.\n", "date": "2024-12-26", "categories": []}
{"id": "2412.19392", "title": "Asymptotically Optimal Search for a Change Point Anomaly under a\n  Composite Hypothesis Model", "abstract": "  We address the problem of searching for a change point in an anomalous\nprocess among a finite set of M processes. Specifically, we address a composite\nhypothesis model in which each process generates measurements following a\ncommon distribution with an unknown parameter (vector). This parameter belongs\nto either a normal or abnormal space depending on the current state of the\nprocess. Before the change point, all processes, including the anomalous one,\nare in a normal state; after the change point, the anomalous process\ntransitions to an abnormal state. Our goal is to design a sequential search\nstrategy that minimizes the Bayes risk by balancing sample complexity and\ndetection accuracy. We propose a deterministic search algorithm with the\nfollowing notable properties. First, we analytically demonstrate that when the\ndistributions of both normal and abnormal processes are unknown, the algorithm\nis asymptotically optimal in minimizing the Bayes risk as the error probability\napproaches zero. In the second setting, where the parameter under the null\nhypothesis is known, the algorithm achieves asymptotic optimality with improved\ndetection time based on the true normal state. Simulation results are presented\nto validate the theoretical findings.\n", "date": "2024-12-27", "categories": []}
{"id": "2405.16771", "title": "ARC: A Generalist Graph Anomaly Detector with In-Context Learning", "abstract": "  Graph anomaly detection (GAD), which aims to identify abnormal nodes that\ndiffer from the majority within a graph, has garnered significant attention.\nHowever, current GAD methods necessitate training specific to each dataset,\nresulting in high training costs, substantial data requirements, and limited\ngeneralizability when being applied to new datasets and domains. To address\nthese limitations, this paper proposes ARC, a generalist GAD approach that\nenables a ``one-for-all'' GAD model to detect anomalies across various graph\ndatasets on-the-fly. Equipped with in-context learning, ARC can directly\nextract dataset-specific patterns from the target dataset using few-shot normal\nsamples at the inference stage, without the need for retraining or fine-tuning\non the target dataset. ARC comprises three components that are well-crafted for\ncapturing universal graph anomaly patterns: 1) smoothness-based feature\nAlignment module that unifies the features of different datasets into a common\nand anomaly-sensitive space; 2) ego-neighbor Residual graph encoder that learns\nabnormality-related node embeddings; and 3) cross-attentive in-Context anomaly\nscoring module that predicts node abnormality by leveraging few-shot normal\nsamples. Extensive experiments on multiple benchmark datasets from various\ndomains demonstrate the superior anomaly detection performance, efficiency, and\ngeneralizability of ARC.\n", "date": "2024-05-27", "categories": []}
{"id": "2408.14947", "title": "ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line\n  Scanning", "abstract": "  Detecting unexpected objects (anomalies) in real time has great potential for\nmonitoring, managing, and protecting the environment. Hyperspectral line-scan\ncameras are a low-cost solution that enhance confidence in anomaly detection\nover RGB and multispectral imagery. However, existing line-scan algorithms are\ntoo slow when using small computers (e.g. those onboard a drone or small\nsatellite), do not adapt to changing scenery, or lack robustness against\ngeometric distortions. This paper introduces the Exponentially moving RX\nalgorithm (ERX) to address these issues, and compares it with four existing\nRX-based anomaly detection methods for hyperspectral line scanning. Three large\nand more complex datasets are also introduced to better assess the practical\nchallenges when using line-scan cameras (two hyperspectral and one\nmultispectral). ERX was evaluated using a Jetson Xavier NX edge computing\nmodule (6-core CPU, 8GB RAM, 20W power draw), achieving the best combination of\nspeed and detection performance. ERX was 9 times faster than the next-best\nalgorithm on the dataset with the highest number of bands (108 band), with an\naverage speed of 561 lines per second on the Jetson. It achieved a 29.3% AUC\nimprovement over the next-best algorithm on the most challenging dataset, while\nshowing greater adaptability through consistently high AUC scores regardless of\nthe camera's starting location. ERX performed robustly across all datasets,\nachieving an AUC of 0.941 on a drone-collected hyperspectral line scan dataset\nwithout geometric corrections (a 16.9% improvement over existing algorithms).\nThis work enables future research on the detection of anomalous objects in real\ntime, adaptive and automatic threshold selection, and real-time field tests.\nThe datasets and the Python code are openly available at:\nhttps://github.com/WiseGamgee/HyperAD, promoting accessibility and future work.\n", "date": "2024-08-27", "categories": []}
{"id": "2412.18298", "title": "Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight", "abstract": "  Video anomaly detection (VAD) has witnessed significant advancements through\nthe integration of large language models (LLMs) and vision-language models\n(VLMs), addressing critical challenges such as interpretability, temporal\nreasoning, and generalization in dynamic, open-world scenarios. This paper\npresents an in-depth review of cutting-edge LLM-/VLM-based methods in 2024,\nfocusing on four key aspects: (i) enhancing interpretability through semantic\ninsights and textual explanations, making visual anomalies more understandable;\n(ii) capturing intricate temporal relationships to detect and localize dynamic\nanomalies across video frames; (iii) enabling few-shot and zero-shot detection\nto minimize reliance on large, annotated datasets; and (iv) addressing\nopen-world and class-agnostic anomalies by using semantic understanding and\nmotion features for spatiotemporal coherence. We highlight their potential to\nredefine the landscape of VAD. Additionally, we explore the synergy between\nvisual and textual modalities offered by LLMs and VLMs, highlighting their\ncombined strengths and proposing future directions to fully exploit the\npotential in enhancing video anomaly detection.\n", "date": "2024-12-24", "categories": []}
{"id": "2410.00320", "title": "PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot\n  3D Anomaly Detection", "abstract": "  Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that\naddresses scenarios where target 3D training samples are unavailable due to\npractical concerns like privacy protection. This paper introduces PointAD, a\nnovel approach that transfers the strong generalization capabilities of CLIP\nfor recognizing 3D anomalies on unseen objects. PointAD provides a unified\nframework to comprehend 3D anomalies from both points and pixels. In this\nframework, PointAD renders 3D anomalies into multiple 2D renderings and\nprojects them back into 3D space. To capture the generic anomaly semantics into\nPointAD, we propose hybrid representation learning that optimizes the learnable\ntext prompts from 3D and 2D through auxiliary point clouds. The collaboration\noptimization between point and pixel representations jointly facilitates our\nmodel to grasp underlying 3D anomaly patterns, contributing to detecting and\nsegmenting anomalies of unseen diverse 3D objects. Through the alignment of 3D\nand 2D space, our model can directly integrate RGB information, further\nenhancing the understanding of 3D anomalies in a plug-and-play manner.\nExtensive experiments show the superiority of PointAD in ZS 3D anomaly\ndetection across diverse unseen objects.\n", "date": "2024-10-01", "categories": []}
{"id": "2412.16200", "title": "Robust Spectral Anomaly Detection in EELS Spectral Images via Three\n  Dimensional Convolutional Variational Autoencoders", "abstract": "  We introduce a Three-Dimensional Convolutional Variational Autoencoder\n(3D-CVAE) for automated anomaly detection in Electron Energy Loss Spectroscopy\nSpectrum Imaging (EELS-SI) data. Our approach leverages the full\nthree-dimensional structure of EELS-SI data to detect subtle spectral anomalies\nwhile preserving both spatial and spectral correlations across the datacube. By\nemploying negative log-likelihood loss and training on bulk spectra, the model\nlearns to reconstruct bulk features characteristic of the defect-free material.\nIn exploring methods for anomaly detection, we evaluated both our 3D-CVAE\napproach and Principal Component Analysis (PCA), testing their performance\nusing Fe L-edge peak shifts designed to simulate material defects. Our results\nshow that 3D-CVAE achieves superior anomaly detection and maintains consistent\nperformance across various shift magnitudes. The method demonstrates clear\nbimodal separation between normal and anomalous spectra, enabling reliable\nclassification. Further analysis verifies that lower dimensional\nrepresentations are robust to anomalies in the data. While performance\nadvantages over PCA diminish with decreasing anomaly concentration, our method\nmaintains high reconstruction quality even in challenging, noise-dominated\nspectral regions. This approach provides a robust framework for unsupervised\nautomated detection of spectral anomalies in EELS-SI data, particularly\nvaluable for analyzing complex material systems.\n", "date": "2024-12-16", "categories": []}
{"id": "2412.16375", "title": "Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time\n  Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's\n  GRACE-FO Verification and Validation", "abstract": "  NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) data are\ncritical for NASA-JPL's tsunami detection, real-time operations, and\noceanographic research. However, these time-series data often contain spikes,\nsteps, and drifts that degrade data quality and obscure essential oceanographic\nfeatures. To address these anomalies, the work introduces an Iterative\nEncoding-Decoding Variational Autoencoders (Iterative Encoding-Decoding VAEs)\nmodel to improve the quality of DART time series. Unlike traditional filtering\nand thresholding methods that risk distorting inherent signal characteristics,\nIterative Encoding-Decoding VAEs progressively remove anomalies while\npreserving the data's latent structure. A hybrid thresholding approach further\nretains genuine oceanographic features near boundaries. Applied to complex DART\ndatasets, this approach yields reconstructions that better maintain key oceanic\nproperties compared to classical statistical techniques, offering improved\nrobustness against spike removal and subtle step changes. The resulting\nhigh-quality data supports critical verification and validation efforts for the\nGRACE-FO mission at NASA-JPL, where accurate surface measurements are essential\nto modeling Earth's gravitational field and global water dynamics. Ultimately,\nthis data processing method enhances tsunami detection and underpins future\nclimate modeling with improved interpretability and reliability.\n", "date": "2024-12-20", "categories": []}
{"id": "2412.16447", "title": "A Generalizable Anomaly Detection Method in Dynamic Graphs", "abstract": "  Anomaly detection aims to identify deviations from normal patterns within\ndata. This task is particularly crucial in dynamic graphs, which are common in\napplications like social networks and cybersecurity, due to their evolving\nstructures and complex relationships. Although recent deep learning-based\nmethods have shown promising results in anomaly detection on dynamic graphs,\nthey often lack of generalizability. In this study, we propose GeneralDyG, a\nmethod that samples temporal ego-graphs and sequentially extracts structural\nand temporal features to address the three key challenges in achieving\ngeneralizability: Data Diversity, Dynamic Feature Capture, and Computational\nCost. Extensive experimental results demonstrate that our proposed GeneralDyG\nsignificantly outperforms state-of-the-art methods on four real-world datasets.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.17297", "title": "Revisiting Multimodal Fusion for 3D Anomaly Detection from an\n  Architectural Perspective", "abstract": "  Existing efforts to boost multimodal fusion of 3D anomaly detection (3D-AD)\nprimarily concentrate on devising more effective multimodal fusion strategies.\nHowever, little attention was devoted to analyzing the role of multimodal\nfusion architecture (topology) design in contributing to 3D-AD. In this paper,\nwe aim to bridge this gap and present a systematic study on the impact of\nmultimodal fusion architecture design on 3D-AD. This work considers the\nmultimodal fusion architecture design at the intra-module fusion level, i.e.,\nindependent modality-specific modules, involving early, middle or late\nmultimodal features with specific fusion operations, and also at the\ninter-module fusion level, i.e., the strategies to fuse those modules. In both\ncases, we first derive insights through theoretically and experimentally\nexploring how architectural designs influence 3D-AD. Then, we extend SOTA\nneural architecture search (NAS) paradigm and propose 3D-ADNAS to\nsimultaneously search across multimodal fusion strategies and modality-specific\nmodules for the first time.Extensive experiments show that 3D-ADNAS obtains\nconsistent improvements in 3D-AD across various model capacities in terms of\naccuracy, frame rate, and memory usage, and it exhibits great potential in\ndealing with few-shot 3D-AD tasks.\n", "date": "2024-12-23", "categories": []}
{"id": "2412.17458", "title": "Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly\n  Detection", "abstract": "  Unsupervised anomaly detection methods can identify surface defects in\nindustrial images by leveraging only normal samples for training. Due to the\nrisk of overfitting when learning from a single class, anomaly synthesis\nstrategies are introduced to enhance detection capability by generating\nartificial anomalies. However, existing strategies heavily rely on anomalous\ntextures from auxiliary datasets. Moreover, their limitations in the coverage\nand directionality of anomaly synthesis may result in a failure to capture\nuseful information and lead to significant redundancy. To address these issues,\nwe propose a novel Progressive Boundary-guided Anomaly Synthesis (PBAS)\nstrategy, which can directionally synthesize crucial feature-level anomalies\nwithout auxiliary textures. It consists of three core components: Approximate\nBoundary Learning (ABL), Anomaly Feature Synthesis (AFS), and Refined Boundary\nOptimization (RBO). To make the distribution of normal samples more compact,\nABL first learns an approximate decision boundary by center constraint, which\nimproves the center initialization through feature alignment. AFS then\ndirectionally synthesizes anomalies with more flexible scales guided by the\nhypersphere distribution of normal features. Since the boundary is so loose\nthat it may contain real anomalies, RBO refines the decision boundary through\nthe binary classification of artificial anomalies and normal features.\nExperimental results show that our method achieves state-of-the-art performance\nand the fastest detection speed on three widely used industrial datasets,\nincluding MVTec AD, VisA, and MPDD. The code will be available at:\nhttps://github.com/cqylunlun/PBAS.\n", "date": "2024-12-23", "categories": []}
{"id": "2309.10230", "title": "LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN\n  Using Diverse Synthetic Data", "abstract": "  LiDAR-based semantic scene understanding is an important module in the modern\nautonomous driving perception stack. However, identifying outlier points in a\nLiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich\ninformation. While former SOTA methods adopt heuristic architectures, we\nrevisit this problem from the perspective of Selective Classification, which\nintroduces a selective function into the standard closed-set classification\nsetup. Our solution is built upon the basic idea of abstaining from choosing\nany inlier categories but learns a point-wise abstaining penalty with a\nmargin-based loss. Apart from learning paradigms, synthesizing outliers to\napproximate unlimited real outliers is also critical, so we propose a strong\nsynthesis pipeline that generates outliers originated from various factors:\nobject categories, sampling patterns and sizes. We demonstrate that learning\ndifferent abstaining penalties, apart from point-wise penalty, for different\ntypes of (synthesized) outliers can further improve the performance. We\nbenchmark our method on SemanticKITTI and nuScenes and achieve SOTA results.\nCodes are available at https://github.com/Daniellli/LiON/.\n", "date": "2023-09-19", "categories": []}
{"id": "2402.11887", "title": "Generative Semi-supervised Graph Anomaly Detection", "abstract": "  This work considers a practical semi-supervised graph anomaly detection (GAD)\nscenario, where part of the nodes in a graph are known to be normal,\ncontrasting to the extensively explored unsupervised setting with a fully\nunlabeled graph. We reveal that having access to the normal nodes, even just a\nsmall percentage of normal nodes, helps enhance the detection performance of\nexisting unsupervised GAD methods when they are adapted to the semi-supervised\nsetting. However, their utilization of these normal nodes is limited. In this\npaper, we propose a novel Generative GAD approach (namely GGAD) for the\nsemi-supervised scenario to better exploit the normal nodes. The key idea is to\ngenerate pseudo anomaly nodes, referred to as 'outlier nodes', for providing\neffective negative node samples in training a discriminative one-class\nclassifier. The main challenge here lies in the lack of ground truth\ninformation about real anomaly nodes. To address this challenge, GGAD is\ndesigned to leverage two important priors about the anomaly nodes -- asymmetric\nlocal affinity and egocentric closeness -- to generate reliable outlier nodes\nthat assimilate anomaly nodes in both graph structure and feature\nrepresentations. Comprehensive experiments on six real-world GAD datasets are\nperformed to establish a benchmark for semi-supervised GAD and show that GGAD\nsubstantially outperforms state-of-the-art unsupervised and semi-supervised GAD\nmethods with varying numbers of training normal nodes. Code will be made\navailable at https://github.com/mala-lab/GGAD.\n", "date": "2024-02-19", "categories": []}
{"id": "2412.14592", "title": "Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry,\n  and Internal Properties", "abstract": "  Object anomaly detection is essential for industrial quality inspection, yet\ntraditional single-sensor methods face critical limitations. They fail to\ncapture the wide range of anomaly types, as single sensors are often\nconstrained to either external appearance, geometric structure, or internal\nproperties. To overcome these challenges, we introduce MulSen-AD, the first\nhigh-resolution, multi-sensor anomaly detection dataset tailored for industrial\napplications. MulSen-AD unifies data from RGB cameras, laser scanners, and\nlock-in infrared thermography, effectively capturing external appearance,\ngeometric deformations, and internal defects. The dataset spans 15 industrial\nproducts with diverse, real-world anomalies. We also present MulSen-AD Bench, a\nbenchmark designed to evaluate multi-sensor methods, and propose\nMulSen-TripleAD, a decision-level fusion algorithm that integrates these three\nmodalities for robust, unsupervised object anomaly detection. Our experiments\ndemonstrate that multi-sensor fusion substantially outperforms single-sensor\napproaches, achieving 96.1% AUROC in object-level detection accuracy. These\nresults highlight the importance of integrating multi-sensor data for\ncomprehensive industrial anomaly detection.\n", "date": "2024-12-19", "categories": []}
{"id": "2408.04604", "title": "Towards High-resolution 3D Anomaly Detection via Group-Level Feature\n  Contrastive Learning", "abstract": "  High-resolution point clouds~(HRPCD) anomaly detection~(AD) plays a critical\nrole in precision machining and high-end equipment manufacturing. Despite\nconsiderable 3D-AD methods that have been proposed recently, they still cannot\nmeet the requirements of the HRPCD-AD task. There are several challenges: i) It\nis difficult to directly capture HRPCD information due to large amounts of\npoints at the sample level; ii) The advanced transformer-based methods usually\nobtain anisotropic features, leading to degradation of the representation; iii)\nThe proportion of abnormal areas is very small, which makes it difficult to\ncharacterize. To address these challenges, we propose a novel group-level\nfeature-based network, called Group3AD, which has a significantly efficient\nrepresentation ability. First, we design an Intercluster Uniformity\nNetwork~(IUN) to present the mapping of different groups in the feature space\nas several clusters, and obtain a more uniform distribution between clusters\nrepresenting different parts of the point clouds in the feature space. Then, an\nIntracluster Alignment Network~(IAN) is designed to encourage groups within the\ncluster to be distributed tightly in the feature space. In addition, we propose\nan Adaptive Group-Center Selection~(AGCS) based on geometric information to\nimprove the pixel density of potential anomalous regions during inference. The\nexperimental results verify the effectiveness of our proposed Group3AD, which\nsurpasses Reg3D-AD by the margin of 5\\% in terms of object-level AUROC on\nReal3D-AD. We provide the code and supplementary information on our website:\nhttps://github.com/M-3LAB/Group3AD.\n", "date": "2024-08-08", "categories": []}
{"id": "2412.13324", "title": "BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised\n  Anomaly Detection", "abstract": "  Image anomaly detection (IAD) is essential in applications such as industrial\ninspection, medical imaging, and security. Despite the progress achieved with\ndeep learning models like Deep Semi-Supervised Anomaly Detection (DeepSAD),\nthese models remain susceptible to backdoor attacks, presenting significant\nsecurity challenges. In this paper, we introduce BadSAD, a novel backdoor\nattack framework specifically designed to target DeepSAD models. Our approach\ninvolves two key phases: trigger injection, where subtle triggers are embedded\ninto normal images, and latent space manipulation, which positions and clusters\nthe poisoned images near normal images to make the triggers appear benign.\nExtensive experiments on benchmark datasets validate the effectiveness of our\nattack strategy, highlighting the severe risks that backdoor attacks pose to\ndeep learning-based anomaly detection systems.\n", "date": "2024-12-17", "categories": []}
{"id": "2411.07574", "title": "Disentangling Tabular Data Towards Better One-Class Anomaly Detection", "abstract": "  Tabular anomaly detection under the one-class classification setting poses a\nsignificant challenge, as it involves accurately conceptualizing \"normal\"\nderived exclusively from a single category to discern anomalies from normal\ndata variations. Capturing the intrinsic correlation among attributes within\nnormal samples presents one promising method for learning the concept. To do\nso, the most recent effort relies on a learnable mask strategy with a\nreconstruction task. However, this wisdom may suffer from the risk of producing\nuniform masks, i.e., essentially nothing is masked, leading to less effective\ncorrelation learning. To address this issue, we presume that attributes related\nto others in normal samples can be divided into two non-overlapping and\ncorrelated subsets, defined as CorrSets, to capture the intrinsic correlation\neffectively. Accordingly, we introduce an innovative method that disentangles\nCorrSets from normal tabular data. To our knowledge, this is a pioneering\neffort to apply the concept of disentanglement for one-class anomaly detection\non tabular data. Extensive experiments on 20 tabular datasets show that our\nmethod substantially outperforms the state-of-the-art methods and leads to an\naverage performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC. Codes\nare available at https://github.com/yjnanan/Disent-AD.\n", "date": "2024-11-12", "categories": []}
{"id": "2412.12154", "title": "PyOD 2: A Python Library for Outlier Detection with LLM-powered Model\n  Selection", "abstract": "  Outlier detection (OD), also known as anomaly detection, is a critical\nmachine learning (ML) task with applications in fraud detection, network\nintrusion detection, clickstream analysis, recommendation systems, and social\nnetwork moderation. Among open-source libraries for outlier detection, the\nPython Outlier Detection (PyOD) library is the most widely adopted, with over\n8,500 GitHub stars, 25 million downloads, and diverse industry usage. However,\nPyOD currently faces three limitations: (1) insufficient coverage of modern\ndeep learning algorithms, (2) fragmented implementations across PyTorch and\nTensorFlow, and (3) no automated model selection, making it hard for\nnon-experts.\n  To address these issues, we present PyOD Version 2 (PyOD 2), which integrates\n12 state-of-the-art deep learning models into a unified PyTorch framework and\nintroduces a large language model (LLM)-based pipeline for automated OD model\nselection. These improvements simplify OD workflows, provide access to 45\nalgorithms, and deliver robust performance on various datasets. In this paper,\nwe demonstrate how PyOD 2 streamlines the deployment and automation of OD\nmodels and sets a new standard in both research and industry. PyOD 2 is\naccessible at\n[https://github.com/yzhao062/pyod](https://github.com/yzhao062/pyod). This\nstudy aligns with the Web Mining and Content Analysis track, addressing topics\nsuch as the robustness of Web mining methods and the quality of\nalgorithmically-generated Web data.\n", "date": "2024-12-11", "categories": []}
{"id": "2412.12617", "title": "PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly\n  Detection", "abstract": "  Point cloud anomaly detection under the anomaly-free setting poses\nsignificant challenges as it requires accurately capturing the features of 3D\nnormal data to identify deviations indicative of anomalies. Current efforts\nfocus on devising reconstruction tasks, such as acquiring normal data\nrepresentations by restoring normal samples from altered, pseudo-anomalous\ncounterparts. Our findings reveal that distributing attention equally across\nnormal and pseudo-anomalous data tends to dilute the model's focus on anomalous\ndeviations. The challenge is further compounded by the inherently disordered\nand sparse nature of 3D point cloud data. In response to those predicaments, we\nintroduce an innovative approach that emphasizes learning point offsets,\ntargeting more informative pseudo-abnormal points, thus fostering more\neffective distillation of normal data representations. We also have crafted an\naugmentation technique that is steered by normal vectors, facilitating the\ncreation of credible pseudo anomalies that enhance the efficiency of the\ntraining process. Our comprehensive experimental evaluation on the\nAnomaly-ShapeNet and Real3D-AD datasets evidences that our proposed method\noutperforms existing state-of-the-art approaches, achieving an average\nenhancement of 9.0% and 1.4% in the AUC-ROC detection metric across these\ndatasets, respectively.\n", "date": "2024-12-17", "categories": []}
{"id": "2412.12850", "title": "Boosting Fine-Grained Visual Anomaly Detection with\n  Coarse-Knowledge-Aware Adversarial Learning", "abstract": "  Many unsupervised visual anomaly detection methods train an auto-encoder to\nreconstruct normal samples and then leverage the reconstruction error map to\ndetect and localize the anomalies. However, due to the powerful modeling and\ngeneralization ability of neural networks, some anomalies can also be well\nreconstructed, resulting in unsatisfactory detection and localization accuracy.\nIn this paper, a small coarsely-labeled anomaly dataset is first collected.\nThen, a coarse-knowledge-aware adversarial learning method is developed to\nalign the distribution of reconstructed features with that of normal features.\nThe alignment can effectively suppress the auto-encoder's reconstruction\nability on anomalies and thus improve the detection accuracy. Considering that\nanomalies often only occupy very small areas in anomalous images, a patch-level\nadversarial learning strategy is further developed. Although no patch-level\nanomalous information is available, we rigorously prove that by simply viewing\nany patch features from anomalous images as anomalies, the proposed\nknowledge-aware method can also align the distribution of reconstructed patch\nfeatures with the normal ones. Experimental results on four medical datasets\nand two industrial datasets demonstrate the effectiveness of our method in\nimproving the detection and localization performance.\n", "date": "2024-12-17", "categories": []}
{"id": "2411.07272", "title": "ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs", "abstract": "  This paper investigates the use of the ASTD language for ensemble anomaly\ndetection in data logs. It uses a sliding window technique for continuous\nlearning in data streams, coupled with updating learning models upon the\ncompletion of each window to maintain accurate detection and align with current\ndata trends. It proposes ASTD patterns for combining learning models,\nespecially in the context of unsupervised learning, which is commonly used for\ndata streams. To facilitate this, a new ASTD operator is proposed, the\nQuantified Flow, which enables the seamless combination of learning models\nwhile ensuring that the specification remains concise. Our contribution is a\nspecification pattern, highlighting the capacity of ASTDs to abstract and\nmodularize anomaly detection systems. The ASTD language provides a unique\napproach to develop data flow anomaly detection systems, grounded in the\ncombination of processes through the graphical representation of the language\noperators. This simplifies the design task for developers, who can focus\nprimarily on defining the functional operations that constitute the system.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.19450", "title": "Unsupervised Learning Approach to Anomaly Detection in Gravitational\n  Wave Data", "abstract": "  Gravitational waves (GW), predicted by Einstein's General Theory of\nRelativity, provide a powerful probe of astrophysical phenomena and fundamental\nphysics. In this work, we propose an unsupervised anomaly detection method\nusing variational autoencoders (VAEs) to analyze GW time-series data. By\ntraining on noise-only data, the VAE accurately reconstructs noise inputs while\nfailing to reconstruct anomalies, such as GW signals, which results in\nmeasurable spikes in the reconstruction error. The method was applied to data\nfrom the LIGO H1 and L1 detectors. Evaluation on testing datasets containing\nboth noise and GW events demonstrated reliable detection, achieving an area\nunder the ROC curve (AUC) of 0.89. This study introduces VAEs as a robust,\nunsupervised approach for identifying anomalies in GW data, which offers a\nscalable framework for detecting known and potentially new phenomena in\nphysics.\n", "date": "2024-11-29", "categories": []}
{"id": "2412.10659", "title": "MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with\n  Spatial Transcriptomics", "abstract": "  The detection of anomalous tissue regions (ATRs) within affected tissues is\ncrucial in clinical diagnosis and pathological studies. Conventional automated\nATR detection methods, primarily based on histology images alone, falter in\ncases where ATRs and normal tissues have subtle visual differences. The recent\nspatial transcriptomics (ST) technology profiles gene expressions across tissue\nregions, offering a molecular perspective for detecting ATRs. However, there is\na dearth of ATR detection methods that effectively harness complementary\ninformation from both histology images and ST. To address this gap, we propose\nMEATRD, a novel ATR detection method that integrates histology image and ST\ndata. MEATRD is trained to reconstruct image patches and gene expression\nprofiles of normal tissue spots (inliers) from their multimodal embeddings,\nfollowed by learning a one-class classification AD model based on latent\nmultimodal reconstruction errors. This strategy harmonizes the strengths of\nreconstruction-based and one-class classification approaches. At the heart of\nMEATRD is an innovative masked graph dual-attention transformer (MGDAT)\nnetwork, which not only facilitates cross-modality and cross-node information\nsharing but also addresses the model over-generalization issue commonly seen in\nreconstruction-based AD methods. Additionally, we demonstrate that\nmodality-specific, task-relevant information is collated and condensed in\nmultimodal bottleneck encoding generated in MGDAT, marking the first\ntheoretical analysis of the informational properties of multimodal bottleneck\nencoding. Extensive evaluations across eight real ST datasets reveal MEATRD's\nsuperior performance in ATR detection, surpassing various state-of-the-art AD\nmethods. Remarkably, MEATRD also proves adept at discerning ATRs that only show\nslight visual deviations from normal tissues.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.10765", "title": "Neural Network Meta Classifier: Improving the Reliability of Anomaly\n  Segmentation", "abstract": "  Deep neural networks (DNNs) are a contemporary solution for semantic\nsegmentation and are usually trained to operate on a predefined closed set of\nclasses. In open-set environments, it is possible to encounter semantically\nunknown objects or anomalies. Road driving is an example of such an environment\nin which, from a safety standpoint, it is important to ensure that a DNN\nindicates it is operating outside of its learned semantic domain. One possible\napproach to anomaly segmentation is entropy maximization, which is paired with\na logistic regression based post-processing step called meta classification,\nwhich is in turn used to improve the reliability of detection of anomalous\npixels. We propose to substitute the logistic regression meta classifier with a\nmore expressive lightweight fully connected neural network. We analyze\nadvantages and drawbacks of the proposed neural network meta classifier and\ndemonstrate its better performance over logistic regression. We also introduce\nthe concept of informative out-of-distribution examples which we show to\nimprove training results when using entropy maximization in practice. Finally,\nwe discuss the loss of interpretability and show that the behavior of logistic\nregression and neural network is strongly correlated.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.11148", "title": "Redefining Normal: A Novel Object-Level Approach for Multi-Object\n  Novelty Detection", "abstract": "  In the realm of novelty detection, accurately identifying outliers in data\nwithout specific class information poses a significant challenge. While current\nmethods excel in single-object scenarios, they struggle with multi-object\nsituations due to their focus on individual objects. Our paper suggests a novel\napproach: redefining `normal' at the object level in training datasets. Rather\nthan the usual image-level view, we consider the most dominant object in a\ndataset as the norm, offering a perspective that is more effective for\nreal-world scenarios. Adapting to our object-level definition of `normal', we\nmodify knowledge distillation frameworks, where a student network learns from a\npre-trained teacher network. Our first contribution, DeFeND(Dense Feature\nFine-tuning on Normal Data), integrates dense feature fine-tuning into the\ndistillation process, allowing the teacher network to focus on object-level\nfeatures with a self-supervised loss. The second is masked knowledge\ndistillation, where the student network works with partially hidden inputs,\nhoning its ability to deduce and generalize from incomplete data. This approach\nnot only fares well in single-object novelty detection but also considerably\nsurpasses existing methods in multi-object contexts. The implementation is\navailable at: https://github.com/SMSD75/Redefining_Normal_ACCV24/tree/main\n", "date": "2024-12-15", "categories": []}
{"id": "2412.11461", "title": "Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation", "abstract": "  Unsupervised anomaly detection (UAD) plays an important role in modern data\nanalytics and it is crucial to provide simple yet effective and guaranteed UAD\nalgorithms for real applications. In this paper, we present a novel UAD method\nfor tabular data by evaluating how much noise is in the data. Specifically, we\npropose to learn a deep neural network from the clean (normal) training dataset\nand a noisy dataset, where the latter is generated by adding highly diverse\nnoises to the clean data. The neural network can learn a reliable decision\nboundary between normal data and anomalous data when the diversity of the\ngenerated noisy data is sufficiently high so that the hard abnormal samples lie\nin the noisy region. Importantly, we provide theoretical guarantees, proving\nthat the proposed method can detect anomalous data successfully, although the\nmethod does not utilize any real anomalous data in the training stage.\nExtensive experiments through more than 60 benchmark datasets demonstrate the\neffectiveness of the proposed method in comparison to 12 baselines of UAD. Our\nmethod obtains a 92.27\\% AUC score and a 1.68 ranking score on average.\nMoreover, compared to the state-of-the-art UAD methods, our method is easier to\nimplement.\n", "date": "2024-12-16", "categories": []}
{"id": "2412.11802", "title": "AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly\n  Detection and Localization", "abstract": "  Unsupervised visual anomaly detection is crucial for enhancing industrial\nproduction quality and efficiency. Among unsupervised methods, reconstruction\napproaches are popular due to their simplicity and effectiveness. The key\naspect of reconstruction methods lies in the restoration of anomalous regions,\nwhich current methods have not satisfactorily achieved. To tackle this issue,\nwe introduce a novel \\uline{A}daptive \\uline{M}ask \\uline{I}npainting\n\\uline{Net}work (AMI-Net) from the perspective of adaptive mask-inpainting. In\ncontrast to traditional reconstruction methods that treat non-semantic image\npixels as targets, our method uses a pre-trained network to extract multi-scale\nsemantic features as reconstruction targets. Given the multiscale nature of\nindustrial defects, we incorporate a training strategy involving random\npositional and quantitative masking. Moreover, we propose an innovative\nadaptive mask generator capable of generating adaptive masks that effectively\nmask anomalous regions while preserving normal regions. In this manner, the\nmodel can leverage the visible normal global contextual information to restore\nthe masked anomalous regions, thereby effectively suppressing the\nreconstruction of defects. Extensive experimental results on the MVTec AD and\nBTAD industrial datasets validate the effectiveness of the proposed method.\nAdditionally, AMI-Net exhibits exceptional real-time performance, striking a\nfavorable balance between detection accuracy and speed, rendering it highly\nsuitable for industrial applications. Code is available at:\nhttps://github.com/luow23/AMI-Net\n", "date": "2024-12-16", "categories": []}
{"id": "2412.10115", "title": "Filter or Compensate: Towards Invariant Representation from Distribution\n  Shift for Anomaly Detection", "abstract": "  Recent Anomaly Detection (AD) methods have achieved great success with\nIn-Distribution (ID) data. However, real-world data often exhibits distribution\nshift, causing huge performance decay on traditional AD methods. From this\nperspective, few previous work has explored AD with distribution shift, and the\ndistribution-invariant normality learning has been proposed based on the\nReverse Distillation (RD) framework. However, we observe the misalignment issue\nbetween the teacher and the student network that causes detection failure,\nthereby propose FiCo, Filter or Compensate, to address the distribution shift\nissue in AD. FiCo firstly compensates the distribution-specific information to\nreduce the misalignment between the teacher and student network via the\nDistribution-Specific Compensation (DiSCo) module, and secondly filters all\nabnormal information to capture distribution-invariant normality with the\nDistribution-Invariant Filter (DiIFi) module. Extensive experiments on three\ndifferent AD benchmarks demonstrate the effectiveness of FiCo, which\noutperforms all existing state-of-the-art (SOTA) methods, and even achieves\nbetter results on the ID scenario compared with RD-based methods. Our code is\navailable at https://github.com/znchen666/FiCo.\n", "date": "2024-12-13", "categories": []}
{"id": "2402.17363", "title": "CGGM: A conditional graph generation model with adaptive sparsity for\n  node anomaly detection in IoT networks", "abstract": "  Dynamic graphs are extensively employed for detecting anomalous behavior in\nnodes within the Internet of Things (IoT). Graph generative models are often\nused to address the issue of imbalanced node categories in dynamic graphs.\nNevertheless, the constraints it faces include the monotonicity of adjacency\nrelationships, the difficulty in constructing multi-dimensional features for\nnodes, and the lack of a method for end-to-end generation of multiple\ncategories of nodes. In this paper, we propose a novel graph generation model,\ncalled CGGM, specifically for generating samples belonging to the minority\nclass. The framework consists two core module: a conditional graph generation\nmodule and a graph-based anomaly detection module. The generative module adapts\nto the sparsity of the matrix by downsampling a noise adjacency matrix, and\nincorporates a multi-dimensional feature encoder based on multi-head\nself-attention to capture latent dependencies among features. Additionally, a\nlatent space constraint is combined with the distribution distance to\napproximate the latent distribution of real data. The graph-based anomaly\ndetection module utilizes the generated balanced dataset to predict the node\nbehaviors. Extensive experiments have shown that CGGM outperforms the\nstate-of-the-art methods in terms of accuracy and divergence. The results also\ndemonstrate CGGM can generated diverse data categories, that enhancing the\nperformance of multi-category classification task.\n", "date": "2024-02-27", "categories": []}
{"id": "2412.09026", "title": "Video Anomaly Detection with Motion and Appearance Guided Patch\n  Diffusion Model", "abstract": "  A recent endeavor in one class of video anomaly detection is to leverage\ndiffusion models and posit the task as a generation problem, where the\ndiffusion model is trained to recover normal patterns exclusively, thus\nreporting abnormal patterns as outliers. Yet, existing attempts neglect the\nvarious formations of anomaly and predict normal samples at the feature level\nregardless that abnormal objects in surveillance videos are often relatively\nsmall. To address this, a novel patch-based diffusion model is proposed,\nspecifically engineered to capture fine-grained local information. We further\nobserve that anomalies in videos manifest themselves as deviations in both\nappearance and motion. Therefore, we argue that a comprehensive solution must\nconsider both of these aspects simultaneously to achieve accurate frame\nprediction. To address this, we introduce innovative motion and appearance\nconditions that are seamlessly integrated into our patch diffusion model. These\nconditions are designed to guide the model in generating coherent and\ncontextually appropriate predictions for both semantic content and motion\nrelations. Experimental results in four challenging video anomaly detection\ndatasets empirically substantiate the efficacy of our proposed approach,\ndemonstrating that it consistently outperforms most existing methods in\ndetecting abnormal behaviors.\n", "date": "2024-12-12", "categories": []}
{"id": "2412.08189", "title": "Breaking the Bias: Recalibrating the Attention of Industrial Anomaly\n  Detection", "abstract": "  Due to the scarcity and unpredictable nature of defect samples, industrial\nanomaly detection (IAD) predominantly employs unsupervised learning. However,\nall unsupervised IAD methods face a common challenge: the inherent bias in\nnormal samples, which causes models to focus on variable regions while\noverlooking potential defects in invariant areas. To effectively overcome this,\nit is essential to decompose and recalibrate attention, guiding the model to\nsuppress irrelevant variations and concentrate on subtle, defect-susceptible\nareas. In this paper, we propose Recalibrating Attention of Industrial Anomaly\nDetection (RAAD), a framework that systematically decomposes and recalibrates\nattention maps. RAAD employs a two-stage process: first, it reduces attention\nbias through quantization, and second, it fine-tunes defect-prone regions for\nimproved sensitivity. Central to this framework is Hierarchical Quantization\nScoring (HQS), which dynamically allocates bit-widths across layers based on\ntheir anomaly detection contributions. HQS dynamically adjusts bit-widths based\non the hierarchical nature of attention maps, compressing lower layers that\nproduce coarse and noisy attention while preserving deeper layers with sharper,\ndefect-focused attention. This approach optimizes both computational efficiency\nand the model' s sensitivity to anomalies. We validate the effectiveness of\nRAAD on 32 datasets using a single 3090ti. Experiments demonstrate that RAAD,\nbalances the complexity and expressive power of the model, enhancing its\nanomaly detection capability.\n", "date": "2024-12-11", "categories": []}
{"id": "2303.16191", "title": "Hard-normal Example-aware Template Mutual Matching for Industrial\n  Anomaly Detection", "abstract": "  Anomaly detectors are widely used in industrial manufacturing to detect and\nlocalize unknown defects in query images. These detectors are trained on\nanomaly-free samples and have successfully distinguished anomalies from most\nnormal samples. However, hard-normal examples are scattered and far apart from\nmost normal samples, and thus they are often mistaken for anomalies by existing\nmethods. To address this issue, we propose Hard-normal Example-aware Template\nMutual Matching (HETMM), an efficient framework to build a robust\nprototype-based decision boundary. Specifically, HETMM employs the proposed\nAffine-invariant Template Mutual Matching (ATMM) to mitigate the affection\nbrought by the affine transformations and easy-normal examples. By mutually\nmatching the pixel-level prototypes within the patch-level search spaces\nbetween query and template set, ATMM can accurately distinguish between\nhard-normal examples and anomalies, achieving low false-positive and\nmissed-detection rates. In addition, we also propose PTS to compress the\noriginal template set for speed-up. PTS selects cluster centres and hard-normal\nexamples to preserve the original decision boundary, allowing this tiny set to\nachieve comparable performance to the original one. Extensive experiments\ndemonstrate that HETMM outperforms state-of-the-art methods, while using a\n60-sheet tiny set can achieve competitive performance and real-time inference\nspeed (around 26.1 FPS) on a Quadro 8000 RTX GPU. HETMM is training-free and\ncan be hot-updated by directly inserting novel samples into the template set,\nwhich can promptly address some incremental learning issues in industrial\nmanufacturing.\n", "date": "2023-03-28", "categories": []}
{"id": "2305.03761", "title": "Weakly-Supervised Anomaly Detection in the Milky Way", "abstract": "  Large-scale astrophysics datasets present an opportunity for new machine\nlearning techniques to identify regions of interest that might otherwise be\noverlooked by traditional searches. To this end, we use Classification Without\nLabels (CWoLa), a weakly-supervised anomaly detection method, to identify cold\nstellar streams within the more than one billion Milky Way stars observed by\nthe Gaia satellite. CWoLa operates without the use of labeled streams or\nknowledge of astrophysical principles. Instead, we train a classifier to\ndistinguish between mixed samples for which the proportions of signal and\nbackground samples are unknown. This computationally lightweight strategy is\nable to detect both simulated streams and the known stream GD-1 in data.\nOriginally designed for high-energy collider physics, this technique may have\nbroad applicability within astrophysics as well as other domains interested in\nidentifying localized anomalies.\n", "date": "2023-05-05", "categories": []}
{"id": "2412.07027", "title": "Deep Learning for Cross-Border Transaction Anomaly Detection in\n  Anti-Money Laundering Systems", "abstract": "  In the context of globalization and the rapid expansion of the digital\neconomy, anti-money laundering (AML) has become a crucial aspect of financial\noversight, particularly in cross-border transactions. The rising complexity and\nscale of international financial flows necessitate more intelligent and\nadaptive AML systems to combat increasingly sophisticated money laundering\ntechniques. This paper explores the application of unsupervised learning models\nin cross-border AML systems, focusing on rule optimization through contrastive\nlearning techniques. Five deep learning models, ranging from basic\nconvolutional neural networks (CNNs) to hybrid CNNGRU architectures, were\ndesigned and tested to assess their performance in detecting abnormal\ntransactions. The results demonstrate that as model complexity increases, so\ndoes the system's detection accuracy and responsiveness. In particular, the\nself-developed hybrid Convolutional-Recurrent Neural Integration Model (CRNIM)\nmodel showed superior performance in terms of accuracy and area under the\nreceiver operating characteristic curve (AUROC). These findings highlight the\npotential of unsupervised learning models to significantly improve the\nintelligence, flexibility, and real-time capabilities of AML systems. By\noptimizing detection rules and enhancing adaptability to emerging money\nlaundering schemes, this research provides both theoretical and practical\ncontributions to the advancement of AML technologies, which are essential for\nsafeguarding the global financial system against illicit activities.\n", "date": "2024-11-21", "categories": []}
{"id": "2412.07539", "title": "Anomaly detection using Diffusion-based methods", "abstract": "  This paper explores the utility of diffusion-based models for anomaly\ndetection, focusing on their efficacy in identifying deviations in both compact\nand high-resolution datasets. Diffusion-based architectures, including\nDenoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers\n(DiTs), are evaluated for their performance using reconstruction objectives. By\nleveraging the strengths of these models, this study benchmarks their\nperformance against traditional anomaly detection methods such as Isolation\nForests, One-Class SVMs, and COPOD. The results demonstrate the superior\nadaptability, scalability, and robustness of diffusion-based methods in\nhandling complex real-world anomaly detection tasks. Key findings highlight the\nrole of reconstruction error in enhancing detection accuracy and underscore the\nscalability of these models to high-dimensional datasets. Future directions\ninclude optimizing encoder-decoder architectures and exploring multi-modal\ndatasets to further advance diffusion-based anomaly detection.\n", "date": "2024-12-10", "categories": []}
{"id": "2412.07579", "title": "Unlocking the Potential of Reverse Distillation for Anomaly Detection", "abstract": "  Knowledge Distillation (KD) is a promising approach for unsupervised Anomaly\nDetection (AD). However, the student network's over-generalization often\ndiminishes the crucial representation differences between teacher and student\nin anomalous regions, leading to detection failures. To addresses this problem,\nthe widely accepted Reverse Distillation (RD) paradigm designs the asymmetry\nteacher and student, using an encoder as teacher and a decoder as student. Yet,\nthe design of RD does not ensure that the teacher encoder effectively\ndistinguishes between normal and abnormal features or that the student decoder\ngenerates anomaly-free features. Additionally, the absence of skip connections\nresults in a loss of fine details during feature reconstruction. To address\nthese issues, we propose RD with Expert, which introduces a novel\nExpert-Teacher-Student network for simultaneous distillation of both the\nteacher encoder and student decoder. The added expert network enhances the\nstudent's ability to generate normal features and optimizes the teacher's\ndifferentiation between normal and abnormal features, reducing missed\ndetections. Additionally, Guided Information Injection is designed to filter\nand transfer features from teacher to student, improving detail reconstruction\nand minimizing false positives. Experiments on several benchmarks prove that\nour method outperforms existing unsupervised AD methods under RD paradigm,\nfully unlocking RD's potential.\n", "date": "2024-12-10", "categories": []}
{"id": "2402.15143", "title": "PUAD: Frustratingly Simple Method for Robust Anomaly Detection", "abstract": "  Developing an accurate and fast anomaly detection model is an important task\nin real-time computer vision applications. There has been much research to\ndevelop a single model that detects either structural or logical anomalies,\nwhich are inherently distinct. The majority of the existing approaches\nimplicitly assume that the anomaly can be represented by identifying the\nanomalous location. However, we argue that logical anomalies, such as the wrong\nnumber of objects, can not be well-represented by the spatial feature maps and\nrequire an alternative approach. In addition, we focused on the possibility of\ndetecting logical anomalies by using an out-of-distribution detection approach\non the feature space, which aggregates the spatial information of the feature\nmap. As a demonstration, we propose a method that incorporates a simple\nout-of-distribution detection method on the feature space against\nstate-of-the-art reconstruction-based approaches. Despite the simplicity of our\nproposal, our method PUAD (Picturable and Unpicturable Anomaly Detection)\nachieves state-of-the-art performance on the MVTec LOCO AD dataset.\n", "date": "2024-02-23", "categories": []}
{"id": "2411.06071", "title": "GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot\n  Anomaly Detection", "abstract": "  Zero-shot anomaly detection (ZSAD) is crucial for detecting anomalous\npatterns in target datasets without using training samples, specifically in\nscenarios where there are distributional differences between the target domain\nand training data or where data scarcity arises because of restricted access.\nAlthough recently pretrained vision-language models demonstrate strong\nzero-shot performance across various visual tasks, they focus on learning class\nsemantics, which makes their direct application to ZSAD challenging. To address\nthis scenario, we propose GlocalCLIP, which uniquely separates global and local\nprompts and jointly optimizes them. This approach enables the object-agnostic\nglocal semantic prompt to effectively capture general normal and anomalous\npatterns without dependency on specific objects in the image. We refine the\ntext prompts for more precise adjustments by utilizing deep-text prompt tuning\nin the text encoder. In the vision encoder, we apply V-V attention layers to\ncapture detailed local image features. Finally, we introduce glocal contrastive\nlearning to improve the complementary learning of global and local prompts,\neffectively detecting anomalous patterns across various domains. The\ngeneralization performance of GlocalCLIP in ZSAD was demonstrated on 15\nreal-world datasets from both the industrial and medical domains, achieving\nsuperior performance compared to existing methods. Code will be made available\nat https://github.com/YUL-git/GlocalCLIP.\n", "date": "2024-11-09", "categories": []}
{"id": "2412.04259", "title": "SCADE: Scalable Framework for Anomaly Detection in High-Performance\n  System", "abstract": "  As command-line interfaces remain integral to high-performance computing\nenvironments, the risk of exploitation through stealthy and complex\ncommand-line abuse grows. Conventional security solutions struggle to detect\nthese anomalies due to their context-specific nature, lack of labeled data, and\nthe prevalence of sophisticated attacks like Living-off-the-Land (LOL). To\naddress this gap, we introduce the Scalable Command-Line Anomaly Detection\nEngine (SCADE), a framework that combines global statistical models with local\ncontext-specific analysis for unsupervised anomaly detection. SCADE leverages\nnovel statistical methods, including BM25 and Log Entropy, alongside dynamic\nthresholding to adaptively detect rare, malicious command-line patterns in low\nsignal-to-noise ratio (SNR) environments. Experimental results show that SCADE\nachieves above 98% SNR in identifying anomalous behavior while minimizing false\npositives. Designed for scalability and precision, SCADE provides an\ninnovative, metadata-enriched approach to anomaly detection, offering a robust\nsolution for cybersecurity in high-computation environments. This work presents\nSCADE's architecture, detection methodology, and its potential for enhancing\nanomaly detection in enterprise systems. We argue that SCADE represents a\nsignificant advancement in unsupervised anomaly detection, offering a robust,\nadaptive framework for security analysts and researchers seeking to enhance\ndetection accuracy in high-computation environments.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.05498", "title": "A New Perspective on Time Series Anomaly Detection: Faster Patch-based\n  Broad Learning System", "abstract": "  Time series anomaly detection (TSAD) has been a research hotspot in both\nacademia and industry in recent years. Deep learning methods have become the\nmainstream research direction due to their excellent performance. However, new\nviewpoints have emerged in recent TSAD research. Deep learning is not required\nfor TSAD due to limitations such as slow deep learning speed. The Broad\nLearning System (BLS) is a shallow network framework that benefits from its\nease of optimization and speed. It has been shown to outperform machine\nlearning approaches while remaining competitive with deep learning. Based on\nthe current situation of TSAD, we propose the Contrastive Patch-based Broad\nLearning System (CPatchBLS). This is a new exploration of patching technique\nand BLS, providing a new perspective for TSAD. We construct Dual-PatchBLS as a\nbase through patching and Simple Kernel Perturbation (SKP) and utilize\ncontrastive learning to capture the differences between normal and abnormal\ndata under different representations. To compensate for the temporal semantic\nloss caused by various patching, we propose CPatchBLS with model level\nintegration, which takes advantage of BLS's fast feature to build model-level\nintegration and improve model detection. Using five real-world series anomaly\ndetection datasets, we confirmed the method's efficacy, outperforming previous\ndeep learning and machine learning methods while retaining a high level of\ncomputing efficiency.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.05641", "title": "Hyperedge Anomaly Detection with Hypergraph Neural Network", "abstract": "  Hypergraph is a data structure that enables us to model higher-order\nassociations among data entities. Conventional graph-structured data can\nrepresent pairwise relationships only, whereas hypergraph enables us to\nassociate any number of entities, which is essential in many real-life\napplications. Hypergraph learning algorithms have been well-studied for\nnumerous problem settings, such as node classification, link prediction, etc.\nHowever, much less research has been conducted on anomaly detection from\nhypergraphs. Anomaly detection identifies events that deviate from the usual\npattern and can be applied to hypergraphs to detect unusual higher-order\nassociations. In this work, we propose an end-to-end hypergraph neural\nnetwork-based model for identifying anomalous associations in a hypergraph. Our\nproposed algorithm operates in an unsupervised manner without requiring any\nlabeled data. Extensive experimentation on several real-life datasets\ndemonstrates the effectiveness of our model in detecting anomalous hyperedges.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.05669", "title": "Detecting outliers by clustering algorithms", "abstract": "  Clustering and outlier detection are two important tasks in data mining.\nOutliers frequently interfere with clustering algorithms to determine the\nsimilarity between objects, resulting in unreliable clustering results.\nCurrently, only a few clustering algorithms (e.g., DBSCAN) have the ability to\ndetect outliers to eliminate interference. For other clustering algorithms, it\nis tedious to introduce another outlier detection task to eliminate outliers\nbefore each clustering process. Obviously, how to equip more clustering\nalgorithms with outlier detection ability is very meaningful. Although a common\nstrategy allows clustering algorithms to detect outliers based on the distance\nbetween objects and clusters, it is contradictory to improving the performance\nof clustering algorithms on the datasets with outliers. In this paper, we\npropose a novel outlier detection approach, called ODAR, for clustering. ODAR\nmaps outliers and normal objects into two separated clusters by feature\ntransformation. As a result, any clustering algorithm can detect outliers by\nidentifying clusters. Experiments show that ODAR is robust to diverse datasets.\nCompared with baseline methods, the clustering algorithms achieve the best on 7\nout of 10 datasets with the help of ODAR, with at least 5% improvement in\naccuracy.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.06015", "title": "siForest: Detecting Network Anomalies with Set-Structured Isolation\n  Forest", "abstract": "  As cyber threats continue to evolve in sophistication and scale, the ability\nto detect anomalous network behavior has become critical for maintaining robust\ncybersecurity defenses. Modern cybersecurity systems face the overwhelming\nchallenge of analyzing billions of daily network interactions to identify\npotential threats, making efficient and accurate anomaly detection algorithms\ncrucial for network defense. This paper investigates the use of variations of\nthe Isolation Forest (iForest) machine learning algorithm for detecting\nanomalies in internet scan data. In particular, it presents the Set-Partitioned\nIsolation Forest (siForest), a novel extension of the iForest method designed\nto detect anomalies in set-structured data. By treating instances such as sets\nof multiple network scans with the same IP address as cohesive units, siForest\neffectively addresses some challenges of analyzing complex, multidimensional\ndatasets. Extensive experiments on synthetic datasets simulating diverse\nanomaly scenarios in network traffic demonstrate that siForest has the\npotential to outperform traditional approaches on some types of internet scan\ndata.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.04473", "title": "Take Package as Language: Anomaly Detection Using Transformer", "abstract": "  Network data packet anomaly detection faces numerous challenges, including\nexploring new anomaly supervision signals, researching weakly supervised\nanomaly detection, and improving model interpretability. This paper proposes\nNIDS-GPT, a GPT-based causal language model for network intrusion detection.\nUnlike previous work, NIDS-GPT innovatively treats each number in the packet as\nan independent \"word\" rather than packet fields, enabling a more fine-grained\ndata representation. We adopt an improved GPT-2 model and design special\ntokenizers and embedding layers to better capture the structure and semantics\nof network data. NIDS-GPT has good scalability, supports unsupervised\npre-training, and enhances model interpretability through attention weight\nvisualization. Experiments on the CICIDS2017 and car-hacking datasets show that\nNIDS-GPT achieves 100\\% accuracy under extreme imbalance conditions, far\nsurpassing traditional methods; it also achieves over 90\\% accuracy in one-shot\nlearning. These results demonstrate NIDS-GPT's excellent performance and\npotential in handling complex network anomaly detection tasks, especially in\ndata-imbalanced and resource-constrained scenarios. The code is available at\n\\url{https://github.com/woshixiaobai2019/nids-gpt.gi\n", "date": "2024-11-15", "categories": []}
{"id": "2412.04780", "title": "Anomaly Detection and Classification in Knowledge Graphs", "abstract": "  Anomalies such as redundant, inconsistent, contradictory, and deficient\nvalues in a Knowledge Graph (KG) are unavoidable, as these graphs are often\ncurated manually, or extracted using machine learning and natural language\nprocessing techniques. Therefore, anomaly detection is a task that can enhance\nthe quality of KGs. In this paper, we propose SEKA (SEeking Knowledge graph\nAnomalies), an unsupervised approach for the detection of abnormal triples and\nentities in KGs. SEKA can help improve the correctness of a KG whilst retaining\nits coverage. We propose an adaption of the Path Rank Algorithm (PRA), named\nthe Corroborative Path Rank Algorithm (CPRA), which is an efficient adaptation\nof PRA that is customized to detect anomalies in KGs. Furthermore, we also\npresent TAXO (TAXOnomy of anomaly types in KGs), a taxonomy of possible anomaly\ntypes that can occur in a KG. This taxonomy provides a classification of the\nanomalies discovered by SEKA with an extensive discussion of possible data\nquality issues in a KG. We evaluate both approaches using the four real-world\nKGs YAGO-1, KBpedia, Wikidata, and DSKG to demonstrate the ability of SEKA and\nTAXO to outperform the baselines.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.04867", "title": "MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection\n  Dataset for Tiny Objects", "abstract": "  We present MANTA, a visual-text anomaly detection dataset for tiny objects.\nThe visual component comprises over 137.3K images across 38 object categories\nspanning five typical domains, of which 8.6K images are labeled as anomalous\nwith pixel-level annotations. Each image is captured from five distinct\nviewpoints to ensure comprehensive object coverage. The text component consists\nof two subsets: Declarative Knowledge, including 875 words that describe common\nanomalies across various domains and specific categories, with detailed\nexplanations for < what, why, how>, including causes and visual\ncharacteristics; and Constructivist Learning, providing 2K multiple-choice\nquestions with varying levels of difficulty, each paired with images and\ncorresponded answer explanations. We also propose a baseline for visual-text\ntasks and conduct extensive benchmarking experiments to evaluate advanced\nmethods across different settings, highlighting the challenges and efficacy of\nour dataset.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.04990", "title": "ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using\n  Smartphone Sensors", "abstract": "  Road anomalies can be defined as irregularities on the road surface or in the\nsurface itself. Some may be intentional (such as speedbumps), accidental (such\nas materials falling off a truck), or the result of roads' excessive use or low\nor no maintenance, such as potholes. Despite their varying origins, these\nirregularities often harm vehicles substantially. Speed bumps are intentionally\nplaced for safety but are dangerous due to their non-standard shape, size, and\nlack of proper markings. Potholes are unintentional and can also cause severe\ndamage. To address the detection of these anomalies, we need an automated road\nmonitoring system. Today, various systems exist that use visual information to\ntrack these anomalies. Still, due to poor lighting conditions and improper or\nmissing markings, they may go undetected and have severe consequences for\npublic transport, automated vehicles, etc. In this paper, the Enhanced\nTemporal-BiLSTM Network (ETLNet) is introduced as a novel approach that\nintegrates two Temporal Convolutional Network (TCN) layers with a Bidirectional\nLong Short-Term Memory (BiLSTM) layer. This combination is tailored to detect\nanomalies effectively irrespective of lighting conditions, as it depends not on\nvisuals but smartphone inertial sensor data. Our methodology employs\naccelerometer and gyroscope sensors, typically in smartphones, to gather data\non road conditions. Empirical evaluations demonstrate that the ETLNet model\nmaintains an F1-score for detecting speed bumps of 99.3%. The ETLNet model's\nrobustness and efficiency significantly advance automated road surface\nmonitoring technologies.\n", "date": "2024-12-06", "categories": []}
{"id": "2305.19770", "title": "Quality In / Quality Out: Data quality more relevant than model choice\n  in anomaly detection with the UGR'16", "abstract": "  Autonomous or self-driving networks are expected to provide a solution to the\nmyriad of extremely demanding new applications with minimal human supervision.\nFor this purpose, the community relies on the development of new Machine\nLearning (ML) models and techniques. %, like the celebrated Deep Learning (DL).\nHowever, ML can only be as good as the data it is fitted with, and data quality\nis an elusive concept difficult to assess. In this paper, we show that\nrelatively minor modifications on a benchmark dataset (UGR'16, a flow-based\nreal-traffic dataset for anomaly detection) cause significantly more impact on\nmodel performance than the specific ML technique considered. We also show that\nthe measured model performance is uncertain, as a result of labelling\ninaccuracies. Our findings illustrate that the widely adopted approach of\ncomparing a set of models in terms of performance results (e.g., in terms of\naccuracy or ROC curves) may lead to incorrect conclusions when done without a\nproper understanding of dataset biases and sensitivity. We contribute a\nmethodology to interpret a model response that can be useful for this\nunderstanding.\n", "date": "2023-05-31", "categories": []}
{"id": "2409.03669", "title": "A method to benchmark high-dimensional process drift detection", "abstract": "  Process curves are multivariate finite time series data coming from\nmanufacturing processes. This paper studies machine learning that detect drifts\nin process curve datasets. A theoretic framework to synthetically generate\nprocess curves in a controlled way is introduced in order to benchmark machine\nlearning algorithms for process drift detection. An evaluation score, called\nthe temporal area under the curve, is introduced, which allows to quantify how\nwell machine learning models unveil curves belonging to drift segments.\nFinally, a benchmark study comparing popular machine learning approaches on\nsynthetic data generated with the introduced framework is presented that shows\nthat existing algorithms often struggle with datasets containing multiple drift\nsegments.\n", "date": "2024-09-05", "categories": []}
{"id": "2410.22623", "title": "PV-VTT: A Privacy-Centric Dataset for Mission-Specific Anomaly Detection\n  and Natural Language Interpretation", "abstract": "  Video crime detection is a significant application of computer vision and\nartificial intelligence. However, existing datasets primarily focus on\ndetecting severe crimes by analyzing entire video clips, often neglecting the\nprecursor activities (i.e., privacy violations) that could potentially prevent\nthese crimes. To address this limitation, we present PV-VTT (Privacy Violation\nVideo To Text), a unique multimodal dataset aimed at identifying privacy\nviolations. PV-VTT provides detailed annotations for both video and text in\nscenarios. To ensure the privacy of individuals in the videos, we only provide\nvideo feature vectors, avoiding the release of any raw video data. This\nprivacy-focused approach allows researchers to use the dataset while protecting\nparticipant confidentiality. Recognizing that privacy violations are often\nambiguous and context-dependent, we propose a Graph Neural Network (GNN)-based\nvideo description model. Our model generates a GNN-based prompt with image for\nLarge Language Model (LLM), which deliver cost-effective and high-quality video\ndescriptions. By leveraging a single video frame along with relevant text, our\nmethod reduces the number of input tokens required, maintaining descriptive\nquality while optimizing LLM API-usage. Extensive experiments validate the\neffectiveness and interpretability of our approach in video description tasks\nand flexibility of our PV-VTT dataset.\n", "date": "2024-10-30", "categories": []}
{"id": "2412.03829", "title": "CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor\n  Based on CLIP", "abstract": "  Industrial anomaly classification (AC) is an indispensable task in industrial\nmanufacturing, which guarantees quality and safety of various product. To\naddress the scarcity of data in industrial scenarios, lots of few-shot anomaly\ndetection methods emerge recently. In this paper, we propose an effective\nfew-shot anomaly classification (FSAC) framework with one-stage training,\ndubbed CLIP-FSAC++. Specifically, we introduce a cross-modality interaction\nmodule named Anomaly Descriptor following image and text encoders, which\nenhances the correlation of visual and text embeddings and adapts the\nrepresentations of CLIP from pre-trained data to target data. In anomaly\ndescriptor, image-to-text cross-attention module is used to obtain\nimage-specific text embeddings and text-to-image cross-attention module is used\nto obtain text-specific visual embeddings. Then these modality-specific\nembeddings are used to enhance original representations of CLIP for better\nmatching ability. Comprehensive experiment results are provided for evaluating\nour method in few-normal shot anomaly classification on VisA and MVTEC-AD for\n1, 2, 4 and 8-shot settings. The source codes are at\nhttps://github.com/Jay-zzcoder/clip-fsac-pp\n", "date": "2024-12-05", "categories": []}
{"id": "2412.04304", "title": "Towards Zero-shot 3D Anomaly Localization", "abstract": "  3D anomaly detection and localization is of great significance for industrial\ninspection. Prior 3D anomaly detection and localization methods focus on the\nsetting that the testing data share the same category as the training data\nwhich is normal. However, in real-world applications, the normal training data\nfor the target 3D objects can be unavailable due to issues like data privacy or\nexport control regulation. To tackle these challenges, we identify a new task\n-- zero-shot 3D anomaly detection and localization, where the training and\ntesting classes do not overlap. To this end, we design 3DzAL, a novel\npatch-level contrastive learning framework based on pseudo anomalies generated\nusing the inductive bias from task-irrelevant 3D xyz data to learn more\nrepresentative feature representations. Furthermore, we train a normalcy\nclassifier network to classify the normal patches and pseudo anomalies and\nutilize the classification result jointly with feature distance to design\nanomaly scores. Instead of directly using the patch point clouds, we introduce\nadversarial perturbations to the input patch xyz data before feeding into the\n3D normalcy classifier for the classification-based anomaly score. We show that\n3DzAL outperforms the state-of-the-art anomaly detection and localization\nperformance.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.02474", "title": "F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain\n  Information", "abstract": "  With the development of society, time series anomaly detection plays an\nimportant role in network and IoT services. However, most existing anomaly\ndetection methods directly analyze time series in the time domain and cannot\ndistinguish some relatively hidden anomaly sequences. We attempt to analyze the\nimpact of frequency on time series from a frequency domain perspective, thus\nproposing a new time series anomaly detection method called F-SE-LSTM. This\nmethod utilizes two sliding windows and fast Fourier transform (FFT) to\nconstruct a frequency matrix. Simultaneously, Squeeze-and-Excitation Networks\n(SENet) and Long Short-Term Memory (LSTM) are employed to extract\nfrequency-related features within and between periods. Through comparative\nexperiments on multiple datasets such as Yahoo Webscope S5 and Numenta Anomaly\nBenchmark, the results demonstrate that the frequency matrix constructed by\nF-SE-LSTM exhibits better discriminative ability than ordinary time domain and\nfrequency domain data. Furthermore, F-SE-LSTM outperforms existing\nstate-of-the-art deep learning anomaly detection methods in terms of anomaly\ndetection capability and execution efficiency.\n", "date": "2024-12-03", "categories": []}
{"id": "2407.02861", "title": "A Self-Supervised Task for Fault Detection in Satellite Multivariate\n  Time Series", "abstract": "  In the space sector, due to environmental conditions and restricted\naccessibility, robust fault detection methods are imperative for ensuring\nmission success and safeguarding valuable assets. This work proposes a novel\napproach leveraging Physics-Informed Real NVP neural networks, renowned for\ntheir ability to model complex and high-dimensional distributions, augmented\nwith a self-supervised task based on sensors' data permutation. It focuses on\nenhancing fault detection within the satellite multivariate time series. The\nexperiments involve various configurations, including pre-training with\nself-supervision, multi-task learning, and standalone self-supervised training.\nResults indicate significant performance improvements across all settings. In\nparticular, employing only the self-supervised loss yields the best overall\nresults, suggesting its efficacy in guiding the network to extract relevant\nfeatures for fault detection. This study presents a promising direction for\nimproving fault detection in space systems and warrants further exploration in\nother datasets and applications.\n", "date": "2024-07-03", "categories": []}
{"id": "2408.14498", "title": "Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection", "abstract": "  Anomaly detection is a crucial task in various domains. Most of the existing\nmethods assume the normal sample data clusters around a single central\nprototype while the real data may consist of multiple categories or subgroups.\nIn addition, existing methods always assume all unlabeled samples are normal\nwhile some of them are inevitably being anomalies. To address these issues, we\npropose a novel anomaly detection framework that can efficiently work with\nlimited labeled anomalies. Specifically, we assume the normal sample data may\nconsist of multiple subgroups, and propose to learn multi-normal prototypes to\nrepresent them with deep embedding clustering and contrastive learning.\nAdditionally, we propose a method to estimate the likelihood of each unlabeled\nsample being normal during model training, which can help to learn more\nefficient data encoder and normal prototypes for anomaly detection. Extensive\nexperiments on various datasets demonstrate the superior performance of our\nmethod compared to state-of-the-art methods.\n", "date": "2024-08-23", "categories": []}
{"id": "2410.21712", "title": "Sliced-Wasserstein-based Anomaly Detection and Open Dataset for\n  Localized Critical Peak Rebates", "abstract": "  In this work, we present a new unsupervised anomaly (outlier) detection (AD)\nmethod using the sliced-Wasserstein metric. This filtering technique is\nconceptually interesting for MLOps pipelines deploying machine learning models\nin critical sectors, e.g., energy, as it offers a conservative data selection.\nAdditionally, we open the first dataset showcasing localized critical peak\nrebate demand response in a northern climate. We demonstrate the capabilities\nof our method on synthetic datasets as well as standard AD datasets and use it\nin the making of a first benchmark for our open-source localized critical peak\nrebate dataset.\n", "date": "2024-10-29", "categories": []}
{"id": "2412.00146", "title": "Knowledge-Augmented Explainable and Interpretable Learning for Anomaly\n  Detection and Diagnosis", "abstract": "  Knowledge-augmented learning enables the combination of knowledge-based and\ndata-driven approaches. For anomaly detection and diagnosis, understandability\nis typically an important factor, especially in high-risk areas. Therefore,\nexplainability and interpretability are also major criteria in such contexts.\nThis chapter focuses on knowledge-augmented explainable and interpretable\nlearning to enhance understandability, transparency and ultimately\ncomputational sensemaking. We exemplify different approaches and methods in the\ndomains of anomaly detection and diagnosis - from comparatively simple\ninterpretable methods towards more advanced neuro-symbolic approaches.\n", "date": "2024-11-28", "categories": []}
{"id": "2412.00860", "title": "Deep evolving semi-supervised anomaly detection", "abstract": "  The aim of this paper is to formalise the task of continual semi-supervised\nanomaly detection (CSAD), with the aim of highlighting the importance of such a\nproblem formulation which assumes as close to real-world conditions as\npossible. After an overview of the relevant definitions of continual\nsemi-supervised learning, its components, anomaly detection extension, and the\ntraining protocols; the paper introduces a baseline model of a variational\nautoencoder (VAE) to work with semi-supervised data along with a continual\nlearning method of deep generative replay with outlier rejection. The results\nshow that such a use of extreme value theory (EVT) applied to anomaly detection\ncan provide promising results even in comparison to an upper baseline of joint\ntraining. The results explore the effects of how much labelled and unlabelled\ndata is present, of which class, and where it is located in the data stream.\nOutlier rejection shows promising initial results where it often surpasses a\nbaseline method of Elastic Weight Consolidation (EWC). A baseline for CSAD is\nput forward along with the specific dataset setups used for reproducability and\ntestability for other practitioners. Future research directions include other\nCSAD settings and further research into efficient continual hyperparameter\ntuning.\n", "date": "2024-12-01", "categories": []}
{"id": "2412.00890", "title": "Exploring Large Vision-Language Models for Robust and Efficient\n  Industrial Anomaly Detection", "abstract": "  Industrial anomaly detection (IAD) plays a crucial role in the maintenance\nand quality control of manufacturing processes. In this paper, we propose a\nnovel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal\nTraining (CLAD), which leverages large vision-language models (LVLMs) to\nimprove both anomaly detection and localization in industrial settings. CLAD\naligns visual and textual features into a shared embedding space using\ncontrastive learning, ensuring that normal instances are grouped together while\nanomalies are pushed apart. Through extensive experiments on two benchmark\nindustrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms\nstate-of-the-art methods in both image-level anomaly detection and pixel-level\nanomaly localization. Additionally, we provide ablation studies and human\nevaluation to validate the importance of key components in our method. Our\napproach not only achieves superior performance but also enhances\ninterpretability by accurately localizing anomalies, making it a promising\nsolution for real-world industrial applications.\n", "date": "2024-12-01", "categories": []}
{"id": "2412.00984", "title": "TGTOD: A Global Temporal Graph Transformer for Outlier Detection at\n  Scale", "abstract": "  While Transformers have revolutionized machine learning on various data,\nexisting Transformers for temporal graphs face limitations in (1) restricted\nreceptive fields, (2) overhead of subgraph extraction, and (3) suboptimal\ngeneralization capability beyond link prediction. In this paper, we rethink\ntemporal graph Transformers and propose TGTOD, a novel end-to-end Temporal\nGraph Transformer for Outlier Detection. TGTOD employs global attention to\nmodel both structural and temporal dependencies within temporal graphs. To\ntackle scalability, our approach divides large temporal graphs into\nspatiotemporal patches, which are then processed by a hierarchical Transformer\narchitecture comprising Patch Transformer, Cluster Transformer, and Temporal\nTransformer. We evaluate TGTOD on three public datasets under two settings,\ncomparing with a wide range of baselines. Our experimental results demonstrate\nthe effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic.\nFurthermore, our efficiency evaluation shows that TGTOD reduces training time\nby 44x compared to existing Transformers for temporal graphs. To foster\nreproducibility, we make our implementation publicly available at\nhttps://github.com/kayzliu/tgtod.\n", "date": "2024-12-01", "categories": []}
{"id": "2410.15909", "title": "Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating\n  Spatial and Temporal Analysis", "abstract": "  In this paper, we propose a new architecture for real-time anomaly detection\nin video data, inspired by human behavior combining spatial and temporal\nanalyses. This approach uses two distinct models: (i) for temporal analysis, a\nrecurrent convolutional network (CNN + RNN) is employed, associating VGG19 and\na GRU to process video sequences; (ii) regarding spatial analysis, it is\nperformed using YOLOv7 to analyze individual images. These two analyses can be\ncarried out either in parallel, with a final prediction that combines the\nresults of both analysis, or in series, where the spatial analysis enriches the\ndata before the temporal analysis. Some experimentations are been made to\ncompare these two architectural configurations with each other, and evaluate\nthe effectiveness of our hybrid approach in video anomaly detection.\n", "date": "2024-10-21", "categories": []}
{"id": "2411.19220", "title": "Automatic Prompt Generation and Grounding Object Detection for Zero-Shot\n  Image Anomaly Detection", "abstract": "  Identifying defects and anomalies in industrial products is a critical\nquality control task. Traditional manual inspection methods are slow,\nsubjective, and error-prone. In this work, we propose a novel zero-shot\ntraining-free approach for automated industrial image anomaly detection using a\nmultimodal machine learning pipeline, consisting of three foundation models.\nOur method first uses a large language model, i.e., GPT-3. generate text\nprompts describing the expected appearances of normal and abnormal products. We\nthen use a grounding object detection model, called Grounding DINO, to locate\nthe product in the image. Finally, we compare the cropped product image patches\nto the generated prompts using a zero-shot image-text matching model, called\nCLIP, to identify any anomalies. Our experiments on two datasets of industrial\nproduct images, namely MVTec-AD and VisA, demonstrate the effectiveness of this\nmethod, achieving high accuracy in detecting various types of defects and\nanomalies without the need for model training. Our proposed model enables\nefficient, scalable, and objective quality control in industrial manufacturing\nsettings.\n", "date": "2024-11-28", "categories": []}
{"id": "2411.19506", "title": "Real-time Anomaly Detection at the L1 Trigger of CMS Experiment", "abstract": "  We present the preparation, deployment, and testing of an autoencoder trained\nfor unbiased detection of new physics signatures in the CMS experiment Global\nTrigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision\nwhether to readout or discard the data from each LHC collision, which occur at\na rate of 40 MHz, within a 50 ns latency. The Neural Network makes a prediction\nfor each event within these constraints, which can be used to select anomalous\nevents for further analysis. The GT test crate is a copy of the main GT system,\nreceiving the same input data, but whose output is not used to trigger the\nreadout of CMS, providing a platform for thorough testing of new trigger\nalgorithms on live data, but without interrupting data taking. We describe the\nmethodology to achieve ultra low latency anomaly detection, and present the\nintegration of the DNN into the GT test crate, as well as the monitoring,\ntesting, and validation of the algorithm during proton collisions.\n", "date": "2024-11-29", "categories": []}
{"id": "2411.19731", "title": "Real-Time Anomaly Detection in Video Streams", "abstract": "  This thesis is part of a CIFRE agreement between the company Othello and the\nLIASD laboratory. The objective is to develop an artificial intelligence system\nthat can detect real-time dangers in a video stream. To achieve this, a novel\napproach combining temporal and spatial analysis has been proposed. Several\navenues have been explored to improve anomaly detection by integrating object\ndetection, human pose detection, and motion analysis. For result\ninterpretability, techniques commonly used for image analysis, such as\nactivation and saliency maps, have been extended to videos, and an original\nmethod has been proposed. The proposed architecture performs binary or\nmulticlass classification depending on whether an alert or the cause needs to\nbe identified. Numerous neural networkmodels have been tested, and three of\nthem have been selected. You Only Looks Once (YOLO) has been used for spatial\nanalysis, a Convolutional Recurrent Neuronal Network (CRNN) composed of VGG19\nand a Gated Recurrent Unit (GRU) for temporal analysis, and a multi-layer\nperceptron for classification. These models handle different types of data and\ncan be combined in parallel or in series. Although the parallel mode is faster,\nthe serial mode is generally more reliable. For training these models,\nsupervised learning was chosen, and two proprietary datasets were created. The\nfirst dataset focuses on objects that may play a potential role in anomalies,\nwhile the second consists of videos containing anomalies or non-anomalies. This\napproach allows for the processing of both continuous video streams and finite\nvideos, providing greater flexibility in detection.\n", "date": "2024-11-29", "categories": []}
{"id": "2411.19875", "title": "Enhanced anomaly detection in well log data through the application of\n  ensemble GANs", "abstract": "  Although generative adversarial networks (GANs) have shown significant\nsuccess in modeling data distributions for image datasets, their application to\nstructured or tabular data, such as well logs, remains relatively\nunderexplored. This study extends the ensemble GANs (EGANs) framework to\ncapture the distribution of well log data and detect anomalies that fall\noutside of these distributions. The proposed approach compares the performance\nof traditional methods, such as Gaussian mixture models (GMMs), with EGANs in\ndetecting anomalies outside the expected data distributions. For the gamma ray\n(GR) dataset, EGANs achieved a precision of 0.62 and F1 score of 0.76,\noutperforming GMM's precision of 0.38 and F1 score of 0.54. Similarly, for\ntravel time (DT), EGANs achieved a precision of 0.70 and F1 score of 0.79,\nsurpassing GMM 0.56 and 0.71. In the neutron porosity (NPHI) dataset, EGANs\nrecorded a precision of 0.53 and F1 score of 0.68, outshining GMM 0.47 and\n0.61. For the bulk density (RHOB) dataset, EGANs achieved a precision of 0.52\nand an F1 score of 0.67, slightly outperforming GMM, which yielded a precision\nof 0.50 and an F1 score of 0.65. This work's novelty lies in applying EGANs for\nwell log data analysis, showcasing their ability to learn data patterns and\nidentify anomalies that deviate from them. This approach offers more reliable\nanomaly detection compared to traditional methods like GMM. The findings\nhighlight the potential of EGANs in enhancing anomaly detection for well log\ndata, delivering significant implications for optimizing drilling strategies\nand reservoir management through more accurate, data-driven insights into\nsubsurface characterization.\n", "date": "2024-11-29", "categories": []}
{"id": "2411.16692", "title": "Leveraging Conversational Generative AI for Anomaly Detection in Digital\n  Substations", "abstract": "  This study addresses critical challenges of cybersecurity in digital\nsubstations by proposing an innovative task-oriented dialogue (ToD) system for\nanomaly detection (AD) in multicast messages, specifically, generic object\noriented substation event (GOOSE) and sampled value (SV) datasets. Leveraging\ngenerative artificial intelligence (GenAI) technology, the proposed framework\ndemonstrates superior error reduction, scalability, and adaptability compared\nwith traditional human-in-the-loop (HITL) processes. Notably, this methodology\noffers significant advantages over machine learning (ML) techniques in terms of\nefficiency and implementation speed when confronting novel and/or unknown cyber\nthreats, while also maintaining model complexity and precision. The research\nemploys advanced performance metrics to conduct a comparative assessment\nbetween the proposed AD and HITL-based AD frameworks, utilizing a\nhardware-in-the-loop (HIL) testbed for generating and extracting features of\nIEC61850 communication messages. This approach presents a promising solution\nfor enhancing the reliability of power system operations in the face of\nevolving cybersecurity challenges.\n", "date": "2024-11-09", "categories": []}
{"id": "2404.13273", "title": "Multi-feature Reconstruction Network using Crossed-mask Restoration for\n  Unsupervised Industrial Anomaly Detection", "abstract": "  Unsupervised anomaly detection using only normal samples is of great\nsignificance for quality inspection in industrial manufacturing. Although\nexisting reconstruction-based methods have achieved promising results, they\nstill face two problems: poor distinguishable information in image\nreconstruction and well abnormal regeneration caused by model\nunder-regularization. To overcome the above issues, we convert the image\nreconstruction into a combination of parallel feature restorations and propose\na multi-feature reconstruction network, MFRNet, using crossed-mask restoration\nin this paper. Specifically, a multi-scale feature aggregator is first\ndeveloped to generate more discriminative hierarchical representations of the\ninput images from a pre-trained model. Subsequently, a crossed-mask generator\nis adopted to randomly cover the extracted feature map, followed by a\nrestoration network based on the transformer structure for high-quality repair\nof the missing regions. Finally, a hybrid loss is equipped to guide model\ntraining and anomaly estimation, which gives consideration to both the pixel\nand structural similarity. Extensive experiments show that our method is highly\ncompetitive with or significantly outperforms other state-of-the-arts on four\npublic available datasets and one self-made dataset.\n", "date": "2024-04-20", "categories": []}
{"id": "2411.17218", "title": "GraphSubDetector: Time Series Subsequence Anomaly Detection via\n  Density-Aware Adaptive Graph Neural Network", "abstract": "  Time series subsequence anomaly detection is an important task in a large\nvariety of real-world applications ranging from health monitoring to AIOps, and\nis challenging due to the following reasons: 1) how to effectively learn\ncomplex dynamics and dependencies in time series; 2) diverse and complicated\nanomalous subsequences as well as the inherent variance and noise of normal\npatterns; 3) how to determine the proper subsequence length for effective\ndetection, which is a required parameter for many existing algorithms. In this\npaper, we present a novel approach to subsequence anomaly detection, namely\nGraphSubDetector. First, it adaptively learns the appropriate subsequence\nlength with a length selection mechanism that highlights the characteristics of\nboth normal and anomalous patterns. Second, we propose a density-aware adaptive\ngraph neural network (DAGNN), which can generate further robust representations\nagainst variance of normal data for anomaly detection by message passing\nbetween subsequences. The experimental results demonstrate the effectiveness of\nthe proposed algorithm, which achieves superior performance on multiple time\nseries anomaly benchmark datasets compared to state-of-the-art algorithms.\n", "date": "2024-11-26", "categories": []}
{"id": "2411.17495", "title": "A Machine Learning-based Anomaly Detection Framework in Life Insurance\n  Contracts", "abstract": "  Life insurance, like other forms of insurance, relies heavily on large\nvolumes of data. The business model is based on an exchange where companies\nreceive payments in return for the promise to provide coverage in case of an\naccident. Thus, trust in the integrity of the data stored in databases is\ncrucial. One method to ensure data reliability is the automatic detection of\nanomalies. While this approach is highly useful, it is also challenging due to\nthe scarcity of labeled data that distinguish between normal and anomalous\ncontracts or inter\\-actions. This manuscript discusses several classical and\nmodern unsupervised anomaly detection methods and compares their performance\nacross two different datasets. In order to facilitate the adoption of these\nmethods by companies, this work also explores ways to automate the process,\nmaking it accessible even to non-data scientists.\n", "date": "2024-11-26", "categories": []}
{"id": "2310.17748", "title": "OrionBench: Benchmarking Time Series Generative Models in the Service of\n  the End-User", "abstract": "  Time series anomaly detection is a vital task in many domains, including\npatient monitoring in healthcare, forecasting in finance, and predictive\nmaintenance in energy industries. This has led to a proliferation of anomaly\ndetection methods, including deep learning-based methods. Benchmarks are\nessential for comparing the performances of these models as they emerge, in a\nfair, rigorous, and reproducible approach. Although several benchmarks for\ncomparing models have been proposed, these usually rely on a one-time execution\nover a limited set of datasets, with comparisons restricted to a few models. We\npropose OrionBench: an end-user centric, continuously maintained benchmarking\nframework for unsupervised time series anomaly detection models. Our framework\nprovides universal abstractions to represent models, hyperparameter\nstandardization, extensibility to add new pipelines and datasets, pipeline\nverification, and frequent releases with published updates of the benchmark. We\ndemonstrate how to use OrionBench, and the performance of pipelines across 17\nreleases published over the course of four years. We also walk through two real\nscenarios we experienced with OrionBench that highlight the importance of\ncontinuous benchmarking for unsupervised time series anomaly detection.\n", "date": "2023-10-26", "categories": []}
{"id": "2312.17679", "title": "Data Augmentation for Supervised Graph Outlier Detection via Latent\n  Diffusion Models", "abstract": "  A fundamental challenge confronting supervised graph outlier detection\nalgorithms is the prevalent problem of class imbalance, where the scarcity of\noutlier instances compared to normal instances often results in suboptimal\nperformance. Recently, generative models, especially diffusion models, have\ndemonstrated their efficacy in synthesizing high-fidelity images. Despite their\nextraordinary generation quality, their potential in data augmentation for\nsupervised graph outlier detection remains largely underexplored. To bridge\nthis gap, we introduce GODM, a novel data augmentation for mitigating class\nimbalance in supervised Graph Outlier detection via latent Diffusion Models.\nExtensive experiments conducted on multiple datasets substantiate the\neffectiveness and efficiency of GODM. The case study further demonstrated the\ngeneration quality of our synthetic data. To foster accessibility and\nreproducibility, we encapsulate GODM into a plug-and-play package and release\nit at PyPI: https://pypi.org/project/godm/.\n", "date": "2023-12-29", "categories": []}
{"id": "2404.10320", "title": "CARE to Compare: A real-world dataset for anomaly detection in wind\n  turbine data", "abstract": "  Anomaly detection plays a crucial role in the field of predictive maintenance\nfor wind turbines, yet the comparison of different algorithms poses a difficult\ntask because domain specific public datasets are scarce. Many comparisons of\ndifferent approaches either use benchmarks composed of data from many different\ndomains, inaccessible data or one of the few publicly available datasets which\nlack detailed information about the faults. Moreover, many publications\nhighlight a couple of case studies where fault detection was successful. With\nthis paper we publish a high quality dataset that contains data from 36 wind\nturbines across 3 different wind farms as well as the most detailed fault\ninformation of any public wind turbine dataset as far as we know. The new\ndataset contains 89 years worth of real-world operating data of wind turbines,\ndistributed across 44 labeled time frames for anomalies that led up to faults,\nas well as 51 time series representing normal behavior. Additionally, the\nquality of training data is ensured by turbine-status-based labels for each\ndata point. Furthermore, we propose a new scoring method, called CARE\n(Coverage, Accuracy, Reliability and Earliness), which takes advantage of the\ninformation depth that is present in the dataset to identify a good all-around\nanomaly detection model. This score considers the anomaly detection\nperformance, the ability to recognize normal behavior properly and the\ncapability to raise as few false alarms as possible while simultaneously\ndetecting anomalies early.\n", "date": "2024-04-16", "categories": []}
{"id": "2409.16225", "title": "VideoPatchCore: An Effective Method to Memorize Normality for Video\n  Anomaly Detection", "abstract": "  Video anomaly detection (VAD) is a crucial task in video analysis and\nsurveillance within computer vision. Currently, VAD is gaining attention with\nmemory techniques that store the features of normal frames. The stored features\nare utilized for frame reconstruction, identifying an abnormality when a\nsignificant difference exists between the reconstructed and input frames.\nHowever, this approach faces several challenges due to the simultaneous\noptimization required for both the memory and encoder-decoder model. These\nchallenges include increased optimization difficulty, complexity of\nimplementation, and performance variability depending on the memory size. To\naddress these challenges,we propose an effective memory method for VAD, called\nVideoPatchCore. Inspired by PatchCore, our approach introduces a structure that\nprioritizes memory optimization and configures three types of memory tailored\nto the characteristics of video data. This method effectively addresses the\nlimitations of existing memory-based methods, achieving good performance\ncomparable to state-of-the-art methods. Furthermore, our method requires no\ntraining and is straightforward to implement, making VAD tasks more accessible.\nOur code is available online at github.com/SkiddieAhn/Paper-VideoPatchCore.\n", "date": "2024-09-24", "categories": []}
{"id": "2410.12830", "title": "Incorporating Metabolic Information into LLMs for Anomaly Detection in\n  Clinical Time-Series", "abstract": "  Anomaly detection in clinical time-series holds significant potential in\nidentifying suspicious patterns in different biological parameters. In this\npaper, we propose a targeted method that incorporates the clinical domain\nknowledge into LLMs to improve their ability to detect anomalies. We introduce\nthe Metabolism Pathway-driven Prompting (MPP) method, which integrates the\ninformation about metabolic pathways to better capture the structural and\ntemporal changes in biological samples. We applied our method for doping\ndetection in sports, focusing on steroid metabolism, and evaluated using\nreal-world data from athletes. The results show that our method improves\nanomaly detection performance by leveraging metabolic context, providing a more\nnuanced and accurate prediction of suspicious samples in athletes' profiles.\n", "date": "2024-10-02", "categories": []}
{"id": "2411.15250", "title": "TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and\n  Key Parameters", "abstract": "  Log-system is an important mechanism for recording the runtime status and\nevents of Web service systems, and anomaly detection in logs is an effective\nmethod of detecting problems. However, manual anomaly detection in logs is\ninefficient, error-prone, and unrealistic. Existing log anomaly detection\nmethods either use the indexes of event templates, or form vectors by embedding\nthe fixed string part of the template as a sentence, or use time parameters for\nsequence analysis. However, log entries often contain features and semantic\ninformation that cannot be fully represented by these methods, resulting in\nmissed and false alarms. In this paper, we propose TPLogAD, a universal\nunsupervised method for analyzing unstructured logs, which performs anomaly\ndetection based on event templates and key parameters. The itemplate2vec and\npara2vec included in TPLogAD are two efficient and easy-to-implement semantic\nrepresentation methods for logs, detecting anomalies in event templates and\nparameters respectively, which has not been achieved in previous work.\nAdditionally, TPLogAD can avoid the interference of log diversity and dynamics\non anomaly detection. Our experiments on four public log datasets show that\nTPLogAD outperforms existing log anomaly detection methods.\n", "date": "2024-11-22", "categories": []}
{"id": "2411.16049", "title": "ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain\n  Shift", "abstract": "  Recent advancements in anomaly detection have shifted focus towards\nMulti-class Unified Anomaly Detection (MUAD), offering more scalable and\npractical alternatives compared to traditional one-class-one-model approaches.\nHowever, existing MUAD methods often suffer from inter-class interference and\nare highly susceptible to domain shifts, leading to substantial performance\ndegradation in real-world applications. In this paper, we propose a novel\nrobust prompt-driven MUAD framework, called ROADS, to address these challenges.\nROADS employs a hierarchical class-aware prompt integration mechanism that\ndynamically encodes class-specific information into our anomaly detector to\nmitigate interference among anomaly classes. Additionally, ROADS incorporates a\ndomain adapter to enhance robustness against domain shifts by learning\ndomain-invariant representations. Extensive experiments on MVTec-AD and VISA\ndatasets demonstrate that ROADS surpasses state-of-the-art methods in both\nanomaly detection and localization, with notable improvements in\nout-of-distribution settings.\n", "date": "2024-11-25", "categories": []}
{"id": "2411.16110", "title": "FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy\n  Training Data", "abstract": "  While the mainstream research in anomaly detection has mainly followed the\none-class classification, practical industrial environments often incur noisy\ntraining data due to annotation errors or lack of labels for new or refurbished\nproducts. To address these issues, we propose a novel learning-based approach\nfor fully unsupervised anomaly detection with unlabeled and potentially\ncontaminated training data. Our method is motivated by two observations, that\ni) the pairwise feature distances between the normal samples are on average\nlikely to be smaller than those between the anomaly samples or heterogeneous\nsamples and ii) pairs of features mutually closest to each other are likely to\nbe homogeneous pairs, which hold if the normal data has smaller variance than\nthe anomaly data. Building on the first observation that nearest-neighbor\ndistances can distinguish between confident normal samples and anomalies, we\npropose a pseudo-labeling strategy using an iteratively reconstructed memory\nbank (IRMB). The second observation is utilized as a new loss function to\npromote class-homogeneity between mutually closest pairs thereby reducing the\nill-posedness of the task. Experimental results on two public industrial\nanomaly benchmarks and semantic anomaly examples validate the effectiveness of\nFUN-AD across different scenarios and anomaly-to-normal ratios. Our code is\navailable at https://github.com/HY-Vision-Lab/FUNAD.\n", "date": "2024-11-25", "categories": []}
{"id": "2411.14509", "title": "End-to-End Convolutional Activation Anomaly Analysis for Anomaly\n  Detection", "abstract": "  We propose an End-to-end Convolutional Activation Anomaly Analysis\n(E2E-CA$^3$), which is a significant extension of A$^3$ anomaly detection\napproach proposed by Sperl, Schulze and B\\\"ottinger, both in terms of\narchitecture and scope of application. In contrast to the original idea, we\nutilize a convolutional autoencoder as a target network, which allows for\nnatural application of the method both to image and tabular data. The alarm\nnetwork is also designed as a CNN, where the activations of convolutional\nlayers from CAE are stacked together into $k+1-$dimensional tensor. Moreover,\nwe combine the classification loss of the alarm network with the reconstruction\nerror of the target CAE, as a \"best of both worlds\" approach, which greatly\nincreases the versatility of the network. The evaluation shows that despite\ngenerally straightforward and lightweight architecture, it has a very promising\nanomaly detection performance on common datasets such as MNIST, CIFAR-10 and\nKDDcup99.\n", "date": "2024-11-21", "categories": []}
{"id": "2411.14515", "title": "Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel\n  Anomaly Detection", "abstract": "  Anomaly detection (AD) is a machine learning task that identifies anomalies\nby learning patterns from normal training data. In many real-world scenarios,\nanomalies vary in severity, from minor anomalies with little risk to severe\nabnormalities requiring immediate attention. However, existing models primarily\noperate in a binary setting, and the anomaly scores they produce are usually\nbased on the deviation of data points from normal data, which may not\naccurately reflect practical severity. In this paper, we address this gap by\nmaking three key contributions. First, we propose a novel setting, Multilevel\nAD (MAD), in which the anomaly score represents the severity of anomalies in\nreal-world applications, and we highlight its diverse applications across\nvarious domains. Second, we introduce a novel benchmark, MAD-Bench, that\nevaluates models not only on their ability to detect anomalies, but also on how\neffectively their anomaly scores reflect severity. This benchmark incorporates\nmultiple types of baselines and real-world applications involving severity.\nFinally, we conduct a comprehensive performance analysis on MAD-Bench. We\nevaluate models on their ability to assign severity-aligned scores, investigate\nthe correspondence between their performance on binary and multilevel\ndetection, and study their robustness. This analysis offers key insights into\nimproving AD models for practical severity alignment. The code framework and\ndatasets used for the benchmark will be made publicly available.\n", "date": "2024-11-21", "categories": []}
{"id": "2411.14953", "title": "Evaluating Vision Transformer Models for Visual Quality Control in\n  Industrial Manufacturing", "abstract": "  One of the most promising use-cases for machine learning in industrial\nmanufacturing is the early detection of defective products using a quality\ncontrol system. Such a system can save costs and reduces human errors due to\nthe monotonous nature of visual inspections. Today, a rich body of research\nexists which employs machine learning methods to identify rare defective\nproducts in unbalanced visual quality control datasets. These methods typically\nrely on two components: A visual backbone to capture the features of the input\nimage and an anomaly detection algorithm that decides if these features are\nwithin an expected distribution. With the rise of transformer architecture as\nvisual backbones of choice, there exists now a great variety of different\ncombinations of these two components, ranging all along the trade-off between\ndetection quality and inference time. Facing this variety, practitioners in the\nfield often have to spend a considerable amount of time on researching the\nright combination for their use-case at hand. Our contribution is to help\npractitioners with this choice by reviewing and evaluating current vision\ntransformer models together with anomaly detection methods. For this, we chose\nSotA models of both disciplines, combined them and evaluated them towards the\ngoal of having small, fast and efficient anomaly detection models suitable for\nindustrial manufacturing. We evaluated the results of our experiments on the\nwell-known MVTecAD and BTAD datasets. Moreover, we give guidelines for choosing\na suitable model architecture for a quality control system in practice,\nconsidering given use-case and hardware constraints.\n", "date": "2024-11-22", "categories": []}
{"id": "2309.02253", "title": "MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for\n  Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance\n  Powertrain Testing", "abstract": "  A clear need for automatic anomaly detection applied to automotive testing\nhas emerged as more and more attention is paid to the data recorded and manual\nevaluation by humans reaches its capacity. Such real-world data is massive,\ndiverse, multivariate and temporal in nature, therefore requiring modelling of\nthe testee behaviour. We propose a variational autoencoder with multi-head\nattention (MA-VAE), which, when trained on unlabelled data, not only provides\nvery few false positives but also manages to detect the majority of the\nanomalies presented. In addition to that, the approach offers a novel way to\navoid the bypass phenomenon, an undesirable behaviour investigated in\nliterature. Lastly, the approach also introduces a new method to remap\nindividual windows to a continuous time series. The results are presented in\nthe context of a real-world industrial data set and several experiments are\nundertaken to further investigate certain aspects of the proposed model. When\nconfigured properly, it is 9% of the time wrong when an anomaly is flagged and\ndiscovers 67% of the anomalies present. Also, MA-VAE has the potential to\nperform well with only a fraction of the training and validation subset,\nhowever, to extract it, a more sophisticated threshold estimation method is\nrequired.\n", "date": "2023-09-05", "categories": []}
{"id": "2408.03747", "title": "Online Model-based Anomaly Detection in Multivariate Time Series:\n  Taxonomy, Survey, Research Challenges and Future Directions", "abstract": "  Time-series anomaly detection plays an important role in engineering\nprocesses, like development, manufacturing and other operations involving\ndynamic systems. These processes can greatly benefit from advances in the\nfield, as state-of-the-art approaches may aid in cases involving, for example,\nhighly dimensional data. To provide the reader with understanding of the\nterminology, this survey introduces a novel taxonomy where a distinction\nbetween online and offline, and training and inference is made. Additionally,\nit presents the most popular data sets and evaluation metrics used in the\nliterature, as well as a detailed analysis. Furthermore, this survey provides\nan extensive overview of the state-of-the-art model-based online semi- and\nunsupervised anomaly detection approaches for multivariate time-series data,\ncategorising them into different model families and other properties. The\nbiggest research challenge revolves around benchmarking, as currently there is\nno reliable way to compare different approaches against one another. This\nproblem is two-fold: on the one hand, public data sets suffers from at least\none fundamental flaw, while on the other hand, there is a lack of intuitive and\nrepresentative evaluation metrics in the field. Moreover, the way most\npublications choose a detection threshold disregards real-world conditions,\nwhich hinders the application in the real world. To allow for tangible advances\nin the field, these issues must be addressed in future work.\n", "date": "2024-08-07", "categories": []}
{"id": "2410.02592", "title": "IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of\n  Both Driver and Passengers", "abstract": "  Recently, in-car monitoring has emerged as a promising technology for\ndetecting early-stage abnormal status of the driver and providing timely alerts\nto prevent traffic accidents. Although training models with multimodal data\nenhances the reliability of abnormal status detection, the scarcity of labeled\ndata and the imbalance of class distribution impede the extraction of critical\nabnormal state features, significantly deteriorating training performance.\nFurthermore, missing modalities due to environment and hardware limitations\nfurther exacerbate the challenge of abnormal status identification. More\nimportantly, monitoring abnormal health conditions of passengers, particularly\nin elderly care, is of paramount importance but remains underexplored. To\naddress these challenges, we introduce our IC3M, an efficient\ncamera-rotation-based multimodal framework for monitoring both driver and\npassengers in a car. Our IC3M comprises two key modules: an adaptive threshold\npseudo-labeling strategy and a missing modality reconstruction. The former\ncustomizes pseudo-labeling thresholds for different classes based on the class\ndistribution, generating class-balanced pseudo labels to guide model training\neffectively, while the latter leverages crossmodality relationships learned\nfrom limited labels to accurately recover missing modalities by distribution\ntransferring from available modalities. Extensive experimental results\ndemonstrate that IC3M outperforms state-of-the-art benchmarks in accuracy,\nprecision, and recall while exhibiting superior robustness under limited\nlabeled data and severe missing modality.\n", "date": "2024-10-03", "categories": []}
{"id": "2312.16852", "title": "Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone", "abstract": "  With the increase of the number of elderly people living alone around the\nworld, there is a growing demand for sensor-based detection of anomalous\nbehaviors. Although smart homes with ambient sensors could be useful for\ndetecting such anomalies, there is a problem of lack of sufficient real data\nfor developing detection algorithms. For coping with this problem, several\nsensor data simulators have been proposed, but they have not been able to model\nappropriately the long-term transitions and correlations between anomalies that\nexist in reality. In this paper, therefore, we propose a novel sensor data\nsimulator that can model these factors in generation of sensor data. Anomalies\nconsidered in this study were classified into three types of \\textit{state\nanomalies}, \\textit{activity anomalies}, and \\textit{moving anomalies}. The\nsimulator produces 10 years data in 100 min. including six anomalies, two for\neach type. Numerical evaluations show that this simulator is superior to the\npast simulators in the sense that it simulates well day-to-day variations of\nreal data.\n", "date": "2023-12-28", "categories": []}
{"id": "2411.06406", "title": "Locally Adaptive One-Class Classifier Fusion with Dynamic $\\ell$p-Norm\n  Constraints for Robust Anomaly Detection", "abstract": "  This paper presents a novel approach to one-class classifier fusion through\nlocally adaptive learning with dynamic $\\ell$p-norm constraints. We introduce a\nframework that dynamically adjusts fusion weights based on local data\ncharacteristics, addressing fundamental challenges in ensemble-based anomaly\ndetection. Our method incorporates an interior-point optimization technique\nthat significantly improves computational efficiency compared to traditional\nFrank-Wolfe approaches, achieving up to 19-fold speed improvements in complex\nscenarios. The framework is extensively evaluated on standard UCI benchmark\ndatasets and specialized temporal sequence datasets, demonstrating superior\nperformance across diverse anomaly types. Statistical validation through\nSkillings-Mack tests confirms our method's significant advantages over existing\napproaches, with consistent top rankings in both pure and non-pure learning\nscenarios. The framework's ability to adapt to local data patterns while\nmaintaining computational efficiency makes it particularly valuable for\nreal-time applications where rapid and accurate anomaly detection is crucial.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.13153", "title": "Long-term Detection System for Six Kinds of Abnormal Behavior of the\n  Elderly Living Alone", "abstract": "  The proportion of elderly people is increasing worldwide, particularly those\nliving alone in Japan. As elderly people get older, their risks of physical\ndisabilities and health issues increase. To automatically discover these issues\nat a low cost in daily life, sensor-based detection in a smart home is\npromising. As part of the effort towards early detection of abnormal behaviors,\nwe propose a simulator-based detection systems for six typical anomalies: being\nsemi-bedridden, being housebound, forgetting, wandering, fall while walking and\nfall while standing. Our detection system can be customized for various room\nlayout, sensor arrangement and resident's characteristics by training detection\nclassifiers using the simulator with the parameters fitted to individual cases.\nConsidering that the six anomalies that our system detects have various\noccurrence durations, such as being housebound for weeks or lying still for\nseconds after a fall, the detection classifiers of our system produce anomaly\nlabels depending on each anomaly's occurrence duration, e.g., housebound per\nday and falls per second. We propose a method that standardizes the processing\nof sensor data, and uses a simple detection approach. Although the validity\ndepends on the realism of the simulation, numerical evaluations using sensor\ndata that includes a variety of resident behavior patterns over nine years as\ntest data show that (1) the methods for detecting wandering and falls are\ncomparable to previous methods, and (2) the methods for detecting being\nsemi-bedridden, being housebound, and forgetting achieve a sensitivity of over\n0.9 with fewer than one false alarm every 50 days.\n", "date": "2024-11-20", "categories": []}
{"id": "2402.19145", "title": "A SAM-guided Two-stream Lightweight Model for Anomaly Detection", "abstract": "  In industrial anomaly detection, model efficiency and mobile-friendliness\nbecome the primary concerns in real-world applications. Simultaneously, the\nimpressive generalization capabilities of Segment Anything (SAM) have garnered\nbroad academic attention, making it an ideal choice for localizing unseen\nanomalies and diverse real-world patterns. In this paper, considering these two\ncritical factors, we propose a SAM-guided Two-stream Lightweight Model for\nunsupervised anomaly detection (STLM) that not only aligns with the two\npractical application requirements but also harnesses the robust generalization\ncapabilities of SAM. We employ two lightweight image encoders, i.e., our\ntwo-stream lightweight module, guided by SAM's knowledge. To be specific, one\nstream is trained to generate discriminative and general feature\nrepresentations in both normal and anomalous regions, while the other stream\nreconstructs the same images without anomalies, which effectively enhances the\ndifferentiation of two-stream representations when facing anomalous regions.\nFurthermore, we employ a shared mask decoder and a feature aggregation module\nto generate anomaly maps. Our experiments conducted on MVTec AD benchmark show\nthat STLM, with about 16M parameters and achieving an inference time in 20ms,\ncompetes effectively with state-of-the-art methods in terms of performance,\n98.26% on pixel-level AUC and 94.92% on PRO. We further experiment on more\ndifficult datasets, e.g., VisA and DAGM, to demonstrate the effectiveness and\ngeneralizability of STLM.\n", "date": "2024-02-29", "categories": []}
{"id": "2407.03634", "title": "SOWA: Adapting Hierarchical Frozen Window Self-Attention to\n  Visual-Language Models for Better Anomaly Detection", "abstract": "  Visual anomaly detection is essential in industrial manufacturing, yet\ntraditional methods often rely heavily on extensive normal datasets and\ntask-specific models, limiting their scalability. Recent advancements in\nlarge-scale vision-language models have significantly enhanced zero- and\nfew-shot anomaly detection. However, these approaches may not fully leverage\nhierarchical features, potentially overlooking nuanced details crucial for\naccurate detection. To address this, we introduce a novel window self-attention\nmechanism based on the CLIP model, augmented with learnable prompts to process\nmulti-level features within a Soldier-Officer Window Self-Attention (SOWA)\nframework. Our method has been rigorously evaluated on five benchmark datasets,\nachieving superior performance by leading in 18 out of 20 metrics, setting a\nnew standard against existing state-of-the-art techniques.\n", "date": "2024-07-04", "categories": []}
{"id": "2312.02491", "title": "Pseudo Replay-based Class Continual Learning for Online New Category\n  Anomaly Detection in Advanced Manufacturing", "abstract": "  The incorporation of advanced sensors and machine learning techniques has\nenabled modern manufacturing enterprises to perform data-driven\nclassification-based anomaly detection based on the sensor data collected in\nmanufacturing processes. However, one critical challenge is that newly\npresented defect category may manifest as the manufacturing process continues,\nresulting in monitoring performance deterioration of previously trained machine\nlearning models. Hence, there is an increasing need for empowering machine\nlearning models to learn continually. Among all continual learning methods,\nmemory-based continual learning has the best performance but faces the\nconstraints of data storage capacity. To address this issue, this paper\ndevelops a novel pseudo replay-based continual learning framework by\nintegrating class incremental learning and oversampling-based data generation.\nWithout storing all the data, the developed framework could generate\nhigh-quality data representing previous classes to train machine learning model\nincrementally when new category anomaly occurs. In addition, it could even\nenhance the monitoring performance since it also effectively improves the data\nquality. The effectiveness of the proposed framework is validated in three\ncases studies, which leverages supervised classification problem for anomaly\ndetection. The experimental results show that the developed method is very\npromising in detecting novel anomaly while maintaining a good performance on\nthe previous task and brings up more flexibility in model architecture.\n", "date": "2023-12-05", "categories": []}
{"id": "2404.19247", "title": "Improved AutoEncoder with LSTM module and KL divergence", "abstract": "  The task of anomaly detection is to separate anomalous data from normal data\nin the dataset. Models such as deep convolutional autoencoder (CAE) network and\ndeep supporting vector data description (SVDD) model have been universally\nemployed and have demonstrated significant success in detecting anomalies.\nHowever, the over-reconstruction ability of CAE network for anomalous data can\neasily lead to high false negative rate in detecting anomalous data. On the\nother hand, the deep SVDD model has the drawback of feature collapse, which\nleads to a decrease of detection accuracy for anomalies. To address these\nproblems, we propose the Improved AutoEncoder with LSTM module and\nKullback-Leibler divergence (IAE-LSTM-KL) model in this paper. An LSTM network\nis added after the encoder to memorize feature representations of normal data.\nIn the meanwhile, the phenomenon of feature collapse can also be mitigated by\npenalizing the featured input to SVDD module via KL divergence. The efficacy of\nthe IAE-LSTM-KL model is validated through experiments on both synthetic and\nreal-world datasets. Experimental results show that IAE-LSTM-KL model yields\nhigher detection accuracy for anomalies. In addition, it is also found that the\nIAE-LSTM-KL model demonstrates enhanced robustness to contaminated outliers in\nthe dataset. All code may be found at\nhttps://github.com/crazyn2/IAE-LSTM-KL_codes\n", "date": "2024-04-30", "categories": []}
{"id": "2411.10765", "title": "Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using\n  Enhanced Long Short-Term Memory Variational Autoencoder", "abstract": "  As core thermal power generation equipment, steam turbines incur significant\nexpenses and adverse effects on operation when facing interruptions like\ndowntime, maintenance, and damage. Accurate anomaly detection is the\nprerequisite for ensuring the safe and stable operation of steam turbines.\nHowever, challenges in steam turbine anomaly detection, including inherent\nanomalies, lack of temporal information analysis, and high-dimensional data\ncomplexity, limit the effectiveness of existing methods. To address these\nchallenges, we proposed an Enhanced Long Short-Term Memory Variational\nAutoencoder using Deep Advanced Features and Gaussian Mixture Model\n(ELSTMVAE-DAF-GMM) for precise unsupervised anomaly detection in unlabeled\ndatasets. Specifically, LSTMVAE, integrating LSTM with VAE, was used to project\nhigh-dimensional time-series data to a low-dimensional phase space. The Deep\nAutoencoder-Local Outlier Factor (DAE-LOF) sample selection mechanism was used\nto eliminate inherent anomalies during training, further improving the model's\nprecision and reliability. The novel deep advanced features (DAF) hybridize\nlatent embeddings and reconstruction discrepancies from the LSTMVAE model and\nprovide a more comprehensive data representation within a continuous and\nstructured phase space, significantly enhancing anomaly detection by\nsynergizing temporal dynamics with data pattern variations. These DAF were\nincorporated into GMM to ensure robust and effective unsupervised anomaly\ndetection. We utilized real operating data from industry steam turbines and\nconducted both comparison and ablation experiments, demonstrating superior\nanomaly detection outcomes characterized by high accuracy and minimal false\nalarm rates compared with existing methods.\n", "date": "2024-11-16", "categories": []}
{"id": "2411.10945", "title": "Anomaly Detection for People with Visual Impairments Using an Egocentric\n  360-Degree Camera", "abstract": "  Recent advancements in computer vision have led to a renewed interest in\ndeveloping assistive technologies for individuals with visual impairments.\nAlthough extensive research has been conducted in the field of computer\nvision-based assistive technologies, most of the focus has been on\nunderstanding contexts in images, rather than addressing their physical safety\nand security concerns. To address this challenge, we propose the first step\ntowards detecting anomalous situations for visually impaired people by\nobserving their entire surroundings using an egocentric 360-degree camera. We\nfirst introduce a novel egocentric 360-degree video dataset called VIEW360\n(Visually Impaired Equipped with Wearable 360-degree camera), which contains\nabnormal activities that visually impaired individuals may encounter, such as\nshoulder surfing and pickpocketing. Furthermore, we propose a new architecture\ncalled the FDPN (Frame and Direction Prediction Network), which facilitates\nframe-level prediction of abnormal events and identifying of their directions.\nFinally, we evaluate our approach on our VIEW360 dataset and the publicly\navailable UCF-Crime and Shanghaitech datasets, demonstrating state-of-the-art\nperformance.\n", "date": "2024-11-17", "categories": []}
{"id": "2411.11003", "title": "TeG: Temporal-Granularity Method for Anomaly Detection with Attention in\n  Smart City Surveillance", "abstract": "  Anomaly detection in video surveillance has recently gained interest from the\nresearch community. Temporal duration of anomalies vary within video streams,\nleading to complications in learning the temporal dynamics of specific events.\nThis paper presents a temporal-granularity method for an anomaly detection\nmodel (TeG) in real-world surveillance, combining spatio-temporal features at\ndifferent time-scales. The TeG model employs multi-head cross-attention blocks\nand multi-head self-attention blocks for this purpose. Additionally, we extend\nthe UCF-Crime dataset with new anomaly types relevant to Smart City research\nproject. The TeG model is deployed and validated in a city surveillance system,\nachieving successful real-time results in industrial settings.\n", "date": "2024-11-17", "categories": []}
{"id": "2410.17142", "title": "Coniferest: a complete active anomaly detection framework", "abstract": "  We present coniferest, an open source generic purpose active anomaly\ndetection framework written in Python. The package design and implemented\nalgorithms are described. Currently, static outlier detection analysis is\nsupported via the Isolation forest algorithm. Moreover, Active Anomaly\nDiscovery (AAD) and Pineforest algorithms are available to tackle active\nanomaly detection problems. The algorithms and package performance are\nevaluated on a series of synthetic datasets. We also describe a few success\ncases which resulted from applying the package to real astronomical data in\nactive anomaly detection tasks within the SNAD project.\n", "date": "2024-10-22", "categories": []}
{"id": "2411.09844", "title": "Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire\n  Prediction", "abstract": "  Wildfires pose a significantly increasing hazard to global ecosystems due to\nthe climate crisis. Due to its complex nature, there is an urgent need for\ninnovative approaches to wildfire prediction, such as machine learning. This\nresearch took a unique approach, differentiating from classical supervised\nlearning, and addressed the gap in unsupervised wildfire prediction using\nautoencoders and clustering techniques for anomaly detection. Historical\nweather and normalised difference vegetation index datasets of Australia for\n2005 - 2021 were utilised. Two main unsupervised approaches were analysed. The\nfirst used a deep autoencoder to obtain latent features, which were then fed\ninto clustering models, isolation forest, local outlier factor and one-class\nSVM for anomaly detection. The second approach used a deep autoencoder to\nreconstruct the input data and use reconstruction errors to identify anomalies.\nLong Short-Term Memory (LSTM) autoencoders and fully connected (FC)\nautoencoders were employed in this part, both in an unsupervised way learning\nonly from nominal data. The FC autoencoder outperformed its counterparts,\nachieving an accuracy of 0.71, an F1-score of 0.74, and an MCC of 0.42. These\nfindings highlight the practicality of this method, as it effectively predicts\nwildfires in the absence of ground truth, utilising an unsupervised learning\ntechnique.\n", "date": "2024-11-14", "categories": []}
{"id": "2411.09310", "title": "Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are\n  We There Yet?", "abstract": "  Zero-shot anomaly detection (ZSAD) offers potential for identifying anomalies\nin medical imaging without task-specific training. In this paper, we evaluate\nCLIP-based models, originally developed for industrial tasks, on brain tumor\ndetection using the BraTS-MET dataset. Our analysis examines their ability to\ndetect medical-specific anomalies with no or minimal supervision, addressing\nthe challenges posed by limited data annotation. While these models show\npromise in transferring general knowledge to medical tasks, their performance\nfalls short of the precision required for clinical use. Our findings highlight\nthe need for further adaptation before CLIP-based models can be reliably\napplied to medical anomaly detection.\n", "date": "2024-11-14", "categories": []}
{"id": "2411.09558", "title": "Adaptive Deviation Learning for Visual Anomaly Detection with Data\n  Contamination", "abstract": "  Visual anomaly detection targets to detect images that notably differ from\nnormal pattern, and it has found extensive application in identifying defective\nparts within the manufacturing industry. These anomaly detection paradigms\npredominantly focus on training detection models using only clean, unlabeled\nnormal samples, assuming an absence of contamination; a condition often unmet\nin real-world scenarios. The performance of these methods significantly depends\non the quality of the data and usually decreases when exposed to noise. We\nintroduce a systematic adaptive method that employs deviation learning to\ncompute anomaly scores end-to-end while addressing data contamination by\nassigning relative importance to the weights of individual instances. In this\napproach, the anomaly scores for normal instances are designed to approximate\nscalar scores obtained from the known prior distribution. Meanwhile, anomaly\nscores for anomaly examples are adjusted to exhibit statistically significant\ndeviations from these reference scores. Our approach incorporates a constrained\noptimization problem within the deviation learning framework to update instance\nweights, resolving this problem for each mini-batch. Comprehensive experiments\non the MVTec and VisA benchmark datasets indicate that our proposed method\nsurpasses competing techniques and exhibits both stability and robustness in\nthe presence of data contamination.\n", "date": "2024-11-14", "categories": []}
{"id": "2411.08755", "title": "Weakly-Supervised Anomaly Detection in Surveillance Videos Based on\n  Two-Stream I3D Convolution Network", "abstract": "  The widespread implementation of urban surveillance systems has necessitated\nmore sophisticated techniques for anomaly detection to ensure enhanced public\nsafety. This paper presents a significant advancement in the field of anomaly\ndetection through the application of Two-Stream Inflated 3D (I3D) Convolutional\nNetworks. These networks substantially outperform traditional 3D Convolutional\nNetworks (C3D) by more effectively extracting spatial and temporal features\nfrom surveillance videos, thus improving the precision of anomaly detection.\nOur research advances the field by implementing a weakly supervised learning\nframework based on Multiple Instance Learning (MIL), which uniquely\nconceptualizes surveillance videos as collections of 'bags' that contain\ninstances (video clips). Each instance is innovatively processed through a\nranking mechanism that prioritizes clips based on their potential to display\nanomalies. This novel strategy not only enhances the accuracy and precision of\nanomaly detection but also significantly diminishes the dependency on extensive\nmanual annotations. Moreover, through meticulous optimization of model\nsettings, including the choice of optimizer, our approach not only establishes\nnew benchmarks in the performance of anomaly detection systems but also offers\na scalable and efficient solution for real-world surveillance applications.\nThis paper contributes significantly to the field of computer vision by\ndelivering a more adaptable, efficient, and context-aware anomaly detection\nsystem, which is poised to redefine practices in urban surveillance.\n", "date": "2024-11-13", "categories": []}
{"id": "2411.08867", "title": "Unsupervised Parameter-free Outlier Detection using HDBSCAN* Outlier\n  Profiles", "abstract": "  In machine learning and data mining, outliers are data points that\nsignificantly differ from the dataset and often introduce irrelevant\ninformation that can induce bias in its statistics and models. Therefore,\nunsupervised methods are crucial to detect outliers if there is limited or no\ninformation about them. Global-Local Outlier Scores based on Hierarchies\n(GLOSH) is an unsupervised outlier detection method within HDBSCAN*, a\nstate-of-the-art hierarchical clustering method. GLOSH estimates outlier scores\nfor each data point by comparing its density to the highest density of the\nregion they reside in the HDBSCAN* hierarchy. GLOSH may be sensitive to\nHDBSCAN*'s minpts parameter that influences density estimation. With limited\nknowledge about the data, choosing an appropriate minpts value beforehand is\nchallenging as one or some minpts values may better represent the underlying\ncluster structure than others. Additionally, in the process of searching for\n``potential outliers'', one has to define the number of outliers n a dataset\nhas, which may be impractical and is often unknown. In this paper, we propose\nan unsupervised strategy to find the ``best'' minpts value, leveraging the\nrange of GLOSH scores across minpts values to identify the value for which\nGLOSH scores can best identify outliers from the rest of the dataset. Moreover,\nwe propose an unsupervised strategy to estimate a threshold for classifying\npoints into inliers and (potential) outliers without the need to pre-define any\nvalue. Our experiments show that our strategies can automatically find the\nminpts value and threshold that yield the best or near best outlier detection\nresults using GLOSH.\n", "date": "2024-11-13", "categories": []}
{"id": "2411.07314", "title": "Anomaly Detection in OKTA Logs using Autoencoders", "abstract": "  Okta logs are used today to detect cybersecurity events using various\nrule-based models with restricted look back periods. These functions have\nlimitations, such as a limited retrospective analysis, a predefined rule set,\nand susceptibility to generating false positives. To address this, we adopt\nunsupervised techniques, specifically employing autoencoders. To properly use\nan autoencoder, we need to transform and simplify the complexity of the log\ndata we receive from our users. This transformed and filtered data is then fed\ninto the autoencoder, and the output is evaluated.\n", "date": "2024-11-11", "categories": []}
{"id": "2411.07753", "title": "Spatially Regularized Graph Attention Autoencoder Framework for\n  Detecting Rainfall Extremes", "abstract": "  We introduce a novel Graph Attention Autoencoder (GAE) with spatial\nregularization to address the challenge of scalable anomaly detection in\nspatiotemporal rainfall data across India from 1990 to 2015. Our model\nleverages a Graph Attention Network (GAT) to capture spatial dependencies and\ntemporal dynamics in the data, further enhanced by a spatial regularization\nterm ensuring geographic coherence. We construct two graph datasets employing\nrainfall, pressure, and temperature attributes from the Indian Meteorological\nDepartment and ERA5 Reanalysis on Single Levels, respectively. Our network\noperates on graph representations of the data, where nodes represent geographic\nlocations, and edges, inferred through event synchronization, denote\nsignificant co-occurrences of rainfall events. Through extensive experiments,\nwe demonstrate that our GAE effectively identifies anomalous rainfall patterns\nacross the Indian landscape. Our work paves the way for sophisticated\nspatiotemporal anomaly detection methodologies in climate science, contributing\nto better climate change preparedness and response strategies.\n", "date": "2024-11-12", "categories": []}
{"id": "2408.06121", "title": "A Methodological Report on Anomaly Detection on Dynamic Knowledge Graphs", "abstract": "  In this paper, we explore different approaches to anomaly detection on\ndynamic knowledge graphs, specifically in a Micro-services environment for\nKubernetes applications. Our approach explores three dynamic knowledge graph\nrepresentations: sequential data, hierarchical data and inter-service\ndependency data, with each representation incorporating increasingly complex\nstructural information of dynamic knowledge graph. Different machine learning\nand deep learning models are tested on these representations. We empirically\nanalyse their performance and propose an approach based on ensemble learning of\nthese models. Our approach significantly outperforms the baseline on the ISWC\n2024 Dynamic Knowledge Graph Anomaly Detection dataset, providing a robust\nsolution for anomaly detection in dynamic complex data.\n", "date": "2024-08-12", "categories": []}
{"id": "2408.16201", "title": "Uni-3DAD: GAN-Inversion Aided Universal 3D Anomaly Detection on\n  Model-free Products", "abstract": "  Anomaly detection is a long-standing challenge in manufacturing systems.\nTraditionally, anomaly detection has relied on human inspectors. However, 3D\npoint clouds have gained attention due to their robustness to environmental\nfactors and their ability to represent geometric data. Existing 3D anomaly\ndetection methods generally fall into two categories. One compares scanned 3D\npoint clouds with design files, assuming these files are always available.\nHowever, such assumptions are often violated in many real-world applications\nwhere model-free products exist, such as fresh produce (i.e., ``Cookie\",\n``Potato\", etc.), dentures, bone, etc. The other category compares patches of\nscanned 3D point clouds with a library of normal patches named memory bank.\nHowever, those methods usually fail to detect incomplete shapes, which is a\nfairly common defect type (i.e., missing pieces of different products). The\nmain challenge is that missing areas in 3D point clouds represent the absence\nof scanned points. This makes it infeasible to compare the missing region with\nexisting point cloud patches in the memory bank. To address these two\nchallenges, we proposed a unified, unsupervised 3D anomaly detection framework\ncapable of identifying all types of defects on model-free products. Our method\nintegrates two detection modules: a feature-based detection module and a\nreconstruction-based detection module. Feature-based detection covers geometric\ndefects, such as dents, holes, and cracks, while the reconstruction-based\nmethod detects missing regions. Additionally, we employ a One-class Support\nVector Machine (OCSVM) to fuse the detection results from both modules. The\nresults demonstrate that (1) our proposed method outperforms the\nstate-of-the-art methods in identifying incomplete shapes and (2) it still\nmaintains comparable performance with the SOTA methods in detecting all other\ntypes of anomalies.\n", "date": "2024-08-29", "categories": []}
{"id": "2411.05822", "title": "SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in\n  Industrial applications", "abstract": "  In this paper, we propose SPACE, a novel anomaly detection methodology that\nintegrates a Feature Encoder (FE) into the structure of the Student-Teacher\nmethod. The proposed method has two key elements: Spatial Consistency\nregularization Loss (SCL) and Feature converter Module (FM). SCL prevents\noverfitting in student models by avoiding excessive imitation of the teacher\nmodel. Simultaneously, it facilitates the expansion of normal data features by\nsteering clear of abnormal areas generated through data augmentation. This dual\nfunctionality ensures a robust boundary between normal and abnormal data. The\nFM prevents the learning of ambiguous information from the FE. This protects\nthe learned features and enables more effective detection of structural and\nlogical anomalies. Through these elements, SPACE is available to minimize the\ninfluence of the FE while integrating various data augmentations.In this study,\nwe evaluated the proposed method on the MVTec LOCO, MVTec AD, and VisA\ndatasets. Experimental results, through qualitative evaluation, demonstrate the\nsuperiority of detection and efficiency of each module compared to\nstate-of-the-art methods.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.06214", "title": "Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model", "abstract": "  Natural gas pipeline leaks pose severe risks, leading to substantial economic\nlosses and potential hazards to human safety. In this study, we develop an\naccurate model for the early prediction of pipeline leaks. To the best of our\nknowledge, unlike previous anomaly detection, this is the first application to\nuse internal pipeline data for early prediction of leaks. The modeling process\naddresses two main challenges: long-term dependencies and sample imbalance.\nFirst, we introduce a dilated convolution-based prediction model to capture\nlong-term dependencies, as dilated convolution expands the model's receptive\nfield without added computational cost. Second, to mitigate sample imbalance,\nwe propose the MKTCN model, which incorporates the Kolmogorov-Arnold Network as\nthe fully connected layer in a dilated convolution model, enhancing network\ngeneralization. Finally, we validate the MKTCN model through extensive\nexperiments on two real-world datasets. Results demonstrate that MKTCN\noutperforms in generalization and classification, particularly under severe\ndata imbalance, and effectively predicts leaks up to 5000 seconds in advance.\nOverall, the MKTCN model represents a significant advancement in early pipeline\nleak prediction, providing robust generalization and improved modeling of the\nlong-term dependencies inherent in multi-dimensional time-series data.\n", "date": "2024-11-09", "categories": []}
{"id": "2411.06427", "title": "UniGAD: Unifying Multi-level Graph Anomaly Detection", "abstract": "  Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or\nsuspicious objects within graph-structured data. Existing methods generally\nfocus on a single graph object type (node, edge, graph, etc.) and often\noverlook the inherent connections among different object types of graph\nanomalies. For instance, a money laundering transaction might involve an\nabnormal account and the broader community it interacts with. To address this,\nwe present UniGAD, the first unified framework for detecting anomalies at node,\nedge, and graph levels jointly. Specifically, we develop the Maximum Rayleigh\nQuotient Subgraph Sampler (MRQSampler) that unifies multi-level formats by\ntransferring objects at each level into graph-level tasks on subgraphs. We\ntheoretically prove that MRQSampler maximizes the accumulated spectral energy\nof subgraphs (i.e., the Rayleigh quotient) to preserve the most significant\nanomaly information. To further unify multi-level training, we introduce a\nnovel GraphStitch Network to integrate information across different levels,\nadjust the amount of sharing required at each level, and harmonize conflicting\ntraining goals. Comprehensive experiments show that UniGAD outperforms both\nexisting GAD methods specialized for a single task and graph prompt-based\napproaches for multiple tasks, while also providing robust zero-shot task\ntransferability. All codes can be found at\nhttps://github.com/lllyyq1121/UniGAD.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.07089", "title": "Towards Characterizing Cyber Networks with Large Language Models", "abstract": "  Threat hunting analyzes large, noisy, high-dimensional data to find sparse\nadversarial behavior. We believe adversarial activities, however they are\ndisguised, are extremely difficult to completely obscure in high dimensional\nspace. In this paper, we employ these latent features of cyber data to find\nanomalies via a prototype tool called Cyber Log Embeddings Model (CLEM). CLEM\nwas trained on Zeek network traffic logs from both a real-world production\nnetwork and an from Internet of Things (IoT) cybersecurity testbed. The model\nis deliberately overtrained on a sliding window of data to characterize each\nwindow closely. We use the Adjusted Rand Index (ARI) to comparing the k-means\nclustering of CLEM output to expert labeling of the embeddings. Our approach\ndemonstrates that there is promise in using natural language modeling to\nunderstand cyber data.\n", "date": "2024-11-11", "categories": []}
{"id": "2407.06000", "title": "Bounding Boxes and Probabilistic Graphical Models: Video Anomaly\n  Detection Simplified", "abstract": "  In this study, we formulate the task of Video Anomaly Detection as a\nprobabilistic analysis of object bounding boxes. We hypothesize that the\nrepresentation of objects via their bounding boxes only, can be sufficient to\nsuccessfully identify anomalous events in a scene. The implied value of this\napproach is increased object anonymization, faster model training and fewer\ncomputational resources. This can particularly benefit applications within\nvideo surveillance running on edge devices such as cameras. We design our model\nbased on human reasoning which lends itself to explaining model output in\nhuman-understandable terms. Meanwhile, the slowest model trains within less\nthan 7 seconds on a 11th Generation Intel Core i9 Processor. While our approach\nconstitutes a drastic reduction of problem feature space in comparison with\nprior art, we show that this does not result in a reduction in performance: the\nresults we report are highly competitive on the benchmark datasets CUHK Avenue\nand ShanghaiTech, and significantly exceed on the latest State-of-the-Art\nresults on StreetScene, which has so far proven to be the most challenging VAD\ndataset.\n", "date": "2024-07-08", "categories": []}
{"id": "2411.05596", "title": "Machine learning-driven Anomaly Detection and Forecasting for Euclid\n  Space Telescope Operations", "abstract": "  State-of-the-art space science missions increasingly rely on automation due\nto spacecraft complexity and the costs of human oversight. The high volume of\ndata, including scientific and telemetry data, makes manual inspection\nchallenging. Machine learning offers significant potential to meet these\ndemands.\n  The Euclid space telescope, in its survey phase since February 2024,\nexemplifies this shift. Euclid's success depends on accurate monitoring and\ninterpretation of housekeeping telemetry and science-derived data. Thousands of\ntelemetry parameters, monitored as time series, may or may not impact the\nquality of scientific data. These parameters have complex interdependencies,\noften due to physical relationships (e.g., proximity of temperature sensors).\nOptimising science operations requires careful anomaly detection and\nidentification of hidden parameter states. Moreover, understanding the\ninteractions between known anomalies and physical quantities is crucial yet\ncomplex, as related parameters may display anomalies with varied timing and\nintensity.\n  We address these challenges by analysing temperature anomalies in Euclid's\ntelemetry from February to August 2024, focusing on eleven temperature\nparameters and 35 covariates. We use a predictive XGBoost model to forecast\ntemperatures based on historical values, detecting anomalies as deviations from\npredictions. A second XGBoost model predicts anomalies from covariates,\ncapturing their relationships to temperature anomalies. We identify the top\nthree anomalies per parameter and analyse their interactions with covariates\nusing SHAP (Shapley Additive Explanations), enabling rapid, automated analysis\nof complex parameter relationships.\n  Our method demonstrates how machine learning can enhance telemetry\nmonitoring, offering scalable solutions for other missions with similar data\nchallenges.\n", "date": "2024-11-08", "categories": []}
{"id": "2307.10869", "title": "Identifying Performance Issues in Cloud Service Systems Based on\n  Relational-Temporal Features", "abstract": "  Cloud systems are susceptible to performance issues, which may cause\nservice-level agreement violations and financial losses. In current practice,\ncrucial metrics are monitored periodically to provide insight into the\noperational status of components. Identifying performance issues is often\nformulated as an anomaly detection problem, which is tackled by analyzing each\nmetric independently. However, this approach overlooks the complex dependencies\nexisting among cloud components. Some graph neural network-based methods take\nboth temporal and relational information into account, however, the correlation\nviolations in the metrics that serve as indicators of underlying performance\nissues are difficult for them to identify. Furthermore, a large volume of\ncomponents in a cloud system results in a vast array of noisy metrics. This\ncomplexity renders it impractical for engineers to fully comprehend the\ncorrelations, making it challenging to identify performance issues accurately.\nTo address these limitations, we propose Identifying Performance Issues based\non Relational-Temporal Features (ISOLATE ), a learning-based approach that\nleverages both the relational and temporal features of metrics to identify\nperformance issues. In particular, it adopts a graph neural network with\nattention to characterizing the relations among metrics and extracts long-term\nand multi-scale temporal patterns using a GRU and a convolution network,\nrespectively. The learned graph attention weights can be further used to\nlocalize the correlation-violated metrics. Moreover, to relieve the impact of\nnoisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags\npseudo-labels based on a small portion of confirmed negative examples.\nExtensive evaluation on both public and industrial datasets shows that ISOLATE\noutperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3.\n", "date": "2023-07-20", "categories": []}
{"id": "2411.04422", "title": "Unsupervised Abnormal Stop Detection for Long Distance Coaches with\n  Low-Frequency GPS", "abstract": "  In our urban life, long distance coaches supply a convenient yet economic\napproach to the transportation of the public. One notable problem is to\ndiscover the abnormal stop of the coaches due to the important reason, i.e.,\nillegal pick up on the way which possibly endangers the safety of passengers.\nIt has become a pressing issue to detect the coach abnormal stop with\nlow-quality GPS. In this paper, we propose an unsupervised method that helps\ntransportation managers to efficiently discover the Abnormal Stop Detection\n(ASD) for long distance coaches. Concretely, our method converts the ASD\nproblem into an unsupervised clustering framework in which both the normal stop\nand the abnormal one are decomposed. Firstly, we propose a stop duration model\nfor the low frequency GPS based on the assumption that a coach changes speed\napproximately in a linear approach. Secondly, we strip the abnormal stops from\nthe normal stop points by the low rank assumption. The proposed method is\nconceptually simple yet efficient, by leveraging low rank assumption to handle\nnormal stop points, our approach enables domain experts to discover the ASD for\ncoaches, from a case study motivated by traffic managers. Datset and code are\npublicly available at: https://github.com/pangjunbiao/IPPs.\n", "date": "2024-11-07", "categories": []}
{"id": "2411.03365", "title": "Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN\n  Autoencoder Approach for Spectral Intrusion Analysis", "abstract": "  In the rapidly evolving landscape of 5G technology, safeguarding Radio\nFrequency (RF) environments against sophisticated intrusions is paramount,\nespecially in dynamic spectrum access and management. This paper presents an\nenhanced experimental model that integrates a self-attention mechanism with a\nRecurrent Neural Network (RNN)-based autoencoder for the detection of anomalous\nspectral activities in 5G networks at the waveform level. Our approach,\ngrounded in time-series analysis, processes in-phase and quadrature (I/Q)\nsamples to identify irregularities that could indicate potential jamming\nattacks. The model's architecture, augmented with a self-attention layer,\nextends the capabilities of RNN autoencoders, enabling a more nuanced\nunderstanding of temporal dependencies and contextual relationships within the\nRF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed\nconstructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a\ncomprehensive stream of data that reflects real-world RF spectrum conditions\nand attack scenarios. The model is trained to reconstruct standard signal\nbehavior, establishing a normative baseline against which deviations,\nindicative of security threats, are identified. The proposed architecture is\ndesigned to balance between detection precision and computational efficiency,\nso the LSTM network, enriched with self-attention, continues to optimize for\nminimal execution latency and power consumption. Conducted on a real-world\nSDR-based testbed, our results demonstrate the model's improved performance and\naccuracy in threat detection.\n  Keywords: self-attention, real-time intrusion detection, RNN autoencoder,\nTransformer architecture, LSTM, time series anomaly detection, 5G Security,\nspectrum access security.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.02465", "title": "See it, Think it, Sorted: Large Multimodal Models are Few-shot Time\n  Series Anomaly Analyzers", "abstract": "  Time series anomaly detection (TSAD) is becoming increasingly vital due to\nthe rapid growth of time series data across various sectors. Anomalies in web\nservice data, for example, can signal critical incidents such as system\nfailures or server malfunctions, necessitating timely detection and response.\nHowever, most existing TSAD methodologies rely heavily on manual feature\nengineering or require extensive labeled training data, while also offering\nlimited interpretability. To address these challenges, we introduce a\npioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA),\nwhich leverages the power of Large Multimodal Models (LMMs) to enhance both the\ndetection and interpretation of anomalies in time series data. By converting\ntime series into visual formats that LMMs can efficiently process, TAMA\nleverages few-shot in-context learning capabilities to reduce dependence on\nextensive labeled datasets. Our methodology is validated through rigorous\nexperimentation on multiple real-world datasets, where TAMA consistently\noutperforms state-of-the-art methods in TSAD tasks. Additionally, TAMA provides\nrich, natural language-based semantic analysis, offering deeper insights into\nthe nature of detected anomalies. Furthermore, we contribute one of the first\nopen-source datasets that includes anomaly detection labels, anomaly type\nlabels, and contextual description, facilitating broader exploration and\nadvancement within this critical field. Ultimately, TAMA not only excels in\nanomaly detection but also provides a comprehensive approach for understanding\nthe underlying causes of anomalies, pushing TSAD forward through innovative\nmethodologies and insights.\n", "date": "2024-11-04", "categories": []}
{"id": "2207.10286", "title": "Comparative Study on Supervised versus Semi-supervised Machine Learning\n  for Anomaly Detection of In-vehicle CAN Network", "abstract": "  As the central nerve of the intelligent vehicle control system, the\nin-vehicle network bus is crucial to the security of vehicle driving. One of\nthe best standards for the in-vehicle network is the Controller Area Network\n(CAN bus) protocol. However, the CAN bus is designed to be vulnerable to\nvarious attacks due to its lack of security mechanisms. To enhance the security\nof in-vehicle networks and promote the research in this area, based upon a\nlarge scale of CAN network traffic data with the extracted valuable features,\nthis study comprehensively compared fully-supervised machine learning with\nsemi-supervised machine learning methods for CAN message anomaly detection.\nBoth traditional machine learning models (including single classifier and\nensemble models) and neural network based deep learning models are evaluated.\nFurthermore, this study proposed a deep autoencoder based semi-supervised\nlearning method applied for CAN message anomaly detection and verified its\nsuperiority over other semi-supervised methods. Extensive experiments show that\nthe fully-supervised methods generally outperform semi-supervised ones as they\nare using more information as inputs. Typically the developed XGBoost based\nmodel obtained state-of-the-art performance with the best accuracy (98.65%),\nprecision (0.9853), and ROC AUC (0.9585) beating other methods reported in the\nliterature.\n", "date": "2022-07-21", "categories": []}
{"id": "2306.02709", "title": "Comparative Study on Semi-supervised Learning Applied for Anomaly\n  Detection in Hydraulic Condition Monitoring System", "abstract": "  Condition-based maintenance is becoming increasingly important in hydraulic\nsystems. However, anomaly detection for these systems remains challenging,\nespecially since that anomalous data is scarce and labeling such data is\ntedious and even dangerous. Therefore, it is advisable to make use of\nunsupervised or semi-supervised methods, especially for semi-supervised\nlearning which utilizes unsupervised learning as a feature extraction mechanism\nto aid the supervised part when only a small number of labels are available.\nThis study systematically compares semi-supervised learning methods applied for\nanomaly detection in hydraulic condition monitoring systems. Firstly, thorough\ndata analysis and feature learning were carried out to understand the\nopen-sourced hydraulic condition monitoring dataset. Then, various methods were\nimplemented and evaluated including traditional stand-alone semi-supervised\nlearning models (e.g., one-class SVM, Robust Covariance), ensemble models\n(e.g., Isolation Forest), and deep neural network based models (e.g.,\nautoencoder, Hierarchical Extreme Learning Machine (HELM)). Typically, this\nstudy customized and implemented an extreme learning machine based\nsemi-supervised HELM model and verified its superiority over other\nsemi-supervised methods. Extensive experiments show that the customized HELM\nmodel obtained state-of-the-art performance with the highest accuracy (99.5%),\nthe lowest false positive rate (0.015), and the best F1-score (0.985) beating\nother semi-supervised methods.\n", "date": "2023-06-05", "categories": []}
{"id": "2409.18427", "title": "Neural Collaborative Filtering to Detect Anomalies in Human Semantic\n  Trajectories", "abstract": "  Human trajectory anomaly detection has become increasingly important across a\nwide range of applications, including security surveillance and public health.\nHowever, existing trajectory anomaly detection methods are primarily focused on\nvehicle-level traffic, while human-level trajectory anomaly detection remains\nunder-explored. Since human trajectory data is often very sparse, machine\nlearning methods have become the preferred approach for identifying complex\npatterns. However, concerns regarding potential biases and the robustness of\nthese models have intensified the demand for more transparent and explainable\nalternatives. In response to these challenges, our research focuses on\ndeveloping a lightweight anomaly detection model specifically designed to\ndetect anomalies in human trajectories. We propose a Neural Collaborative\nFiltering approach to model and predict normal mobility. Our method is designed\nto model users' daily patterns of life without requiring prior knowledge,\nthereby enhancing performance in scenarios where data is sparse or incomplete,\nsuch as in cold start situations. Our algorithm consists of two main modules.\nThe first is the collaborative filtering module, which applies collaborative\nfiltering to model normal mobility of individual humans to places of interest.\nThe second is the neural module, responsible for interpreting the complex\nspatio-temporal relationships inherent in human trajectory data. To validate\nour approach, we conducted extensive experiments using simulated and real-world\ndatasets comparing to numerous state-of-the-art trajectory anomaly detection\napproaches.\n", "date": "2024-09-27", "categories": []}
{"id": "2411.00914", "title": "AAD-LLM: Adaptive Anomaly Detection Using Large Language Models", "abstract": "  For data-constrained, complex and dynamic industrial environments, there is a\ncritical need for transferable and multimodal methodologies to enhance anomaly\ndetection and therefore, prevent costs associated with system failures.\nTypically, traditional PdM approaches are not transferable or multimodal. This\nwork examines the use of Large Language Models (LLMs) for anomaly detection in\ncomplex and dynamic manufacturing systems. The research aims to improve the\ntransferability of anomaly detection models by leveraging Large Language Models\n(LLMs) and seeks to validate the enhanced effectiveness of the proposed\napproach in data-sparse industrial applications. The research also seeks to\nenable more collaborative decision-making between the model and plant operators\nby allowing for the enriching of input series data with semantics.\nAdditionally, the research aims to address the issue of concept drift in\ndynamic industrial settings by integrating an adaptability mechanism. The\nliterature review examines the latest developments in LLM time series tasks\nalongside associated adaptive anomaly detection methods to establish a robust\ntheoretical framework for the proposed architecture. This paper presents a\nnovel model framework (AAD-LLM) that doesn't require any training or finetuning\non the dataset it is applied to and is multimodal. Results suggest that anomaly\ndetection can be converted into a \"language\" task to deliver effective,\ncontext-aware detection in data-constrained industrial applications. This work,\ntherefore, contributes significantly to advancements in anomaly detection\nmethodologies.\n", "date": "2024-11-01", "categories": []}
{"id": "2411.01817", "title": "High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A\n  Novel Approach", "abstract": "  Graph Convolutional Network (GCN) are widely used in Graph Anomaly Detection\n(GAD) due to their natural compatibility with graph structures, resulting in\nsignificant performance improvements. However, most researchers approach GAD as\na graph node classification task and often rely on low-pass filters or feature\naggregation from neighboring nodes. This paper proposes a novel approach by\nintroducing a High-Pass Graph Convolution Network (HP-GCN) for GAD. The\nproposed HP-GCN leverages high-frequency components to detect anomalies, as\nanomalies tend to increase high-frequency signals within the network of normal\nnodes. Additionally, isolated nodes, which lack interactions with other nodes,\npresent a challenge for Graph Neural Network (GNN). To address this, the model\nsegments the graph into isolated nodes and nodes within connected subgraphs.\nIsolated nodes learn their features through Multi-Layer Perceptron (MLP),\nenhancing detection accuracy. The model is evaluated and validated on YelpChi,\nAmazon, T-Finance, and T-Social datasets. The results showed that the proposed\nHP-GCN can achieve anomaly detection accuracy of 96.10%, 98.16%, 96.46%, and\n98.94%, respectively. The findings demonstrate that the HP-GCN outperforms\nexisting GAD methods based on spatial domain GNN as well as those using\nlow-pass and band-pass filters in spectral domain GCN. The findings underscore\nthe effectiveness of this method in improving anomaly detection performance.\nSource code can be found at: https://github.com/meteor0033/High-pass_GAD.git.\n", "date": "2024-11-04", "categories": []}
{"id": "2405.14755", "title": "Large language models can be zero-shot anomaly detectors for time\n  series?", "abstract": "  Recent studies have shown the ability of large language models to perform a\nvariety of tasks, including time series forecasting. The flexible nature of\nthese models allows them to be used for many applications. In this paper, we\npresent a novel study of large language models used for the challenging task of\ntime series anomaly detection. This problem entails two aspects novel for LLMs:\nthe need for the model to identify part of the input sequence (or multiple\nparts) as anomalous; and the need for it to work with time series data rather\nthan the traditional text input. We introduce sigllm, a framework for time\nseries anomaly detection using large language models. Our framework includes a\ntime-series-to-text conversion module, as well as end-to-end pipelines that\nprompt language models to perform time series anomaly detection. We investigate\ntwo paradigms for testing the abilities of large language models to perform the\ndetection task. First, we present a prompt-based detection method that directly\nasks a language model to indicate which elements of the input are anomalies.\nSecond, we leverage the forecasting capability of a large language model to\nguide the anomaly detection process. We evaluated our framework on 11 datasets\nspanning various sources and 10 pipelines. We show that the forecasting method\nsignificantly outperformed the prompting method in all 11 datasets with respect\nto the F1 score. Moreover, while large language models are capable of finding\nanomalies, state-of-the-art deep learning models are still superior in\nperformance, achieving results 30% better than large language models.\n", "date": "2024-05-23", "categories": []}
{"id": "2402.04857", "title": "Advancing Video Anomaly Detection: A Concise Review and a New Dataset", "abstract": "  Video Anomaly Detection (VAD) finds widespread applications in security\nsurveillance, traffic monitoring, industrial monitoring, and healthcare.\nDespite extensive research efforts, there remains a lack of concise reviews\nthat provide insightful guidance for researchers. Such reviews would serve as\nquick references to grasp current challenges, research trends, and future\ndirections. In this paper, we present such a review, examining models and\ndatasets from various perspectives. We emphasize the critical relationship\nbetween model and dataset, where the quality and diversity of datasets\nprofoundly influence model performance, and dataset development adapts to the\nevolving needs of emerging approaches. Our review identifies practical issues,\nincluding the absence of comprehensive datasets with diverse scenarios. To\naddress this, we introduce a new dataset, Multi-Scenario Anomaly Detection\n(MSAD), comprising 14 distinct scenarios captured from various camera views.\nOur dataset has diverse motion patterns and challenging variations, such as\ndifferent lighting and weather conditions, providing a robust foundation for\ntraining superior models. We conduct an in-depth analysis of recent\nrepresentative models using MSAD and highlight its potential in addressing the\nchallenges of detecting anomalies across diverse and evolving surveillance\nscenarios. [Project website: https://msad-dataset.github.io/]\n", "date": "2024-02-07", "categories": []}
{"id": "2410.24178", "title": "AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal\n  Properties", "abstract": "  Anomaly detection is widely used for identifying critical errors and\nsuspicious behaviors, but current methods lack interpretability. We leverage\ncommon properties of existing methods and recent advances in generative models\nto introduce counterfactual explanations for anomaly detection. Given an input,\nwe generate its counterfactual as a diffusion-based repair that shows what a\nnon-anomalous version should have looked like. A key advantage of this approach\nis that it enables a domain-independent formal specification of explainability\ndesiderata, offering a unified framework for generating and evaluating\nexplanations. We demonstrate the effectiveness of our anomaly explainability\nframework, AR-Pro, on vision (MVTec, VisA) and time-series (SWaT, WADI, HAI)\nanomaly datasets. The code used for the experiments is accessible at:\nhttps://github.com/xjiae/arpro.\n", "date": "2024-10-31", "categories": []}
{"id": "2408.15874", "title": "Robust Statistical Scaling of Outlier Scores: Improving the Quality of\n  Outlier Probabilities for Outliers (Extended Version)", "abstract": "  Outlier detection algorithms typically assign an outlier score to each\nobservation in a dataset, indicating the degree to which an observation is an\noutlier. However, these scores are often not comparable across algorithms and\ncan be difficult for humans to interpret. Statistical scaling addresses this\nproblem by transforming outlier scores into outlier probabilities without using\nground-truth labels, thereby improving interpretability and comparability\nacross algorithms. However, the quality of this transformation can be different\nfor outliers and inliers. Missing outliers in scenarios where they are of\nparticular interest - such as healthcare, finance, or engineering - can be\ncostly or dangerous. Thus, ensuring good probabilities for outliers is\nessential. This paper argues that statistical scaling, as commonly used in the\nliterature, does not produce equally good probabilities for outliers as for\ninliers. Therefore, we propose robust statistical scaling, which uses robust\nestimators to improve the probabilities for outliers. We evaluate several\nvariants of our method against other outlier score transformations for\nreal-world datasets and outlier detection algorithms, where it can improve the\nprobabilities for outliers.\n", "date": "2024-08-28", "categories": []}
{"id": "2410.22735", "title": "MIXAD: Memory-Induced Explainable Time Series Anomaly Detection", "abstract": "  For modern industrial applications, accurately detecting and diagnosing\nanomalies in multivariate time series data is essential. Despite such need,\nmost state-of-the-art methods often prioritize detection performance over model\ninterpretability. Addressing this gap, we introduce MIXAD (Memory-Induced\nExplainable Time Series Anomaly Detection), a model designed for interpretable\nanomaly detection. MIXAD leverages a memory network alongside spatiotemporal\nprocessing units to understand the intricate dynamics and topological\nstructures inherent in sensor relationships. We also introduce a novel anomaly\nscoring method that detects significant shifts in memory activation patterns\nduring anomalies. Our approach not only ensures decent detection performance\nbut also outperforms state-of-the-art baselines by 34.30% and 34.51% in\ninterpretability metrics.\n", "date": "2024-10-30", "categories": []}
{"id": "2410.22256", "title": "Hypergraph-based multi-scale spatio-temporal graph convolution network\n  for Time-Series anomaly detection", "abstract": "  Multivariate time series anomaly detection technology plays an important role\nin many fields including aerospace, water treatment, cloud service providers,\netc. Excellent anomaly detection models can greatly improve work efficiency and\navoid major economic losses. However, with the development of technology, the\nincreasing size and complexity of data, and the lack of labels for relevant\nabnormal data, it is becoming increasingly challenging to perform effective and\naccurate anomaly detection in high-dimensional and complex data sets. In this\npaper, we propose a hypergraph based spatiotemporal graph convolutional neural\nnetwork model STGCN_Hyper, which explicitly captures high-order, multi-hop\ncorrelations between multiple variables through a hypergraph based dynamic\ngraph structure learning module. On this basis, we further use the hypergraph\nbased spatiotemporal graph convolutional network to utilize the learned\nhypergraph structure to effectively propagate and aggregate one-hop and\nmulti-hop related node information in the convolutional network, thereby\nobtaining rich spatial information. Furthermore, through the multi-scale TCN\ndilated convolution module, the STGCN_hyper model can also capture the\ndependencies of features at different scales in the temporal dimension. An\nunsupervised anomaly detector based on PCA and GMM is also integrated into the\nSTGCN_hyper model. Through the anomaly score of the detector, the model can\ndetect the anomalies in an unsupervised way. Experimental results on multiple\ntime series datasets show that our model can flexibly learn the multi-scale\ntime series features in the data and the dependencies between features, and\noutperforms most existing baseline models in terms of precision, recall,\nF1-score on anomaly detection tasks. Our code is available on:\nhttps://git.ecdf.ed.ac.uk/msc-23-24/s2044819\n", "date": "2024-10-29", "categories": []}
{"id": "2301.04660", "title": "Anomalies, Representations, and Self-Supervision", "abstract": "  We develop a self-supervised method for density-based anomaly detection using\ncontrastive learning, and test it using event-level anomaly data from CMS\nADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the\nbackground data to mimic non-Standard-Model events in a model-agnostic way. It\nuses a permutation-invariant Transformer Encoder architecture to map the\nobjects measured in a collider event to the representation space, where the\ndata augmentations define a representation space which is sensitive to\npotential anomalous features. An AutoEncoder trained on background\nrepresentations then computes anomaly scores for a variety of signals in the\nrepresentation space. With AnomalyCLR we find significant improvements on\nperformance metrics for all signals when compared to the raw data baseline.\n", "date": "2023-01-11", "categories": []}
{"id": "2312.03804", "title": "How Low Can You Go? Surfacing Prototypical In-Distribution Samples for\n  Unsupervised Anomaly Detection", "abstract": "  Unsupervised anomaly detection (UAD) alleviates large labeling efforts by\ntraining exclusively on unlabeled in-distribution data and detecting outliers\nas anomalies. Generally, the assumption prevails that large training datasets\nallow the training of higher-performing UAD models. However, in this work, we\nshow that UAD with extremely few training samples can already match -- and in\nsome cases even surpass -- the performance of training with the whole training\ndataset. Building upon this finding, we propose an unsupervised method to\nreliably identify prototypical samples to further boost UAD performance. We\ndemonstrate the utility of our method on seven different established UAD\nbenchmarks from computer vision, industrial defect detection, and medicine.\nWith just 25 selected samples, we even exceed the performance of full training\nin $25/67$ categories in these benchmarks. Additionally, we show that the\nprototypical in-distribution samples identified by our proposed method\ngeneralize well across models and datasets and that observing their sample\nselection criteria allows for a successful manual selection of small subsets of\nhigh-performing samples. Our code is available at\nhttps://anonymous.4open.science/r/uad_prototypical_samples/\n", "date": "2023-12-06", "categories": []}
{"id": "2410.20047", "title": "ResAD: A Simple Framework for Class Generalizable Anomaly Detection", "abstract": "  This paper explores the problem of class-generalizable anomaly detection,\nwhere the objective is to train one unified AD model that can generalize to\ndetect anomalies in diverse classes from different domains without any\nretraining or fine-tuning on the target data. Because normal feature\nrepresentations vary significantly across classes, this will cause the widely\nstudied one-for-one AD models to be poorly classgeneralizable (i.e.,\nperformance drops dramatically when used for new classes). In this work, we\npropose a simple but effective framework (called ResAD) that can be directly\napplied to detect anomalies in new classes. Our main insight is to learn the\nresidual feature distribution rather than the initial feature distribution. In\nthis way, we can significantly reduce feature variations. Even in new classes,\nthe distribution of normal residual features would not remarkably shift from\nthe learned distribution. Therefore, the learned model can be directly adapted\nto new classes. ResAD consists of three components: (1) a Feature Converter\nthat converts initial features into residual features; (2) a simple and shallow\nFeature Constraintor that constrains normal residual features into a spatial\nhypersphere for further reducing feature variations and maintaining consistency\nin feature scales among different classes; (3) a Feature Distribution Estimator\nthat estimates the normal residual feature distribution, anomalies can be\nrecognized as out-of-distribution. Despite the simplicity, ResAD can achieve\nremarkable anomaly detection results when directly used in new classes. The\ncode is available at https://github.com/xcyao00/ResAD.\n", "date": "2024-10-26", "categories": []}
{"id": "2410.20366", "title": "Rethinking Reconstruction-based Graph-Level Anomaly Detection:\n  Limitations and a Simple Remedy", "abstract": "  Graph autoencoders (Graph-AEs) learn representations of given graphs by\naiming to accurately reconstruct them. A notable application of Graph-AEs is\ngraph-level anomaly detection (GLAD), whose objective is to identify graphs\nwith anomalous topological structures and/or node features compared to the\nmajority of the graph population. Graph-AEs for GLAD regard a graph with a high\nmean reconstruction error (i.e. mean of errors from all node pairs and/or\nnodes) as anomalies. Namely, the methods rest on the assumption that they would\nbetter reconstruct graphs with similar characteristics to the majority. We,\nhowever, report non-trivial counter-examples, a phenomenon we call\nreconstruction flip, and highlight the limitations of the existing\nGraph-AE-based GLAD methods. Specifically, we empirically and theoretically\ninvestigate when this assumption holds and when it fails. Through our analyses,\nwe further argue that, while the reconstruction errors for a given graph are\neffective features for GLAD, leveraging the multifaceted summaries of the\nreconstruction errors, beyond just mean, can further strengthen the features.\nThus, we propose a novel and simple GLAD method, named MUSE. The key innovation\nof MUSE involves taking multifaceted summaries of reconstruction errors as\ngraph features for GLAD. This surprisingly simple method obtains SOTA\nperformance in GLAD, performing best overall among 14 methods across 10\ndatasets.\n", "date": "2024-10-27", "categories": []}
{"id": "2305.08197", "title": "A Dataset Fusion Algorithm for Generalised Anomaly Detection in\n  Homogeneous Periodic Time Series Datasets", "abstract": "  The generalisation of Neural Networks (NN) to multiple datasets is often\noverlooked in literature due to NNs typically being optimised for specific data\nsources. This becomes especially challenging in time-series-based multi-dataset\nmodels due to difficulties in fusing sequential data from different sensors and\ncollection specifications. In a commercial environment, however, generalisation\ncan effectively utilise available data and computational power, which is\nessential in the context of Green AI, the sustainable development of AI models.\nThis paper introduces \"Dataset Fusion,\" a novel dataset composition algorithm\nfor fusing periodic signals from multiple homogeneous datasets into a single\ndataset while retaining unique features for generalised anomaly detection. The\nproposed approach, tested on a case study of 3-phase current data from 2\ndifferent homogeneous Induction Motor (IM) fault datasets using an unsupervised\nLSTMCaps NN, significantly outperforms conventional training approaches with an\nAverage F1 score of 0.879 and effectively generalises across all datasets. The\nproposed approach was also tested with varying percentages of the training\ndata, in line with the principles of Green AI. Results show that using only\n6.25\\% of the training data, translating to a 93.7\\% reduction in computational\npower, results in a mere 4.04\\% decrease in performance, demonstrating the\nadvantages of the proposed approach in terms of both performance and\ncomputational efficiency. Moreover, the algorithm's effectiveness under\nnon-ideal conditions highlights its potential for practical use in real-world\napplications.\n", "date": "2023-05-14", "categories": []}
{"id": "2404.18525", "title": "Enabling Efficient and Flexible Interpretability of Data-driven Anomaly\n  Detection in Industrial Processes with AcME-AD", "abstract": "  While Machine Learning has become crucial for Industry 4.0, its opaque nature\nhinders trust and impedes the transformation of valuable insights into\nactionable decision, a challenge exacerbated in the evolving Industry 5.0 with\nits human-centric focus. This paper addresses this need by testing the\napplicability of AcME-AD in industrial settings. This recently developed\nframework facilitates fast and user-friendly explanations for anomaly\ndetection. AcME-AD is model-agnostic, offering flexibility, and prioritizes\nreal-time efficiency. Thus, it seems suitable for seamless integration with\nindustrial Decision Support Systems. We present the first industrial\napplication of AcME-AD, showcasing its effectiveness through experiments. These\ntests demonstrate AcME-AD's potential as a valuable tool for explainable AD and\nfeature-based root cause analysis within industrial environments, paving the\nway for trustworthy and actionable insights in the age of Industry 5.0.\n", "date": "2024-04-29", "categories": []}
{"id": "2410.03306", "title": "Selective Test-Time Adaptation for Unsupervised Anomaly Detection using\n  Neural Implicit Representations", "abstract": "  Deep learning models in medical imaging often encounter challenges when\nadapting to new clinical settings unseen during training. Test-time adaptation\noffers a promising approach to optimize models for these unseen domains, yet\nits application in anomaly detection (AD) remains largely unexplored. AD aims\nto efficiently identify deviations from normative distributions; however, full\nadaptation, including pathological shifts, may inadvertently learn the\nanomalies it intends to detect. We introduce a novel concept of selective\ntest-time adaptation that utilizes the inherent characteristics of deep\npre-trained features to adapt selectively in a zero-shot manner to any test\nimage from an unseen domain. This approach employs a model-agnostic,\nlightweight multi-layer perceptron for neural implicit representations,\nenabling the adaptation of outputs from any reconstruction-based AD method\nwithout altering the source-trained model. Rigorous validation in brain AD\ndemonstrated that our strategy substantially enhances detection accuracy for\nmultiple conditions and different target distributions. Specifically, our\nmethod improves the detection rates by up to 78% for enlarged ventricles and\n24% for edemas.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.19136", "title": "Context-Aware Trajectory Anomaly Detection", "abstract": "  Trajectory anomaly detection is crucial for effective decision-making in\nurban and human mobility management. Existing methods of trajectory anomaly\ndetection generally focus on training a trajectory generative model and\nevaluating the likelihood of reconstructing a given trajectory. However,\nprevious work often lacks important contextual information on the trajectory,\nsuch as the agent's information (e.g., agent ID) or geographic information\n(e.g., Points of Interest (POI)), which could provide additional information on\naccurately capturing anomalous behaviors. To fill this gap, we propose a\ncontext-aware anomaly detection approach that models contextual information\nrelated to trajectories. The proposed method is based on a trajectory\nreconstruction framework guided by contextual factors such as agent ID and\ncontextual POI embedding. The injection of contextual information aims to\nimprove the performance of anomaly detection. We conducted experiments in two\ncities and demonstrated that the proposed approach significantly outperformed\nexisting methods by effectively modeling contextual information. Overall, this\npaper paves a new direction for advancing trajectory anomaly detection.\n", "date": "2024-10-24", "categories": []}
{"id": "2410.19535", "title": "Detection of Emerging Infectious Diseases in Lung CT based on Spatial\n  Anomaly Patterns", "abstract": "  Fast detection of emerging diseases is important for containing their spread\nand treating patients effectively. Local anomalies are relevant, but often\nnovel diseases involve familiar disease patterns in new spatial distributions.\nTherefore, established local anomaly detection approaches may fail to identify\nthem as new. Here, we present a novel approach to detect the emergence of new\ndisease phenotypes exhibiting distinct patterns of the spatial distribution of\nlesions. We first identify anomalies in lung CT data, and then compare their\ndistribution in a continually acquired new patient cohorts with historic\npatient population observed over a long prior period. We evaluate how\naccumulated evidence collected in the stream of patients is able to detect the\nonset of an emerging disease. In a gram-matrix based representation derived\nfrom the intermediate layers of a three-dimensional convolutional neural\nnetwork, newly emerging clusters indicate emerging diseases.\n", "date": "2024-10-25", "categories": []}
{"id": "2410.19722", "title": "Temporal Convolution-based Hybrid Model Approach with Representation\n  Learning for Real-Time Acoustic Anomaly Detection", "abstract": "  The early detection of potential failures in industrial machinery components\nis paramount for ensuring the reliability and safety of operations, thereby\npreserving Machine Condition Monitoring (MCM). This research addresses this\nimperative by introducing an innovative approach to Real-Time Acoustic Anomaly\nDetection. Our method combines semi-supervised temporal convolution with\nrepresentation learning and a hybrid model strategy with Temporal Convolutional\nNetworks (TCN) to handle various intricate anomaly patterns found in acoustic\ndata effectively. The proposed model demonstrates superior performance compared\nto established research in the field, underscoring the effectiveness of this\napproach. Not only do we present quantitative evidence of its superiority, but\nwe also employ visual representations, such as t-SNE plots, to further\nsubstantiate the model's efficacy.\n", "date": "2024-10-25", "categories": []}
{"id": "2405.11881", "title": "Out-of-Distribution Detection with a Single Unconditional Diffusion\n  Model", "abstract": "  Out-of-distribution (OOD) detection is a critical task in machine learning\nthat seeks to identify abnormal samples. Traditionally, unsupervised methods\nutilize a deep generative model for OOD detection. However, such approaches\nrequire a new model to be trained for each inlier dataset. This paper explores\nwhether a single model can perform OOD detection across diverse tasks. To that\nend, we introduce Diffusion Paths (DiffPath), which uses a single diffusion\nmodel originally trained to perform unconditional generation for OOD detection.\nWe introduce a novel technique of measuring the rate-of-change and curvature of\nthe diffusion paths connecting samples to the standard normal. Extensive\nexperiments show that with a single model, DiffPath is competitive with prior\nwork using individual models on a variety of OOD tasks involving different\ndistributions. Our code is publicly available at\nhttps://github.com/clear-nus/diffpath.\n", "date": "2024-05-20", "categories": []}
{"id": "2405.19882", "title": "PixOOD: Pixel-Level Out-of-Distribution Detection", "abstract": "  We propose a dense image prediction out-of-distribution detection algorithm,\ncalled PixOOD, which does not require training on samples of anomalous data and\nis not designed for a specific application which avoids traditional training\nbiases. In order to model the complex intra-class variability of the\nin-distribution data at the pixel level, we propose an online data condensation\nalgorithm which is more robust than standard K-means and is easily trainable\nthrough SGD. We evaluate PixOOD on a wide range of problems. It achieved\nstate-of-the-art results on four out of seven datasets, while being competitive\non the rest. The source code is available at https://github.com/vojirt/PixOOD.\n", "date": "2024-05-30", "categories": []}
{"id": "2410.18487", "title": "Graph Pre-Training Models Are Strong Anomaly Detectors", "abstract": "  Graph Anomaly Detection (GAD) is a challenging and practical research topic\nwhere Graph Neural Networks (GNNs) have recently shown promising results. The\neffectiveness of existing GNNs in GAD has been mainly attributed to the\nsimultaneous learning of node representations and the classifier in an\nend-to-end manner. Meanwhile, graph pre-training, the two-stage learning\nparadigm such as DGI and GraphMAE, has shown potential in leveraging unlabeled\ngraph data to enhance downstream tasks, yet its impact on GAD remains\nunder-explored. In this work, we show that graph pre-training models are strong\ngraph anomaly detectors. Specifically, we demonstrate that pre-training is\nhighly competitive, markedly outperforming the state-of-the-art end-to-end\ntraining models when faced with limited supervision. To understand this\nphenomenon, we further uncover pre-training enhances the detection of distant,\nunder-represented, unlabeled anomalies that go beyond 2-hop neighborhoods of\nknown anomalies, shedding light on its superior performance against end-to-end\nmodels. Moreover, we extend our examination to the potential of pre-training in\ngraph-level anomaly detection. We envision this work to stimulate a\nre-evaluation of pre-training's role in GAD and offer valuable insights for\nfuture research.\n", "date": "2024-10-24", "categories": []}
{"id": "2410.18875", "title": "Exploring the Universe with SNAD: Anomaly Detection in Astronomy", "abstract": "  SNAD is an international project with a primary focus on detecting\nastronomical anomalies within large-scale surveys, using active learning and\nother machine learning algorithms. The work carried out by SNAD not only\ncontributes to the discovery and classification of various astronomical\nphenomena but also enhances our understanding and implementation of machine\nlearning techniques within the field of astrophysics. This paper provides a\nreview of the SNAD project and summarizes the advancements and achievements\nmade by the team over several years.\n", "date": "2024-10-24", "categories": []}
{"id": "2401.01984", "title": "AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed\n  and Low Tolerance", "abstract": "  Recent advances in visual anomaly detection research have seen AUROC and\nAUPRO scores on public benchmark datasets such as MVTec and VisA converge\ntowards perfect recall, giving the impression that these benchmarks are\nnear-solved. However, high AUROC and AUPRO scores do not always reflect\nqualitative performance, which limits the validity of these metrics in\nreal-world applications. We argue that the artificial ceiling imposed by the\nlack of an adequate evaluation metric restrains progression of the field, and\nit is crucial that we revisit the evaluation metrics used to rate our\nalgorithms. In response, we introduce Per-IMage Overlap (PIMO), a novel metric\nthat addresses the shortcomings of AUROC and AUPRO. PIMO retains the\nrecall-based nature of the existing metrics but introduces two distinctions:\nthe assignment of curves (and respective area under the curve) is per-image,\nand its X-axis relies solely on normal images. Measuring recall per image\nsimplifies instance score indexing and is more robust to noisy annotations. As\nwe show, it also accelerates computation and enables the usage of statistical\ntests to compare models. By imposing low tolerance for false positives on\nnormal images, PIMO provides an enhanced model validation procedure and\nhighlights performance variations across datasets. Our experiments demonstrate\nthat PIMO offers practical advantages and nuanced performance insights that\nredefine anomaly detection benchmarks -- notably challenging the perception\nthat MVTec AD and VisA datasets have been solved by contemporary models.\nAvailable on GitHub: https://github.com/jpcbertoldo/aupimo.\n", "date": "2024-01-03", "categories": []}
{"id": "2405.16164", "title": "Acquiring Better Load Estimates by Combining Anomaly and Change Point\n  Detection in Power Grid Time-series Measurements", "abstract": "  In this paper we present novel methodology for automatic anomaly and switch\nevent filtering to improve load estimation in power grid systems. By leveraging\nunsupervised methods with supervised optimization, our approach prioritizes\ninterpretability while ensuring robust and generalizable performance on unseen\ndata. Through experimentation, a combination of binary segmentation for change\npoint detection and statistical process control for anomaly detection emerges\nas the most effective strategy, specifically when ensembled in a novel\nsequential manner. Results indicate the clear wasted potential when filtering\nis not applied. The automatic load estimation is also fairly accurate, with\napproximately 90% of estimates falling within a 10% error margin, with only a\nsingle significant failure in both the minimum and maximum load estimates\nacross 60 measurements in the test set. Our methodology's interpretability\nmakes it particularly suitable for critical infrastructure planning, thereby\nenhancing decision-making processes.\n", "date": "2024-05-25", "categories": []}
{"id": "2408.04377", "title": "Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon", "abstract": "  Anomaly detection in time series data is a critical challenge across various\ndomains. Traditional methods typically focus on identifying anomalies in\nimmediate subsequent steps, often underestimating the significance of temporal\ndynamics such as delay time and horizons of anomalies, which generally require\nextensive post-analysis. This paper introduces a novel approach for time series\nanomaly prediction, incorporating temporal information directly into the\nprediction results. We propose a new dataset specifically designed to evaluate\nthis approach and conduct comprehensive experiments using several\nstate-of-the-art methods. Our results demonstrate the efficacy of our approach\nin providing timely and accurate anomaly predictions, setting a new benchmark\nfor future research in this field.\n", "date": "2024-08-08", "categories": []}
{"id": "2410.18051", "title": "Real time anomalies detection on video", "abstract": "  Nowadays, many places use security cameras. Unfortunately, when an incident\noccurs, these technologies are used to show past events. So it can be\nconsidered as a deterrence tool than a detection tool. In this article, we will\npropose a deep learning approach trying to solve this problematic. This\napproach uses convolutional models (CNN) to extract relevant characteristics\nlinked to the video images, theses characteristics will form times series to be\nanalyzed by LSTM / GRU models.\n", "date": "2024-10-23", "categories": []}
{"id": "2409.17841", "title": "Machine Learning-based vs Deep Learning-based Anomaly Detection in\n  Multivariate Time Series for Spacecraft Attitude Sensors", "abstract": "  In the framework of Failure Detection, Isolation and Recovery (FDIR) on\nspacecraft, new AI-based approaches are emerging in the state of the art to\novercome the limitations commonly imposed by traditional threshold checking.\n  The present research aims at characterizing two different approaches to the\nproblem of stuck values detection in multivariate time series coming from\nspacecraft attitude sensors. The analysis reveals the performance differences\nin the two approaches, while commenting on their interpretability and\ngeneralization to different scenarios.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.05383", "title": "Deep Learning for Video Anomaly Detection: A Review", "abstract": "  Video anomaly detection (VAD) aims to discover behaviors or events deviating\nfrom the normality in videos. As a long-standing task in the field of computer\nvision, VAD has witnessed much good progress. In the era of deep learning, with\nthe explosion of architectures of continuously growing capability and capacity,\na great variety of deep learning based methods are constantly emerging for the\nVAD task, greatly improving the generalization ability of detection algorithms\nand broadening the application scenarios. Therefore, such a multitude of\nmethods and a large body of literature make a comprehensive survey a pressing\nnecessity. In this paper, we present an extensive and comprehensive research\nreview, covering the spectrum of five different categories, namely,\nsemi-supervised, weakly supervised, fully supervised, unsupervised and open-set\nsupervised VAD, and we also delve into the latest VAD works based on\npre-trained large models, remedying the limitations of past reviews in terms of\nonly focusing on semi-supervised VAD and small model based methods. For the VAD\ntask with different levels of supervision, we construct a well-organized\ntaxonomy, profoundly discuss the characteristics of different types of methods,\nand show their performance comparisons. In addition, this review involves the\npublic datasets, open-source codes, and evaluation metrics covering all the\naforementioned VAD tasks. Finally, we provide several important research\ndirections for the VAD community.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.13602", "title": "MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning\n  and Entropy-based Scoring", "abstract": "  Anomaly detection (AD) plays a pivotal role in multimedia applications for\ndetecting defective products and automating quality inspection. Deep learning\n(DL) models typically require large-scale annotated data, which are often\nhighly imbalanced since anomalies are usually scarce. The black box nature of\nthese models prohibits them from being trusted by users. To address these\nchallenges, we propose MeLIAD, a novel methodology for interpretable anomaly\ndetection, which unlike the previous methods is based on metric learning and\nachieves interpretability by design without relying on any prior distribution\nassumptions of true anomalies. MeLIAD requires only a few samples of anomalies\nfor training, without employing any augmentation techniques, and is inherently\ninterpretable, providing visualizations that offer insights into why an image\nis identified as anomalous. This is achieved by introducing a novel trainable\nentropy-based scoring component for the identification and localization of\nanomalous instances, and a novel loss function that jointly optimizes the\nanomaly scoring component with a metric learning objective. Experiments on five\npublic benchmark datasets, including quantitative and qualitative evaluation of\ninterpretability, demonstrate that MeLIAD achieves improved anomaly detection\nand localization performance compared to state-of-the-art methods.\n", "date": "2024-09-20", "categories": []}
{"id": "2410.15405", "title": "XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous\n  Driving Systems", "abstract": "  The rapid advancement of autonomous vehicle (AV) technology has introduced\nsignificant challenges in ensuring transportation security and reliability.\nTraditional AI models for anomaly detection in AVs are often opaque, posing\ndifficulties in understanding and trusting their decision making processes.\nThis paper proposes a novel feature ensemble framework that integrates multiple\nExplainable AI (XAI) methods: SHAP, LIME, and DALEX with various AI models to\nenhance both anomaly detection and interpretability. By fusing top features\nidentified by these XAI methods across six diverse AI models (Decision Trees,\nRandom Forests, Deep Neural Networks, K Nearest Neighbors, Support Vector\nMachines, and AdaBoost), the framework creates a robust and comprehensive set\nof features critical for detecting anomalies. These feature sets, produced by\nour feature ensemble framework, are evaluated using independent classifiers\n(CatBoost, Logistic Regression, and LightGBM) to ensure unbiased performance.\nWe evaluated our feature ensemble approach on two popular autonomous driving\ndatasets (VeReMi and Sensor) datasets. Our feature ensemble technique\ndemonstrates improved accuracy, robustness, and transparency of AI models,\ncontributing to safer and more trustworthy autonomous driving systems.\n", "date": "2024-10-20", "categories": []}
{"id": "2410.15997", "title": "MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection\n  with Multi-scale Reconstructive Contrast", "abstract": "  Many methods have been proposed for unsupervised time series anomaly\ndetection. Despite some progress, research on predicting future anomalies is\nstill relatively scarce. Predicting anomalies is particularly challenging due\nto the diverse reaction time and the lack of labeled data. To address these\nchallenges, we propose MultiRC to integrate reconstructive and contrastive\nlearning for joint learning of anomaly prediction and detection, with\nmulti-scale structure and adaptive dominant period mask to deal with the\ndiverse reaction time. MultiRC also generates negative samples to provide\nessential training momentum for the anomaly prediction tasks and prevent model\ndegradation. We evaluate seven benchmark datasets from different fields. For\nboth anomaly prediction and detection tasks, MultiRC outperforms existing\nstate-of-the-art methods.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.16255", "title": "Revisiting Deep Feature Reconstruction for Logical and Structural\n  Industrial Anomaly Detection", "abstract": "  Industrial anomaly detection is crucial for quality control and predictive\nmaintenance, but it presents challenges due to limited training data, diverse\nanomaly types, and external factors that alter object appearances. Existing\nmethods commonly detect structural anomalies, such as dents and scratches, by\nleveraging multi-scale features from image patches extracted through deep\npre-trained networks. However, significant memory and computational demands\noften limit their practical application. Additionally, detecting logical\nanomalies-such as images with missing or excess elements-requires an\nunderstanding of spatial relationships that traditional patch-based methods\nfail to capture. In this work, we address these limitations by focusing on Deep\nFeature Reconstruction (DFR), a memory- and compute-efficient approach for\ndetecting structural anomalies. We further enhance DFR into a unified\nframework, called ULSAD, which is capable of detecting both structural and\nlogical anomalies. Specifically, we refine the DFR training objective to\nimprove performance in structural anomaly detection, while introducing an\nattention-based loss mechanism using a global autoencoder-like network to\nhandle logical anomaly detection. Our empirical evaluation across five\nbenchmark datasets demonstrates the performance of ULSAD in detecting and\nlocalizing both structural and logical anomalies, outperforming eight\nstate-of-the-art methods. An extensive ablation study further highlights the\ncontribution of each component to the overall performance improvement. Our code\nis available at https://github.com/sukanyapatra1997/ULSAD-2024.git\n", "date": "2024-10-21", "categories": []}
{"id": "2405.17525", "title": "SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection", "abstract": "  The smoothing issue in graph learning leads to indistinguishable node\nrepresentations, posing significant challenges for graph-related tasks.\nHowever, our experiments reveal that this problem can uncover underlying\nproperties of node anomaly detection (NAD) that previous research has missed.\nWe introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing\nPatterns (NSP), which indicate that the representations of anomalous nodes are\nharder to smooth than those of normal ones. In addition, we explore the\ntheoretical implications of these patterns, demonstrating the potential\nbenefits of ISP and NSP for NAD tasks. Motivated by these findings, we propose\nSmoothGNN, a novel unsupervised NAD framework. First, we design a learning\ncomponent to explicitly capture ISP for detecting node anomalies. Second, we\ndesign a spectral graph neural network to implicitly learn ISP to enhance\ndetection. Third, we design an effective coefficient based on our findings that\nNSP can serve as coefficients for node representations, aiding in the\nidentification of anomalous nodes. Furthermore, we devise a novel anomaly\nmeasure to calculate loss functions and anomalous scores for nodes, reflecting\nthe properties of NAD using ISP and NSP. Extensive experiments on 9 real\ndatasets show that SmoothGNN outperforms the best rival by an average of 14.66%\nin AUC and 7.28% in Average Precision, with 75x running time speedup,\nvalidating the effectiveness and efficiency of our framework.\n", "date": "2024-05-27", "categories": []}
{"id": "2407.02057", "title": "HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised\n  Graph-Level Anomaly Detection", "abstract": "  Unsupervised graph-level anomaly detection (UGAD) has garnered increasing\nattention in recent years due to its significance. Most existing methods that\nrely on traditional GNNs mainly consider pairwise relationships between\nfirst-order neighbors, which is insufficient to capture the complex high-order\ndependencies often associated with anomalies. This limitation underscores the\nnecessity of exploring high-order node interactions in UGAD. In addition, most\nprevious works ignore the underlying properties (e.g., hierarchy and power-law\nstructure) which are common in real-world graph datasets and therefore are\nindispensable factors in the UGAD task. In this paper, we propose a novel Dual\nHyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection\n(HC-GLAD in short). To exploit high-order node group information, we construct\nhypergraphs based on pre-designed gold motifs and subsequently perform\nhypergraph convolution. Furthermore, to preserve the hierarchy of real-world\ngraphs, we introduce hyperbolic geometry into this field and conduct both graph\nand hypergraph embedding learning in hyperbolic space with the hyperboloid\nmodel. To the best of our knowledge, this is the first work to simultaneously\napply hypergraph with node group information and hyperbolic geometry in this\nfield. Extensive experiments on 13 real-world datasets of different fields\ndemonstrate the superiority of HC-GLAD on the UGAD task. The code is available\nat https://github.com/Yali-F/HC-GLAD.\n", "date": "2024-07-02", "categories": []}
{"id": "2410.12817", "title": "Interactive Explainable Anomaly Detection for Industrial Settings", "abstract": "  Being able to recognise defects in industrial objects is a key element of\nquality assurance in production lines. Our research focuses on visual anomaly\ndetection in RGB images. Although Convolutional Neural Networks (CNNs) achieve\nhigh accuracies in this task, end users in industrial environments receive the\nmodel's decisions without additional explanations. Therefore, it is of interest\nto enrich the model's outputs with further explanations to increase confidence\nin the model and speed up anomaly detection. In our work, we focus on (1)\nCNN-based classification models and (2) the further development of a\nmodel-agnostic explanation algorithm for black-box classifiers. Additionally,\n(3) we demonstrate how we can establish an interactive interface that allows\nusers to further correct the model's output. We present our NearCAIPI\nInteraction Framework, which improves AI through user interaction, and show how\nthis approach increases the system's trustworthiness. We also illustrate how\nNearCAIPI can integrate human feedback into an interactive process chain.\n", "date": "2024-10-01", "categories": []}
{"id": "2410.13363", "title": "Statistical testing on generative AI anomaly detection tools in\n  Alzheimer's Disease diagnosis", "abstract": "  Alzheimer's Disease is challenging to diagnose due to our limited\nunderstanding of its mechanism and large heterogeneity among patients.\nNeurodegeneration is studied widely as a biomarker for clinical diagnosis,\nwhich can be measured from time series MRI progression. On the other hand,\ngenerative AI has shown promise in anomaly detection in medical imaging and\nused for tasks including tumor detection. However, testing the reliability of\nsuch data-driven methods is non-trivial due to the issue of double-dipping in\nhypothesis testing. In this work, we propose to solve this issue with selective\ninference and develop a reliable generative AI method for Alzheimer's\nprediction. We show that compared to traditional statistical methods with\nhighly inflated p-values, selective inference successfully controls the false\ndiscovery rate under the desired alpha level while retaining statistical power.\nIn practice, our pipeline could assist clinicians in Alzheimer's diagnosis and\nearly intervention.\n", "date": "2024-10-17", "categories": []}
{"id": "2408.02181", "title": "AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing\n  Pipelines", "abstract": "  Anomaly detection in manufacturing pipelines remains a critical challenge,\nintensified by the complexity and variability of industrial environments. This\npaper introduces AssemAI, an interpretable image-based anomaly detection system\ntailored for smart manufacturing pipelines. Utilizing a curated image dataset\nfrom an industry-focused rocket assembly pipeline, we address the challenge of\nimbalanced image data and demonstrate the importance of image-based methods in\nanomaly detection. Our primary contributions include deriving an image dataset,\nfine-tuning an object detection model YOLO-FF, and implementing a custom\nanomaly detection model for assembly pipelines. The proposed approach leverages\ndomain knowledge in data preparation, model development and reasoning. We\nimplement several anomaly detection models on the derived image dataset,\nincluding a Convolutional Neural Network, Vision Transformer (ViT), and\npre-trained versions of these models. Additionally, we incorporate\nexplainability techniques at both user and model levels, utilizing ontology for\nuser-level explanations and SCORE-CAM for in-depth feature and model analysis.\nFinally, the best-performing anomaly detection model and YOLO-FF are deployed\nin a real-time setting. Our results include ablation studies on the baselines\nand a comprehensive evaluation of the proposed system. This work highlights the\nbroader impact of advanced image-based anomaly detection in enhancing the\nreliability and efficiency of smart manufacturing processes. The image dataset,\ncodes to reproduce the results and additional experiments are available at\nhttps://github.com/renjithk4/AssemAI.\n", "date": "2024-08-05", "categories": []}
{"id": "2410.12080", "title": "SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection", "abstract": "  Image-based Pose-Agnostic 3D Anomaly Detection is an important task that has\nemerged in industrial quality control. This task seeks to find anomalies from\nquery images of a tested object given a set of reference images of an\nanomaly-free object. The challenge is that the query views (a.k.a poses) are\nunknown and can be different from the reference views. Currently, new methods\nsuch as OmniposeAD and SplatPose have emerged to bridge the gap by synthesizing\npseudo reference images at the query views for pixel-to-pixel comparison.\nHowever, none of these methods can infer in real-time, which is critical in\nindustrial quality control for massive production. For this reason, we propose\nSplatPose+, which employs a hybrid representation consisting of a Structure\nfrom Motion (SfM) model for localization and a 3D Gaussian Splatting (3DGS)\nmodel for Novel View Synthesis. Although our proposed pipeline requires the\ncomputation of an additional SfM model, it offers real-time inference speeds\nand faster training compared to SplatPose. Quality-wise, we achieved a new SOTA\non the Pose-agnostic Anomaly Detection benchmark with the Multi-Pose Anomaly\nDetection (MAD-SIM) dataset.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.12206", "title": "Abnormality Forecasting: Time Series Anomaly Prediction via Future\n  Context Modeling", "abstract": "  Identifying anomalies from time series data plays an important role in\nvarious fields such as infrastructure security, intelligent operation and\nmaintenance, and space exploration. Current research focuses on detecting the\nanomalies after they occur, which can lead to significant financial/reputation\nloss or infrastructure damage. In this work we instead study a more practical\nyet very challenging problem, time series anomaly prediction, aiming at\nproviding early warnings for abnormal events before their occurrence. To tackle\nthis problem, we introduce a novel principled approach, namely future context\nmodeling (FCM). Its key insight is that the future abnormal events in a target\nwindow can be accurately predicted if their preceding observation window\nexhibits any subtle difference to normal data. To effectively capture such\ndifferences, FCM first leverages long-term forecasting models to generate a\ndiscriminative future context based on the observation data, aiming to amplify\nthose subtle but unusual difference. It then models a normality correlation of\nthe observation data with the forecasting future context to complement the\nnormality modeling of the observation data in foreseeing possible abnormality\nin the target window. A joint variate-time attention learning is also\nintroduced in FCM to leverage both temporal signals and features of the time\nseries data for more discriminative normality modeling in the aforementioned\ntwo views. Comprehensive experiments on five datasets demonstrate that FCM\ngains good recall rate (70\\%+) on multiple datasets and significantly\noutperforms all baselines in F1 score. Code is available at\nhttps://github.com/mala-lab/FCM.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.12328", "title": "Improved Anomaly Detection through Conditional Latent Space VAE\n  Ensembles", "abstract": "  We propose a novel Conditional Latent space Variational Autoencoder (CL-VAE)\nto perform improved pre-processing for anomaly detection on data with known\ninlier classes and unknown outlier classes. This proposed variational\nautoencoder (VAE) improves latent space separation by conditioning on\ninformation within the data. The method fits a unique prior distribution to\neach class in the dataset, effectively expanding the classic prior distribution\nfor VAEs to include a Gaussian mixture model. An ensemble of these VAEs are\nmerged in the latent spaces to form a group consensus that greatly improves the\naccuracy of anomaly detection across data sets. Our approach is compared\nagainst the capabilities of a typical VAE, a CNN, and a PCA, with regards AUC\nfor anomaly detection. The proposed model shows increased accuracy in anomaly\ndetection, achieving an AUC of 97.4% on the MNIST dataset compared to 95.7% for\nthe second best model. In addition, the CL-VAE shows increased benefits from\nensembling, a more interpretable latent space, and an increased ability to\nlearn patterns in complex data with limited model sizes.\n", "date": "2024-10-16", "categories": []}
{"id": "2404.08827", "title": "\"Don't forget to put the milk back!\" Dataset for Enabling Embodied\n  Agents to Detect Anomalous Situations", "abstract": "  Home robots intend to make their users lives easier. Our work assists in this\ngoal by enabling robots to inform their users of dangerous or unsanitary\nanomalies in their home. Some examples of these anomalies include the user\nleaving their milk out, forgetting to turn off the stove, or leaving poison\naccessible to children. To move towards enabling home robots with these\nabilities, we have created a new dataset, which we call SafetyDetect. The\nSafetyDetect dataset consists of 1000 anomalous home scenes, each of which\ncontains unsafe or unsanitary situations for an agent to detect. Our approach\nutilizes large language models (LLMs) alongside both a graph representation of\nthe scene and the relationships between the objects in the scene. Our key\ninsight is that this connected scene graph and the object relationships it\nencodes enables the LLM to better reason about the scene -- especially as it\nrelates to detecting dangerous or unsanitary situations. Our most promising\napproach utilizes GPT-4 and pursues a categorization technique where object\nrelations from the scene graph are classified as normal, dangerous, unsanitary,\nor dangerous for children. This method is able to correctly identify over 90%\nof anomalous scenarios in the SafetyDetect Dataset. Additionally, we conduct\nreal world experiments on a ClearPath TurtleBot where we generate a scene graph\nfrom visuals of the real world scene, and run our approach with no\nmodification. This setup resulted in little performance loss. The SafetyDetect\nDataset and code will be released to the public upon this papers publication.\n", "date": "2024-04-12", "categories": []}
{"id": "2408.03888", "title": "Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection", "abstract": "  Knowledge distillation based on student-teacher network is one of the\nmainstream solution paradigms for the challenging unsupervised Anomaly\nDetection task, utilizing the difference in representation capabilities of the\nteacher and student networks to implement anomaly localization. However,\nover-generalization of the student network to the teacher network may lead to\nnegligible differences in representation capabilities of anomaly, thus\naffecting the detection effectiveness. Existing methods address the possible\nover-generalization by using differentiated students and teachers from the\nstructural perspective or explicitly expanding distilled information from the\ncontent perspective, which inevitably result in an increased likelihood of\nunderfitting of the student network and poor anomaly detection capabilities in\nanomaly center or edge. In this paper, we propose Dual-Modeling Decouple\nDistillation (DMDD) for the unsupervised anomaly detection. In DMDD, a Decouple\nStudent-Teacher Network is proposed to decouple the initial student features\ninto normality and abnormality features. We further introduce Dual-Modeling\nDistillation based on normal-anomaly image pairs, fitting normality features of\nanomalous image and the teacher features of the corresponding normal image,\nwidening the distance between abnormality features and the teacher features in\nanomalous regions. Synthesizing these two distillation ideas, we achieve\nanomaly detection which focuses on both edge and center of anomaly. Finally, a\nMulti-perception Segmentation Network is proposed to achieve focused anomaly\nmap fusion based on multiple attention. Experimental results on MVTec AD show\nthat DMDD surpasses SOTA localization performance of previous knowledge\ndistillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on\nPRO.\n", "date": "2024-08-07", "categories": []}
{"id": "2410.11307", "title": "CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor\n  Detection", "abstract": "  Artificial intelligence aids in brain tumor detection via MRI scans,\nenhancing the accuracy and reducing the workload of medical professionals.\nHowever, in scenarios with extremely limited medical images, traditional deep\nlearning approaches tend to fail due to the absence of anomalous images.\nAnomaly detection also suffers from ineffective feature extraction due to vague\ntraining process. Our work introduces a novel two-stage anomaly detection\nalgorithm called CONSULT (CONtrastive Self-sUpervised Learning for few-shot\nTumor detection). The first stage of CONSULT fine-tunes a pre-trained feature\nextractor specifically for MRI brain images, using a synthetic data generation\npipeline to create tumor-like data. This process overcomes the lack of anomaly\nsamples and enables the integration of attention mechanisms to focus on\nanomalous image segments. The first stage is to overcome the shortcomings of\ncurrent anomaly detection in extracting features in high-variation data by\nincorporating Context-Aware Contrastive Learning and Self-supervised Feature\nAdversarial Learning. The second stage of CONSULT uses PatchCore for\nconventional feature extraction via the fine-tuned weights from the first\nstage. To summarize, we propose a self-supervised training scheme for anomaly\ndetection, enhancing model performance and data reliability. Furthermore, our\nproposed contrastive loss, Tritanh Loss, stabilizes learning by offering a\nunique solution all while enhancing gradient flow. Finally, CONSULT achieves\nsuperior performance in few-shot brain tumor detection, demonstrating\nsignificant improvements over PatchCore by 9.4%, 12.9%, 10.2%, and 6.0% for 2,\n4, 6, and 8 shots, respectively, while training exclusively on healthy images.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.11591", "title": "PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge", "abstract": "  Visual Anomaly Detection (VAD) has gained significant research attention for\nits ability to identify anomalous images and pinpoint the specific areas\nresponsible for the anomaly. A key advantage of VAD is its unsupervised nature,\nwhich eliminates the need for costly and time-consuming labeled data\ncollection. However, despite its potential for real-world applications, the\nliterature has given limited focus to resource-efficient VAD, particularly for\ndeployment on edge devices. This work addresses this gap by leveraging\nlightweight neural networks to reduce memory and computation requirements,\nenabling VAD deployment on resource-constrained edge devices. We benchmark the\nmajor VAD algorithms within this framework and demonstrate the feasibility of\nedge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a\nnovel algorithm, Partially Shared Teacher-student (PaSTe), designed to address\nthe high resource demands of the existing Student Teacher Feature Pyramid\nMatching (STFPM) approach. Our results show that PaSTe decreases the inference\ntime by 25%, while reducing the training time by 33% and peak RAM usage during\ntraining by 76%. These improvements make the VAD process significantly more\nefficient, laying a solid foundation for real-world deployment on edge devices.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.00054", "title": "Transferable Unsupervised Outlier Detection Framework for Human Semantic\n  Trajectories", "abstract": "  Semantic trajectories, which enrich spatial-temporal data with textual\ninformation such as trip purposes or location activities, are key for\nidentifying outlier behaviors critical to healthcare, social security, and\nurban planning. Traditional outlier detection relies on heuristic rules, which\nrequires domain knowledge and limits its ability to identify unseen outliers.\nBesides, there lacks a comprehensive approach that can jointly consider\nmulti-modal data across spatial, temporal, and textual dimensions. Addressing\nthe need for a domain-agnostic model, we propose the Transferable Outlier\nDetection for Human Semantic Trajectories (TOD4Traj) framework.TOD4Traj first\nintroduces a modality feature unification module to align diverse data feature\nrepresentations, enabling the integration of multi-modal information and\nenhancing transferability across different datasets. A contrastive learning\nmodule is further pro-posed for identifying regular mobility patterns both\ntemporally and across populations, allowing for a joint detection of outliers\nbased on individual consistency and group majority patterns. Our experimental\nresults have shown TOD4Traj's superior performance over existing models,\ndemonstrating its effectiveness and adaptability in detecting human trajectory\noutliers across various datasets.\n", "date": "2024-09-28", "categories": []}
{"id": "2410.09284", "title": "Enhanced Federated Anomaly Detection Through Autoencoders Using Summary\n  Statistics-Based Thresholding", "abstract": "  In Federated Learning (FL), anomaly detection (AD) is a challenging task due\nto the decentralized nature of data and the presence of non-IID data\ndistributions. This study introduces a novel federated threshold calculation\nmethod that leverages summary statistics from both normal and anomalous data to\nimprove the accuracy and robustness of anomaly detection using autoencoders\n(AE) in a federated setting. Our approach aggregates local summary statistics\nacross clients to compute a global threshold that optimally separates anomalies\nfrom normal data while ensuring privacy preservation. We conducted extensive\nexperiments using publicly available datasets, including Credit Card Fraud\nDetection, Shuttle, and Covertype, under various data distribution scenarios.\nThe results demonstrate that our method consistently outperforms existing\nfederated and local threshold calculation techniques, particularly in handling\nnon-IID data distributions. This study also explores the impact of different\ndata distribution scenarios and the number of clients on the performance of\nfederated anomaly detection. Our findings highlight the potential of using\nsummary statistics for threshold calculation in improving the scalability and\naccuracy of federated anomaly detection systems.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.08390", "title": "KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on\n  Graph Data", "abstract": "  Graph-based anomaly detection is pivotal in diverse security applications,\nsuch as fraud detection in transaction networks and intrusion detection for\nnetwork traffic. Standard approaches, including Graph Neural Networks (GNNs),\noften struggle to generalize across shifting data distributions. Meanwhile,\nreal-world domain knowledge is more stable and a common existing component of\nreal-world detection strategies. To explicitly integrate such knowledge into\ndata-driven models such as GCNs, we propose KnowGraph, which integrates domain\nknowledge with data-driven learning for enhanced graph-based anomaly detection.\nKnowGraph comprises two principal components: (1) a statistical learning\ncomponent that utilizes a main model for the overarching detection task,\naugmented by multiple specialized knowledge models that predict domain-specific\nsemantic entities; (2) a reasoning component that employs probabilistic\ngraphical models to execute logical inferences based on model outputs, encoding\ndomain knowledge through weighted first-order logic formulas. Extensive\nexperiments on these large-scale real-world datasets show that KnowGraph\nconsistently outperforms state-of-the-art baselines in both transductive and\ninductive settings, achieving substantial gains in average precision when\ngeneralizing to completely unseen test graphs. Further ablation studies\ndemonstrate the effectiveness of the proposed reasoning component in improving\ndetection performance, especially under extreme class imbalance. These results\nhighlight the potential of integrating domain knowledge into data-driven models\nfor high-stakes, graph-based security applications.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.08629", "title": "Towards Cross-domain Few-shot Graph Anomaly Detection", "abstract": "  Few-shot graph anomaly detection (GAD) has recently garnered increasing\nattention, which aims to discern anomalous patterns among abundant unlabeled\ntest nodes under the guidance of a limited number of labeled training nodes.\nExisting few-shot GAD approaches typically adopt meta-training methods trained\non richly labeled auxiliary networks to facilitate rapid adaptation to target\nnetworks that possess sparse labels. However, these proposed methods often\nassume that the auxiliary and target networks exist in the same data\ndistributions-an assumption rarely holds in practical settings. This paper\nexplores a more prevalent and complex scenario of cross-domain few-shot GAD,\nwhere the goal is to identify anomalies within sparsely labeled target graphs\nusing auxiliary graphs from a related, yet distinct domain. The challenge here\nis nontrivial owing to inherent data distribution discrepancies between the\nsource and target domains, compounded by the uncertainties of sparse labeling\nin the target domain. In this paper, we propose a simple and effective\nframework, termed CDFS-GAD, specifically designed to tackle the aforementioned\nchallenges. CDFS-GAD first introduces a domain-adaptive graph contrastive\nlearning module, which is aimed at enhancing cross-domain feature alignment.\nThen, a prompt tuning module is further designed to extract domain-specific\nfeatures tailored to each domain. Moreover, a domain-adaptive hypersphere\nclassification loss is proposed to enhance the discrimination between normal\nand anomalous instances under minimal supervision, utilizing domain-sensitive\nnorms. Lastly, a self-training strategy is introduced to further refine the\npredicted scores, enhancing its reliability in few-shot settings. Extensive\nexperiments on twelve real-world cross-domain data pairs demonstrate the\neffectiveness of the proposed CDFS-GAD framework in comparison to various\nexisting GAD methods.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.08877", "title": "Interdependency Matters: Graph Alignment for Multivariate Time Series\n  Anomaly Detection", "abstract": "  Anomaly detection in multivariate time series (MTS) is crucial for various\napplications in data mining and industry. Current industrial methods typically\napproach anomaly detection as an unsupervised learning task, aiming to identify\ndeviations by estimating the normal distribution in noisy, label-free datasets.\nThese methods increasingly incorporate interdependencies between channels\nthrough graph structures to enhance accuracy. However, the role of\ninterdependencies is more critical than previously understood, as shifts in\ninterdependencies between MTS channels from normal to anomalous data are\nsignificant. This observation suggests that \\textit{anomalies could be detected\nby changes in these interdependency graph series}. To capitalize on this\ninsight, we introduce MADGA (MTS Anomaly Detection via Graph Alignment), which\nredefines anomaly detection as a graph alignment (GA) problem that explicitly\nutilizes interdependencies for anomaly detection. MADGA dynamically transforms\nsubsequences into graphs to capture the evolving interdependencies, and Graph\nalignment is performed between these graphs, optimizing an alignment plan that\nminimizes cost, effectively minimizing the distance for normal data and\nmaximizing it for anomalous data. Uniquely, our GA approach involves explicit\nalignment of both nodes and edges, employing Wasserstein distance for nodes and\nGromov-Wasserstein distance for edges. To our knowledge, this is the first\napplication of GA to MTS anomaly detection that explicitly leverages\ninterdependency for this purpose. Extensive experiments on diverse real-world\ndatasets validate the effectiveness of MADGA, demonstrating its capability to\ndetect anomalies and differentiate interdependencies, consistently achieving\nstate-of-the-art across various scenarios.\n", "date": "2024-10-11", "categories": []}
{"id": "2310.05468", "title": "Enhancing Interpretability and Generalizability in Extended Isolation\n  Forests", "abstract": "  Anomaly Detection (AD) focuses on identifying unusual behaviors in complex\ndatasets. Machine Learning (ML) algorithms and Decision Support Systems (DSSs)\nprovide effective solutions for AD, but detecting anomalies alone may not be\nenough, especially in engineering, where diagnostics and maintenance are\ncrucial. Users need clear explanations to support root cause analysis and build\ntrust in the model. The unsupervised nature of AD, however, makes\ninterpretability a challenge. This paper introduces Extended Isolation Forest\nFeature Importance (ExIFFI), a method that explains predictions made by\nExtended Isolation Forest (EIF) models, which split data using hyperplanes.\nExIFFI provides explanations at both global and local levels by leveraging\nfeature importance.\n  We also present an improved version, Enhanced Extended Isolation Forest\n(EIF+), designed to enhance the model's ability to detect unseen anomalies\nthrough a revised splitting strategy. Using five synthetic and eleven\nreal-world datasets, we conduct a comparative analysis, evaluating unsupervised\nAD methods with the Average Precision metric. EIF+ consistently outperforms EIF\nacross all datasets when trained without anomalies, demonstrating better\ngeneralization.\n  To assess ExIFFI's interpretability, we introduce the Area Under the Curve of\nFeature Selection (AUC\\_FS), a novel metric using feature selection as a proxy\ntask. ExIFFI outperforms other unsupervised interpretability methods on 8 of 11\nreal-world datasets and successfully identifies anomalous features in synthetic\ndatasets. When trained only on inliers, ExIFFI also outperforms competing\nmodels on real-world data and accurately detects anomalous features in\nsynthetic datasets. We provide open-source code to encourage further research\nand reproducibility.\n", "date": "2023-10-09", "categories": []}
{"id": "2404.00847", "title": "Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised\n  Video Anomaly Detection: A New Baseline", "abstract": "  Unsupervised (US) video anomaly detection (VAD) in surveillance applications\nis gaining more popularity recently due to its practical real-world\napplications. As surveillance videos are privacy sensitive and the availability\nof large-scale video data may enable better US-VAD systems, collaborative\nlearning can be highly rewarding in this setting. However, due to the extremely\nchallenging nature of the US-VAD task, where learning is carried out without\nany annotations, privacy-preserving collaborative learning of US-VAD systems\nhas not been studied yet. In this paper, we propose a new baseline for anomaly\ndetection capable of localizing anomalous events in complex surveillance videos\nin a fully unsupervised fashion without any labels on a privacy-preserving\nparticipant-based distributed training configuration. Additionally, we propose\nthree new evaluation protocols to benchmark anomaly detection approaches on\nvarious scenarios of collaborations and data availability. Based on these\nprotocols, we modify existing VAD datasets to extensively evaluate our approach\nas well as existing US SOTA methods on two large-scale datasets including\nUCF-Crime and XD-Violence. All proposed evaluation protocols, dataset splits,\nand codes are available here: https://github.com/AnasEmad11/CLAP\n", "date": "2024-04-01", "categories": []}
{"id": "2406.08810", "title": "Few-Shot Anomaly Detection via Category-Agnostic Registration Learning", "abstract": "  Most existing anomaly detection (AD) methods require a dedicated model for\neach category. Such a paradigm, despite its promising results, is\ncomputationally expensive and inefficient, thereby failing to meet the\nrequirements for realworld applications. Inspired by how humans detect\nanomalies, by comparing a query image to known normal ones, this article\nproposes a novel few-shot AD (FSAD) framework. Using a training set of normal\nimages from various categories, registration, aiming to align normal images of\nthe same categories, is leveraged as the proxy task for self-supervised\ncategory-agnostic representation learning. At test time, an image and its\ncorresponding support set, consisting of a few normal images from the same\ncategory, are supplied, and anomalies are identified by comparing the\nregistered features of the test image to its corresponding support image\nfeatures. Such a setup enables the model to generalize to novel test\ncategories. It is, to our best knowledge, the first FSAD method that requires\nno model fine-tuning for novel categories: enabling a single model to be\napplied to all categories. Extensive experiments demonstrate the effectiveness\nof the proposed method. Particularly, it improves the current state-of-the-art\n(SOTA) for FSAD by 11.3% and 8.3% on the MVTec and MPDD benchmarks,\nrespectively. The source code is available at\nhttps://github.com/Haoyan-Guan/CAReg.\n", "date": "2024-06-13", "categories": []}
{"id": "2409.11596", "title": "Outlier Detection with Cluster Catch Digraphs", "abstract": "  This paper introduces a novel family of outlier detection algorithms based on\nCluster Catch Digraphs (CCDs), specifically tailored to address the challenges\nof high dimensionality and varying cluster shapes, which deteriorate the\nperformance of most traditional outlier detection methods. We propose the\nUniformity-Based CCD with Mutual Catch Graph (U-MCCD), the Uniformity- and\nNeighbor-Based CCD with Mutual Catch Graph (UN-MCCD), and their shape-adaptive\nvariants (SU-MCCD and SUN-MCCD), which are designed to detect outliers in data\nsets with arbitrary cluster shapes and high dimensions. We present the\nadvantages and shortcomings of these algorithms and provide the motivation or\nneed to define each particular algorithm. Through comprehensive Monte Carlo\nsimulations, we assess their performance and demonstrate the robustness and\neffectiveness of our algorithms across various settings and contamination\nlevels. We also illustrate the use of our algorithms on various real-life data\nsets. The U-MCCD algorithm efficiently identifies outliers while maintaining\nhigh true negative rates, and the SU-MCCD algorithm shows substantial\nimprovement in handling non-uniform clusters. Additionally, the UN-MCCD and\nSUN-MCCD algorithms address the limitations of existing methods in\nhigh-dimensional spaces by utilizing Nearest Neighbor Distances (NND) for\nclustering and outlier detection. Our results indicate that these novel\nalgorithms offer substantial advancements in the accuracy and adaptability of\noutlier detection, providing a valuable tool for various real-world\napplications.\n  Keyword: Outlier detection, Graph-based clustering, Cluster catch digraphs,\n$k$-nearest-neighborhood, Mutual catch graphs, Nearest neighbor distance.\n", "date": "2024-09-17", "categories": []}
{"id": "2410.06576", "title": "On The Relationship between Visual Anomaly-free and Anomalous\n  Representations", "abstract": "  Anomaly Detection is an important problem within computer vision, having\nvariety of real-life applications. Yet, the current set of solutions to this\nproblem entail known, systematic shortcomings. Specifically, contemporary\nsurface Anomaly Detection task assumes the presence of multiple specific\nanomaly classes e.g. cracks, rusting etc., unlike one-class classification\nmodel of past. However, building a deep learning model in such setup remains a\nchallenge because anomalies arise rarely, and hence anomaly samples are quite\nscarce. Transfer learning has been a preferred paradigm in such situations. But\nthe typical source domains with large dataset sizes e.g. ImageNet, JFT-300M,\nLAION-2B do not correlate well with the domain of surfaces and materials, an\nimportant premise of transfer learning. In this paper, we make an important\nhypothesis and show, by exhaustive experimentation, that the space of\nanomaly-free visual patterns of the normal samples correlates well with each of\nthe various spaces of anomalous patterns of the class-specific anomaly samples.\nThe first results of using this hypothesis in transfer learning have indeed\nbeen quite encouraging. We expect that finding such a simple closeby domain\nthat readily entails large number of samples, and which also oftentimes shows\ninterclass separability though with narrow margins, will be a useful discovery.\nEspecially, it is expected to improve domain adaptation for anomaly detection,\nand few-shot learning for anomaly detection, making in-the-wild anomaly\ndetection realistically possible in future.\n", "date": "2024-10-09", "categories": []}
{"id": "2410.03732", "title": "Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly\n  Detection in Cellular Networks", "abstract": "  The rapid growth in mobile broadband usage and increasing subscribers have\nmade it crucial to ensure reliable network performance. As mobile networks grow\nmore complex, especially during peak hours, manual collection of Key\nPerformance Indicators (KPIs) is time-consuming due to the vast data involved.\nDetecting network failures and identifying unusual behavior during busy periods\nis vital to assess network health. Researchers have applied Deep Learning (DL)\nand Machine Learning (ML) techniques to understand network behavior by\npredicting throughput, analyzing call records, and detecting outages. However,\nthese methods often require significant computational power, large labeled\ndatasets, and are typically specialized, making retraining for new scenarios\ncostly and time-intensive.\n  This study introduces a novel approach Multi-Scale Convolutional LSTM with\nTransfer Learning (TL) to detect anomalies in cellular networks. The model is\ninitially trained from scratch using a publicly available dataset to learn\ntypical network behavior. Transfer Learning is then employed to fine-tune the\nmodel by applying learned weights to different datasets. We compare the\nperformance of the model trained from scratch with that of the fine-tuned model\nusing TL. To address class imbalance and gain deeper insights, Exploratory Data\nAnalysis (EDA) and the Synthetic Minority Over-sampling Technique (SMOTE) are\napplied. Results demonstrate that the model trained from scratch achieves 99%\naccuracy after 100 epochs, while the fine-tuned model reaches 95% accuracy on a\ndifferent dataset after just 20 epochs.\n", "date": "2024-09-30", "categories": []}
{"id": "2410.03904", "title": "Did You Hear That? Introducing AADG: A Framework for Generating\n  Benchmark Data in Audio Anomaly Detection", "abstract": "  We introduce a novel, general-purpose audio generation framework specifically\ndesigned for anomaly detection and localization. Unlike existing datasets that\npredominantly focus on industrial and machine-related sounds, our framework\nfocuses a broader range of environments, particularly useful in real-world\nscenarios where only audio data are available, such as in video-derived or\ntelephonic audio. To generate such data, we propose a new method inspired by\nthe LLM-Modulo framework, which leverages large language models(LLMs) as world\nmodels to simulate such real-world scenarios. This tool is modular allowing a\nplug-and-play approach. It operates by first using LLMs to predict plausible\nreal-world scenarios. An LLM further extracts the constituent sounds, the order\nand the way in which these should be merged to create coherent wholes. Much\nlike the LLM-Modulo framework, we include rigorous verification of each output\nstage, ensuring the reliability of the generated data. The data produced using\nthe framework serves as a benchmark for anomaly detection applications,\npotentially enhancing the performance of models trained on audio data,\nparticularly in handling out-of-distribution cases. Our contributions thus fill\na critical void in audio anomaly detection resources and provide a scalable\ntool for generating diverse, realistic audio data.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.04289", "title": "Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings\n  Methods", "abstract": "  Accurate anomaly detection is critical in vision-based infrastructure\ninspection, where it helps prevent costly failures and enhances safety.\nSelf-Supervised Learning (SSL) offers a promising approach by learning robust\nrepresentations from unlabeled data. However, its application in anomaly\ndetection remains underexplored. This paper addresses this gap by providing a\ncomprehensive evaluation of SSL methods for real-world anomaly detection,\nfocusing on sewer infrastructure. Using the Sewer-ML dataset, we evaluate\nlightweight models such as ViT-Tiny and ResNet-18 across SSL frameworks,\nincluding BYOL, Barlow Twins, SimCLR, DINO, and MAE, under varying class\nimbalance levels. Through 250 experiments, we rigorously assess the performance\nof these SSL methods to ensure a robust and comprehensive evaluation. Our\nfindings highlight the superiority of joint-embedding methods like SimCLR and\nBarlow Twins over reconstruction-based approaches such as MAE, which struggle\nto maintain performance under class imbalance. Furthermore, we find that the\nSSL model choice is more critical than the backbone architecture. Additionally,\nwe emphasize the need for better label-free assessments of SSL representations,\nas current methods like RankMe fail to adequately evaluate representation\nquality, making cross-validation without labels infeasible. Despite the\nremaining performance gap between SSL and supervised models, these findings\nhighlight the potential of SSL to enhance anomaly detection, paving the way for\nfurther research in this underexplored area of SSL applications.\n", "date": "2024-10-05", "categories": []}
{"id": "2410.01011", "title": "Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies\n  with an Integrated Statistical and Neural Framework", "abstract": "  Existing methods for anomaly detection often fall short due to their\ninability to handle the complexity, heterogeneity, and high dimensionality\ninherent in real-world mobility data. In this paper, we propose DeepBayesic, a\nnovel framework that integrates Bayesian principles with deep neural networks\nto model the underlying multivariate distributions from sparse and complex\ndatasets. Unlike traditional models, DeepBayesic is designed to manage\nheterogeneous inputs, accommodating both continuous and categorical data to\nprovide a more comprehensive understanding of mobility patterns. The framework\nfeatures customized neural density estimators and hybrid architectures,\nallowing for flexibility in modeling diverse feature distributions and enabling\nthe use of specialized neural networks tailored to different data types. Our\napproach also leverages agent embeddings for personalized anomaly detection,\nenhancing its ability to distinguish between normal and anomalous behaviors for\nindividual agents. We evaluate our approach on several mobility datasets,\ndemonstrating significant improvements over state-of-the-art anomaly detection\nmethods. Our results indicate that incorporating personalization and advanced\nsequence modeling techniques can substantially enhance the ability to detect\nsubtle and complex anomalies in spatiotemporal event sequences.\n", "date": "2024-10-01", "categories": []}
{"id": "2406.14398", "title": "ATAC-Net: Zoomed view works better for Anomaly Detection", "abstract": "  The application of deep learning in visual anomaly detection has gained\nwidespread popularity due to its potential use in quality control and\nmanufacturing. Current standard methods are Unsupervised, where a clean dataset\nis utilised to detect deviations and flag anomalies during testing. However,\nincorporating a few samples when the type of anomalies is known beforehand can\nsignificantly enhance performance. Thus, we propose ATAC-Net, a framework that\ntrains to detect anomalies from a minimal set of known prior anomalies.\nFurthermore, we introduce attention-guided cropping, which provides a closer\nview of suspect regions during the training phase. Our framework is a reliable\nand easy-to-understand system for detecting anomalies, and we substantiate its\nsuperiority to some of the current state-of-the-art techniques in a comparable\nsetting.\n", "date": "2024-06-20", "categories": []}
{"id": "2410.02087", "title": "HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks", "abstract": "  Identifying unusual brain activity is a crucial task in neuroscience\nresearch, as it aids in the early detection of brain disorders. It is common to\nrepresent brain networks as graphs, and researchers have developed various\ngraph-based machine learning methods for analyzing them. However, the majority\nof existing graph learning tools for the brain face a combination of the\nfollowing three key limitations. First, they focus only on pairwise\ncorrelations between regions of the brain, limiting their ability to capture\nsynchronized activity among larger groups of regions. Second, they model the\nbrain network as a static network, overlooking the temporal changes in the\nbrain. Third, most are designed only for classifying brain networks as healthy\nor disordered, lacking the ability to identify abnormal brain activity patterns\nlinked to biomarkers associated with disorders. To address these issues, we\npresent HyperBrain, an unsupervised anomaly detection framework for temporal\nhypergraph brain networks. HyperBrain models fMRI time series data as temporal\nhypergraphs capturing dynamic higher-order interactions. It then uses a novel\ncustomized temporal walk (BrainWalk) and neural encodings to detect abnormal\nco-activations among brain regions. We evaluate the performance of HyperBrain\nin both synthetic and real-world settings for Autism Spectrum Disorder and\nAttention Deficit Hyperactivity Disorder(ADHD). HyperBrain outperforms all\nother baselines on detecting abnormal co-activations in brain networks.\nFurthermore, results obtained from HyperBrain are consistent with clinical\nresearch on these brain disorders. Our findings suggest that learning temporal\nand higher-order connections in the brain provides a promising approach to\nuncover intricate connectivity patterns in brain networks, offering improved\ndiagnosis.\n", "date": "2024-10-02", "categories": []}
{"id": "2403.04151", "title": "Dual-path Frequency Discriminators for Few-shot Anomaly Detection", "abstract": "  Few-shot anomaly detection (FSAD) plays a crucial role in industrial\nmanufacturing. However, existing FSAD methods encounter difficulties leveraging\na limited number of normal samples, frequently failing to detect and locate\ninconspicuous anomalies in the spatial domain. We have further discovered that\nthese subtle anomalies would be more noticeable in the frequency domain. In\nthis paper, we propose a Dual-Path Frequency Discriminators (DFD) network from\na frequency perspective to tackle these issues. The original spatial images are\ntransformed into multi-frequency images, making them more conducive to the\ntailored discriminators in detecting anomalies. Additionally, the\ndiscriminators learn a joint representation with forms of pseudo-anomalies.\nExtensive experiments conducted on MVTec AD and VisA benchmarks demonstrate\nthat our DFD surpasses current state-of-the-art methods. The code is available\nat \\url{https://github.com/yuhbai/DFD}.\n", "date": "2024-03-07", "categories": []}
{"id": "2406.06370", "title": "UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving", "abstract": "  Dealing with atypical traffic scenarios remains a challenging task in\nautonomous driving. However, most anomaly detection approaches cannot be\ntrained on raw sensor data but require exposure to outlier data and powerful\nsemantic segmentation models trained in a supervised fashion. This limits the\nrepresentation of normality to labeled data, which does not scale well. In this\nwork, we revisit unsupervised anomaly detection and present UMAD, leveraging\ngenerative world models and unsupervised image segmentation. Our method\noutperforms state-of-the-art unsupervised anomaly detection.\n", "date": "2024-06-10", "categories": []}
{"id": "2401.13551", "title": "Interleaving One-Class and Weakly-Supervised Models with Adaptive\n  Thresholding for Unsupervised Video Anomaly Detection", "abstract": "  Video Anomaly Detection (VAD) has been extensively studied under the settings\nof One-Class Classification (OCC) and Weakly-Supervised learning (WS), which\nhowever both require laborious human-annotated normal/abnormal labels. In this\npaper, we study Unsupervised VAD (UVAD) that does not depend on any label by\ncombining OCC and WS into a unified training framework. Specifically, we extend\nOCC to weighted OCC (wOCC) and propose a wOCC-WS interleaving training module,\nwhere the two models automatically generate pseudo-labels for each other. We\nface two challenges to make the combination effective: (1) Models' performance\nfluctuates occasionally during the training process due to the inevitable\nrandomness of the pseudo labels. (2) Thresholds are needed to divide pseudo\nlabels, making the training depend on the accuracy of user intervention. For\nthe first problem, we propose to use wOCC requiring soft labels instead of OCC\ntrained with hard zero/one labels, as soft labels exhibit high consistency\nthroughout different training cycles while hard labels are prone to sudden\nchanges. For the second problem, we repeat the interleaving training module\nmultiple times, during which we propose an adaptive thresholding strategy that\ncan progressively refine a rough threshold to a relatively optimal threshold,\nwhich reduces the influence of user interaction. A benefit of employing OCC and\nWS methods to compose a UVAD method is that we can incorporate the most recent\nOCC or WS model into our framework. Experiments demonstrate the effectiveness\nof the proposed UVAD framework.\n", "date": "2024-01-24", "categories": []}
{"id": "2405.18050", "title": "Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs", "abstract": "  Anomaly detection in continuous-time dynamic graphs is an emerging field yet\nunder-explored in the context of learning algorithms. In this paper, we pioneer\nstructured analyses of link-level anomalies and graph representation learning\nfor identifying categorically anomalous graph links. First, we introduce a\nfine-grained taxonomy for edge-level anomalies leveraging structural, temporal,\nand contextual graph properties. Based on these properties, we introduce a\nmethod for generating and injecting typed anomalies into graphs. Next, we\nintroduce a novel method to generate continuous-time dynamic graphs featuring\nconsistencies across either or combinations of time, structure, and context. To\nenable temporal graph learning methods to detect specific types of anomalous\nlinks rather than the bare existence of a link, we extend the generic link\nprediction setting by: (1) conditioning link existence on contextual edge\nattributes; and (2) refining the training regime to accommodate diverse\nperturbations in the negative edge sampler. Comprehensive benchmarks on\nsynthetic and real-world datasets -- featuring synthetic and labeled organic\nanomalies and employing six state-of-the-art link prediction methods --\nvalidate our taxonomy and generation processes for anomalies and benign graphs,\nas well as our approach to adapting methods for anomaly detection. Our results\nreveal that different learning methods excel in capturing different aspects of\ngraph normality and detecting different types of anomalies. We conclude with a\ncomprehensive list of findings highlighting opportunities for future research.\n", "date": "2024-05-28", "categories": []}
{"id": "2409.19136", "title": "Kinematic Detection of Anomalies in Human Trajectory Data", "abstract": "  Historically, much of the research in understanding, modeling, and mining\nhuman trajectory data has focused on where an individual stays. Thus, the focus\nof existing research has been on where a user goes. On the other hand, the\nstudy of how a user moves between locations has great potential for new\nresearch opportunities. Kinematic features describe how an individual moves\nbetween locations and can be used for tasks such as identification of\nindividuals or anomaly detection. Unfortunately, data availability and quality\nchallenges make kinematic trajectory mining difficult. In this paper, we\nleverage the Geolife dataset of human trajectories to investigate the viability\nof using kinematic features to identify individuals and detect anomalies. We\nshow that humans have an individual \"kinematic profile\" which can be used as a\nstrong signal to identify individual humans. We experimentally show that, for\nthe two use-cases of individual identification and anomaly detection, simple\nkinematic features fed to standard classification and anomaly detection\nalgorithms significantly improve results.\n", "date": "2024-09-27", "categories": []}
{"id": "2409.19623", "title": "MCDDPM: Multichannel Conditional Denoising Diffusion Model for\n  Unsupervised Anomaly Detection in Brain MRI", "abstract": "  Detecting anomalies in brain MRI scans using supervised deep learning methods\npresents challenges due to anatomical diversity and labor-intensive requirement\nof pixel-level annotations. Generative models like Denoising Diffusion\nProbabilistic Model (DDPM) and their variants like pDDPM, mDDPM, cDDPM have\nrecently emerged to be powerful alternatives to perform unsupervised anomaly\ndetection in brain MRI scans. These methods leverage frame-level labels of\nhealthy brains to generate healthy tissues in brain MRI scans. During\ninference, when an anomalous (or unhealthy) scan image is presented as an\ninput, these models generate a healthy scan image corresponding to the input\nanomalous scan, and the difference map between the generated healthy scan image\nand the original anomalous scan image provide the necessary pixel level\nidentification of abnormal tissues. The generated healthy images from the DDPM,\npDDPM and mDDPM models however suffer from fidelity issues and contain\nartifacts that do not have medical significance. While cDDPM achieves slightly\nbetter fidelity and artifact suppression, it requires huge memory footprint and\nis computationally expensive than the other DDPM based models. In this work, we\npropose an improved version of DDPM called Multichannel Conditional Denoising\nDiffusion Probabilistic Model (MCDDPM) for unsupervised anomaly detection in\nbrain MRI scans. Our proposed model achieves high fidelity by making use of\nadditional information from the healthy images during the training process,\nenriching the representation power of DDPM models, with a computational cost\nand memory requirements on par with DDPM, pDDPM and mDDPM models. Experimental\nresults on multiple datasets (e.g. BraTS20, BraTS21) demonstrate promising\nperformance of the proposed method. The code is available at\nhttps://github.com/vivekkumartri/MCDDPM.\n", "date": "2024-09-29", "categories": []}
{"id": "2409.20208", "title": "Constraining Anomaly Detection with Anomaly-Free Regions", "abstract": "  We propose the novel concept of anomaly-free regions (AFR) to improve anomaly\ndetection. An AFR is a region in the data space for which it is known that\nthere are no anomalies inside it, e.g., via domain knowledge. This region can\ncontain any number of normal data points and can be anywhere in the data space.\nAFRs have the key advantage that they constrain the estimation of the\ndistribution of non-anomalies: The estimated probability mass inside the AFR\nmust be consistent with the number of normal data points inside the AFR. Based\non this insight, we provide a solid theoretical foundation and a reference\nimplementation of anomaly detection using AFRs. Our empirical results confirm\nthat anomaly detection constrained via AFRs improves upon unconstrained anomaly\ndetection. Specifically, we show that, when equipped with an estimated AFR, an\nefficient algorithm based on random guessing becomes a strong baseline that\nseveral widely-used methods struggle to overcome. On a dataset with a\nground-truth AFR available, the current state of the art is outperformed.\n", "date": "2024-09-30", "categories": []}
{"id": "2409.20353", "title": "CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset", "abstract": "  Machine learning models are increasingly being deployed in real-world\ncontexts. However, systematic studies on their transferability to specific and\ncritical applications are underrepresented in the research literature. An\nimportant example is visual anomaly detection (VAD) for robotic power line\ninspection. While existing VAD methods perform well in controlled environments,\nreal-world scenarios present diverse and unexpected anomalies that current\ndatasets fail to capture. To address this gap, we introduce\n$\\textit{CableInspect-AD}$, a high-quality, publicly available dataset created\nand annotated by domain experts from Hydro-Qu\\'ebec, a Canadian public utility.\nThis dataset includes high-resolution images with challenging real-world\nanomalies, covering defects with varying severity levels. To address the\nchallenges of collecting diverse anomalous and nominal examples for setting a\ndetection threshold, we propose an enhancement to the celebrated PatchCore\nalgorithm. This enhancement enables its use in scenarios with limited labeled\ndata. We also present a comprehensive evaluation protocol based on\ncross-validation to assess models' performances. We evaluate our\n$\\textit{Enhanced-PatchCore}$ for few-shot and many-shot detection, and\nVision-Language Models for zero-shot detection. While promising, these models\nstruggle to detect all anomalies, highlighting the dataset's value as a\nchallenging benchmark for the broader research community. Project page:\nhttps://mila-iqia.github.io/cableinspect-ad/.\n", "date": "2024-09-30", "categories": []}
{"id": "2409.18235", "title": "Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous\n  Data in Deep Neural Networks", "abstract": "  Deep neural networks (DNNs), while increasingly deployed in many\napplications, struggle with robustness against anomalous and\nout-of-distribution (OOD) data. Current OOD benchmarks often oversimplify,\nfocusing on single-object tasks and not fully representing complex real-world\nanomalies. This paper introduces a new, straightforward method employing graph\nstructures and topological features to effectively detect both far-OOD and\nnear-OOD data. We convert images into networks of interconnected human\nunderstandable features or visual concepts. Through extensive testing on two\nnovel tasks, including ablation studies with large vocabularies and diverse\ntasks, we demonstrate the method's effectiveness. This approach enhances DNN\nresilience to OOD data and promises improved performance in various\napplications.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.18874", "title": "CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly\n  Detection and Forecasting", "abstract": "  Anomaly detection in network traffic is crucial for maintaining the security\nof computer networks and identifying malicious activities. One of the primary\napproaches to anomaly detection are methods based on forecasting. Nevertheless,\nextensive real-world network datasets for forecasting and anomaly detection\ntechniques are missing, potentially causing performance overestimation of\nanomaly detection algorithms. This manuscript addresses this gap by introducing\na dataset comprising time series data of network entities' behavior, collected\nfrom the CESNET3 network. The dataset was created from 40 weeks of network\ntraffic of 275 thousand active IP addresses. The ISP origin of the presented\ndata ensures a high level of variability among network entities, which forms a\nunique and authentic challenge for forecasting and anomaly detection models. It\nprovides valuable insights into the practical deployment of forecast-based\nanomaly detection approaches.\n", "date": "2024-09-27", "categories": []}
{"id": "2406.09825", "title": "Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of\n  Anomalous Behavior in Bio-regenerative Life Support System Telemetry", "abstract": "  The detection of abnormal or critical system states is essential in condition\nmonitoring. While much attention is given to promptly identifying anomalies, a\nretrospective analysis of these anomalies can significantly enhance our\ncomprehension of the underlying causes of observed undesired behavior. This\naspect becomes particularly critical when the monitored system is deployed in a\nvital environment. In this study, we delve into anomalies within the domain of\nBio-Regenerative Life Support Systems (BLSS) for space exploration and analyze\nanomalies found in telemetry data stemming from the EDEN ISS space greenhouse\nin Antarctica. We employ time series clustering on anomaly detection results to\ncategorize various types of anomalies in both uni- and multivariate settings.\nWe then assess the effectiveness of these methods in identifying systematic\nanomalous behavior. Additionally, we illustrate that the anomaly detection\nmethods MDI and DAMP produce complementary results, as previously indicated by\nresearch.\n", "date": "2024-06-14", "categories": []}
{"id": "2409.14109", "title": "Vision-Language Models Assisted Unsupervised Video Anomaly Detection", "abstract": "  Video anomaly detection is a subject of great interest across industrial and\nacademic domains due to its crucial role in computer vision applications.\nHowever, the inherent unpredictability of anomalies and the scarcity of anomaly\nsamples present significant challenges for unsupervised learning methods. To\novercome the limitations of unsupervised learning, which stem from a lack of\ncomprehensive prior knowledge about anomalies, we propose VLAVAD\n(Video-Language Models Assisted Anomaly Detection). Our method employs a\ncross-modal pre-trained model that leverages the inferential capabilities of\nlarge language models (LLMs) in conjunction with a Selective-Prompt Adapter\n(SPA) for selecting semantic space. Additionally, we introduce a Sequence State\nSpace Module (S3M) that detects temporal inconsistencies in semantic features.\nBy mapping high-dimensional visual features to low-dimensional semantic ones,\nour method significantly enhance the interpretability of unsupervised anomaly\ndetection. Our proposed approach effectively tackles the challenge of detecting\nelusive anomalies that are hard to discern over periods, achieving SOTA on the\nchallenging ShanghaiTech dataset.\n", "date": "2024-09-21", "categories": []}
{"id": "2409.14816", "title": "VARADE: a Variational-based AutoRegressive model for Anomaly Detection\n  on the Edge", "abstract": "  Detecting complex anomalies on massive amounts of data is a crucial task in\nIndustry 4.0, best addressed by deep learning. However, available solutions are\ncomputationally demanding, requiring cloud architectures prone to latency and\nbandwidth issues. This work presents VARADE, a novel solution implementing a\nlight autoregressive framework based on variational inference, which is best\nsuited for real-time execution on the edge. The proposed approach was validated\non a robotic arm, part of a pilot production line, and compared with several\nstate-of-the-art algorithms, obtaining the best trade-off between anomaly\ndetection accuracy, power consumption and inference frequency on two different\nedge platforms.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.17330", "title": "VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection", "abstract": "  Semantic segmentation networks have achieved significant success under the\nassumption of independent and identically distributed data. However, these\nnetworks often struggle to detect anomalies from unknown semantic classes due\nto the limited set of visual concepts they are typically trained on. To address\nthis issue, anomaly segmentation often involves fine-tuning on outlier samples,\nnecessitating additional efforts for data collection, labeling, and model\nretraining. Seeking to avoid this cumbersome work, we take a different approach\nand propose to incorporate Vision-Language (VL) encoders into existing anomaly\ndetectors to leverage the semantically broad VL pre-training for improved\noutlier awareness. Additionally, we propose a new scoring function that enables\ndata- and training-free outlier supervision via textual prompts. The resulting\nVL4AD model, which includes max-logit prompt ensembling and a class-merging\nstrategy, achieves competitive performance on widely used benchmark datasets,\nthereby demonstrating the potential of vision-language models for pixel-wise\nanomaly detection.\n", "date": "2024-09-25", "categories": []}
{"id": "2409.17485", "title": "Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly\n  Detection", "abstract": "  Medical anomaly detection (AD) is crucial in pathological identification and\nlocalization. Current methods typically rely on uncertainty estimation in deep\nensembles to detect anomalies, assuming that ensemble learners should agree on\nnormal samples while exhibiting disagreement on unseen anomalies in the output\nspace. However, these methods may suffer from inadequate disagreement on\nanomalies or diminished agreement on normal samples. To tackle these issues, we\npropose D2UE, a Diversified Dual-space Uncertainty Estimation framework for\nmedical anomaly detection. To effectively balance agreement and disagreement\nfor anomaly detection, we propose Redundancy-Aware Repulsion (RAR), which uses\na similarity kernel that remains invariant to both isotropic scaling and\northogonal transformations, explicitly promoting diversity in learners' feature\nspace. Moreover, to accentuate anomalous regions, we develop Dual-Space\nUncertainty (DSU), which utilizes the ensemble's uncertainty in input and\noutput spaces. In input space, we first calculate gradients of reconstruction\nerror with respect to input images. The gradients are then integrated with\nreconstruction outputs to estimate uncertainty for inputs, enabling effective\nanomaly discrimination even when output space disagreement is minimal. We\nconduct a comprehensive evaluation of five medical benchmarks with different\nbackbones. Experimental results demonstrate the superiority of our method to\nstate-of-the-art methods and the effectiveness of each component in our\nframework. Our code is available at https://github.com/Rubiscol/D2UE.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.16821", "title": "XAI-guided Insulator Anomaly Detection for Imbalanced Datasets", "abstract": "  Power grids serve as a vital component in numerous industries, seamlessly\ndelivering electrical energy to industrial processes and technologies, making\ntheir safe and reliable operation indispensable. However, powerlines can be\nhard to inspect due to difficult terrain or harsh climatic conditions.\nTherefore, unmanned aerial vehicles are increasingly deployed to inspect\npowerlines, resulting in a substantial stream of visual data which requires\nswift and accurate processing. Deep learning methods have become widely popular\nfor this task, proving to be a valuable asset in fault detection. In\nparticular, the detection of insulator defects is crucial for predicting\npowerline failures, since their malfunction can lead to transmission\ndisruptions. It is therefore of great interest to continuously maintain and\nrigorously inspect insulator components. In this work we propose a novel\npipeline to tackle this task. We utilize state-of-the-art object detection to\ndetect and subsequently classify individual insulator anomalies. Our approach\naddresses dataset challenges such as imbalance and motion-blurred images\nthrough a fine-tuning methodology which allows us to alter the classification\nfocus of the model by increasing the classification accuracy of anomalous\ninsulators. In addition, we employ explainable-AI tools for precise\nlocalization and explanation of anomalies. This proposed method contributes to\nthe field of anomaly detection, particularly vision-based industrial inspection\nand predictive maintenance. We significantly improve defect detection accuracy\nby up to 13%, while also offering a detailed analysis of model\nmis-classifications and localization quality, showcasing the potential of our\nmethod on real-world data.\n", "date": "2024-09-25", "categories": []}
{"id": "2306.12955", "title": "Triggering Dark Showers with Conditional Dual Auto-Encoders", "abstract": "  We present a family of conditional dual auto-encoders (CoDAEs) for generic\nand model-independent new physics searches at colliders. New physics signals,\nwhich arise from new types of particles and interactions, are considered in our\nstudy as anomalies causing deviations in data with respect to expected\nbackground events. In this work, we perform a normal-only anomaly detection,\nwhich employs only background samples, to search for manifestations of a dark\nversion of strong force applying (variational) auto-encoders on raw detector\nimages, which are large and highly sparse, without leveraging any physics-based\npre-processing or strong assumption on the signals. The proposed CoDAE has a\ndual-encoder design, which is general and can learn an auxiliary yet compact\nlatent space through spatial conditioning, showing a neat improvement over\ncompetitive physics-based baselines and related approaches, therefore also\nreducing the gap with fully supervised models. It is the first time an\nunsupervised model is shown to exhibit excellent discrimination against\nmultiple dark shower models, illustrating the suitability of this method as an\naccurate, fast, model-independent algorithm to deploy, e.g., in the real-time\nevent triggering systems of Large Hadron Collider experiments such as ATLAS and\nCMS.\n", "date": "2023-06-22", "categories": []}
{"id": "2409.15304", "title": "Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled\n  Graph Neural Networks", "abstract": "  Recently, there has been a substantial amount of interest in GNN-based\nanomaly detection. Existing efforts have focused on simultaneously mastering\nthe node representations and the classifier necessary for identifying\nabnormalities with relatively shallow models to create an embedding. Therefore,\nthe existing state-of-the-art models are incapable of capturing nonlinear\nnetwork information and producing suboptimal outcomes. In this thesis, we\ndeploy decoupled GNNs to overcome this issue. Specifically, we decouple the\nessential node representations and classifier for detecting anomalies. In\naddition, for node representation learning, we develop a GNN architecture with\ntwo modules for aggregating node feature information to produce the final node\nembedding. Finally, we conduct empirical experiments to verify the\neffectiveness of our proposed approach. The findings demonstrate that decoupled\ntraining along with the global context enhanced representation of the nodes is\nsuperior to the state-of-the-art models in terms of AUC and introduces a novel\nway of capturing the node information.\n", "date": "2024-09-04", "categories": []}
{"id": "2409.15366", "title": "Trajectory Anomaly Detection with Language Models", "abstract": "  This paper presents a novel approach for trajectory anomaly detection using\nan autoregressive causal-attention model, termed LM-TAD. This method leverages\nthe similarities between language statements and trajectories, both of which\nconsist of ordered elements requiring coherence through external rules and\ncontextual variations. By treating trajectories as sequences of tokens, our\nmodel learns the probability distributions over trajectories, enabling the\nidentification of anomalous locations with high precision. We incorporate\nuser-specific tokens to account for individual behavior patterns, enhancing\nanomaly detection tailored to user context. Our experiments demonstrate the\neffectiveness of LM-TAD on both synthetic and real-world datasets. In\nparticular, the model outperforms existing methods on the Pattern of Life (PoL)\ndataset by detecting user-contextual anomalies and achieves competitive results\non the Porto taxi dataset, highlighting its adaptability and robustness.\nAdditionally, we introduce the use of perplexity and surprisal rate metrics for\ndetecting outliers and pinpointing specific anomalous locations within\ntrajectories. The LM-TAD framework supports various trajectory representations,\nincluding GPS coordinates, staypoints, and activity types, proving its\nversatility in handling diverse trajectory data. Moreover, our approach is\nwell-suited for online trajectory anomaly detection, significantly reducing\ncomputational latency by caching key-value states of the attention mechanism,\nthereby avoiding repeated computations.\n", "date": "2024-09-18", "categories": []}
{"id": "2409.15957", "title": "ASD-Diffusion: Anomalous Sound Detection with Diffusion Models", "abstract": "  Unsupervised Anomalous Sound Detection (ASD) aims to design a generalizable\nmethod that can be used to detect anomalies when only normal sounds are given.\nIn this paper, Anomalous Sound Detection based on Diffusion Models\n(ASD-Diffusion) is proposed for ASD in real-world factories. In our pipeline,\nthe anomalies in acoustic features are reconstructed from their noisy corrupted\nfeatures into their approximate normal pattern. Secondly, a post-processing\nanomalies filter algorithm is proposed to detect anomalies that exhibit\nsignificant deviation from the original input after reconstruction.\nFurthermore, denoising diffusion implicit model is introduced to accelerate the\ninference speed by a longer sampling interval of the denoising process. The\nproposed method is innovative in the application of diffusion models as a new\nscheme. Experimental results on the development set of DCASE 2023 challenge\ntask 2 outperform the baseline by 7.75%, demonstrating the effectiveness of the\nproposed method.\n", "date": "2024-09-24", "categories": []}
{"id": "2409.15986", "title": "Exploring the Impact of Outlier Variability on Anomaly Detection\n  Evaluation Metrics", "abstract": "  Anomaly detection is a dynamic field, in which the evaluation of models plays\na critical role in understanding their effectiveness. The selection and\ninterpretation of the evaluation metrics are pivotal, particularly in scenarios\nwith varying amounts of anomalies. This study focuses on examining the\nbehaviors of three widely used anomaly detection metrics under different\nconditions: F1 score, Receiver Operating Characteristic Area Under Curve (ROC\nAUC), and Precision-Recall Curve Area Under Curve (AUCPR). Our study critically\nanalyzes the extent to which these metrics provide reliable and distinct\ninsights into model performance, especially considering varying levels of\noutlier fractions and contamination thresholds in datasets.\n  Through a comprehensive experimental setup involving widely recognized\nalgorithms for anomaly detection, we present findings that challenge the\nconventional understanding of these metrics and reveal nuanced behaviors under\nvarying conditions. We demonstrated that while the F1 score and AUCPR are\nsensitive to outlier fractions, the ROC AUC maintains consistency and is\nunaffected by such variability. Additionally, under conditions of a fixed\noutlier fraction in the test set, we observe an alignment between ROC AUC and\nAUCPR, indicating that the choice between these two metrics may be less\ncritical in such scenarios.\n  The results of our study contribute to a more refined understanding of metric\nselection and interpretation in anomaly detection, offering valuable insights\nfor both researchers and practitioners in the field.\n", "date": "2024-09-24", "categories": []}
{"id": "2409.14796", "title": "Research on Dynamic Data Flow Anomaly Detection based on Machine\n  Learning", "abstract": "  The sophistication and diversity of contemporary cyberattacks have rendered\nthe use of proxies, gateways, firewalls, and encrypted tunnels as a standalone\ndefensive strategy inadequate. Consequently, the proactive identification of\ndata anomalies has emerged as a prominent area of research within the field of\ndata security. The majority of extant studies concentrate on sample equilibrium\ndata, with the consequence that the detection effect is not optimal in the\ncontext of unbalanced data. In this study, the unsupervised learning method is\nemployed to identify anomalies in dynamic data flows. Initially,\nmulti-dimensional features are extracted from real-time data, and a clustering\nalgorithm is utilised to analyse the patterns of the data. This enables the\npotential outliers to be automatically identified. By clustering similar data,\nthe model is able to detect data behaviour that deviates significantly from\nnormal traffic without the need for labelled data. The results of the\nexperiments demonstrate that the proposed method exhibits high accuracy in the\ndetection of anomalies across a range of scenarios. Notably, it demonstrates\nrobust and adaptable performance, particularly in the context of unbalanced\ndata.\n", "date": "2024-09-23", "categories": []}
{"id": "2309.05630", "title": "Boundary Peeling: Outlier Detection Method Using One-Class Peeling", "abstract": "  Unsupervised outlier detection constitutes a crucial phase within data\nanalysis and remains a dynamic realm of research. A good outlier detection\nalgorithm should be computationally efficient, robust to tuning parameter\nselection, and perform consistently well across diverse underlying data\ndistributions. We introduce One-Class Boundary Peeling, an unsupervised outlier\ndetection algorithm. One-class Boundary Peeling uses the average signed\ndistance from iteratively-peeled, flexible boundaries generated by one-class\nsupport vector machines. One-class Boundary Peeling has robust hyperparameter\nsettings and, for increased flexibility, can be cast as an ensemble method. In\nsynthetic data simulations One-Class Boundary Peeling outperforms all state of\nthe art methods when no outliers are present while maintaining comparable or\nsuperior performance in the presence of outliers, as compared to benchmark\nmethods. One-Class Boundary Peeling performs competitively in terms of correct\nclassification, AUC, and processing time using common benchmark data sets.\n", "date": "2023-09-11", "categories": []}
{"id": "2402.19295", "title": "Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical\n  Bayesian Modelling", "abstract": "  Population-based structural health monitoring (PBSHM), aims to share\ninformation between members of a population. An offshore wind (OW) farm could\nbe considered as a population of nominally-identical wind-turbine structures.\nHowever, benign variations exist among members, such as geometry, sea-bed\nconditions and temperature differences. These factors could influence\nstructural properties and therefore the dynamic response, making it more\ndifficult to detect structural problems via traditional SHM techniques. This\npaper explores the use of a hierarchical Bayesian model to infer expected soil\nstiffness distributions at both population and local levels, as a basis to\nperform anomaly detection, in the form of scour, for new and existing turbines.\nTo do this, observations of natural frequency will be generated as though they\nare from a small population of wind turbines. Differences between individual\nobservations will be introduced by postulating distributions over the soil\nstiffness and measurement noise, as well as reducing soil depth (to represent\nscour), in the case of anomaly detection.\n", "date": "2024-02-29", "categories": []}
{"id": "2409.12785", "title": "Investigation on domain adaptation of additive manufacturing monitoring\n  systems to enhance digital twin reusability", "abstract": "  Powder bed fusion (PBF) is an emerging metal additive manufacturing (AM)\ntechnology that enables rapid fabrication of complex geometries. However,\ndefects such as pores and balling may occur and lead to structural\nunconformities, thus compromising the mechanical performance of the part. This\nhas become a critical challenge for quality assurance as the nature of some\ndefects is stochastic during the process and invisible from the exterior. To\naddress this issue, digital twin (DT) using machine learning (ML)-based\nmodeling can be deployed for AM process monitoring and control. Melt pool is\none of the most commonly observed physical phenomena for process monitoring,\nusually by high-speed cameras. Once labeled and preprocessed, the melt pool\nimages are used to train ML-based models for DT applications such as process\nanomaly detection and print quality evaluation. Nonetheless, the reusability of\nDTs is restricted due to the wide variability of AM settings, including AM\nmachines and monitoring instruments. The performance of the ML models trained\nusing the dataset collected from one setting is usually compromised when\napplied to other settings. This paper proposes a knowledge transfer pipeline\nbetween different AM settings to enhance the reusability of AM DTs. The source\nand target datasets are collected from the National Institute of Standards and\nTechnology and National Cheng Kung University with different cameras,\nmaterials, AM machines, and process parameters. The proposed pipeline consists\nof four steps: data preprocessing, data augmentation, domain alignment, and\ndecision alignment. Compared with the model trained only using the source\ndataset, this pipeline increased the melt pool anomaly detection accuracy by\n31% without any labeled training data from the target dataset.\n", "date": "2024-09-19", "categories": []}
{"id": "2409.13053", "title": "Towards Unbiased Evaluation of Time-series Anomaly Detector", "abstract": "  Time series anomaly detection (TSAD) is an evolving area of research\nmotivated by its critical applications, such as detecting seismic activity,\nsensor failures in industrial plants, predicting crashes in the stock market,\nand so on. Across domains, anomalies occur significantly less frequently than\nnormal data, making the F1-score the most commonly adopted metric for anomaly\ndetection. However, in the case of time series, it is not straightforward to\nuse standard F1-score because of the dissociation between `time points' and\n`time events'. To accommodate this, anomaly predictions are adjusted, called as\npoint adjustment (PA), before the $F_1$-score evaluation. However, these\nadjustments are heuristics-based, and biased towards true positive detection,\nresulting in over-estimated detector performance. In this work, we propose an\nalternative adjustment protocol called ``Balanced point adjustment'' (BA). It\naddresses the limitations of existing point adjustment methods and provides\nguarantees of fairness backed by axiomatic definitions of TSAD evaluation.\n", "date": "2024-09-19", "categories": []}
{"id": "2409.13162", "title": "Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection\n  Framework", "abstract": "  Detecting anomalies within point clouds is crucial for various industrial\napplications, but traditional unsupervised methods face challenges due to data\nacquisition costs, early-stage production constraints, and limited\ngeneralization across product categories. To overcome these challenges, we\nintroduce the Multi-View Projection (MVP) framework, leveraging pre-trained\nVision-Language Models (VLMs) to detect anomalies. Specifically, MVP projects\npoint cloud data into multi-view depth images, thereby translating point cloud\nanomaly detection into image anomaly detection. Following zero-shot image\nanomaly detection methods, pre-trained VLMs are utilized to detect anomalies on\nthese depth images. Given that pre-trained VLMs are not inherently tailored for\nzero-shot point cloud anomaly detection and may lack specificity, we propose\nthe integration of learnable visual and adaptive text prompting techniques to\nfine-tune these VLMs, thereby enhancing their detection performance. Extensive\nexperiments on the MVTec 3D-AD and Real3D-AD demonstrate our proposed MVP\nframework's superior zero-shot anomaly detection performance and the prompting\ntechniques' effectiveness. Real-world evaluations on automotive plastic part\ninspection further showcase that the proposed method can also be generalized to\npractical unseen scenarios. The code is available at\nhttps://github.com/hustCYQ/MVP-PCLIP.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.13437", "title": "Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative\n  Models", "abstract": "  Brain imaging has allowed neuroscientists to analyze brain morphology in\ngenetic and neurodevelopmental disorders, such as Down syndrome, pinpointing\nregions of interest to unravel the neuroanatomical underpinnings of cognitive\nimpairment and memory deficits. However, the connections between brain anatomy,\ncognitive performance and comorbidities like Alzheimer's disease are still\npoorly understood in the Down syndrome population. The latest advances in\nartificial intelligence constitute an opportunity for developing automatic\ntools to analyze large volumes of brain magnetic resonance imaging scans,\novercoming the bottleneck of manual analysis. In this study, we propose the use\nof generative models for detecting brain alterations in people with Down\nsyndrome affected by various degrees of neurodegeneration caused by Alzheimer's\ndisease. To that end, we evaluate state-of-the-art brain anomaly detection\nmodels based on Variational Autoencoders and Diffusion Models, leveraging a\nproprietary dataset of brain magnetic resonance imaging scans. Following a\ncomprehensive evaluation process, our study includes several key analyses.\nFirst, we conducted a qualitative evaluation by expert neuroradiologists.\nSecond, we performed both quantitative and qualitative reconstruction fidelity\nstudies for the generative models. Third, we carried out an ablation study to\nexamine how the incorporation of histogram post-processing can enhance model\nperformance. Finally, we executed a quantitative volumetric analysis of\nsubcortical structures. Our findings indicate that some models effectively\ndetect the primary alterations characterizing Down syndrome's brain anatomy,\nincluding a smaller cerebellum, enlarged ventricles, and cerebral cortex\nreduction, as well as the parietal lobe alterations caused by Alzheimer's\ndisease.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.13466", "title": "Global Outlier Detection in a Federated Learning Setting with Isolation\n  Forest", "abstract": "  We present a novel strategy for detecting global outliers in a federated\nlearning setting, targeting in particular cross-silo scenarios. Our approach\ninvolves the use of two servers and the transmission of masked local data from\nclients to one of the servers. The masking of the data prevents the disclosure\nof sensitive information while still permitting the identification of outliers.\nMoreover, to further safeguard privacy, a permutation mechanism is implemented\nso that the server does not know which client owns any masked data point. The\nserver performs outlier detection on the masked data, using either Isolation\nForest or its extended version, and then communicates outlier information back\nto the clients, allowing them to identify and remove outliers in their local\ndatasets before starting any subsequent federated model training. This approach\nprovides comparable results to a centralized execution of Isolation Forest\nalgorithms on plain data.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.12726", "title": "Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for\n  Early Detection of Cloud Services' User Anomalies", "abstract": "  Ensuring the security of cloud environments is imperative for sustaining\norganizational growth and operational efficiency. As the ubiquity of cloud\nservices continues to rise, the inevitability of cyber threats underscores the\nimportance of preemptive detection. This paper introduces a pioneering\ntime-based embedding approach for Cloud Services Graph-based Anomaly Detection\n(CS-GAD), utilizing a Graph Neural Network (GNN) to discern anomalous user\nbehavior during interactions with cloud services. Our method employs a dynamic\ntripartite graph representation to encapsulate the evolving interactions among\ncloud services, users, and their activities over time. Leveraging GNN models in\neach time frame, our approach generates a graph embedding wherein each user is\nassigned a score based on their historical activity, facilitating the\nidentification of unusual behavior. Results demonstrate a notable reduction in\nfalse positive rates (2-9%) compared to prevailing methods, coupled with a\ncommendable true positive rate (100%). The contributions of this work encompass\nearly detection capabilities, a low false positive rate, an innovative\ntripartite graph representation incorporating action types, the introduction of\na new cloud services dataset featuring various user attacks, and an open-source\nimplementation for community collaboration in advancing cloud service security.\n", "date": "2024-09-19", "categories": []}
{"id": "2405.13031", "title": "A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in\n  Text", "abstract": "  Anomaly detection (AD) is a fast growing and popular domain among established\napplications like vision and time series. We observe a rich literature for\nthese applications, but anomaly detection in text is only starting to blossom.\nRecently, self-supervised methods with self-attention mechanism have been the\nmost popular choice. While recent works have proposed a working ground for\nbuilding and benchmarking state of the art approaches, we propose two principal\ncontributions in this paper: contextual anomaly contamination and a novel\nensemble-based approach. Our method, Textual Anomaly Contamination (TAC),\nallows to contaminate inlier classes with either independent or contextual\nanomalies. In the literature, it appears that this distinction is not\nperformed. For finding contextual anomalies, we propose RoSAE, a Robust\nSubspace Local Recovery Autoencoder Ensemble. All autoencoders of the ensemble\npresent a different latent representation through local manifold learning.\nBenchmark shows that our approach outperforms recent works on both independent\nand contextual anomalies, while being more robust. We also provide 8 dataset\ncomparison instead of only relying to Reuters and 20 Newsgroups corpora.\n", "date": "2024-05-16", "categories": []}
{"id": "2409.11534", "title": "Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to\n  Screening Mammogram", "abstract": "  Out-of-distribution (OOD) detection is crucial for enhancing the\ngeneralization of AI models used in mammogram screening. Given the challenge of\nlimited prior knowledge about OOD samples in external datasets, unsupervised\ngenerative learning is a preferable solution which trains the model to discern\nthe normal characteristics of in-distribution (ID) data. The hypothesis is that\nduring inference, the model aims to reconstruct ID samples accurately, while\nOOD samples exhibit poorer reconstruction due to their divergence from\nnormality. Inspired by state-of-the-art (SOTA) hybrid architectures combining\nCNNs and transformers, we developed a novel backbone - HAND, for detecting OOD\nfrom large-scale digital screening mammogram studies. To boost the learning\nefficiency, we incorporated synthetic OOD samples and a parallel discriminator\nin the latent space to distinguish between ID and OOD samples. Gradient\nreversal to the OOD reconstruction loss penalizes the model for learning OOD\nreconstructions. An anomaly score is computed by weighting the reconstruction\nand discriminator loss. On internal RSNA mammogram held-out test and external\nMayo clinic hand-curated dataset, the proposed HAND model outperformed\nencoder-based and GAN-based baselines, and interestingly, it also outperformed\nthe hybrid CNN+transformer baselines. Therefore, the proposed HAND pipeline\noffers an automated efficient computational solution for domain-specific\nquality checks in external screening mammograms, yielding actionable insights\nwithout direct exposure to the private medical imaging data.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.11807", "title": "Constraint Guided AutoEncoders for Joint Optimization of Condition\n  Indicator Estimation and Anomaly Detection in Machine Condition Monitoring", "abstract": "  The main goal of machine condition monitoring is, as the name implies, to\nmonitor the condition of industrial applications. The objective of this\nmonitoring can be mainly split into two problems. A diagnostic problem, where\nnormal data should be distinguished from anomalous data, otherwise called\nAnomaly Detection (AD), or a prognostic problem, where the aim is to predict\nthe evolution of a Condition Indicator (CI) that reflects the condition of an\nasset throughout its life time. When considering machine condition monitoring,\nit is expected that this CI shows a monotonic behavior, as the condition of a\nmachine gradually degrades over time. This work proposes an extension to\nConstraint Guided AutoEncoders (CGAE), which is a robust AD method, that\nenables building a single model that can be used for both AD and CI estimation.\nFor the purpose of improved CI estimation the extension incorporates a\nconstraint that enforces the model to have monotonically increasing CI\npredictions over time. Experimental results indicate that the proposed\nalgorithm performs similar, or slightly better, than CGAE, with regards to AD,\nwhile improving the monotonic behavior of the CI.\n", "date": "2024-09-18", "categories": []}
{"id": "2408.06620", "title": "Unveiling the Flaws: A Critical Analysis of Initialization Effect on\n  Time Series Anomaly Detection", "abstract": "  Deep learning for time-series anomaly detection (TSAD) has gained significant\nattention over the past decade. Despite the reported improvements in several\npapers, the practical application of these models remains limited. Recent\nstudies have cast doubt on these models, attributing their results to flawed\nevaluation techniques. However, the impact of initialization has largely been\noverlooked. This paper provides a critical analysis of the initialization\neffects on TSAD model performance. Our extensive experiments reveal that TSAD\nmodels are highly sensitive to hyperparameters such as window size, seed\nnumber, and normalization. This sensitivity often leads to significant\nvariability in performance, which can be exploited to artificially inflate the\nreported efficacy of these models. We demonstrate that even minor changes in\ninitialization parameters can result in performance variations that overshadow\nthe claimed improvements from novel model architectures. Our findings highlight\nthe need for rigorous evaluation protocols and transparent reporting of\npreprocessing steps to ensure the reliability and fairness of anomaly detection\nmethods. This paper calls for a more cautious interpretation of TSAD\nadvancements and encourages the development of more robust and transparent\nevaluation practices to advance the field and its practical applications.\n", "date": "2024-08-13", "categories": []}
{"id": "2409.10951", "title": "Fair Anomaly Detection For Imbalanced Groups", "abstract": "  Anomaly detection (AD) has been widely studied for decades in many real-world\napplications, including fraud detection in finance, and intrusion detection for\ncybersecurity, etc. Due to the imbalanced nature between protected and\nunprotected groups and the imbalanced distributions of normal examples and\nanomalies, the learning objectives of most existing anomaly detection methods\ntend to solely concentrate on the dominating unprotected group. Thus, it has\nbeen recognized by many researchers about the significance of ensuring model\nfairness in anomaly detection. However, the existing fair anomaly detection\nmethods tend to erroneously label most normal examples from the protected group\nas anomalies in the imbalanced scenario where the unprotected group is more\nabundant than the protected group. This phenomenon is caused by the improper\ndesign of learning objectives, which statistically focus on learning the\nfrequent patterns (i.e., the unprotected group) while overlooking the\nunder-represented patterns (i.e., the protected group). To address these\nissues, we propose FairAD, a fairness-aware anomaly detection method targeting\nthe imbalanced scenario. It consists of a fairness-aware contrastive learning\nmodule and a rebalancing autoencoder module to ensure fairness and handle the\nimbalanced data issue, respectively. Moreover, we provide the theoretical\nanalysis that shows our proposed contrastive learning regularization guarantees\ngroup fairness. Empirical studies demonstrate the effectiveness and efficiency\nof FairAD across multiple real-world datasets.\n", "date": "2024-09-17", "categories": []}
{"id": "2310.10461", "title": "Model Selection of Anomaly Detectors in the Absence of Labeled\n  Validation Data", "abstract": "  Anomaly detection is the task of identifying abnormal samples in large\nunlabeled datasets. While the advent of foundation models has produced powerful\nzero-shot anomaly detection methods, their deployment in practice is often\nhindered by the absence of labeled validation data -- without it, their\ndetection performance cannot be evaluated reliably. In this work, we propose\nSWSA (Selection With Synthetic Anomalies): a general-purpose framework to\nselect image-based anomaly detectors without labeled validation data. Instead\nof collecting labeled validation data, we generate synthetic anomalies without\nany training or fine-tuning, using only a small support set of normal images.\nOur synthetic anomalies are used to create detection tasks that compose a\nvalidation framework for model selection. In an empirical study, we evaluate\nSWSA with three types of synthetic anomalies and on two selection tasks: model\nselection of image-based anomaly detectors and prompt selection for CLIP-based\nanomaly detection. SWSA often selects models and prompts that match selections\nmade with a ground-truth validation set, outperforming baseline selection\nstrategies.\n", "date": "2023-10-16", "categories": []}
{"id": "2404.17961", "title": "Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex\n  Driving Scenes", "abstract": "  In anomaly segmentation for complex driving scenes, state-of-the-art\napproaches utilize anomaly scoring functions to calculate anomaly scores. For\nthese functions, accurately predicting the logits of inlier classes for each\npixel is crucial for precisely inferring the anomaly score. However, in\nreal-world driving scenarios, the diversity of scenes often results in\ndistorted manifolds of pixel embeddings in the space. This effect is not\nconducive to directly using the pixel embeddings for the logit prediction\nduring inference, a concern overlooked by existing methods. To address this\nproblem, we propose a novel method called Random Walk on Pixel Manifolds\n(RWPM). RWPM utilizes random walks to reveal the intrinsic relationships among\npixels to refine the pixel embeddings. The refined pixel embeddings alleviate\nthe distortion of manifolds, improving the accuracy of anomaly scores. Our\nextensive experiments show that RWPM consistently improve the performance of\nthe existing anomaly segmentation methods and achieve the best results. Code is\navailable at: \\url{https://github.com/ZelongZeng/RWPM}.\n", "date": "2024-04-27", "categories": []}
{"id": "2406.13987", "title": "Image anomaly detection and prediction scheme based on SSA optimized\n  ResNet50-BiGRU model", "abstract": "  Image anomaly detection is a popular research direction, with many methods\nemerging in recent years due to rapid advancements in computing. The use of\nartificial intelligence for image anomaly detection has been widely studied. By\nanalyzing images of athlete posture and movement, it is possible to predict\ninjury status and suggest necessary adjustments. Most existing methods rely on\nconvolutional networks to extract information from irrelevant pixel data,\nlimiting model accuracy. This paper introduces a network combining Residual\nNetwork (ResNet) and Bidirectional Gated Recurrent Unit (BiGRU), which can\npredict potential injury types and provide early warnings by analyzing changes\nin muscle and bone poses from video images. To address the high complexity of\nthis network, the Sparrow search algorithm was used for optimization.\nExperiments conducted on four datasets demonstrated that our model has the\nsmallest error in image anomaly detection compared to other models, showing\nstrong adaptability. This provides a new approach for anomaly detection and\npredictive analysis in images, contributing to the sustainable development of\nhuman health and performance.\n", "date": "2024-06-20", "categories": []}
{"id": "2407.05639", "title": "Deep Learning-based Anomaly Detection and Log Analysis for Computer\n  Networks", "abstract": "  Computer network anomaly detection and log analysis, as an important topic in\nthe field of network security, has been a key task to ensure network security\nand system reliability. First, existing network anomaly detection and log\nanalysis methods are often challenged by high-dimensional data and complex\nnetwork topologies, resulting in unstable performance and high false-positive\nrates. In addition, traditional methods are usually difficult to handle\ntime-series data, which is crucial for anomaly detection and log analysis.\nTherefore, we need a more efficient and accurate method to cope with these\nproblems. To compensate for the shortcomings of current methods, we propose an\ninnovative fusion model that integrates Isolation Forest, GAN (Generative\nAdversarial Network), and Transformer with each other, and each of them plays a\nunique role. Isolation Forest is used to quickly identify anomalous data\npoints, and GAN is used to generate synthetic data with the real data\ndistribution characteristics to augment the training dataset, while the\nTransformer is used for modeling and context extraction on time series data.\nThe synergy of these three components makes our model more accurate and robust\nin anomaly detection and log analysis tasks. We validate the effectiveness of\nthis fusion model in an extensive experimental evaluation. Experimental results\nshow that our model significantly improves the accuracy of anomaly detection\nwhile reducing the false alarm rate, which helps to detect potential network\nproblems in advance. The model also performs well in the log analysis task and\nis able to quickly identify anomalous behaviors, which helps to improve the\nstability of the system. The significance of this study is that it introduces\nadvanced deep learning techniques, which work anomaly detection and log\nanalysis.\n", "date": "2024-07-08", "categories": []}
{"id": "2409.04834", "title": "Reducing Events to Augment Log-based Anomaly Detection Models: An\n  Empirical Study", "abstract": "  As software systems grow increasingly intricate, the precise detection of\nanomalies have become both essential and challenging. Current log-based anomaly\ndetection methods depend heavily on vast amounts of log data leading to\ninefficient inference and potential misguidance by noise logs. However, the\nquantitative effects of log reduction on the effectiveness of anomaly detection\nremain unexplored. Therefore, we first conduct a comprehensive study on six\ndistinct models spanning three datasets. Through the study, the impact of log\nquantity and their effectiveness in representing anomalies is qualifies,\nuncovering three distinctive log event types that differently influence model\nperformance. Drawing from these insights, we propose LogCleaner: an efficient\nmethodology for the automatic reduction of log events in the context of anomaly\ndetection. Serving as middleware between software systems and models,\nLogCleaner continuously updates and filters anti-events and duplicative-events\nin the raw generated logs. Experimental outcomes highlight LogCleaner's\ncapability to reduce over 70% of log events in anomaly detection, accelerating\nthe model's inference speed by approximately 300%, and universally improving\nthe performance of models for anomaly detection.\n", "date": "2024-09-07", "categories": []}
{"id": "2409.09742", "title": "OML-AD: Online Machine Learning for Anomaly Detection in Time Series\n  Data", "abstract": "  Time series are ubiquitous and occur naturally in a variety of applications\n-- from data recorded by sensors in manufacturing processes, over financial\ndata streams to climate data. Different tasks arise, such as regression,\nclassification or segmentation of the time series. However, to reliably solve\nthese challenges, it is important to filter out abnormal observations that\ndeviate from the usual behavior of the time series. While many anomaly\ndetection methods exist for independent data and stationary time series, these\nmethods are not applicable to non-stationary time series. To allow for\nnon-stationarity in the data, while simultaneously detecting anomalies, we\npropose OML-AD, a novel approach for anomaly detection (AD) based on online\nmachine learning (OML). We provide an implementation of OML-AD within the\nPython library River and show that it outperforms state-of-the-art baseline\nmethods in terms of accuracy and computational efficiency.\n", "date": "2024-09-15", "categories": []}
{"id": "2409.09804", "title": "Abnormal Event Detection In Videos Using Deep Embedding", "abstract": "  Abnormal event detection or anomaly detection in surveillance videos is\ncurrently a challenge because of the diversity of possible events. Due to the\nlack of anomalous events at training time, anomaly detection requires the\ndesign of learning methods without supervision. In this work we propose an\nunsupervised approach for video anomaly detection with the aim to jointly\noptimize the objectives of the deep neural network and the anomaly detection\ntask using a hybrid architecture. Initially, a convolutional autoencoder is\npre-trained in an unsupervised manner with a fusion of depth, motion and\nappearance features. In the second step, we utilize the encoder part of the\npre-trained autoencoder and extract the embeddings of the fused input. Now, we\njointly train/ fine tune the encoder to map the embeddings to a hypercenter.\nThus, embeddings of normal data fall near the hypercenter, whereas embeddings\nof anomalous data fall far away from the hypercenter.\n", "date": "2024-09-15", "categories": []}
{"id": "2409.10069", "title": "Enhancing Anomaly Detection via Generating Diversified and\n  Hard-to-distinguish Synthetic Anomalies", "abstract": "  Unsupervised anomaly detection is a daunting task, as it relies solely on\nnormality patterns from the training data to identify unseen anomalies during\ntesting. Recent approaches have focused on leveraging domain-specific\ntransformations or perturbations to generate synthetic anomalies from normal\nsamples. The objective here is to acquire insights into normality patterns by\nlearning to differentiate between normal samples and these crafted anomalies.\nHowever, these approaches often encounter limitations when domain-specific\ntransformations are not well-specified such as in tabular data, or when it\nbecomes trivial to distinguish between them. To address these issues, we\nintroduce a novel domain-agnostic method that employs a set of conditional\nperturbators and a discriminator. The perturbators are trained to generate\ninput-dependent perturbations, which are subsequently utilized to construct\nsynthetic anomalies, and the discriminator is trained to distinguish normal\nsamples from them. We ensure that the generated anomalies are both diverse and\nhard to distinguish through two key strategies: i) directing perturbations to\nbe orthogonal to each other and ii) constraining perturbations to remain in\nproximity to normal samples. Throughout experiments on real-world datasets, we\ndemonstrate the superiority of our method over state-of-the-art benchmarks,\nwhich is evident not only in image data but also in tabular data, where\ndomain-specific transformation is not readily accessible. Additionally, we\nempirically confirm the adaptability of our method to semi-supervised settings,\ndemonstrating its capacity to incorporate supervised signals to enhance anomaly\ndetection performance even further.\n", "date": "2024-09-16", "categories": []}
{"id": "2407.08838", "title": "Deep Learning for Network Anomaly Detection under Data Contamination:\n  Evaluating Robustness and Mitigating Performance Degradation", "abstract": "  Deep learning (DL) has emerged as a crucial tool in network anomaly detection\n(NAD) for cybersecurity. While DL models for anomaly detection excel at\nextracting features and learning patterns from data, they are vulnerable to\ndata contamination -- the inadvertent inclusion of attack-related data in\ntraining sets presumed benign. This study evaluates the robustness of six\nunsupervised DL algorithms against data contamination using our proposed\nevaluation protocol. Results demonstrate significant performance degradation in\nstate-of-the-art anomaly detection algorithms when exposed to contaminated\ndata, highlighting the critical need for self-protection mechanisms in DL-based\nNAD models. To mitigate this vulnerability, we propose an enhanced auto-encoder\nwith a constrained latent representation, allowing normal data to cluster more\ndensely around a learnable center in the latent space. Our evaluation reveals\nthat this approach exhibits improved resistance to data contamination compared\nto existing methods, offering a promising direction for more robust NAD\nsystems.\n", "date": "2024-07-11", "categories": []}
{"id": "2409.08521", "title": "Optimal Classification-based Anomaly Detection with Neural Networks:\n  Theory and Practice", "abstract": "  Anomaly detection is an important problem in many application areas, such as\nnetwork security. Many deep learning methods for unsupervised anomaly detection\nproduce good empirical performance but lack theoretical guarantees. By casting\nanomaly detection into a binary classification problem, we establish\nnon-asymptotic upper bounds and a convergence rate on the excess risk on\nrectified linear unit (ReLU) neural networks trained on synthetic anomalies.\nOur convergence rate on the excess risk matches the minimax optimal rate in the\nliterature. Furthermore, we provide lower and upper bounds on the number of\nsynthetic anomalies that can attain this optimality. For practical\nimplementation, we relax some conditions to improve the search for the\nempirical risk minimizer, which leads to competitive performance to other\nclassification-based methods for anomaly detection. Overall, our work provides\nthe first theoretical guarantees of unsupervised neural network-based anomaly\ndetectors and empirical insights on how to design them well.\n", "date": "2024-09-13", "categories": []}
{"id": "2406.18197", "title": "Human-Free Automated Prompting for Vision-Language Anomaly Detection:\n  Prompt Optimization with Meta-guiding Prompt Scheme", "abstract": "  Pre-trained vision-language models (VLMs) are highly adaptable to various\ndownstream tasks through few-shot learning, making prompt-based anomaly\ndetection a promising approach. Traditional methods depend on human-crafted\nprompts that require prior knowledge of specific anomaly types. Our goal is to\ndevelop a human-free prompt-based anomaly detection framework that optimally\nlearns prompts through data-driven methods, eliminating the need for human\nintervention. The primary challenge in this approach is the lack of anomalous\nsamples during the training phase. Additionally, the Vision Transformer\n(ViT)-based image encoder in VLMs is not ideal for pixel-wise anomaly\nsegmentation due to a locality feature mismatch between the original image and\nthe output feature map. To tackle the first challenge, we have developed the\nObject-Attention Anomaly Generation Module (OAGM) to synthesize anomaly samples\nfor training. Furthermore, our Meta-Guiding Prompt-Tuning Scheme (MPTS)\niteratively adjusts the gradient-based optimization direction of learnable\nprompts to avoid overfitting to the synthesized anomalies. For the second\nchallenge, we propose Locality-Aware Attention, which ensures that each local\npatch feature attends only to nearby patch features, preserving the locality\nfeatures corresponding to their original locations. This framework allows for\nthe optimal prompt embeddings by searching in the continuous latent space via\nbackpropagation, free from human semantic constraints. Additionally, the\nmodified locality-aware attention improves the precision of pixel-wise anomaly\nsegmentation.\n", "date": "2024-06-26", "categories": []}
{"id": "2409.06831", "title": "Atom dimension adaptation for infinite set dictionary learning", "abstract": "  Recent work on dictionary learning with set-atoms has shown benefits in\nanomaly detection. Instead of viewing an atom as a single vector, these methods\nallow building sparse representations with atoms taken from a set around a\ncentral vector; the set can be a cone or may have a probability distribution\nassociated to it. We propose a method for adaptively adjusting the size of\nset-atoms in Gaussian and cone dictionary learning. The purpose of the\nalgorithm is to match the atom sizes with their contribution in representing\nthe signals. The proposed algorithm not only decreases the representation\nerror, but also improves anomaly detection, for a class of anomalies called\n`dependency'. We obtain better detection performance than state-of-the-art\nmethods.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.07135", "title": "Unsupervised Novelty Detection Methods Benchmarking with Wavelet\n  Decomposition", "abstract": "  Novelty detection is a critical task in various engineering fields. Numerous\napproaches to novelty detection rely on supervised or semi-supervised learning,\nwhich requires labelled datasets for training. However, acquiring labelled\ndata, when feasible, can be expensive and time-consuming. For these reasons,\nunsupervised learning is a powerful alternative that allows performing novelty\ndetection without needing labelled samples. In this study, numerous\nunsupervised machine learning algorithms for novelty detection are compared,\nhighlighting their strengths and weaknesses in the context of vibration\nsensing. The proposed framework uses a continuous metric, unlike most\ntraditional methods that merely flag anomalous samples without quantifying the\ndegree of anomaly. Moreover, a new dataset is gathered from an actuator\nvibrating at specific frequencies to benchmark the algorithms and evaluate the\nframework. Novel conditions are introduced by altering the input wave signal.\nOur findings offer valuable insights into the adaptability and robustness of\nunsupervised learning techniques for real-world novelty detection applications.\n", "date": "2024-09-11", "categories": []}
{"id": "2310.07511", "title": "Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery", "abstract": "  Remote sensing anomaly detector can find the objects deviating from the\nbackground as potential targets for Earth monitoring. Given the diversity in\nearth anomaly types, designing a transferring model with cross-modality\ndetection ability should be cost-effective and flexible to new earth\nobservation sources and anomaly types. However, the current anomaly detectors\naim to learn the certain background distribution, the trained model cannot be\ntransferred to unseen images. Inspired by the fact that the deviation metric\nfor score ranking is consistent and independent from the image distribution,\nthis study exploits the learning target conversion from the varying background\ndistribution to the consistent deviation metric. We theoretically prove that\nthe large-margin condition in labeled samples ensures the transferring ability\nof learned deviation metric. To satisfy this condition, two large margin losses\nfor pixel-level and feature-level deviation ranking are proposed respectively.\nSince the real anomalies are difficult to acquire, anomaly simulation\nstrategies are designed to compute the model loss. With the large-margin\nlearning for deviation metric, the trained model achieves cross-modality\ndetection ability in five modalities including hyperspectral, visible light,\nsynthetic aperture radar (SAR), infrared and low-light in zero-shot manner.\n", "date": "2023-10-11", "categories": []}
{"id": "2409.06285", "title": "Context Enhancement with Reconstruction as Sequence for Unified\n  Unsupervised Anomaly Detection", "abstract": "  Unsupervised anomaly detection (AD) aims to train robust detection models\nusing only normal samples, while can generalize well to unseen anomalies.\nRecent research focuses on a unified unsupervised AD setting in which only one\nmodel is trained for all classes, i.e., n-class-one-model paradigm.\nFeature-reconstruction-based methods achieve state-of-the-art performance in\nthis scenario. However, existing methods often suffer from a lack of sufficient\ncontextual awareness, thereby compromising the quality of the reconstruction.\nTo address this issue, we introduce a novel Reconstruction as Sequence (RAS)\nmethod, which enhances the contextual correspondence during feature\nreconstruction from a sequence modeling perspective. In particular, based on\nthe transformer technique, we integrate a specialized RASFormer block into RAS.\nThis block enables the capture of spatial relationships among different image\nregions and enhances sequential dependencies throughout the reconstruction\nprocess. By incorporating the RASFormer block, our RAS method achieves superior\ncontextual awareness capabilities, leading to remarkable performance.\nExperimental results show that our RAS significantly outperforms competing\nmethods, well demonstrating the effectiveness and superiority of our method.\nOur code is available at https://github.com/Nothingtolose9979/RAS.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.06367", "title": "Texture-AD: An Anomaly Detection Dataset and Benchmark for Real\n  Algorithm Development", "abstract": "  Anomaly detection is a crucial process in industrial manufacturing and has\nmade significant advancements recently. However, there is a large variance\nbetween the data used in the development and the data collected by the\nproduction environment. Therefore, we present the Texture-AD benchmark based on\nrepresentative texture-based anomaly detection to evaluate the effectiveness of\nunsupervised anomaly detection algorithms in real-world applications. This\ndataset includes images of 15 different cloth, 14 semiconductor wafers and 10\nmetal plates acquired under different optical schemes. In addition, it includes\nmore than 10 different types of defects produced during real manufacturing\nprocesses, such as scratches, wrinkles, color variations and point defects,\nwhich are often more difficult to detect than existing datasets. All anomalous\nareas are provided with pixel-level annotations to facilitate comprehensive\nevaluation using anomaly detection models. Specifically, to adapt to diverse\nproducts in automated pipelines, we present a new evaluation method and results\nof baseline algorithms. The experimental results show that Texture-AD is a\ndifficult challenge for state-of-the-art algorithms. To our knowledge,\nTexture-AD is the first dataset to be devoted to evaluating industrial defect\ndetection algorithms in the real world. The dataset is available at\nhttps://XXX.\n", "date": "2024-09-10", "categories": []}
{"id": "2211.00372", "title": "Meta-Learning for Unsupervised Outlier Detection with Optimal Transport", "abstract": "  Automated machine learning has been widely researched and adopted in the\nfield of supervised classification and regression, but progress in unsupervised\nsettings has been limited. We propose a novel approach to automate outlier\ndetection based on meta-learning from previous datasets with outliers. Our\npremise is that the selection of the optimal outlier detection technique\ndepends on the inherent properties of the data distribution. We leverage\noptimal transport in particular, to find the dataset with the most similar\nunderlying distribution, and then apply the outlier detection techniques that\nproved to work best for that data distribution. We evaluate the robustness of\nour approach and find that it outperforms the state of the art methods in\nunsupervised outlier detection. This approach can also be easily generalized to\nautomate other unsupervised settings.\n", "date": "2022-11-01", "categories": []}
{"id": "2402.15374", "title": "Outlier detection by ensembling uncertainty with negative objectness", "abstract": "  Outlier detection is an essential capability in safety-critical applications\nof supervised visual recognition. Most of the existing methods deliver best\nresults by encouraging standard closed-set models to produce low-confidence\npredictions in negative training data. However, that approach conflates\nprediction uncertainty with recognition of the negative class. We therefore\nreconsider direct prediction of K+1 logits that correspond to K groundtruth\nclasses and one outlier class. This setup allows us to formulate a novel\nanomaly score as an ensemble of in-distribution uncertainty and the posterior\nof the outlier class which we term negative objectness. Now outliers can be\nindependently detected due to i) high prediction uncertainty or ii) similarity\nwith negative data. We embed our method into a dense prediction architecture\nwith mask-level recognition over K+2 classes. The training procedure encourages\nthe novel K+2-th class to learn negative objectness at pasted negative\ninstances. Our models outperform the current state-of-the art on standard\nbenchmarks for image-wide and pixel-level outlier detection with and without\ntraining on real negative data.\n", "date": "2024-02-23", "categories": []}
{"id": "2406.07487", "title": "GLAD: Towards Better Reconstruction with Global and Local Adaptive\n  Diffusion Models for Unsupervised Anomaly Detection", "abstract": "  Diffusion models have shown superior performance on unsupervised anomaly\ndetection tasks. Since trained with normal data only, diffusion models tend to\nreconstruct normal counterparts of test images with certain noises added.\nHowever, these methods treat all potential anomalies equally, which may cause\ntwo main problems. From the global perspective, the difficulty of\nreconstructing images with different anomalies is uneven. Therefore, instead of\nutilizing the same setting for all samples, we propose to predict a particular\ndenoising step for each sample by evaluating the difference between image\ncontents and the priors extracted from diffusion models. From the local\nperspective, reconstructing abnormal regions differs from normal areas even in\nthe same image. Theoretically, the diffusion model predicts a noise for each\nstep, typically following a standard Gaussian distribution. However, due to the\ndifference between the anomaly and its potential normal counterpart, the\npredicted noise in abnormal regions will inevitably deviate from the standard\nGaussian distribution. To this end, we propose introducing synthetic abnormal\nsamples in training to encourage the diffusion models to break through the\nlimitation of standard Gaussian distribution, and a spatial-adaptive feature\nfusion scheme is utilized during inference. With the above modifications, we\npropose a global and local adaptive diffusion model (abbreviated to GLAD) for\nunsupervised anomaly detection, which introduces appealing flexibility and\nachieves anomaly-free reconstruction while retaining as much normal information\nas possible. Extensive experiments are conducted on three commonly used anomaly\ndetection datasets (MVTec-AD, MPDD, and VisA) and a printed circuit board\ndataset (PCB-Bank) we integrated, showing the effectiveness of the proposed\nmethod.\n", "date": "2024-06-11", "categories": []}
{"id": "2409.05035", "title": "Deep Generic Representations for Domain-Generalized Anomalous Sound\n  Detection", "abstract": "  Developing a reliable anomalous sound detection (ASD) system requires\nrobustness to noise, adaptation to domain shifts, and effective performance\nwith limited training data. Current leading methods rely on extensive labeled\ndata for each target machine type to train feature extractors using\nOutlier-Exposure (OE) techniques, yet their performance on the target domain\nremains sub-optimal. In this paper, we present \\textit{GenRep}, which utilizes\ngeneric feature representations from a robust, large-scale pre-trained feature\nextractor combined with kNN for domain-generalized ASD, without the need for\nfine-tuning. \\textit{GenRep} incorporates MemMixup, a simple approach for\naugmenting the target memory bank using nearest source samples, paired with a\ndomain normalization technique to address the imbalance between source and\ntarget domains. \\textit{GenRep} outperforms the best OE-based approach without\na need for labeled data with an Official Score of 73.79\\% on the DCASE2023T2\nEval set and demonstrates robustness under limited data scenarios. The code is\navailable open-source.\n", "date": "2024-09-08", "categories": []}
{"id": "2409.05319", "title": "Machine Anomalous Sound Detection Using Spectral-temporal Modulation\n  Representations Derived from Machine-specific Filterbanks", "abstract": "  Early detection of factory machinery malfunctions is crucial in industrial\napplications. In machine anomalous sound detection (ASD), different machines\nexhibit unique vibration-frequency ranges based on their physical properties.\nMeanwhile, the human auditory system is adept at tracking both temporal and\nspectral dynamics of machine sounds. Consequently, integrating the\ncomputational auditory models of the human auditory system with\nmachine-specific properties can be an effective approach to machine ASD. We\nfirst quantified the frequency importances of four types of machines using the\nFisher ratio (F-ratio). The quantified frequency importances were then used to\ndesign machine-specific non-uniform filterbanks (NUFBs), which extract the log\nnon-uniform spectrum (LNS) feature. The designed NUFBs have a narrower\nbandwidth and higher filter distribution density in frequency regions with\nrelatively high F-ratios. Finally, spectral and temporal modulation\nrepresentations derived from the LNS feature were proposed. These proposed LNS\nfeature and modulation representations are input into an autoencoder\nneural-network-based detector for ASD. The quantification results from the\ntraining set of the Malfunctioning Industrial Machine Investigation and\nInspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the\ndistinguishing information between normal and anomalous sounds of different\nmachines is encoded non-uniformly in the frequency domain. By highlighting\nthese important frequency regions using NUFBs, the LNS feature can\nsignificantly enhance performance using the metric of AUC (area under the\nreceiver operating characteristic curve) under various SNR conditions.\nFurthermore, modulation representations can further improve performance.\nSpecifically, temporal modulation is effective for fans, pumps, and sliders,\nwhile spectral modulation is particularly effective for valves.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.05346", "title": "GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced\n  Driver Assistance System", "abstract": "  For electric vehicles, the Adaptive Cruise Control (ACC) in Advanced Driver\nAssistance Systems (ADAS) is designed to assist braking based on driving\nconditions, road inclines, predefined deceleration strengths, and user braking\npatterns. However, the driving data collected during the development of ADAS\nare generally limited and lack diversity. This deficiency leads to late or\naggressive braking for different users. Crucially, it is necessary to\neffectively identify anomalies, such as unexpected or inconsistent braking\npatterns in ADAS, especially given the challenge of working with unlabelled,\nlimited, and noisy datasets from real-world electric vehicles. In order to\ntackle the aforementioned challenges in ADAS, we propose Graph Neural\nControlled Differential Equation Normalizing Flow (GDFlow), a model that\nleverages Normalizing Flow (NF) with Neural Controlled Differential Equations\n(NCDE) to learn the distribution of normal driving patterns continuously.\nCompared to the traditional clustering or anomaly detection algorithms, our\napproach effectively captures the spatio-temporal information from different\nsensor data and more accurately models continuous changes in driving patterns.\nAdditionally, we introduce a quantile-based maximum likelihood objective to\nimprove the likelihood estimate of the normal data near the boundary of the\ndistribution, enhancing the model's ability to distinguish between normal and\nanomalous patterns. We validate GDFlow using real-world electric vehicle\ndriving data that we collected from Hyundai IONIQ5 and GV80EV, achieving\nstate-of-the-art performance compared to six baselines across four dataset\nconfigurations of different vehicle types and drivers. Furthermore, our model\noutperforms the latest anomaly detection methods across four time series\nbenchmark datasets. Our approach demonstrates superior efficiency in inference\ntime compared to existing methods.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.05378", "title": "Memoryless Multimodal Anomaly Detection via Student-Teacher Network and\n  Signed Distance Learning", "abstract": "  Unsupervised anomaly detection is a challenging computer vision task, in\nwhich 2D-based anomaly detection methods have been extensively studied.\nHowever, multimodal anomaly detection based on RGB images and 3D point clouds\nrequires further investigation. The existing methods are mainly inspired by\nmemory bank based methods commonly used in 2D-based anomaly detection, which\nmay cost extra memory for storing mutimodal features. In present study, a novel\nmemoryless method MDSS is proposed for multimodal anomaly detection, which\nemploys a light-weighted student-teacher network and a signed distance function\nto learn from RGB images and 3D point clouds respectively, and complements the\nanomaly information from the two modalities. Specifically, a student-teacher\nnetwork is trained with normal RGB images and masks generated from point clouds\nby a dynamic loss, and the anomaly score map could be obtained from the\ndiscrepancy between the output of student and teacher. Furthermore, the signed\ndistance function learns from normal point clouds to predict the signed\ndistances between points and surface, and the obtained signed distances are\nused to generate anomaly score map. Subsequently, the anomaly score maps are\naligned to generate the final anomaly score map for detection. The experimental\nresults indicate that MDSS is comparable but more stable than the SOTA memory\nbank based method Shape-guided, and furthermore performs better than other\nbaseline methods.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.05389", "title": "A Novel Representation of Periodic Pattern and Its Application to\n  Untrained Anomaly Detection", "abstract": "  There are a variety of industrial products that possess periodic textures or\nsurfaces, such as carbon fiber textiles and display panels. Traditional\nimage-based quality inspection methods for these products require identifying\nthe periodic patterns from normal images (without anomaly and noise) and\nsubsequently detecting anomaly pixels with inconsistent appearances. However,\nit remains challenging to accurately extract the periodic pattern from a single\nimage in the presence of unknown anomalies and measurement noise. To deal with\nthis challenge, this paper proposes a novel self-representation of the periodic\nimage defined on a set of continuous parameters. In this way, periodic pattern\nlearning can be embedded into a joint optimization framework, which is named\nperiodic-sparse decomposition, with simultaneously modeling the sparse\nanomalies and Gaussian noise. Finally, for the real-world industrial images\nthat may not strictly satisfy the periodic assumption, we propose a novel\npixel-level anomaly scoring strategy to enhance the performance of anomaly\ndetection. Both simulated and real-world case studies demonstrate the\neffectiveness of the proposed methodology for periodic pattern learning and\nanomaly detection.\n", "date": "2024-09-09", "categories": []}
{"id": "2409.05611", "title": "Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly\n  Detection", "abstract": "  Most unsupervised anomaly detection methods based on representations of\nnormal samples to distinguish anomalies have recently made remarkable progress.\nHowever, existing methods only learn a single decision boundary for\ndistinguishing the samples within the training dataset, neglecting the\nvariation in feature distribution for normal samples even in the same category\nin the real world. Furthermore, it was not considered that a distribution bias\nstill exists between the test set and the train set. Therefore, we propose an\nAdapted-MoE which contains a routing network and a series of expert models to\nhandle multiple distributions of same-category samples by divide and conquer.\nSpecifically, we propose a routing network based on representation learning to\nroute same-category samples into the subclasses feature space. Then, a series\nof expert models are utilized to learn the representation of various normal\nsamples and construct several independent decision boundaries. We propose the\ntest-time adaption to eliminate the bias between the unseen test sample\nrepresentation and the feature distribution learned by the expert model. Our\nexperiments are conducted on a dataset that provides multiple subclasses from\nthree categories, namely Texture AD benchmark. The Adapted-MoE significantly\nimproves the performance of the baseline model, achieving 2.18%-7.20% and\n1.57%-16.30% increase in I-AUROC and P-AUROC, which outperforms the current\nstate-of-the-art methods. Our code is available at https://github.com/.\n", "date": "2024-09-09", "categories": []}
{"id": "2212.11192", "title": "Continual Learning Approaches for Anomaly Detection", "abstract": "  Anomaly Detection is a relevant problem that arises in numerous real-world\napplications, especially when dealing with images. However, there has been\nlittle research for this task in the Continual Learning setting. In this work,\nwe introduce a novel approach called SCALE (SCALing is Enough) to perform\nCompressed Replay in a framework for Anomaly Detection in Continual Learning\nsetting. The proposed technique scales and compresses the original images using\na Super Resolution model which, to the best of our knowledge, is studied for\nthe first time in the Continual Learning setting. SCALE can achieve a high\nlevel of compression while maintaining a high level of image reconstruction\nquality. In conjunction with other Anomaly Detection approaches, it can achieve\noptimal results. To validate the proposed approach, we use a real-world dataset\nof images with pixel-based anomalies, with the scope to provide a reliable\nbenchmark for Anomaly Detection in the context of Continual Learning, serving\nas a foundation for further advancements in the field.\n", "date": "2022-12-21", "categories": []}
{"id": "2409.03024", "title": "NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks", "abstract": "  Collecting real-world mobility data is challenging. It is often fraught with\nprivacy concerns, logistical difficulties, and inherent biases. Moreover,\naccurately annotating anomalies in large-scale data is nearly impossible, as it\ndemands meticulous effort to distinguish subtle and complex patterns. These\nchallenges significantly impede progress in geospatial anomaly detection\nresearch by restricting access to reliable data and complicating the rigorous\nevaluation, comparison, and benchmarking of methodologies. To address these\nlimitations, we introduce a synthetic mobility dataset, NUMOSIM, that provides\na controlled, ethical, and diverse environment for benchmarking anomaly\ndetection techniques. NUMOSIM simulates a wide array of realistic mobility\nscenarios, encompassing both typical and anomalous behaviours, generated\nthrough advanced deep learning models trained on real mobility data. This\napproach allows NUMOSIM to accurately replicate the complexities of real-world\nmovement patterns while strategically injecting anomalies to challenge and\nevaluate detection algorithms based on how effectively they capture the\ninterplay between demographic, geospatial, and temporal factors. Our goal is to\nadvance geospatial mobility analysis by offering a realistic benchmark for\nimproving anomaly detection and mobility modeling techniques. To support this,\nwe provide open access to the NUMOSIM dataset, along with comprehensive\ndocumentation, evaluation metrics, and benchmark results.\n", "date": "2024-09-04", "categories": []}
{"id": "2309.02854", "title": "A Critical Review of Common Log Data Sets Used for Evaluation of\n  Sequence-based Anomaly Detection Techniques", "abstract": "  Log data store event execution patterns that correspond to underlying\nworkflows of systems or applications. While most logs are informative, log data\nalso include artifacts that indicate failures or incidents. Accordingly, log\ndata are often used to evaluate anomaly detection techniques that aim to\nautomatically disclose unexpected or otherwise relevant system behavior\npatterns. Recently, detection approaches leveraging deep learning have\nincreasingly focused on anomalies that manifest as changes of sequential\npatterns within otherwise normal event traces. Several publicly available data\nsets, such as HDFS, BGL, Thunderbird, OpenStack, and Hadoop, have since become\nstandards for evaluating these anomaly detection techniques, however, the\nappropriateness of these data sets has not been closely investigated in the\npast. In this paper we therefore analyze six publicly available log data sets\nwith focus on the manifestations of anomalies and simple techniques for their\ndetection. Our findings suggest that most anomalies are not directly related to\nsequential manifestations and that advanced detection techniques are not\nrequired to achieve high detection rates on these data sets.\n", "date": "2023-09-06", "categories": []}
{"id": "2409.02973", "title": "SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in\n  Streaming Data Analysis", "abstract": "  Streaming data analysis is increasingly required in applications, e.g., IoT,\ncybersecurity, robotics, mechatronics or cyber-physical systems. Despite its\nrelevance, it is still an emerging field with open challenges. SDO is a recent\nanomaly detection method designed to meet requirements of speed,\ninterpretability and intuitive parameterization. In this work, we present\nSDOoop, which extends the capabilities of SDO's streaming version to retain\ntemporal information of data structures. SDOoop spots contextual anomalies\nundetectable by traditional algorithms, while enabling the inspection of data\ngeometries, clusters and temporal patterns. We used SDOoop to model real\nnetwork communications in critical infrastructures and extract patterns that\ndisclose their dynamics. Moreover, we evaluated SDOoop with data from intrusion\ndetection and natural science domains and obtained performances equivalent or\nsuperior to state-of-the-art approaches. Our results show the high potential of\nnew model-based methods to analyze and explain streaming data. Since SDOoop\noperates with constant per-sample space and time complexity, it is ideal for\nbig data, being able to instantly process large volumes of information. SDOoop\nconforms to next-generation machine learning, which, in addition to accuracy\nand speed, is expected to provide highly interpretable and informative models.\n", "date": "2024-09-04", "categories": []}
{"id": "2409.03046", "title": "Oddballness: universal anomaly detection with language models", "abstract": "  We present a new method to detect anomalies in texts (in general: in\nsequences of any data), using language models, in a totally unsupervised\nmanner. The method considers probabilities (likelihoods) generated by a\nlanguage model, but instead of focusing on low-likelihood tokens, it considers\na new metric introduced in this paper: oddballness. Oddballness measures how\n``strange'' a given token is according to the language model. We demonstrate in\ngrammatical error detection tasks (a specific case of text anomaly detection)\nthat oddballness is better than just considering low-likelihood events, if a\ntotally unsupervised setup is assumed.\n", "date": "2024-09-04", "categories": []}
{"id": "2409.03236", "title": "Unveiling Context-Related Anomalies: Knowledge Graph Empowered\n  Decoupling of Scene and Action for Human-Related Video Anomaly Detection", "abstract": "  Detecting anomalies in human-related videos is crucial for surveillance\napplications. Current methods primarily include appearance-based and\naction-based techniques. Appearance-based methods rely on low-level visual\nfeatures such as color, texture, and shape. They learn a large number of pixel\npatterns and features related to known scenes during training, making them\neffective in detecting anomalies within these familiar contexts. However, when\nencountering new or significantly changed scenes, i.e., unknown scenes, they\noften fail because existing SOTA methods do not effectively capture the\nrelationship between actions and their surrounding scenes, resulting in low\ngeneralization. In contrast, action-based methods focus on detecting anomalies\nin human actions but are usually less informative because they tend to overlook\nthe relationship between actions and their scenes, leading to incorrect\ndetection. For instance, the normal event of running on the beach and the\nabnormal event of running on the street might both be considered normal due to\nthe lack of scene information. In short, current methods struggle to integrate\nlow-level visual and high-level action features, leading to poor anomaly\ndetection in varied and complex scenes. To address this challenge, we propose a\nnovel decoupling-based architecture for human-related video anomaly detection\n(DecoAD). DecoAD significantly improves the integration of visual and action\nfeatures through the decoupling and interweaving of scenes and actions, thereby\nenabling a more intuitive and accurate understanding of complex behaviors and\nscenes. DecoAD supports fully supervised, weakly supervised, and unsupervised\nsettings.\n", "date": "2024-09-05", "categories": []}
{"id": "2409.03657", "title": "Unsupervised Anomaly Detection and Localization with Generative\n  Adversarial Networks", "abstract": "  We propose a novel unsupervised anomaly detection approach using generative\nadversarial networks and SOP-derived spectrograms. Demonstrating remarkable\nefficacy, our method achieves over 97% accuracy on SOP datasets from both\nsubmarine and terrestrial fiber links, all achieved without the need for\nlabelled data.\n", "date": "2024-09-05", "categories": []}
{"id": "2409.02290", "title": "Unsupervised Welding Defect Detection Using Audio And Video", "abstract": "  In this work we explore the application of AI to robotic welding. Robotic\nwelding is a widely used technology in many industries, but robots currently do\nnot have the capability to detect welding defects which get introduced due to\nvarious reasons in the welding process. We describe how deep-learning methods\ncan be applied to detect weld defects in real-time by recording the welding\nprocess with microphones and a camera. Our findings are based on a large\ndatabase with more than 4000 welding samples we collected which covers\ndifferent weld types, materials and various defect categories. All deep\nlearning models are trained in an unsupervised fashion because the space of\npossible defects is large and the defects in our data may contain biases. We\ndemonstrate that a reliable real-time detection of most categories of weld\ndefects is feasible both from audio and video, with improvements achieved by\ncombining both modalities. Specifically, the multi-modal approach achieves an\naverage Area-under-ROC-Curve (AUC) of 0.92 over all eleven defect types in our\ndata. We conclude the paper with an analysis of the results by defect type and\na discussion of future work.\n", "date": "2024-09-03", "categories": []}
{"id": "2211.05244", "title": "Deep Learning for Time Series Anomaly Detection: A Survey", "abstract": "  Time series anomaly detection has applications in a wide range of research\nfields and applications, including manufacturing and healthcare. The presence\nof anomalies can indicate novel or unexpected events, such as production\nfaults, system defects, or heart fluttering, and is therefore of particular\ninterest. The large size and complex patterns of time series have led\nresearchers to develop specialised deep learning models for detecting anomalous\npatterns. This survey focuses on providing structured and comprehensive\nstate-of-the-art time series anomaly detection models through the use of deep\nlearning. It providing a taxonomy based on the factors that divide anomaly\ndetection models into different categories. Aside from describing the basic\nanomaly detection technique for each category, the advantages and limitations\nare also discussed. Furthermore, this study includes examples of deep anomaly\ndetection in time series across various application domains in recent years. It\nfinally summarises open issues in research and challenges faced while adopting\ndeep anomaly detection models.\n", "date": "2022-11-09", "categories": []}
{"id": "2301.11418", "title": "Parkinson gait modelling from an anomaly deep representation", "abstract": "  Parkinson's Disease (PD) is associated with gait movement disorders, such as\nbradykinesia, stiffness, tremors and postural instability, caused by\nprogressive dopamine deficiency. Today, some approaches have implemented\nlearning representations to quantify kinematic patterns during locomotion,\nsupporting clinical procedures such as diagnosis and treatment planning. These\napproaches assumes a large amount of stratified and labeled data to optimize\ndiscriminative representations. Nonetheless these considerations may restrict\nthe approaches to be operable in real scenarios during clinical practice. This\nwork introduces a self-supervised generative representation to learn\ngait-motion-related patterns, under the pretext of video reconstruction and an\nanomaly detection framework. This architecture is trained following a one-class\nweakly supervised learning to avoid inter-class variance and approach the\nmultiple relationships that represent locomotion. The proposed approach was\nvalidated with 14 PD patients and 23 control subjects, and trained with the\ncontrol population only, achieving an AUC of 95%, homocedasticity level of 70%\nand shapeness level of 70% in the classification task considering its\ngeneralization.\n", "date": "2023-01-26", "categories": []}
{"id": "2308.09296", "title": "CARLA: Self-supervised Contrastive Representation Learning for Time\n  Series Anomaly Detection", "abstract": "  One main challenge in time series anomaly detection (TSAD) is the lack of\nlabelled data in many real-life scenarios. Most of the existing anomaly\ndetection methods focus on learning the normal behaviour of unlabelled time\nseries in an unsupervised manner. The normal boundary is often defined tightly,\nresulting in slight deviations being classified as anomalies, consequently\nleading to a high false positive rate and a limited ability to generalise\nnormal patterns. To address this, we introduce a novel end-to-end\nself-supervised ContrAstive Representation Learning approach for time series\nAnomaly detection (CARLA). While existing contrastive learning methods assume\nthat augmented time series windows are positive samples and temporally distant\nwindows are negative samples, we argue that these assumptions are limited as\naugmentation of time series can transform them to negative samples, and a\ntemporally distant window can represent a positive sample. Our contrastive\napproach leverages existing generic knowledge about time series anomalies and\ninjects various types of anomalies as negative samples. Therefore, CARLA not\nonly learns normal behaviour but also learns deviations indicating anomalies.\nIt creates similar representations for temporally closed windows and distinct\nones for anomalies. Additionally, it leverages the information about\nrepresentations' neighbours through a self-supervised approach to classify\nwindows based on their nearest/furthest neighbours to further enhance the\nperformance of anomaly detection. In extensive tests on seven major real-world\ntime series anomaly detection datasets, CARLA shows superior performance over\nstate-of-the-art self-supervised and unsupervised TSAD methods. Our research\nshows the potential of contrastive representation learning to advance time\nseries anomaly detection.\n", "date": "2023-08-18", "categories": []}
{"id": "2402.03846", "title": "Efficient Generation of Hidden Outliers for Improved Outlier Detection", "abstract": "  Outlier generation is a popular technique used for solving important outlier\ndetection tasks. Generating outliers with realistic behavior is challenging.\nPopular existing methods tend to disregard the 'multiple views' property of\noutliers in high-dimensional spaces. The only existing method accounting for\nthis property falls short in efficiency and effectiveness. We propose BISECT, a\nnew outlier generation method that creates realistic outliers mimicking said\nproperty. To do so, BISECT employs a novel proposition introduced in this\narticle stating how to efficiently generate said realistic outliers. Our method\nhas better guarantees and complexity than the current methodology for\nrecreating 'multiple views'. We use the synthetic outliers generated by BISECT\nto effectively enhance outlier detection in diverse datasets, for multiple use\ncases. For instance, oversampling with BISECT reduced the error by up to 3\ntimes when compared with the baselines.\n", "date": "2024-02-06", "categories": []}
{"id": "2402.10802", "title": "TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly\n  Detection Models", "abstract": "  Time series anomaly detection (TSAD) has gained significant attention due to\nits real-world applications to improve the stability of modern software\nsystems. However, there is no effective way to verify whether they can meet the\nrequirements for real-world deployment. Firstly, current algorithms typically\ntrain a specific model for each time series. Maintaining such many models is\nimpractical in a large-scale system with tens of thousands of curves. The\nperformance of using merely one unified model to detect anomalies remains\nunknown. Secondly, most TSAD models are trained on the historical part of a\ntime series and are tested on its future segment. In distributed systems,\nhowever, there are frequent system deployments and upgrades, with new,\npreviously unseen time series emerging daily. The performance of testing newly\nincoming unseen time series on current TSAD algorithms remains unknown. Lastly,\nthe assumptions of the evaluation metrics in existing benchmarks are far from\npractical demands. To solve the above-mentioned problems, we propose an\nindustrial-grade benchmark TimeSeriesBench. We assess the performance of\nexisting algorithms across more than 168 evaluation settings and provide\ncomprehensive analysis for the future design of anomaly detection algorithms.\nAn industrial dataset is also released along with TimeSeriesBench.\n", "date": "2024-02-16", "categories": []}
{"id": "2403.01169", "title": "Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection", "abstract": "  Most models for weakly supervised video anomaly detection (WS-VAD) rely on\nmultiple instance learning, aiming to distinguish normal and abnormal snippets\nwithout specifying the type of anomaly. However, the ambiguous nature of\nanomaly definitions across contexts may introduce inaccuracy in discriminating\nabnormal and normal events. To show the model what is anomalous, a novel\nframework is proposed to guide the learning of suspected anomalies from event\nprompts. Given a textual prompt dictionary of potential anomaly events and the\ncaptions generated from anomaly videos, the semantic anomaly similarity between\nthem could be calculated to identify the suspected events for each video\nsnippet. It enables a new multi-prompt learning process to constrain the\nvisual-semantic features across all videos, as well as provides a new way to\nlabel pseudo anomalies for self-training. To demonstrate its effectiveness,\ncomprehensive experiments and detailed ablation studies are conducted on four\ndatasets, namely XD-Violence, UCF-Crime, TAD, and ShanghaiTech. Our proposed\nmodel outperforms most state-of-the-art methods in terms of AP or AUC (86.5\\%,\n\\hl{90.4}\\%, 94.4\\%, and 97.4\\%). Furthermore, it shows promising performance\nin open-set and cross-dataset cases. The data, code, and models can be found\nat: \\url{https://github.com/shiwoaz/lap}.\n", "date": "2024-03-02", "categories": []}
{"id": "2403.12172", "title": "Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video\n  Anomaly Detection", "abstract": "  Skeleton-based video anomaly detection (SVAD) is a crucial task in computer\nvision. Accurately identifying abnormal patterns or events enables operators to\npromptly detect suspicious activities, thereby enhancing safety. Achieving this\ndemands a comprehensive understanding of human motions, both at body and region\nlevels, while also accounting for the wide variations of performing a single\naction. However, existing studies fail to simultaneously address these crucial\nproperties. This paper introduces a novel, practical and lightweight framework,\nnamely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video\nAnomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD.\nGiCiSAD consists of three novel modules: the Graph Attention-based Forecasting\nmodule to capture the spatio-temporal dependencies inherent in the data, the\nGraph-level Jigsaw Puzzle Maker module to distinguish subtle region-level\ndiscrepancies between normal and abnormal motions, and the Graph-based\nConditional Diffusion model to generate a wide spectrum of human motions.\nExtensive experiments on four widely used skeleton-based video datasets show\nthat GiCiSAD outperforms existing methods with significantly fewer training\nparameters, establishing it as the new state-of-the-art.\n", "date": "2024-03-18", "categories": []}
{"id": "2408.00872", "title": "Online Detection of Anomalies in Temporal Knowledge Graphs with\n  Interpretability", "abstract": "  Temporal knowledge graphs (TKGs) are valuable resources for capturing\nevolving relationships among entities, yet they are often plagued by noise,\nnecessitating robust anomaly detection mechanisms. Existing dynamic graph\nanomaly detection approaches struggle to capture the rich semantics introduced\nby node and edge categories within TKGs, while TKG embedding methods lack\ninterpretability, undermining the credibility of anomaly detection. Moreover,\nthese methods falter in adapting to pattern changes and semantic drifts\nresulting from knowledge updates. To tackle these challenges, we introduce\nAnoT, an efficient TKG summarization method tailored for interpretable online\nanomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule\ngraph, enabling flexible inference of complex patterns in TKGs. When new\nknowledge emerges, AnoT maps it onto a node in the rule graph and traverses the\nrule graph recursively to derive the anomaly score of the knowledge. The\ntraversal yields reachable nodes that furnish interpretable evidence for the\nvalidity or the anomalous of the new knowledge. Overall, AnoT embodies a\ndetector-updater-monitor architecture, encompassing a detector for offline TKG\nsummarization and online scoring, an updater for real-time rule graph updates\nbased on emerging knowledge, and a monitor for estimating the approximation\nerror of the rule graph. Experimental results on four real-world datasets\ndemonstrate that AnoT surpasses existing methods significantly in terms of\naccuracy and interoperability. All of the raw datasets and the implementation\nof AnoT are provided in https://github.com/zjs123/ANoT.\n", "date": "2024-08-01", "categories": []}
{"id": "2408.15628", "title": "CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection", "abstract": "  To improve logical anomaly detection, some previous works have integrated\nsegmentation techniques with conventional anomaly detection methods. Although\nthese methods are effective, they frequently lead to unsatisfactory\nsegmentation results and require manual annotations. To address these\ndrawbacks, we develop an unsupervised component segmentation technique that\nleverages foundation models to autonomously generate training labels for a\nlightweight segmentation network without human labeling. Integrating this new\nsegmentation technique with our proposed Patch Histogram module and the\nLocal-Global Student-Teacher (LGST) module, we achieve a detection AUROC of\n95.3% in the MVTec LOCO AD dataset, which surpasses previous SOTA methods.\nFurthermore, our proposed method provides lower latency and higher throughput\nthan most existing approaches.\n", "date": "2024-08-28", "categories": []}
{"id": "2409.00556", "title": "FADE: Few-shot/zero-shot Anomaly Detection Engine using Large\n  Vision-Language Model", "abstract": "  Automatic image anomaly detection is important for quality inspection in the\nmanufacturing industry. The usual unsupervised anomaly detection approach is to\ntrain a model for each object class using a dataset of normal samples. However,\na more realistic problem is zero-/few-shot anomaly detection where zero or only\na few normal samples are available. This makes the training of object-specific\nmodels challenging. Recently, large foundation vision-language models have\nshown strong zero-shot performance in various downstream tasks. While these\nmodels have learned complex relationships between vision and language, they are\nnot specifically designed for the tasks of anomaly detection. In this paper, we\npropose the Few-shot/zero-shot Anomaly Detection Engine (FADE) which leverages\nthe vision-language CLIP model and adjusts it for the purpose of industrial\nanomaly detection. Specifically, we improve language-guided anomaly\nsegmentation 1) by adapting CLIP to extract multi-scale image patch embeddings\nthat are better aligned with language and 2) by automatically generating an\nensemble of text prompts related to industrial anomaly detection. 3) We use\nadditional vision-based guidance from the query and reference images to further\nimprove both zero-shot and few-shot anomaly detection. On the MVTec-AD (and\nVisA) dataset, FADE outperforms other state-of-the-art methods in anomaly\nsegmentation with pixel-AUROC of 89.6% (91.5%) in zero-shot and 95.4% (97.5%)\nin 1-normal-shot. Code is available at https://github.com/BMVC-FADE/BMVC-FADE.\n", "date": "2024-08-31", "categories": []}
{"id": "2409.00942", "title": "VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via\n  Hierarchical Vector Quantization", "abstract": "  Normalizing flows, a category of probabilistic models famed for their\ncapabilities in modeling complex data distributions, have exhibited remarkable\nefficacy in unsupervised anomaly detection. This paper explores the potential\nof normalizing flows in multi-class anomaly detection, wherein the normal data\nis compounded with multiple classes without providing class labels. Through the\nintegration of vector quantization (VQ), we empower the flow models to\ndistinguish different concepts of multi-class normal data in an unsupervised\nmanner, resulting in a novel flow-based unified method, named VQ-Flow.\nSpecifically, our VQ-Flow leverages hierarchical vector quantization to\nestimate two relative codebooks: a Conceptual Prototype Codebook (CPC) for\nconcept distinction and its concomitant Concept-Specific Pattern Codebook\n(CSPC) to capture concept-specific normal patterns. The flow models in VQ-Flow\nare conditioned on the concept-specific patterns captured in CSPC, capable of\nmodeling specific normal patterns associated with different concepts. Moreover,\nCPC further enables our VQ-Flow for concept-aware distribution modeling,\nfaithfully mimicking the intricate multi-class normal distribution through a\nmixed Gaussian distribution reparametrized on the conceptual prototypes.\nThrough the introduction of vector quantization, the proposed VQ-Flow advances\nthe state-of-the-art in multi-class anomaly detection within a unified training\nscheme, yielding the Det./Loc. AUROC of 99.5%/98.3% on MVTec AD. The codebase\nis publicly available at https://github.com/cool-xuan/vqflow.\n", "date": "2024-09-02", "categories": []}
{"id": "2409.01676", "title": "Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health\n  Indicator Derivation in Rotating Machines: Advancing Early Fault Detection\n  and Condition Monitoring", "abstract": "  Deriving health indicators of rotating machines is crucial for their\nmaintenance. However, this process is challenging for the prevalent adopted\nintelligent methods since they may take the whole data distributions, not only\nintroducing noise interference but also lacking the explainability. To address\nthese issues, we propose a diffusion-based weakly-supervised approach for\nderiving health indicators of rotating machines, enabling early fault detection\nand continuous monitoring of condition evolution. This approach relies on a\nclassifier-free diffusion model trained using healthy samples and a few\nanomalies. This model generates healthy samples. and by comparing the\ndifferences between the original samples and the generated ones in the envelope\nspectrum, we construct an anomaly map that clearly identifies faults. Health\nindicators are then derived, which can explain the fault types and mitigate\nnoise interference. Comparative studies on two cases demonstrate that the\nproposed method offers superior health monitoring effectiveness and robustness\ncompared to baseline models.\n", "date": "2024-09-03", "categories": []}
{"id": "2409.01885", "title": "Activity-Guided Industrial Anomalous Sound Detection against\n  Interferences", "abstract": "  We address a practical scenario of anomaly detection for industrial sound\ndata, where the sound of a target machine is corrupted by background noise and\ninterference from neighboring machines. Overcoming this challenge is difficult\nsince the interference is often virtually indistinguishable from the target\nmachine without additional information. To address the issue, we propose SSAD,\na framework of source separation (SS) followed by anomaly detection (AD), which\nleverages machine activity information, often readily available in practical\nsettings. SSAD consists of two components: (i) activity-informed SS, enabling\neffective source separation even given interference with similar timbre, and\n(ii) two-step masking, robustifying anomaly detection by emphasizing anomalies\naligned with the machine activity. Our experiments demonstrate that SSAD\nachieves comparable accuracy to a baseline with full access to clean signals,\nwhile SSAD is provided only a corrupted signal and activity information. In\naddition, thanks to the activity-informed SS and AD with the two-step masking,\nSSAD outperforms standard approaches, particularly in cases with interference.\nIt highlights the practical efficacy of SSAD in addressing the complexities of\nanomaly detection in industrial sound data.\n", "date": "2024-09-03", "categories": []}
{"id": "2408.15113", "title": "AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial\n  Anomaly Detection", "abstract": "  Visual inspection, or industrial anomaly detection, is one of the most common\nquality control types in manufacturing. The task is to identify the presence of\nan anomaly given an image, e.g., a missing component on an image of a circuit\nboard, for subsequent manual inspection. While industrial anomaly detection has\nseen a surge in recent years, most anomaly detection methods still utilize\nknowledge only from normal samples, failing to leverage the information from\nthe frequently available anomalous samples. Additionally, they heavily rely on\nvery general feature extractors pre-trained on common image classification\ndatasets. In this paper, we address these shortcomings and propose the new\nanomaly detection system AnomalousPatchCore~(APC) based on a feature extractor\nfine-tuned with normal and anomalous in-domain samples and a subsequent memory\nbank for identifying unusual features. To fine-tune the feature extractor in\nAPC, we propose three auxiliary tasks that address the different aspects of\nanomaly detection~(classification vs. localization) and mitigate the effect of\nthe imbalance between normal and anomalous samples. Our extensive evaluation on\nthe MVTec dataset shows that APC outperforms state-of-the-art systems in\ndetecting anomalies, which is especially important in industrial anomaly\ndetection given the subsequent manual inspection. In detailed ablation studies,\nwe further investigate the properties of our APC.\n", "date": "2024-08-27", "categories": []}
{"id": "2405.07626", "title": "AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using\n  Large Language Models", "abstract": "  Detecting anomaly edges for dynamic graphs aims to identify edges\nsignificantly deviating from the normal pattern and can be applied in various\ndomains, such as cybersecurity, financial transactions and AIOps. With the\nevolving of time, the types of anomaly edges are emerging and the labeled\nanomaly samples are few for each type. Current methods are either designed to\ndetect randomly inserted edges or require sufficient labeled data for model\ntraining, which harms their applicability for real-world applications. In this\npaper, we study this problem by cooperating with the rich knowledge encoded in\nlarge language models(LLMs) and propose a method, namely AnomalyLLM. To align\nthe dynamic graph with LLMs, AnomalyLLM pre-trains a dynamic-aware encoder to\ngenerate the representations of edges and reprograms the edges using the\nprototypes of word embeddings. Along with the encoder, we design an in-context\nlearning framework that integrates the information of a few labeled samples to\nachieve few-shot anomaly detection. Experiments on four datasets reveal that\nAnomalyLLM can not only significantly improve the performance of few-shot\nanomaly detection, but also achieve superior results on new anomalies without\nany update of model parameters.\n", "date": "2024-05-13", "categories": []}
{"id": "2408.15958", "title": "Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume", "abstract": "  Current anomaly detection methods excel with benchmark industrial data but\nstruggle with natural images and medical data due to varying definitions of\n'normal' and 'abnormal.' This makes accurate identification of deviations in\nthese fields particularly challenging. Especially for 3D brain MRI data, all\nthe state-of-the-art models are reconstruction-based with 3D convolutional\nneural networks which are memory-intensive, time-consuming and producing noisy\noutputs that require further post-processing. We propose a framework called\nSimple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained\non ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature\nextractor to reduce computational cost. We aggregate the extracted features to\nperform anomaly detection tasks on 3D brain MRI volumes. Our model integrates a\nconditional normalizing flow to calculate log likelihood of features and\nemploys the Semi-Push-Pull Mechanism to enhance anomaly detection accuracy. The\nresults indicate improved performance, showcasing our model's remarkable\nadaptability and effectiveness when addressing the challenges exists in brain\nMRI data. In addition, for the large-scale 3D brain volumes, our model\nSimpleSliceNet outperforms the state-of-the-art 2D and 3D models in terms of\naccuracy, memory usage and time consumption. Code is available at:\nhttps://anonymous.4open.science/r/SimpleSliceNet-8EA3.\n", "date": "2024-08-28", "categories": []}
{"id": "2312.11470", "title": "An Improved Anomaly Detection Model for Automated Inspection of Power\n  Line Insulators", "abstract": "  Inspection of insulators is important to ensure reliable operation of the\npower system. Deep learning is being increasingly exploited to automate the\ninspection process by leveraging object detection models to analyse aerial\nimages captured by drones. A purely object detection-based approach, however,\nsuffers from class imbalance-induced poor performance, which can be accentuated\nfor infrequent and hard-to-detect incipient faults. This article proposes the\nuse of anomaly detection along with object detection in a two-stage approach\nfor incipient fault detection in a data-efficient manner. An explainable\nconvolutional one-class classifier is adopted for anomaly detection. The\none-class formulation reduces the reliance on plentifully available images of\nfaulty insulators, while the explainability of the model is expected to promote\nadoption by the industry. A modified loss function is developed that addresses\ncomputational and interpretability issues with the existing model, also\nallowing for the integration of other losses. The superiority of the novel loss\nfunction is demonstrated with MVTec-AD dataset. The models are trained for\ninsulator inspection with two datasets -- representing data-abundant and\ndata-scarce scenarios -- in unsupervised and semi-supervised settings. The\nresults suggest that including as few as five real anomalies in the training\ndataset significantly improves the model's performance and enables reliable\ndetection of rarely occurring incipient faults in insulators.\n", "date": "2023-11-14", "categories": []}
{"id": "2408.14499", "title": "SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban\n  Substations", "abstract": "  District Heating (DH) systems are essential for energy-efficient urban\nheating. However, despite the advancements in automated fault detection and\ndiagnosis (FDD), DH still faces challenges in operational faults that impact\nefficiency. This study introduces the Shared Nearest Neighbor Enhanced District\nHeating Anomaly Detection (SHEDAD) approach, designed to approximate the DH\nnetwork topology and allow for local anomaly detection without disclosing\nsensitive information, such as substation locations. The approach leverages a\nmulti-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial\nneighborhood creation. Moreover, it introduces a merging technique that reduces\nnoise and eliminates trivial edges. We use the Median Absolute Deviation (MAD)\nand modified z-scores to flag anomalous substations. The results reveal that\nSHEDAD outperforms traditional clustering methods, achieving significantly\nlower intra-cluster variance and distance. Additionally, SHEDAD effectively\nisolates and identifies two distinct categories of anomalies: supply\ntemperatures and substation performance. We identified 30 anomalous substations\nand reached a sensitivity of approximately 65\\% and specificity of\napproximately 97\\%. By focusing on this subset of poor-performing substations\nin the network, SHEDAD enables more targeted and effective maintenance\ninterventions, which can reduce energy usage while optimizing network\nperformance.\n", "date": "2024-08-23", "categories": []}
{"id": "2408.14753", "title": "CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy\n  Concerns", "abstract": "  Machine anomalous sound detection (ASD) has emerged as one of the most\npromising applications in the Industrial Internet of Things (IIoT) due to its\nunprecedented efficacy in mitigating risks of malfunctions and promoting\nproduction efficiency. Previous works mainly investigated the machine ASD task\nunder centralized settings. However, developing the ASD system under\ndecentralized settings is crucial in practice, since the machine data are\ndispersed in various factories and the data should not be explicitly shared due\nto privacy concerns. To enable these factories to cooperatively develop a\nscalable ASD model while preserving their privacy, we propose a novel framework\nnamed CoopASD, where each factory trains an ASD model on its local dataset, and\na central server aggregates these local models periodically. We employ a\npre-trained model as the backbone of the ASD model to improve its robustness\nand develop specialized techniques to stabilize the model under a completely\nnon-iid and domain shift setting. Compared with previous state-of-the-art\n(SOTA) models trained in centralized settings, CoopASD showcases competitive\nresults with negligible degradation of 0.08%. We also conduct extensive\nablation studies to demonstrate the effectiveness of CoopASD.\n", "date": "2024-08-27", "categories": []}
{"id": "2408.14756", "title": "Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation\n  Models", "abstract": "  Recent advancements in time-series anomaly detection have relied on deep\nlearning models to handle the diverse behaviors of time-series data. However,\nthese models often suffer from unstable training and require extensive\nhyperparameter tuning, leading to practical limitations. Although foundation\nmodels present a potential solution, their use in time series is limited. To\novercome these issues, we propose an innovative image-based, training-free\ntime-series anomaly detection (ITF-TAD) approach. ITF-TAD converts time-series\ndata into images using wavelet transform and compresses them into a single\nrepresentation, leveraging image foundation models for anomaly detection. This\napproach achieves high-performance anomaly detection without unstable neural\nnetwork training or hyperparameter tuning. Furthermore, ITF-TAD identifies\nanomalies across different frequencies, providing users with a detailed\nvisualization of anomalies and their corresponding frequencies. Comprehensive\nexperiments on five benchmark datasets, including univariate and multivariate\ntime series, demonstrate that ITF-TAD offers a practical and effective solution\nwith performance exceeding or comparable to that of deep models.\n", "date": "2024-08-27", "categories": []}
{"id": "2305.10668", "title": "MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly\n  Detection", "abstract": "  Graph anomaly detection has long been an important problem in various domains\npertaining to information security such as financial fraud, social spam and\nnetwork intrusion. The majority of existing methods are performed in an\nunsupervised manner, as labeled anomalies in a large scale are often too\nexpensive to acquire. However, the identified anomalies may turn out to be\nuninteresting data instances due to the lack of prior knowledge. In real-world\nscenarios, it is often feasible to obtain limited labeled anomalies, which have\ngreat potential to advance graph anomaly detection. However, the work exploring\nlimited labeled anomalies and a large amount of unlabeled nodes in graphs to\ndetect anomalies is relatively limited. Therefore, in this paper, we study an\nimportant problem of few-shot graph anomaly detection. Nonetheless, it is\nchallenging to fully leverage the information of few-shot anomalous nodes due\nto the irregularity of anomalies and the overfitting issue in the few-shot\nlearning. To tackle the above challenges, we propose a novel meta-learning\nbased framework, MetaGAD, that learns to adapt the knowledge from\nself-supervised learning to few-shot supervised learning for graph anomaly\ndetection. In specific, we formulate the problem as a bi-level optimization,\nensuring MetaGAD converging to minimizing the validation loss, thus enhancing\nthe generalization capacity. The comprehensive experiments on six real-world\ndatasets with synthetic anomalies and \"organic\" anomalies (available in the\ndatasets) demonstrate the effectiveness of MetaGAD in detecting anomalies with\nfew-shot anomalies. The code is available at\nhttps://github.com/XiongxiaoXu/MetaGAD.\n", "date": "2023-05-18", "categories": []}
{"id": "2307.10529", "title": "Fast Unsupervised Deep Outlier Model Selection with Hypernetworks", "abstract": "  Outlier detection (OD) finds many applications with a rich literature of\nnumerous techniques. Deep neural network based OD (DOD) has seen a recent surge\nof attention thanks to the many advances in deep learning. In this paper, we\nconsider a critical-yet-understudied challenge with unsupervised DOD, that is,\neffective hyperparameter (HP) tuning/model selection. While several prior work\nreport the sensitivity of OD models to HPs, it becomes ever so critical for the\nmodern DOD models that exhibit a long list of HPs. We introduce HYPER for\ntuning DOD models, tackling two fundamental challenges: (1) validation without\nsupervision (due to lack of labeled anomalies), and (2) efficient search of the\nHP/model space (due to exponential growth in the number of HPs). A key idea is\nto design and train a novel hypernetwork (HN) that maps HPs onto optimal\nweights of the main DOD model. In turn, HYPER capitalizes on a single HN that\ncan dynamically generate weights for many DOD models (corresponding to varying\nHPs), which offers significant speed-up. In addition, it employs meta-learning\non historical OD tasks with labels to train a proxy validation function,\nlikewise trained with our proposed HN efficiently. Extensive experiments on 35\nOD tasks show that HYPER achieves high performance against 8 baselines with\nsignificant efficiency gains.\n", "date": "2023-07-20", "categories": []}
{"id": "2406.09131", "title": "OLGA: One-cLass Graph Autoencoder", "abstract": "  One-class learning (OCL) comprises a set of techniques applied when\nreal-world problems have a single class of interest. The usual procedure for\nOCL is learning a hypersphere that comprises instances of this class and,\nideally, repels unseen instances from any other classes. Besides, several OCL\nalgorithms for graphs have been proposed since graph representation learning\nhas succeeded in various fields. These methods may use a two-step strategy,\ninitially representing the graph and, in a second step, classifying its nodes.\nOn the other hand, end-to-end methods learn the node representations while\nclassifying the nodes in one learning process. We highlight three main gaps in\nthe literature on OCL for graphs: (i) non-customized representations for OCL;\n(ii) the lack of constraints on hypersphere parameters learning; and (iii) the\nmethods' lack of interpretability and visualization. We propose One-cLass Graph\nAutoencoder (OLGA). OLGA is end-to-end and learns the representations for the\ngraph nodes while encapsulating the interest instances by combining two loss\nfunctions. We propose a new hypersphere loss function to encapsulate the\ninterest instances. OLGA combines this new hypersphere loss with the graph\nautoencoder reconstruction loss to improve model learning. OLGA achieved\nstate-of-the-art results and outperformed six other methods with a\nstatistically significant difference from five methods. Moreover, OLGA learns\nlow-dimensional representations maintaining the classification performance with\nan interpretable model representation learning and results.\n", "date": "2024-06-13", "categories": []}
{"id": "2408.13561", "title": "Variational Autoencoder for Anomaly Detection: A Comparative Study", "abstract": "  This paper aims to conduct a comparative analysis of contemporary Variational\nAutoencoder (VAE) architectures employed in anomaly detection, elucidating\ntheir performance and behavioral characteristics within this specific task. The\narchitectural configurations under consideration encompass the original VAE\nbaseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE\nincorporating a vision transformer (ViT-VAE). The findings reveal that ViT-VAE\nexhibits exemplary performance across various scenarios, whereas VAE-GRF may\nnecessitate more intricate hyperparameter tuning to attain its optimal\nperformance state. Additionally, to mitigate the propensity for over-reliance\non results derived from the widely used MVTec dataset, this paper leverages the\nrecently-public MiAD dataset for benchmarking. This deliberate inclusion seeks\nto enhance result competitiveness by alleviating the impact of domain-specific\nmodels tailored exclusively for MVTec, thereby contributing to a more robust\nevaluation framework. Codes is available at\nhttps://github.com/endtheme123/VAE-compare.git.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.13643", "title": "Temporal Divide-and-Conquer Anomaly Actions Localization in\n  Semi-Supervised Videos with Hierarchical Transformer", "abstract": "  Anomaly action detection and localization play an essential role in security\nand advanced surveillance systems. However, due to the tremendous amount of\nsurveillance videos, most of the available data for the task is unlabeled or\nsemi-labeled with the video class known, but the location of the anomaly event\nis unknown. In this work, we target anomaly localization in semi-supervised\nvideos. While the mainstream direction in addressing this task is focused on\nsegment-level multi-instance learning and the generation of pseudo labels, we\naim to explore a promising yet unfulfilled direction to solve the problem by\nlearning the temporal relations within videos in order to locate anomaly\nevents. To this end, we propose a hierarchical transformer model designed to\nevaluate the significance of observed actions in anomalous videos with a\ndivide-and-conquer strategy along the temporal axis. Our approach segments a\nparent video hierarchically into multiple temporal children instances and\nmeasures the influence of the children nodes in classifying the abnormality of\nthe parent video. Evaluating our model on two well-known anomaly detection\ndatasets, UCF-crime and ShanghaiTech, proves its ability to interpret the\nobserved actions within videos and localize the anomalous ones. Our proposed\napproach outperforms previous works relying on segment-level multiple-instance\nlearning approaches while reaching a promising performance compared to the more\nrecent pseudo-labeling-based approaches.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.13082", "title": "Multivariate Time-Series Anomaly Detection based on Enhancing Graph\n  Attention Networks with Topological Analysis", "abstract": "  Unsupervised anomaly detection in time series is essential in industrial\napplications, as it significantly reduces the need for manual intervention.\nMultivariate time series pose a complex challenge due to their feature and\ntemporal dimensions. Traditional methods use Graph Neural Networks (GNNs) or\nTransformers to analyze spatial while RNNs to model temporal dependencies.\nThese methods focus narrowly on one dimension or engage in coarse-grained\nfeature extraction, which can be inadequate for large datasets characterized by\nintricate relationships and dynamic changes. This paper introduces a novel\ntemporal model built on an enhanced Graph Attention Network (GAT) for\nmultivariate time series anomaly detection called TopoGDN. Our model analyzes\nboth time and feature dimensions from a fine-grained perspective. First, we\nintroduce a multi-scale temporal convolution module to extract detailed\ntemporal features. Additionally, we present an augmented GAT to manage complex\ninter-feature dependencies, which incorporates graph topology into node\nfeatures across multiple scales, a versatile, plug-and-play enhancement that\nsignificantly boosts the performance of GAT. Our experimental results confirm\nthat our approach surpasses the baseline models on four datasets, demonstrating\nits potential for widespread application in fields requiring robust anomaly\ndetection. The code is available at https://github.com/ljj-cyber/TopoGDN.\n", "date": "2024-08-23", "categories": []}
{"id": "2408.03143", "title": "SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast\n  and Reliable Surface Defect Detection", "abstract": "  The aim of surface defect detection is to identify and localise abnormal\nregions on the surfaces of captured objects, a task that's increasingly\ndemanded across various industries. Current approaches frequently fail to\nfulfil the extensive demands of these industries, which encompass high\nperformance, consistency, and fast operation, along with the capacity to\nleverage the entirety of the available training data. Addressing these gaps, we\nintroduce SuperSimpleNet, an innovative discriminative model that evolved from\nSimpleNet. This advanced model significantly enhances its predecessor's\ntraining consistency, inference time, as well as detection performance.\nSuperSimpleNet operates in an unsupervised manner using only normal training\nimages but also benefits from labelled abnormal training images when they are\navailable. SuperSimpleNet achieves state-of-the-art results in both the\nsupervised and the unsupervised settings, as demonstrated by experiments across\nfour challenging benchmark datasets. Code:\nhttps://github.com/blaz-r/SuperSimpleNet .\n", "date": "2024-08-06", "categories": []}
{"id": "2408.11935", "title": "Explainable Anomaly Detection: Counterfactual driven What-If Analysis", "abstract": "  There exists three main areas of study inside of the field of predictive\nmaintenance: anomaly detection, fault diagnosis, and remaining useful life\nprediction. Notably, anomaly detection alerts the stakeholder that an anomaly\nis occurring. This raises two fundamental questions: what is causing the fault\nand how can we fix it? Inside of the field of explainable artificial\nintelligence, counterfactual explanations can give that information in the form\nof what changes to make to put the data point into the opposing class, in this\ncase \"healthy\". The suggestions are not always actionable which may raise the\ninterest in asking \"what if we do this instead?\" In this work, we provide a\nproof of concept for utilizing counterfactual explanations as what-if analysis.\nWe perform this on the PRONOSTIA dataset with a temporal convolutional network\nas the anomaly detector. Our method presents the counterfactuals in the form of\na what-if analysis for this base problem to inspire future work for more\ncomplex systems and scenarios.\n", "date": "2024-08-21", "categories": []}
{"id": "2408.12527", "title": "UMAD: University of Macau Anomaly Detection Benchmark Dataset", "abstract": "  Anomaly detection is critical in surveillance systems and patrol robots by\nidentifying anomalous regions in images for early warning. Depending on whether\nreference data are utilized, anomaly detection can be categorized into anomaly\ndetection with reference and anomaly detection without reference. Currently,\nanomaly detection without reference, which is closely related to\nout-of-distribution (OoD) object detection, struggles with learning anomalous\npatterns due to the difficulty of collecting sufficiently large and diverse\nanomaly datasets with the inherent rarity and novelty of anomalies.\nAlternatively, anomaly detection with reference employs the scheme of change\ndetection to identify anomalies by comparing semantic changes between a\nreference image and a query one. However, there are very few ADr works due to\nthe scarcity of public datasets in this domain. In this paper, we aim to\naddress this gap by introducing the UMAD Benchmark Dataset. To our best\nknowledge, this is the first benchmark dataset designed specifically for\nanomaly detection with reference in robotic patrolling scenarios, e.g., where\nan autonomous robot is employed to detect anomalous objects by comparing a\nreference and a query video sequences. The reference sequences can be taken by\nthe robot along a specified route when there are no anomalous objects in the\nscene. The query sequences are captured online by the robot when it is\npatrolling in the same scene following the same route. Our benchmark dataset is\nelaborated such that each query image can find a corresponding reference based\non accurate robot localization along the same route in the prebuilt 3D map,\nwith which the reference and query images can be geometrically aligned using\nadaptive warping. Besides the proposed benchmark dataset, we evaluate the\nbaseline models of ADr on this dataset.\n", "date": "2024-08-22", "categories": []}
{"id": "2408.11359", "title": "Hypergraph Learning based Recommender System for Anomaly Detection,\n  Control and Optimization", "abstract": "  Anomaly detection is fundamental yet, challenging problem with practical\napplications in industry. The current approaches neglect the higher-order\ndependencies within the networks of interconnected sensors in the\nhigh-dimensional time series(multisensor data) for anomaly detection. To this\nend, we present a self-adapting anomaly detection framework for joint learning\nof (a) discrete hypergraph structure and (b) modeling the temporal trends and\nspatial relations among the interdependent sensors using the hierarchical\nencoder-decoder architecture to overcome the challenges. The hypergraph\nrepresentation learning-based framework exploits the relational inductive\nbiases in the hypergraph-structured data to learn the pointwise\nsingle-step-ahead forecasts through the self-supervised autoregressive task and\npredicts the anomalies based on the forecast error. Furthermore, our framework\nincentivizes learning the anomaly-diagnosis ontology through a differentiable\napproach. It derives the anomaly information propagation-based computational\nhypergraphs for root cause analysis and provides recommendations through an\noffline, optimal predictive control policy to remedy an anomaly. We conduct\nextensive experiments to evaluate the proposed method on the benchmark datasets\nfor fair and rigorous comparison with the popular baselines. The proposed\nmethod outperforms the baseline models and achieves SOTA performance. We report\nthe ablation studies to support the efficacy of the framework.\n", "date": "2024-08-21", "categories": []}
{"id": "2301.09489", "title": "Contracting Skeletal Kinematics for Human-Related Video Anomaly\n  Detection", "abstract": "  Detecting the anomaly of human behavior is paramount to timely recognizing\nendangering situations, such as street fights or elderly falls. However,\nanomaly detection is complex since anomalous events are rare and because it is\nan open set recognition task, i.e., what is anomalous at inference has not been\nobserved at training. We propose COSKAD, a novel model that encodes skeletal\nhuman motion by a graph convolutional network and learns to COntract SKeletal\nkinematic embeddings onto a latent hypersphere of minimum volume for Video\nAnomaly Detection. We propose three latent spaces: the commonly-adopted\nEuclidean and the novel spherical and hyperbolic. All variants outperform the\nstate-of-the-art on the most recent UBnormal dataset, for which we contribute a\nhuman-related version with annotated skeletons. COSKAD sets a new\nstate-of-the-art on the human-related versions of ShanghaiTech Campus and CUHK\nAvenue, with performance comparable to video-based methods. Source code and\ndataset will be released upon acceptance.\n", "date": "2023-01-23", "categories": []}
{"id": "2405.19823", "title": "Joint Selective State Space Model and Detrending for Robust Time Series\n  Anomaly Detection", "abstract": "  Deep learning-based sequence models are extensively employed in Time Series\nAnomaly Detection (TSAD) tasks due to their effective sequential modeling\ncapabilities. However, the ability of TSAD is limited by two key challenges:\n(i) the ability to model long-range dependency and (ii) the generalization\nissue in the presence of non-stationary data. To tackle these challenges, an\nanomaly detector that leverages the selective state space model known for its\nproficiency in capturing long-term dependencies across various domains is\nproposed. Additionally, a multi-stage detrending mechanism is introduced to\nmitigate the prominent trend component in non-stationary data to address the\ngeneralization issue. Extensive experiments conducted on realworld public\ndatasets demonstrate that the proposed methods surpass all 12 compared baseline\nmethods.\n", "date": "2024-05-30", "categories": []}
{"id": "2408.10798", "title": "Universal Novelty Detection Through Adaptive Contrastive Learning", "abstract": "  Novelty detection is a critical task for deploying machine learning models in\nthe open world. A crucial property of novelty detection methods is\nuniversality, which can be interpreted as generalization across various\ndistributions of training or test data. More precisely, for novelty detection,\ndistribution shifts may occur in the training set or the test set. Shifts in\nthe training set refer to cases where we train a novelty detector on a new\ndataset and expect strong transferability. Conversely, distribution shifts in\nthe test set indicate the methods' performance when the trained model\nencounters a shifted test sample. We experimentally show that existing methods\nfalter in maintaining universality, which stems from their rigid inductive\nbiases. Motivated by this, we aim for more generalized techniques that have\nmore adaptable inductive biases. In this context, we leverage the fact that\ncontrastive learning provides an efficient framework to easily switch and adapt\nto new inductive biases through the proper choice of augmentations in forming\nthe negative pairs. We propose a novel probabilistic auto-negative pair\ngeneration method AutoAugOOD, along with contrastive learning, to yield a\nuniversal novelty detector method. Our experiments demonstrate the superiority\nof our method under different distribution shifts in various image benchmark\ndatasets. Notably, our method emerges universality in the lens of adaptability\nto different setups of novelty detection, including one-class, unlabeled\nmulti-class, and labeled multi-class settings. Code:\nhttps://github.com/mojtaba-nafez/UNODE\n", "date": "2024-08-20", "categories": []}
{"id": "2305.18440", "title": "Black-Box Anomaly Attribution", "abstract": "  When the prediction of a black-box machine learning model deviates from the\ntrue observation, what can be said about the reason behind that deviation? This\nis a fundamental and ubiquitous question that the end user in a business or\nindustrial AI application often asks. The deviation may be due to a sub-optimal\nblack-box model, or it may be simply because the sample in question is an\noutlier. In either case, one would ideally wish to obtain some form of\nattribution score -- a value indicative of the extent to which an input\nvariable is responsible for the anomaly.\n  In the present paper we address this task of ``anomaly attribution,''\nparticularly in the setting in which the model is black-box and the training\ndata are not available. Specifically, we propose a novel likelihood-based\nattribution framework we call the ``likelihood compensation (LC),'' in which\nthe responsibility score is equated with the correction on each input variable\nneeded to attain the highest possible likelihood. We begin by showing formally\nwhy mainstream model-agnostic explanation methods, such as the local linear\nsurrogate modeling and Shapley values, are not designed to explain anomalies.\nIn particular, we show that they are ``deviation-agnostic,'' namely, that their\nexplanations are blind to the fact that there is a deviation in the model\nprediction for the sample of interest. We do this by positioning these existing\nmethods under the unified umbrella of a function family we call the\n``integrated gradient family.'' We validate the effectiveness of the proposed\nLC approach using publicly available data sets. We also conduct a case study\nwith a real-world building energy prediction task and confirm its usefulness in\npractice based on expert feedback.\n", "date": "2023-05-29", "categories": []}
{"id": "2408.08888", "title": "A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to\n  Astronomical Time-Series", "abstract": "  Automating anomaly detection is an open problem in many scientific fields,\nparticularly in time-domain astronomy, where modern telescopes generate\nmillions of alerts per night. Currently, most anomaly detection algorithms for\nastronomical time-series rely either on hand-crafted features or on features\ngenerated through unsupervised representation learning, coupled with standard\nanomaly detection algorithms. In this work, we introduce a novel approach that\nleverages the latent space of a neural network classifier for anomaly\ndetection. We then propose a new method called Multi-Class Isolation Forests\n(MCIF), which trains separate isolation forests for each class to derive an\nanomaly score for an object based on its latent space representation. This\napproach significantly outperforms a standard isolation forest when distinct\nclusters exist in the latent space. Using a simulated dataset emulating the\nZwicky Transient Facility (54 anomalies and 12,040 common), our anomaly\ndetection pipeline discovered $46\\pm3$ anomalies ($\\sim 85\\%$ recall) after\nfollowing up the top 2,000 ($\\sim 15\\%$) ranked objects. Furthermore, our\nclassifier-based approach outperforms or approaches the performance of other\nstate-of-the-art anomaly detection pipelines. Our novel method demonstrates\nthat existing and new classifiers can be effectively repurposed for real-time\nanomaly detection. The code used in this work, including a Python package, is\npublicly available, https://github.com/Rithwik-G/AstroMCAD.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.09533", "title": "AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly\n  Localization", "abstract": "  Recent advances in anomaly generation approaches alleviate the effect of data\ninsufficiency on task of anomaly localization. While effective, most of them\nlearn multiple large generative models on different datasets and cumbersome\nanomaly prediction models for different classes. To address the limitations, we\npropose a novel scalable framework, named AnomalyFactory, that unifies\nunsupervised anomaly generation and localization with same network\narchitecture. It starts with a BootGenerator that combines structure of a\ntarget edge map and appearance of a reference color image with the guidance of\na learned heatmap. Then, it proceeds with a FlareGenerator that receives\nsupervision signals from the BootGenerator and reforms the heatmap to indicate\nanomaly locations in the generated image. Finally, it easily transforms the\nsame network architecture to a BlazeDetector that localizes anomaly pixels with\nthe learned heatmap by converting the anomaly images generated by the\nFlareGenerator to normal images. By manipulating the target edge maps and\ncombining them with various reference images, AnomalyFactory generates\nauthentic and diversity samples cross domains. Comprehensive experiments\ncarried on 5 datasets, including MVTecAD, VisA, MVTecLOCO, MADSim and RealIAD,\ndemonstrate that our approach is superior to competitors in generation\ncapability and scalability.\n", "date": "2024-08-18", "categories": []}
{"id": "2207.09792", "title": "Unsupervised Industrial Anomaly Detection via Pattern Generative and\n  Contrastive Networks", "abstract": "  It is hard to collect enough flaw images for training deep learning network\nin industrial production. Therefore, existing industrial anomaly detection\nmethods prefer to use CNN-based unsupervised detection and localization network\nto achieve this task. However, these methods always fail when there are\nvarieties happened in new signals since traditional end-to-end networks suffer\nbarriers of fitting nonlinear model in high-dimensional space. Moreover, they\nhave a memory library by clustering the feature of normal images essentially,\nwhich cause it is not robust to texture change. To this end, we propose the\nVision Transformer based (VIT-based) unsupervised anomaly detection network. It\nutilizes a hierarchical task learning and human experience to enhance its\ninterpretability. Our network consists of pattern generation and comparison\nnetworks. Pattern generation network uses two VIT-based encoder modules to\nextract the feature of two consecutive image patches, then uses VIT-based\ndecoder module to learn the human designed style of these features and predict\nthe third image patch. After this, we use the Siamese-based network to compute\nthe similarity of the generation image patch and original image patch. Finally,\nwe refine the anomaly localization by the bi-directional inference strategy.\nComparison experiments on public dataset MVTec dataset show our method achieves\n99.8% AUC, which surpasses previous state-of-the-art methods. In addition, we\ngive a qualitative illustration on our own leather and cloth datasets. The\naccurate segment results strongly prove the accuracy of our method in anomaly\ndetection.\n", "date": "2022-07-20", "categories": []}
{"id": "2403.09039", "title": "Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality", "abstract": "  Anomaly detection in dynamic graphs presents a significant challenge due to\nthe temporal evolution of graph structures and attributes. The conventional\napproaches that tackle this problem typically employ an unsupervised learning\nframework, capturing normality patterns with exclusive normal data during\ntraining and identifying deviations as anomalies during testing. However, these\nmethods face critical drawbacks: they either only depend on proxy tasks for\nrepresentation without directly pinpointing normal patterns, or they neglect to\ndifferentiate between spatial and temporal normality patterns. More recent\nmethods that use contrastive learning with negative sampling also face high\ncomputational costs, limiting their scalability to large graphs. To address\nthese challenges, we introduce a novel Spatial-Temporal memories-enhanced graph\nautoencoder (STRIPE). Initially, STRIPE employs Graph Neural Networks (GNNs)\nand gated temporal convolution layers to extract spatial and temporal features.\nThen STRIPE incorporates separate spatial and temporal memory networks to\ncapture and store prototypes of normal patterns, respectively. These stored\npatterns are retrieved and integrated with encoded graph embeddings through a\nmutual attention mechanism. Finally, the integrated features are fed into the\ndecoder to reconstruct the graph streams which serve as the proxy task for\nanomaly detection. This comprehensive approach not only minimizes\nreconstruction errors but also emphasizes the compactness and distinctiveness\nof the embeddings w.r.t. the nearest memory prototypes. Extensive experiments\non six benchmark datasets demonstrate the effectiveness and efficiency of\nSTRIPE, where STRIPE significantly outperforms existing methods with 5.8%\nimprovement in AUC scores and 4.62X faster in training time.\n", "date": "2024-03-14", "categories": []}
{"id": "2408.07718", "title": "Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly\n  Detection", "abstract": "  Training data sets intended for unsupervised anomaly detection, typically\npresumed to be anomaly-free, often contain anomalies (or contamination), a\nchallenge that significantly undermines model performance. Most robust\nunsupervised anomaly detection models rely on contamination ratio information\nto tackle contamination. However, in reality, contamination ratio may be\ninaccurate. We investigate on the impact of inaccurate contamination ratio\ninformation in robust unsupervised anomaly detection. We verify whether they\nare resilient to misinformed contamination ratios. Our investigation on 6\nbenchmark data sets reveals that such models are not adversely affected by\nexposure to misinformation. In fact, they can exhibit improved performance when\nprovided with such inaccurate contamination ratios.\n", "date": "2024-08-14", "categories": []}
{"id": "2408.07819", "title": "Regularized Contrastive Partial Multi-view Outlier Detection", "abstract": "  In recent years, multi-view outlier detection (MVOD) methods have advanced\nsignificantly, aiming to identify outliers within multi-view datasets. A key\npoint is to better detect class outliers and class-attribute outliers, which\nonly exist in multi-view data. However, existing methods either is not able to\nreduce the impact of outliers when learning view-consistent information, or\nstruggle in cases with varying neighborhood structures. Moreover, most of them\ndo not apply to partial multi-view data in real-world scenarios. To overcome\nthese drawbacks, we propose a novel method named Regularized Contrastive\nPartial Multi-view Outlier Detection (RCPMOD). In this framework, we utilize\ncontrastive learning to learn view-consistent information and distinguish\noutliers by the degree of consistency. Specifically, we propose (1) An\noutlier-aware contrastive loss with a potential outlier memory bank to\neliminate their bias motivated by a theoretical analysis. (2) A neighbor\nalignment contrastive loss to capture the view-shared local structural\ncorrelation. (3) A spreading regularization loss to prevent the model from\noverfitting over outliers. With the Cross-view Relation Transfer technique, we\ncould easily impute the missing view samples based on the features of\nneighbors. Experimental results on four benchmark datasets demonstrate that our\nproposed approach could outperform state-of-the-art competitors under different\nsettings.\n", "date": "2024-08-02", "categories": []}
{"id": "2308.00429", "title": "Patch-wise Auto-Encoder for Visual Anomaly Detection", "abstract": "  Anomaly detection without priors of the anomalies is challenging. In the\nfield of unsupervised anomaly detection, traditional auto-encoder (AE) tends to\nfail based on the assumption that by training only on normal images, the model\nwill not be able to reconstruct abnormal images correctly. On the contrary, we\npropose a novel patch-wise auto-encoder (Patch AE) framework, which aims at\nenhancing the reconstruction ability of AE to anomalies instead of weakening\nit. Each patch of image is reconstructed by corresponding spatially distributed\nfeature vector of the learned feature representation, i.e., patch-wise\nreconstruction, which ensures anomaly-sensitivity of AE. Our method is simple\nand efficient. It advances the state-of-the-art performances on Mvtec AD\nbenchmark, which proves the effectiveness of our model. It shows great\npotential in practical industrial application scenarios.\n", "date": "2023-08-01", "categories": []}
{"id": "2408.07623", "title": "Latent Anomaly Detection Through Density Matrices", "abstract": "  This paper introduces a novel anomaly detection framework that combines the\nrobust statistical principles of density-estimation-based anomaly detection\nmethods with the representation-learning capabilities of deep learning models.\nThe method originated from this framework is presented in two different\nversions: a shallow approach employing a density-estimation model based on\nadaptive Fourier features and density matrices, and a deep approach that\nintegrates an autoencoder to learn a low-dimensional representation of the\ndata. By estimating the density of new samples, both methods are able to find\nnormality scores. The methods can be seamlessly integrated into an end-to-end\narchitecture and optimized using gradient-based optimization techniques. To\nevaluate their performance, extensive experiments were conducted on various\nbenchmark datasets. The results demonstrate that both versions of the method\ncan achieve comparable or superior performance when compared to other\nstate-of-the-art methods. Notably, the shallow approach performs better on\ndatasets with fewer dimensions, while the autoencoder-based approach shows\nimproved performance on datasets with higher dimensions.\n", "date": "2024-08-14", "categories": []}
{"id": "2408.05905", "title": "Weakly Supervised Video Anomaly Detection and Localization with\n  Spatio-Temporal Prompts", "abstract": "  Current weakly supervised video anomaly detection (WSVAD) task aims to\nachieve frame-level anomalous event detection with only coarse video-level\nannotations available. Existing works typically involve extracting global\nfeatures from full-resolution video frames and training frame-level classifiers\nto detect anomalies in the temporal dimension. However, most anomalous events\ntend to occur in localized spatial regions rather than the entire video frames,\nwhich implies existing frame-level feature based works may be misled by the\ndominant background information and lack the interpretation of the detected\nanomalies. To address this dilemma, this paper introduces a novel method called\nSTPrompt that learns spatio-temporal prompt embeddings for weakly supervised\nvideo anomaly detection and localization (WSVADL) based on pre-trained\nvision-language models (VLMs). Our proposed method employs a two-stream network\nstructure, with one stream focusing on the temporal dimension and the other\nprimarily on the spatial dimension. By leveraging the learned knowledge from\npre-trained VLMs and incorporating natural motion priors from raw videos, our\nmodel learns prompt embeddings that are aligned with spatio-temporal regions of\nvideos (e.g., patches of individual frames) for identify specific local regions\nof anomalies, enabling accurate video anomaly detection while mitigating the\ninfluence of background information. Without relying on detailed\nspatio-temporal annotations or auxiliary object detection/tracking, our method\nachieves state-of-the-art performance on three public benchmarks for the WSVADL\ntask.\n", "date": "2024-08-12", "categories": []}
{"id": "2212.03637", "title": "Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation\n  and Analysis of State-of-the-art Methods", "abstract": "  Unsupervised anomaly detection in time-series has been extensively\ninvestigated in the literature. Notwithstanding the relevance of this topic in\nnumerous application fields, a comprehensive and extensive evaluation of recent\nstate-of-the-art techniques taking into account real-world constraints is still\nneeded. Some efforts have been made to compare existing unsupervised\ntime-series anomaly detection methods rigorously. However, only standard\nperformance metrics, namely precision, recall, and F1-score are usually\nconsidered. Essential aspects for assessing their practical relevance are\ntherefore neglected. This paper proposes an in-depth evaluation study of recent\nunsupervised anomaly detection techniques in time-series. Instead of relying\nsolely on standard performance metrics, additional yet informative metrics and\nprotocols are taken into account. In particular, (i) more elaborate performance\nmetrics specifically tailored for time-series are used; (ii) the model size and\nthe model stability are studied; (iii) an analysis of the tested approaches\nwith respect to the anomaly type is provided; and (iv) a clear and unique\nprotocol is followed for all experiments. Overall, this extensive analysis aims\nto assess the maturity of state-of-the-art time-series anomaly detection, give\ninsights regarding their applicability under real-world setups and provide to\nthe community a more complete evaluation protocol.\n", "date": "2022-12-06", "categories": []}
{"id": "2312.07495", "title": "Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly\n  Detection", "abstract": "  This work studies a challenging and practical issue known as multi-class\nunsupervised anomaly detection (MUAD). This problem requires only normal images\nfor training while simultaneously testing both normal and anomaly images across\nmultiple classes. Existing reconstruction-based methods typically adopt\npyramidal networks as encoders and decoders to obtain multi-resolution\nfeatures, often involving complex sub-modules with extensive handcraft\nengineering. In contrast, a plain Vision Transformer (ViT) showcasing a more\nstraightforward architecture has proven effective in multiple domains,\nincluding detection and segmentation tasks. It is simpler, more effective, and\nelegant. Following this spirit, we explore the use of only plain ViT features\nfor MUAD. We first abstract a Meta-AD concept by synthesizing current\nreconstruction-based methods. Subsequently, we instantiate a novel ViT-based\nViTAD structure, designed incrementally from both global and local\nperspectives. This model provide a strong baseline to facilitate future\nresearch. Additionally, this paper uncovers several intriguing findings for\nfurther investigation. Finally, we comprehensively and fairly benchmark various\napproaches using eight metrics. Utilizing a basic training regimen with only an\nMSE loss, ViTAD achieves state-of-the-art results and efficiency on MVTec AD,\nVisA, and Uni-Medical datasets. \\Eg, achieving 85.4 mAD that surpasses UniAD by\n+3.0 for the MVTec AD dataset, and it requires only 1.1 hours and 2.3G GPU\nmemory to complete model training on a single V100 that can serve as a strong\nbaseline to facilitate the development of future research. Full code is\navailable at https://zhangzjn.github.io/projects/ViTAD/.\n", "date": "2023-12-12", "categories": []}
{"id": "2408.05562", "title": "What Matters in Autonomous Driving Anomaly Detection: A Weakly\n  Supervised Horizon", "abstract": "  Video anomaly detection (VAD) in autonomous driving scenario is an important\ntask, however it involves several challenges due to the ego-centric views and\nmoving camera. Due to this, it remains largely under-explored. While recent\ndevelopments in weakly-supervised VAD methods have shown remarkable progress in\ndetecting critical real-world anomalies in static camera scenario, the\ndevelopment and validation of such methods are yet to be explored for moving\ncamera VAD. This is mainly due to existing datasets like DoTA not following\ntraining pre-conditions of weakly-supervised learning. In this paper, we aim to\npromote weakly-supervised method development for autonomous driving VAD. We\nreorganize the DoTA dataset and aim to validate recent powerful\nweakly-supervised VAD methods on moving camera scenarios. Further, we provide a\ndetailed analysis of what modifications on state-of-the-art methods can\nsignificantly improve the detection performance. Towards this, we propose a\n\"feature transformation block\" and through experimentation we show that our\npropositions can empower existing weakly-supervised VAD methods significantly\nin improving the VAD in autonomous driving. Our codes/dataset/demo will be\nreleased at github.com/ut21/WSAD-Driving\n", "date": "2024-08-10", "categories": []}
{"id": "2408.04817", "title": "Performance Metric for Multiple Anomaly Score Distributions with\n  Discrete Severity Levels", "abstract": "  The rise of smart factories has heightened the demand for automated\nmaintenance, and normal-data-based anomaly detection has proved particularly\neffective in environments where anomaly data are scarce. This method, which\ndoes not require anomaly data during training, has prompted researchers to\nfocus not only on detecting anomalies but also on classifying severity levels\nby using anomaly scores. However, the existing performance metrics, such as the\narea under the receiver operating characteristic curve (AUROC), do not\neffectively reflect the performance of models in classifying severity levels\nbased on anomaly scores. To address this limitation, we propose the weighted\nsum of the area under the receiver operating characteristic curve (WS-AUROC),\nwhich combines AUROC with a penalty for severity level differences. We\nconducted various experiments using different penalty assignment methods:\nuniform penalty regardless of severity level differences, penalty based on\nseverity level index differences, and penalty based on actual physical\nquantities that cause anomalies. The latter method was the most sensitive.\nAdditionally, we propose an anomaly detector that achieves clear separation of\ndistributions and outperforms the ablation models on the WS-AUROC and AUROC\nmetrics.\n", "date": "2024-08-09", "categories": []}
{"id": "2408.04839", "title": "Adversarially Robust Industrial Anomaly Detection Through Diffusion\n  Model", "abstract": "  Deep learning-based industrial anomaly detection models have achieved\nremarkably high accuracy on commonly used benchmark datasets. However, the\nrobustness of those models may not be satisfactory due to the existence of\nadversarial examples, which pose significant threats to the practical\ndeployment of deep anomaly detectors. Recently, it has been shown that\ndiffusion models can be used to purify the adversarial noises and thus build a\nrobust classifier against adversarial attacks. Unfortunately, we found that\nnaively applying this strategy in anomaly detection (i.e., placing a purifier\nbefore an anomaly detector) will suffer from a high anomaly miss rate since the\npurifying process can easily remove both the anomaly signal and the adversarial\nperturbations, causing the later anomaly detector failed to detect anomalies.\nTo tackle this issue, we explore the possibility of performing anomaly\ndetection and adversarial purification simultaneously. We propose a simple yet\neffective adversarially robust anomaly detection method, \\textit{AdvRAD}, that\nallows the diffusion model to act both as an anomaly detector and adversarial\npurifier. We also extend our proposed method for certified robustness to $l_2$\nnorm bounded perturbations. Through extensive experiments, we show that our\nproposed method exhibits outstanding (certified) adversarial robustness while\nalso maintaining equally strong anomaly detection performance on par with the\nstate-of-the-art methods on industrial anomaly detection benchmark datasets.\n", "date": "2024-08-09", "categories": []}
{"id": "2408.05191", "title": "Cross-Domain Learning for Video Anomaly Detection with Limited\n  Supervision", "abstract": "  Video Anomaly Detection (VAD) automates the identification of unusual events,\nsuch as security threats in surveillance videos. In real-world applications,\nVAD models must effectively operate in cross-domain settings, identifying rare\nanomalies and scenarios not well-represented in the training data. However,\nexisting cross-domain VAD methods focus on unsupervised learning, resulting in\nperformance that falls short of real-world expectations. Since acquiring weak\nsupervision, i.e., video-level labels, for the source domain is cost-effective,\nwe conjecture that combining it with external unlabeled data has notable\npotential to enhance cross-domain performance. To this end, we introduce a\nnovel weakly-supervised framework for Cross-Domain Learning (CDL) in VAD that\nincorporates external data during training by estimating its prediction bias\nand adaptively minimizing that using the predicted uncertainty. We demonstrate\nthe effectiveness of the proposed CDL framework through comprehensive\nexperiments conducted in various configurations on two large-scale VAD\ndatasets: UCF-Crime and XD-Violence. Our method significantly surpasses the\nstate-of-the-art works in cross-domain evaluations, achieving an average\nabsolute improvement of 19.6% on UCF-Crime and 12.87% on XD-Violence.\n", "date": "2024-08-09", "categories": []}
{"id": "2408.04236", "title": "Cluster-Wide Task Slowdown Detection in Cloud System", "abstract": "  Slow task detection is a critical problem in cloud operation and maintenance\nsince it is highly related to user experience and can bring substantial\nliquidated damages. Most anomaly detection methods detect it from a single-task\naspect. However, considering millions of concurrent tasks in large-scale cloud\ncomputing clusters, it becomes impractical and inefficient. Moreover,\nsingle-task slowdowns are very common and do not necessarily indicate a\nmalfunction of a cluster due to its violent fluctuation nature in a virtual\nenvironment. Thus, we shift our attention to cluster-wide task slowdowns by\nutilizing the duration time distribution of tasks across a cluster, so that the\ncomputation complexity is not relevant to the number of tasks.\n  The task duration time distribution often exhibits compound periodicity and\nlocal exceptional fluctuations over time. Though transformer-based methods are\none of the most powerful methods to capture these time series normal variation\npatterns, we empirically find and theoretically explain the flaw of the\nstandard attention mechanism in reconstructing subperiods with low amplitude\nwhen dealing with compound periodicity.\n  To tackle these challenges, we propose SORN (i.e., Skimming Off subperiods in\ndescending amplitude order and Reconstructing Non-slowing fluctuation), which\nconsists of a Skimming Attention mechanism to reconstruct the compound\nperiodicity and a Neural Optimal Transport module to distinguish cluster-wide\nslowdowns from other exceptional fluctuations. Furthermore, since anomalies in\nthe training set are inevitable in a practical scenario, we propose a picky\nloss function, which adaptively assigns higher weights to reliable time slots\nin the training set. Extensive experiments demonstrate that SORN outperforms\nstate-of-the-art methods on multiple real-world industrial datasets.\n", "date": "2024-08-08", "categories": []}
{"id": "2408.04442", "title": "FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly\n  Detection in Tabular Data", "abstract": "  The emergence of federated learning (FL) presents a promising approach to\nleverage decentralized data while preserving privacy. Furthermore, the\ncombination of FL and anomaly detection is particularly compelling because it\nallows for detecting rare and critical anomalies (usually also rare in locally\ngathered data) in sensitive data from multiple sources, such as cybersecurity\nand healthcare. However, benchmarking the performance of anomaly detection\nmethods in FL environments remains an underexplored area. This paper introduces\nFedAD-Bench, a unified benchmark for evaluating unsupervised anomaly detection\nalgorithms within the context of FL. We systematically analyze and compare the\nperformance of recent deep learning anomaly detection models under federated\nsettings, which were typically assessed solely in centralized settings.\nFedAD-Bench encompasses diverse datasets and metrics to provide a holistic\nevaluation. Through extensive experiments, we identify key challenges such as\nmodel aggregation inefficiencies and metric unreliability. We present insights\ninto FL's regularization effects, revealing scenarios in which it outperforms\ncentralized approaches due to its inherent ability to mitigate overfitting. Our\nwork aims to establish a standardized benchmark to guide future research and\ndevelopment in federated anomaly detection, promoting reproducibility and fair\ncomparison across studies.\n", "date": "2024-08-08", "categories": []}
{"id": "2310.12294", "title": "Open-Set Multivariate Time-Series Anomaly Detection", "abstract": "  Numerous methods for time-series anomaly detection (TSAD) have emerged in\nrecent years, most of which are unsupervised and assume that only normal\nsamples are available during the training phase, due to the challenge of\nobtaining abnormal data in real-world scenarios. Still, limited samples of\nabnormal data are often available, albeit they are far from representative of\nall possible anomalies. Supervised methods can be utilized to classify normal\nand seen anomalies, but they tend to overfit to the seen anomalies present\nduring training, hence, they fail to generalize to unseen anomalies. We propose\nthe first algorithm to address the open-set TSAD problem, called Multivariate\nOpen-Set Time-Series Anomaly Detector (MOSAD), that leverages only a few shots\nof labeled anomalies during the training phase in order to achieve superior\nanomaly detection performance compared to both supervised and unsupervised TSAD\nalgorithms. MOSAD is a novel multi-head TSAD framework with a shared\nrepresentation space and specialized heads, including the Generative head, the\nDiscriminative head, and the Anomaly-Aware Contrastive head. The latter\nproduces a superior representation space for anomaly detection compared to\nconventional supervised contrastive learning. Extensive experiments on three\nreal-world datasets establish MOSAD as a new state-of-the-art in the TSAD\nfield.\n", "date": "2023-10-18", "categories": []}
{"id": "2408.03475", "title": "Can LLMs Serve As Time Series Anomaly Detectors?", "abstract": "  An emerging topic in large language models (LLMs) is their application to\ntime series forecasting, characterizing mainstream and patternable\ncharacteristics of time series. A relevant but rarely explored and more\nchallenging question is whether LLMs can detect and explain time series\nanomalies, a critical task across various real-world applications. In this\npaper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3,\nin detecting and explaining anomalies in time series. Our studies reveal that:\n1) LLMs cannot be directly used for time series anomaly detection. 2) By\ndesigning prompt strategies such as in-context learning and chain-of-thought\nprompting, GPT-4 can detect time series anomalies with results competitive to\nbaseline methods. 3) We propose a synthesized dataset to automatically generate\ntime series anomalies with corresponding explanations. By applying instruction\nfine-tuning on this dataset, LLaMA3 demonstrates improved performance in time\nseries anomaly detection tasks. In summary, our exploration shows the promising\npotential of LLMs as time series anomaly detectors.\n", "date": "2024-08-06", "categories": []}
{"id": "2408.03654", "title": "Unsupervised Detection of Fetal Brain Anomalies using Denoising\n  Diffusion Models", "abstract": "  Congenital malformations of the brain are among the most common fetal\nabnormalities that impact fetal development. Previous anomaly detection methods\non ultrasound images are based on supervised learning, rely on manual\nannotations, and risk missing underrepresented categories. In this work, we\nframe fetal brain anomaly detection as an unsupervised task using diffusion\nmodels. To this end, we employ an inpainting-based Noise Agnostic Anomaly\nDetection approach that identifies the abnormality using\ndiffusion-reconstructed fetal brain images from multiple noise levels. Our\napproach only requires normal fetal brain ultrasound images for training,\naddressing the limited availability of abnormal data. Our experiments on a\nreal-world clinical dataset show the potential of using unsupervised methods\nfor fetal brain anomaly detection. Additionally, we comprehensively evaluate\nhow different noise types affect diffusion models in the fetal anomaly\ndetection domain.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.03014", "title": "CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly\n  Detection", "abstract": "  In this paper, we address the problem of unsupervised video anomaly detection\n(UVAD). The task aims to detect abnormal events in test video using unlabeled\nvideos as training data. The presence of anomalies in the training data poses a\nsignificant challenge in this task, particularly because they form clusters in\nthe feature space. We refer to this property as the \"Anomaly Cluster\" issue.\nThe condensed nature of these anomalies makes it difficult to distinguish\nbetween normal and abnormal data in the training set. Consequently, training\nconventional anomaly detection techniques using an unlabeled dataset often\nleads to sub-optimal results. To tackle this difficulty, we propose a new\nmethod called Cleansed k-Nearest Neighbor (CKNN), which explicitly filters out\nthe Anomaly Clusters by cleansing the training dataset. Following the k-nearest\nneighbor algorithm in the feature space provides powerful anomaly detection\ncapability. Although the identified Anomaly Cluster issue presents a\nsignificant challenge to applying k-nearest neighbor in UVAD, our proposed\ncleansing scheme effectively addresses this problem. We evaluate the proposed\nmethod on various benchmark datasets and demonstrate that CKNN outperforms the\nprevious state-of-the-art UVAD method by up to 8.5% (from 82.0 to 89.0) in\nterms of AUROC. Moreover, we emphasize that the performance of the proposed\nmethod is comparable to that of the state-of-the-art method trained using\nanomaly-free data.\n", "date": "2024-08-06", "categories": []}
{"id": "2312.05959", "title": "VAE-IF: Deep feature extraction with averaging for fully unsupervised\n  artifact detection in routinely acquired ICU time-series", "abstract": "  Artifacts are a common problem in physiological time series collected from\nintensive care units (ICU) and other settings. They affect the quality and\nreliability of clinical research and patient care. Manual annotation of\nartifacts is costly and time-consuming, rendering it impractical. Automated\nmethods are desired. Here, we propose a novel fully unsupervised approach to\ndetect artifacts in clinical-standard, minute-by-minute resolution ICU data\nwithout any prior labeling or signal-specific knowledge. Our approach combines\na variational autoencoder (VAE) and an isolation forest (IF) into a hybrid\nmodel to learn features and identify anomalies in different types of vital\nsigns, such as blood pressure, heart rate, and intracranial pressure. We\nevaluate our approach on a real-world ICU dataset and compare it with\nsupervised benchmark models based on long short-term memory (LSTM) and XGBoost\nand statistical methods such as ARIMA. We show that our unsupervised approach\nachieves comparable sensitivity to fully supervised methods and generalizes\nwell to an external dataset. We also visualize the latent space learned by the\nVAE and demonstrate its ability to disentangle clean and noisy samples. Our\napproach offers a promising solution for cleaning ICU data in clinical research\nand practice without the need for any labels whatsoever.\n", "date": "2023-12-10", "categories": []}
{"id": "2408.01960", "title": "AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion\n  Model", "abstract": "  Anomaly detection is a critical task in industrial manufacturing, aiming to\nidentify defective parts of products. Most industrial anomaly detection methods\nassume the availability of sufficient normal data for training. This assumption\nmay not hold true due to the cost of labeling or data privacy policies.\nAdditionally, mainstream methods require training bespoke models for different\nobjects, which incurs heavy costs and lacks flexibility in practice. To address\nthese issues, we seek help from Stable Diffusion (SD) model due to its\ncapability of zero/few-shot inpainting, which can be leveraged to inpaint\nanomalous regions as normal. In this paper, a few-shot multi-class anomaly\ndetection framework that adopts Stable Diffusion model is proposed, named\nAnomalySD. To adapt SD to anomaly detection task, we design different\nhierarchical text descriptions and the foreground mask mechanism for\nfine-tuning SD. In the inference stage, to accurately mask anomalous regions\nfor inpainting, we propose multi-scale mask strategy and prototype-guided mask\nstrategy to handle diverse anomalous regions. Hierarchical text prompts are\nalso utilized to guide the process of inpainting in the inference stage. The\nanomaly score is estimated based on inpainting result of all masks. Extensive\nexperiments on the MVTec-AD and VisA datasets demonstrate the superiority of\nour approach. We achieved anomaly classification and segmentation results of\n93.6%/94.8% AUROC on the MVTec-AD dataset and 86.1%/96.5% AUROC on the VisA\ndataset under multi-class and one-shot settings.\n", "date": "2024-08-04", "categories": []}
{"id": "2408.00792", "title": "A Scalable and Generalized Deep Learning Framework for Anomaly Detection\n  in Surveillance Videos", "abstract": "  Anomaly detection in videos is challenging due to the complexity, noise, and\ndiverse nature of activities such as violence, shoplifting, and vandalism.\nWhile deep learning (DL) has shown excellent performance in this area, existing\napproaches have struggled to apply DL models across different anomaly tasks\nwithout extensive retraining. This repeated retraining is time-consuming,\ncomputationally intensive, and unfair. To address this limitation, a new DL\nframework is introduced in this study, consisting of three key components:\ntransfer learning to enhance feature generalization, model fusion to improve\nfeature representation, and multi-task classification to generalize the\nclassifier across multiple tasks without training from scratch when new task is\nintroduced. The framework's main advantage is its ability to generalize without\nrequiring retraining from scratch for each new task. Empirical evaluations\ndemonstrate the framework's effectiveness, achieving an accuracy of 97.99% on\nthe RLVS dataset (violence detection), 83.59% on the UCF dataset (shoplifting\ndetection), and 88.37% across both datasets using a single classifier without\nretraining. Additionally, when tested on an unseen dataset, the framework\nachieved an accuracy of 87.25%. The study also utilizes two explainability\ntools to identify potential biases, ensuring robustness and fairness. This\nresearch represents the first successful resolution of the generalization issue\nin anomaly detection, marking a significant advancement in the field.\n", "date": "2024-07-17", "categories": []}
{"id": "2408.00006", "title": "Synthetic Time Series for Anomaly Detection in Cloud Microservices", "abstract": "  This paper proposes a framework for time series generation built to\ninvestigate anomaly detection in cloud microservices. In the field of cloud\ncomputing, ensuring the reliability of microservices is of paramount concern\nand yet a remarkably challenging task. Despite the large amount of research in\nthis area, validation of anomaly detection algorithms in realistic environments\nis difficult to achieve. To address this challenge, we propose a framework to\nmimic the complex time series patterns representative of both normal and\nanomalous cloud microservices behaviors. We detail the pipeline implementation\nthat allows deployment and management of microservices as well as the\ntheoretical approach required to generate anomalies. Two datasets generated\nusing the proposed framework have been made publicly available through GitHub.\n", "date": "2024-07-21", "categories": []}
{"id": "2311.12550", "title": "Explainable Time Series Anomaly Detection using Masked Latent Generative\n  Modeling", "abstract": "  We present a novel time series anomaly detection method that achieves\nexcellent detection accuracy while offering a superior level of explainability.\nOur proposed method, TimeVQVAE-AD, leverages masked generative modeling adapted\nfrom the cutting-edge time series generation method known as TimeVQVAE. The\nprior model is trained on the discrete latent space of a time-frequency domain.\nNotably, the dimensional semantics of the time-frequency domain are preserved\nin the latent space, enabling us to compute anomaly scores across different\nfrequency bands, which provides a better insight into the detected anomalies.\nAdditionally, the generative nature of the prior model allows for sampling\nlikely normal states for detected anomalies, enhancing the explainability of\nthe detected anomalies through counterfactuals. Our experimental evaluation on\nthe UCR Time Series Anomaly archive demonstrates that TimeVQVAE-AD\nsignificantly surpasses the existing methods in terms of detection accuracy and\nexplainability. We provide our implementation on GitHub:\nhttps://github.com/ML4ITS/TimeVQVAE-AnomalyDetection.\n", "date": "2023-11-21", "categories": []}
{"id": "2401.03151", "title": "Semi-supervised learning via DQN for log anomaly detection", "abstract": "  Log anomaly detection is a critical component in modern software system\nsecurity and maintenance, serving as a crucial support and basis for system\nmonitoring, operation, and troubleshooting. It aids operations personnel in\ntimely identification and resolution of issues. However, current methods in log\nanomaly detection still face challenges such as underutilization of unlabeled\ndata, imbalance between normal and anomaly class data, and high rates of false\npositives and false negatives, leading to insufficient effectiveness in anomaly\nrecognition. In this study, we propose a semi-supervised log anomaly detection\nmethod named DQNLog, which integrates deep reinforcement learning to enhance\nanomaly detection performance by leveraging a small amount of labeled data and\nlarge-scale unlabeled data. To address issues of imbalanced data and\ninsufficient labeling, we design a state transition function biased towards\nanomalies based on cosine similarity, aiming to capture semantic-similar\nanomalies rather than favoring the majority class. To enhance the model's\ncapability in learning anomalies, we devise a joint reward function that\nencourages the model to utilize labeled anomalies and explore unlabeled\nanomalies, thereby reducing false positives and false negatives. Additionally,\nto prevent the model from deviating from normal trajectories due to\nmisestimation, we introduce a regularization term in the loss function to\nensure the model retains prior knowledge during updates. We evaluate DQNLog on\nthree widely used datasets, demonstrating its ability to effectively utilize\nlarge-scale unlabeled data and achieve promising results across all\nexperimental datasets.\n", "date": "2024-01-06", "categories": []}
{"id": "2405.10608", "title": "ECATS: Explainable-by-design concept-based anomaly detection for time\n  series", "abstract": "  Deep learning methods for time series have already reached excellent\nperformances in both prediction and classification tasks, including anomaly\ndetection. However, the complexity inherent in Cyber Physical Systems (CPS)\ncreates a challenge when it comes to explainability methods. To overcome this\ninherent lack of interpretability, we propose ECATS, a concept-based\nneuro-symbolic architecture where concepts are represented as Signal Temporal\nLogic (STL) formulae. Leveraging kernel-based methods for STL, concept\nembeddings are learnt in an unsupervised manner through a cross-attention\nmechanism. The network makes class predictions through these concept\nembeddings, allowing for a meaningful explanation to be naturally extracted for\neach input. Our preliminary experiments with a simple CPS-based dataset show\nthat our model is able to achieve great classification performance while\nensuring local interpretability.\n", "date": "2024-05-17", "categories": []}
{"id": "2407.20695", "title": "Time Series Anomaly Detection with CNN for Environmental Sensors in\n  Healthcare-IoT", "abstract": "  This research develops a new method to detect anomalies in time series data\nusing Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed\nmethod creates a Distributed Denial of Service (DDoS) attack using an IoT\nnetwork simulator, Cooja, which emulates environmental sensors such as\ntemperature and humidity. CNNs detect anomalies in time series data, resulting\nin a 92\\% accuracy in identifying possible attacks.\n", "date": "2024-07-30", "categories": []}
{"id": "2402.15988", "title": "Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and\n  Evaluation", "abstract": "  The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect\nanomalous nodes in an input graph while avoiding biased predictions against\nindividuals from sensitive subgroups. However, the current literature does not\ncomprehensively discuss this problem, nor does it provide realistic datasets\nthat encompass actual graph structures, anomaly labels, and sensitive\nattributes. To bridge this gap, we introduce a formal definition of the FairGAD\nproblem and present two novel datasets constructed from the social media\nplatforms Reddit and Twitter. These datasets comprise 1.2 million and 400,000\nedges associated with 9,000 and 47,000 nodes, respectively, and leverage\npolitical leanings as sensitive attributes and misinformation spreaders as\nanomaly labels. We demonstrate that our FairGAD datasets significantly differ\nfrom the synthetic datasets used by the research community. Using our datasets,\nwe investigate the performance-fairness trade-off in nine existing GAD and\nnon-graph AD methods on five state-of-the-art fairness methods. Our code and\ndatasets are available at https://github.com/nigelnnk/FairGAD\n", "date": "2024-02-25", "categories": []}
{"id": "2404.09871", "title": "Explainable Online Unsupervised Anomaly Detection for Cyber-Physical\n  Systems via Causal Discovery from Time Series", "abstract": "  Online unsupervised detection of anomalies is crucial to guarantee the\ncorrect operation of cyber-physical systems and the safety of humans\ninteracting with them. State-of-the-art approaches based on deep learning via\nneural networks achieve outstanding performance at anomaly recognition,\nevaluating the discrepancy between a normal model of the system (with no\nanomalies) and the real-time stream of sensor time series. However, large\ntraining data and time are typically required, and explainability is still a\nchallenge to identify the root of the anomaly and implement predictive\nmaintainance. In this paper, we use causal discovery to learn a normal causal\ngraph of the system, and we evaluate the persistency of causal links during\nreal-time acquisition of sensor data to promptly detect anomalies. On two\nbenchmark anomaly detection datasets, we show that our method has higher\ntraining efficiency, outperforms the accuracy of state-of-the-art neural\narchitectures and correctly identifies the sources of >10 different anomalies.\nThe code is at https://github.com/Isla-lab/causal_anomaly_detection.\n", "date": "2024-04-15", "categories": []}
{"id": "2407.19646", "title": "Foundations for Unfairness in Anomaly Detection -- Case Studies in\n  Facial Imaging Data", "abstract": "  Deep anomaly detection (AD) is perhaps the most controversial of data\nanalytic tasks as it identifies entities that are then specifically targeted\nfor further investigation or exclusion. Also controversial is the application\nof AI to facial imaging data. This work explores the intersection of these two\nareas to understand two core questions: \"Who\" these algorithms are being unfair\nto and equally important \"Why\". Recent work has shown that deep AD can be\nunfair to different groups despite being unsupervised with a recent study\nshowing that for portraits of people: men of color are far more likely to be\nchosen to be outliers. We study the two main categories of AD algorithms:\nautoencoder-based and single-class-based which effectively try to compress all\nthe instances with those that can not be easily compressed being deemed to be\noutliers. We experimentally verify sources of unfairness such as the\nunder-representation of a group (e.g. people of color are relatively rare),\nspurious group features (e.g. men are often photographed with hats), and group\nlabeling noise (e.g. race is subjective). We conjecture that lack of\ncompressibility is the main foundation and the others cause it but experimental\nresults show otherwise and we present a natural hierarchy amongst them.\n", "date": "2024-07-29", "categories": []}
{"id": "2407.19849", "title": "Normality Addition via Normality Detection in Industrial Image Anomaly\n  Detection Models", "abstract": "  The task of image anomaly detection (IAD) aims to identify deviations from\nnormality in image data. These anomalies are patterns that deviate\nsignificantly from what the IAD model has learned from the data during\ntraining. However, in real-world scenarios, the criteria for what constitutes\nnormality often change, necessitating the reclassification of previously\nanomalous instances as normal. To address this challenge, we propose a new\nscenario termed \"normality addition,\" involving the post-training adjustment of\ndecision boundaries to incorporate new normalities. To address this challenge,\nwe propose a method called Normality Addition via Normality Detection (NAND),\nleveraging a vision-language model. NAND performs normality detection which\ndetect patterns related to the intended normality within images based on\ntextual descriptions. We then modify the results of a pre-trained IAD model to\nimplement this normality addition. Using the benchmark dataset in IAD, MVTec\nAD, we establish an evaluation protocol for the normality addition task and\nempirically demonstrate the effectiveness of the NAND method.\n", "date": "2024-07-29", "categories": []}
{"id": "2407.19951", "title": "Can I trust my anomaly detection system? A case study based on\n  explainable AI", "abstract": "  Generative models based on variational autoencoders are a popular technique\nfor detecting anomalies in images in a semi-supervised context. A common\napproach employs the anomaly score to detect the presence of anomalies, and it\nis known to reach high level of accuracy on benchmark datasets. However, since\nanomaly scores are computed from reconstruction disparities, they often obscure\nthe detection of various spurious features, raising concerns regarding their\nactual efficacy. This case study explores the robustness of an anomaly\ndetection system based on variational autoencoder generative models through the\nuse of eXplainable AI methods. The goal is to get a different perspective on\nthe real performances of anomaly detectors that use reconstruction differences.\nIn our case study we discovered that, in many cases, samples are detected as\nanomalous for the wrong or misleading factors.\n", "date": "2024-07-29", "categories": []}
{"id": "2407.18439", "title": "Impact of Recurrent Neural Networks and Deep Learning Frameworks on\n  Real-time Lightweight Time Series Anomaly Detection", "abstract": "  Real-time lightweight time series anomaly detection has become increasingly\ncrucial in cybersecurity and many other domains. Its ability to adapt to\nunforeseen pattern changes and swiftly identify anomalies enables prompt\nresponses and critical decision-making. While several such anomaly detection\napproaches have been introduced in recent years, they primarily utilize a\nsingle type of recurrent neural networks (RNNs) and have been implemented in\nonly one deep learning framework. It is unclear how the use of different types\nof RNNs available in various deep learning frameworks affects the performance\nof these anomaly detection approaches due to the absence of comprehensive\nevaluations. Arbitrarily choosing a RNN variant and a deep learning framework\nto implement an anomaly detection approach may not reflect its true performance\nand could potentially mislead users into favoring one approach over another. In\nthis paper, we aim to study the influence of various types of RNNs available in\npopular deep learning frameworks on real-time lightweight time series anomaly\ndetection. We reviewed several state-of-the-art approaches and implemented a\nrepresentative anomaly detection approach using well-known RNN variants\nsupported by three widely recognized deep learning frameworks. A comprehensive\nevaluation is then conducted to analyze the performance of each implementation\nacross real-world, open-source time series datasets. The evaluation results\nprovide valuable guidance for selecting the appropriate RNN variant and deep\nlearning framework for real-time, lightweight time series anomaly detection.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.18450", "title": "Textile Anomaly Detection: Evaluation of the State-of-the-Art for\n  Automated Quality Inspection of Carpet", "abstract": "  In this study, state-of-the-art unsupervised detection models were evaluated\nfor the purpose of automated anomaly inspection of wool carpets. A custom\ndataset of four unique types of carpet textures was created to thoroughly test\nthe models and their robustness in detecting subtle anomalies in complex\ntextures. Due to the requirements of an inline inspection system in a\nmanufacturing use case, the metrics of importance in this study were accuracy\nin detecting anomalous areas, the number of false detections, and the inference\ntimes of each model for real-time performance. Of the evaluated models, the\nstudent-teacher network based methods were found on average to yield the\nhighest detection accuracy and lowest false detection rates. When trained on a\nmulti-class dataset the models were found to yield comparable if not better\nresults than single-class training. Finally, in terms of detection speed, with\nexception to the generative model, all other evaluated models were found to\nhave comparable inference times on a GPU, with an average of 0.16s per image.\nOn a CPU, most of these models typically produced results between 1.5 to 2\ntimes the respective GPU inference times.\n", "date": "2024-07-26", "categories": []}
{"id": "2401.00773", "title": "Unsupervised Outlier Detection using Random Subspace and Subsampling\n  Ensembles of Dirichlet Process Mixtures", "abstract": "  Probabilistic mixture models are recognized as effective tools for\nunsupervised outlier detection owing to their interpretability and global\ncharacteristics. Among these, Dirichlet process mixture models stand out as a\nstrong alternative to conventional finite mixture models for both clustering\nand outlier detection tasks. Unlike finite mixture models, Dirichlet process\nmixtures are infinite mixture models that automatically determine the number of\nmixture components based on the data. Despite their advantages, the adoption of\nDirichlet process mixture models for unsupervised outlier detection has been\nlimited by challenges related to computational inefficiency and sensitivity to\noutliers in the construction of outlier detectors. Additionally, Dirichlet\nprocess Gaussian mixtures struggle to effectively model non-Gaussian data with\ndiscrete or binary features. To address these challenges, we propose a novel\noutlier detection method that utilizes ensembles of Dirichlet process Gaussian\nmixtures. This unsupervised algorithm employs random subspace and subsampling\nensembles to ensure efficient computation and improve the robustness of the\noutlier detector. The ensemble approach further improves the suitability of the\nproposed method for detecting outliers in non-Gaussian data. Furthermore, our\nmethod uses variational inference for Dirichlet process mixtures, which ensures\nboth efficient and rapid computation. Empirical analyses using benchmark\ndatasets demonstrate that our method outperforms existing approaches in\nunsupervised outlier detection.\n", "date": "2024-01-01", "categories": []}
{"id": "2402.11933", "title": "SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via\n  Self-Supervised Learning", "abstract": "  To detect anomalies in real-world graphs, such as social, email, and\nfinancial networks, various approaches have been developed. While they\ntypically assume static input graphs, most real-world graphs grow over time,\nnaturally represented as edge streams. In this context, we aim to achieve three\ngoals: (a) instantly detecting anomalies as they occur, (b) adapting to\ndynamically changing states, and (c) handling the scarcity of dynamic anomaly\nlabels. In this paper, we propose SLADE (Self-supervised Learning for Anomaly\nDetection in Edge Streams) for rapid detection of dynamic anomalies in edge\nstreams, without relying on labels. SLADE detects the shifts of nodes into\nabnormal states by observing deviations in their interaction patterns over\ntime. To this end, it trains a deep neural network to perform two\nself-supervised tasks: (a) minimizing drift in node representations and (b)\ngenerating long-term interaction patterns from short-term ones. Failure in\nthese tasks for a node signals its deviation from the norm. Notably, the neural\nnetwork and tasks are carefully designed so that all required operations can be\nperformed in constant time (w.r.t. the graph size) in response to each new edge\nin the input stream. In dynamic anomaly detection across four real-world\ndatasets, SLADE outperforms nine competing methods, even those leveraging label\nsupervision.\n", "date": "2024-02-19", "categories": []}
{"id": "2404.09302", "title": "High Significant Fault Detection in Azure Core Workload Insights", "abstract": "  Azure Core workload insights have time-series data with different metric\nunits. Faults or Anomalies are observed in these time-series data owing to\nfaults observed with respect to metric name, resources region, dimensions, and\nits dimension value associated with the data. For Azure Core, an important task\nis to highlight faults or anomalies to the user on a dashboard that they can\nperceive easily. The number of anomalies reported should be highly significant\nand in a limited number, e.g., 5-20 anomalies reported per hour. The reported\nanomalies will have significant user perception and high reconstruction error\nin any time-series forecasting model. Hence, our task is to automatically\nidentify 'high significant anomalies' and their associated information for user\nperception.\n", "date": "2024-04-14", "categories": []}
{"id": "2407.17545", "title": "Large Language Models for Anomaly Detection in Computational Workflows:\n  from Supervised Fine-Tuning to In-Context Learning", "abstract": "  Anomaly detection in computational workflows is critical for ensuring system\nreliability and security. However, traditional rule-based methods struggle to\ndetect novel anomalies. This paper leverages large language models (LLMs) for\nworkflow anomaly detection by exploiting their ability to learn complex data\npatterns. Two approaches are investigated: 1) supervised fine-tuning (SFT),\nwhere pre-trained LLMs are fine-tuned on labeled data for sentence\nclassification to identify anomalies, and 2) in-context learning (ICL) where\nprompts containing task descriptions and examples guide LLMs in few-shot\nanomaly detection without fine-tuning. The paper evaluates the performance,\nefficiency, generalization of SFT models, and explores zero-shot and few-shot\nICL prompts and interpretability enhancement via chain-of-thought prompting.\nExperiments across multiple workflow datasets demonstrate the promising\npotential of LLMs for effective anomaly detection in complex executions.\n", "date": "2024-07-24", "categories": []}
{"id": "2407.17705", "title": "ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with\n  Feature Reconstruction and Refinement", "abstract": "  Unsupervised anomaly localization on industrial textured images has achieved\nremarkable results through reconstruction-based methods, yet existing\napproaches based on image reconstruction and feature reconstruc-tion each have\ntheir own shortcomings. Firstly, image-based methods tend to reconstruct both\nnormal and anomalous regions well, which lead to over-generalization.\nFeature-based methods contain a large amount of distin-guishable semantic\ninformation, however, its feature structure is redundant and lacks anomalous\ninformation, which leads to significant reconstruction errors. In this paper,\nwe propose an Anomaly Localization method based on Mamba with Feature\nReconstruction and Refinement(ALMRR) which re-constructs semantic features\nbased on Mamba and then refines them through a feature refinement module. To\nequip the model with prior knowledge of anomalies, we enhance it by adding\nartificially simulated anomalies to the original images. Unlike image\nreconstruction or repair, the features of synthesized defects are repaired\nalong with those of normal areas. Finally, the aligned features containing rich\nsemantic information are fed in-to the refinement module to obtain the anomaly\nmap. Extensive experiments have been conducted on the MVTec-AD-Textured dataset\nand other real-world industrial dataset, which has demonstrated superior\nperformance com-pared to state-of-the-art (SOTA) methods.\n", "date": "2024-07-25", "categories": []}
{"id": "2407.17909", "title": "Separating Novel Features for Logical Anomaly Detection: A\n  Straightforward yet Effective Approach", "abstract": "  Vision-based inspection algorithms have significantly contributed to quality\ncontrol in industrial settings, particularly in addressing structural defects\nlike dent and contamination which are prevalent in mass production. Extensive\nresearch efforts have led to the development of related benchmarks such as\nMVTec AD (Bergmann et al., 2019). However, in industrial settings, there can be\ninstances of logical defects, where acceptable items are found in unsuitable\nlocations or product pairs do not match as expected. Recent methods tackling\nlogical defects effectively employ knowledge distillation to generate\ndifference maps. Knowledge distillation (KD) is used to learn normal data\ndistribution in unsupervised manner. Despite their effectiveness, these methods\noften overlook the potential false negatives. Excessive similarity between the\nteacher network and student network can hinder the generation of a suitable\ndifference map for logical anomaly detection. This technical report provides\ninsights on handling potential false negatives by utilizing a simple constraint\nin KD-based logical anomaly detection methods. We select EfficientAD as a\nstate-of-the-art baseline and apply a margin-based constraint to its\nunsupervised learning scheme. Applying this constraint, we can improve the\nAUROC for MVTec LOCO AD by 1.3 %.\n", "date": "2024-07-25", "categories": []}
{"id": "2402.18293", "title": "Continuous Memory Representation for Anomaly Detection", "abstract": "  There have been significant advancements in anomaly detection in an\nunsupervised manner, where only normal images are available for training.\nSeveral recent methods aim to detect anomalies based on a memory, comparing or\nreconstructing the input with directly stored normal features (or trained\nfeatures with normal images). However, such memory-based approaches operate on\na discrete feature space implemented by the nearest neighbor or attention\nmechanism, suffering from poor generalization or an identity shortcut issue\noutputting the same as input, respectively. Furthermore, the majority of\nexisting methods are designed to detect single-class anomalies, resulting in\nunsatisfactory performance when presented with multiple classes of objects. To\ntackle all of the above challenges, we propose CRAD, a novel anomaly detection\nmethod for representing normal features within a \"continuous\" memory, enabled\nby transforming spatial features into coordinates and mapping them to\ncontinuous grids. Furthermore, we carefully design the grids tailored for\nanomaly detection, representing both local and global normal features and\nfusing them effectively. Our extensive experiments demonstrate that CRAD\nsuccessfully generalizes the normal features and mitigates the identity\nshortcut, furthermore, CRAD effectively handles diverse classes in a single\nmodel thanks to the high-granularity continuous representation. In an\nevaluation using the MVTec AD dataset, CRAD significantly outperforms the\nprevious state-of-the-art method by reducing 65.0% of the error for multi-class\nunified anomaly detection. The project page is available at\nhttps://tae-mo.github.io/crad/.\n", "date": "2024-02-28", "categories": []}
{"id": "2407.17083", "title": "When Text and Images Don't Mix: Bias-Correcting Language-Image\n  Similarity Scores for Anomaly Detection", "abstract": "  Contrastive Language-Image Pre-training (CLIP) achieves remarkable\nperformance in various downstream tasks through the alignment of image and text\ninput embeddings and holds great promise for anomaly detection. However, our\nempirical experiments show that the embeddings of text inputs unexpectedly\ntightly cluster together, far away from image embeddings, contrary to the\nmodel's contrastive training objective to align image-text input pairs. We show\nthat this phenomenon induces a `similarity bias' - in which false negative and\nfalse positive errors occur due to bias in the similarities between images and\nthe normal label text embeddings. To address this bias, we propose a novel\nmethodology called BLISS which directly accounts for this similarity bias\nthrough the use of an auxiliary, external set of text inputs. BLISS is simple,\nit does not require strong inductive biases about anomalous behaviour nor an\nexpensive training process, and it significantly outperforms baseline methods\non benchmark image datasets, even when access to normal data is extremely\nlimited.\n", "date": "2024-07-24", "categories": []}
{"id": "2305.03098", "title": "Unsupervised anomaly localization in high-resolution breast scans using\n  deep pluralistic image completion", "abstract": "  Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a\ndifficult task due to natural tumor rarity, breast tissue variability, and high\nresolution. Given the scarcity of abnormal images and the abundance of normal\nimages for this problem, an anomaly detection/localization approach could be\nwell-suited. However, most anomaly localization research in machine learning\nfocuses on non-medical datasets, and we find that these methods fall short when\nadapted to medical imaging datasets. The problem is alleviated when we solve\nthe task from the image completion perspective, in which the presence of\nanomalies can be indicated by a discrepancy between the original appearance and\nits auto-completion conditioned on the surroundings. However, there are often\nmany valid normal completions given the same surroundings, especially in the\nDBT dataset, making this evaluation criterion less precise. To address such an\nissue, we consider pluralistic image completion by exploring the distribution\nof possible completions instead of generating fixed predictions. This is\nachieved through our novel application of spatial dropout on the completion\nnetwork during inference time only, which requires no additional training cost\nand is effective at generating diverse completions. We further propose minimum\ncompletion distance (MCD), a new metric for detecting anomalies, thanks to\nthese stochastic completions. We provide theoretical as well as empirical\nsupport for the superiority over existing methods of using the proposed method\nfor anomaly localization. On the DBT dataset, our model outperforms other\nstate-of-the-art methods by at least 10\\% AUROC for pixel-level detection.\n", "date": "2023-05-04", "categories": []}
{"id": "2401.17052", "title": "Retrieval Augmented Deep Anomaly Detection for Tabular Data", "abstract": "  Deep learning for tabular data has garnered increasing attention in recent\nyears, yet employing deep models for structured data remains challenging. While\nthese models excel with unstructured data, their efficacy with structured data\nhas been limited. Recent research has introduced retrieval-augmented models to\naddress this gap, demonstrating promising results in supervised tasks such as\nclassification and regression. In this work, we investigate using\nretrieval-augmented models for anomaly detection on tabular data. We propose a\nreconstruction-based approach in which a transformer model learns to\nreconstruct masked features of \\textit{normal} samples. We test the\neffectiveness of KNN-based and attention-based modules to select relevant\nsamples to help in the reconstruction process of the target sample. Our\nexperiments on a benchmark of 31 tabular datasets reveal that augmenting this\nreconstruction-based anomaly detection (AD) method with sample-sample\ndependencies via retrieval modules significantly boosts performance. The\npresent work supports the idea that retrieval module are useful to augment any\ndeep AD method to enhance anomaly detection on tabular data.\n", "date": "2024-01-30", "categories": []}
{"id": "2406.07176", "title": "RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image\n  Anomaly Detection", "abstract": "  Robustness against noisy imaging is crucial for practical image anomaly\ndetection systems. This study introduces a Robust Anomaly Detection (RAD)\ndataset with free views, uneven illuminations, and blurry collections to\nsystematically evaluate the robustness of current anomaly detection methods.\nSpecifically, RAD aims to identify foreign objects on working platforms as\nanomalies. The collection process incorporates various sources of imaging\nnoise, such as viewpoint changes, uneven illuminations, and blurry collections,\nto replicate real-world inspection scenarios. Subsequently, we assess and\nanalyze 11 state-of-the-art unsupervised and zero-shot methods on RAD. Our\nfindings indicate that: 1) Variations in viewpoint, illumination, and blurring\naffect anomaly detection methods to varying degrees; 2) Methods relying on\nmemory banks and assisted by synthetic anomalies demonstrate stronger\nrobustness; 3) Effectively leveraging the general knowledge of foundational\nmodels is a promising avenue for enhancing the robustness of anomaly detection\nmethods. The dataset is available at https://github.com/hustCYQ/RAD-dataset.\n", "date": "2024-06-11", "categories": []}
{"id": "2407.10299", "title": "Follow the Rules: Reasoning for Video Anomaly Detection with Large\n  Language Models", "abstract": "  Video Anomaly Detection (VAD) is crucial for applications such as security\nsurveillance and autonomous driving. However, existing VAD methods provide\nlittle rationale behind detection, hindering public trust in real-world\ndeployments. In this paper, we approach VAD with a reasoning framework.\nAlthough Large Language Models (LLMs) have shown revolutionary reasoning\nability, we find that their direct use falls short of VAD. Specifically, the\nimplicit knowledge pre-trained in LLMs focuses on general context and thus may\nnot apply to every specific real-world VAD scenario, leading to inflexibility\nand inaccuracy. To address this, we propose AnomalyRuler, a novel rule-based\nreasoning framework for VAD with LLMs. AnomalyRuler comprises two main stages:\ninduction and deduction. In the induction stage, the LLM is fed with few-shot\nnormal reference samples and then summarizes these normal patterns to induce a\nset of rules for detecting anomalies. The deduction stage follows the induced\nrules to spot anomalous frames in test videos. Additionally, we design rule\naggregation, perception smoothing, and robust reasoning strategies to further\nenhance AnomalyRuler's robustness. AnomalyRuler is the first reasoning approach\nfor the one-class VAD task, which requires only few-normal-shot prompting\nwithout the need for full-shot training, thereby enabling fast adaption to\nvarious VAD scenarios. Comprehensive experiments across four VAD benchmarks\ndemonstrate AnomalyRuler's state-of-the-art detection performance and reasoning\nability. AnomalyRuler is open-source and available at:\nhttps://github.com/Yuchen413/AnomalyRuler\n", "date": "2024-07-14", "categories": []}
{"id": "2407.15763", "title": "Towards Open-World Object-based Anomaly Detection via Self-Supervised\n  Outlier Synthesis", "abstract": "  Object detection is a pivotal task in computer vision that has received\nsignificant attention in previous years. Nonetheless, the capability of a\ndetector to localise objects out of the training distribution remains\nunexplored. Whilst recent approaches in object-level out-of-distribution (OoD)\ndetection heavily rely on class labels, such approaches contradict truly\nopen-world scenarios where the class distribution is often unknown. In this\ncontext, anomaly detection focuses on detecting unseen instances rather than\nclassifying detections as OoD. This work aims to bridge this gap by leveraging\nan open-world object detector and an OoD detector via virtual outlier\nsynthesis. This is achieved by using the detector backbone features to first\nlearn object pseudo-classes via self-supervision. These pseudo-classes serve as\nthe basis for class-conditional virtual outlier sampling of anomalous features\nthat are classified by an OoD head. Our approach empowers our overall object\ndetector architecture to learn anomaly-aware feature representations without\nrelying on class labels, hence enabling truly open-world object anomaly\ndetection. Empirical validation of our approach demonstrates its effectiveness\nacross diverse datasets encompassing various imaging modalities (visible,\ninfrared, and X-ray). Moreover, our method establishes state-of-the-art\nperformance on object-level anomaly detection, achieving an average recall\nscore improvement of over 5.4% for natural images and 23.5% for a security\nX-ray dataset compared to the current approaches. In addition, our method\ndetects anomalies in datasets where current approaches fail. Code available at\nhttps://github.com/KostadinovShalon/oln-ssos.\n", "date": "2024-07-22", "categories": []}
{"id": "2309.02460", "title": "Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs", "abstract": "  Cryptocurrencies are rapidly expanding and becoming vital in digital\nfinancial markets. However, the rise in cryptocurrency-related illicit\nactivities has led to significant losses for users. To protect the security of\nthese platforms, it is critical to identify illicit accounts effectively.\nCurrent detection methods mainly depend on feature engineering or are\ninadequate to leverage the complex information within cryptocurrency\ntransaction networks, resulting in suboptimal performance. In this paper, we\npresent DIAM, an effective method for detecting illicit accounts in\ncryptocurrency transaction networks modeled by directed multi-graphs with\nattributed edges. DIAM first features an Edge2Seq module that captures\nintrinsic transaction patterns from parallel edges by considering edge\nattributes and their directed sequences, to generate effective node\nrepresentations. Then in DIAM, we design a multigraph Discrepancy (MGD) module\nwith a tailored message passing mechanism to capture the discrepant features\nbetween normal and illicit nodes over the multigraph topology, assisted by an\nattention mechanism. DIAM integrates these techniques for end-to-end training\nto detect illicit accounts from legitimate ones. Extensive experiments,\ncomparing against 15 existing solutions on 4 large cryptocurrency datasets of\nBitcoin and Ethereum, demonstrate that DIAM consistently outperforms others in\naccurately identifying illicit accounts. For example, on a Bitcoin dataset with\n20 million nodes and 203 million edges, DIAM attains an F1 score of 96.55%,\nmarkedly surpassing the runner-up's score of 83.92%. The code is available at\nhttps://github.com/TommyDzh/DIAM.\n", "date": "2023-09-04", "categories": []}
{"id": "2407.00148", "title": "Localizing Anomalies via Multiscale Score Matching Analysis", "abstract": "  Anomaly detection and localization in medical imaging remain critical\nchallenges in healthcare. This paper introduces Spatial-MSMA (Multiscale Score\nMatching Analysis), a novel unsupervised method for anomaly localization in\nvolumetric brain MRIs. Building upon the MSMA framework, our approach\nincorporates spatial information and conditional likelihoods to enhance anomaly\ndetection capabilities. We employ a flexible normalizing flow model conditioned\non patch positions and global image features to estimate patch-wise anomaly\nscores. The method is evaluated on a dataset of 1,650 T1- and T2-weighted brain\nMRIs from typically developing children, with simulated lesions added to the\ntest set. Spatial-MSMA significantly outperforms existing methods, including\nreconstruction-based, generative-based, and interpretation-based approaches, in\nlesion detection and segmentation tasks. Our model achieves superior\nperformance in both distance-based metrics (99th percentile Hausdorff Distance:\n$7.05 \\pm 0.61$, Mean Surface Distance: $2.10 \\pm 0.43$) and component-wise\nmetrics (True Positive Rate: $0.83 \\pm 0.01$, Positive Predictive Value: $0.96\n\\pm 0.01$). These results demonstrate Spatial-MSMA's potential for accurate and\ninterpretable anomaly localization in medical imaging, with implications for\nimproved diagnosis and treatment planning in clinical settings. Our code is\navailable at~\\url{https://github.com/ahsanMah/sade/}.\n", "date": "2024-06-28", "categories": []}
{"id": "2407.13251", "title": "Motif-Consistent Counterfactuals with Adversarial Refinement for\n  Graph-Level Anomaly Detection", "abstract": "  Graph-level anomaly detection is significant in diverse domains. To improve\ndetection performance, counterfactual graphs have been exploited to benefit the\ngeneralization capacity by learning causal relations. Most existing studies\ndirectly introduce perturbations (e.g., flipping edges) to generate\ncounterfactual graphs, which are prone to alter the semantics of generated\nexamples and make them off the data manifold, resulting in sub-optimal\nperformance. To address these issues, we propose a novel approach,\nMotif-consistent Counterfactuals with Adversarial Refinement (MotifCAR), for\ngraph-level anomaly detection. The model combines the motif of one graph, the\ncore subgraph containing the identification (category) information, and the\ncontextual subgraph (non-motif) of another graph to produce a raw\ncounterfactual graph. However, the produced raw graph might be distorted and\ncannot satisfy the important counterfactual properties: Realism, Validity,\nProximity and Sparsity. Towards that, we present a Generative Adversarial\nNetwork (GAN)-based graph optimizer to refine the raw counterfactual graphs. It\nadopts the discriminator to guide the generator to generate graphs close to\nrealistic data, i.e., meet the property Realism. Further, we design the motif\nconsistency to force the motif of the generated graphs to be consistent with\nthe realistic graphs, meeting the property Validity. Also, we devise the\ncontextual loss and connection loss to control the contextual subgraph and the\nnewly added links to meet the properties Proximity and Sparsity. As a result,\nthe model can generate high-quality counterfactual graphs. Experiments\ndemonstrate the superiority of MotifCAR.\n", "date": "2024-07-18", "categories": []}
{"id": "2211.15597", "title": "Lightning Fast Video Anomaly Detection via Adversarial Knowledge\n  Distillation", "abstract": "  We propose a very fast frame-level model for anomaly detection in video,\nwhich learns to detect anomalies by distilling knowledge from multiple highly\naccurate object-level teacher models. To improve the fidelity of our student,\nwe distill the low-resolution anomaly maps of the teachers by jointly applying\nstandard and adversarial distillation, introducing an adversarial discriminator\nfor each teacher to distinguish between target and generated anomaly maps. We\nconduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2),\nshowing that our method is over 7 times faster than the fastest competing\nmethod, and between 28 and 62 times faster than object-centric models, while\nobtaining comparable results to recent methods. Our evaluation also indicates\nthat our model achieves the best trade-off between speed and accuracy, due to\nits previously unheard-of speed of 1480 FPS. In addition, we carry out a\ncomprehensive ablation study to justify our architectural design choices. Our\ncode is freely available at: https://github.com/ristea/fast-aed.\n", "date": "2022-11-28", "categories": []}
{"id": "2407.12276", "title": "VCP-CLIP: A visual context prompting model for zero-shot anomaly\n  segmentation", "abstract": "  Recently, large-scale vision-language models such as CLIP have demonstrated\nimmense potential in zero-shot anomaly segmentation (ZSAS) task, utilizing a\nunified model to directly detect anomalies on any unseen product with\npainstakingly crafted text prompts. However, existing methods often assume that\nthe product category to be inspected is known, thus setting product-specific\ntext prompts, which is difficult to achieve in the data privacy scenarios.\nMoreover, even the same type of product exhibits significant differences due to\nspecific components and variations in the production process, posing\nsignificant challenges to the design of text prompts. In this end, we propose a\nvisual context prompting model (VCP-CLIP) for ZSAS task based on CLIP. The\ninsight behind VCP-CLIP is to employ visual context prompting to activate\nCLIP's anomalous semantic perception ability. In specific, we first design a\nPre-VCP module to embed global visual information into the text prompt, thus\neliminating the necessity for product-specific prompts. Then, we propose a\nnovel Post-VCP module, that adjusts the text embeddings utilizing the\nfine-grained features of the images. In extensive experiments conducted on 10\nreal-world industrial anomaly segmentation datasets, VCP-CLIP achieved\nstate-of-the-art performance in ZSAS task. The code is available at\nhttps://github.com/xiaozhen228/VCP-CLIP.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.12427", "title": "GeneralAD: Anomaly Detection Across Domains by Attending to Distorted\n  Features", "abstract": "  In the domain of anomaly detection, methods often excel in either high-level\nsemantic or low-level industrial benchmarks, rarely achieving cross-domain\nproficiency. Semantic anomalies are novelties that differ in meaning from the\ntraining set, like unseen objects in self-driving cars. In contrast, industrial\nanomalies are subtle defects that preserve semantic meaning, such as cracks in\nairplane components. In this paper, we present GeneralAD, an anomaly detection\nframework designed to operate in semantic, near-distribution, and industrial\nsettings with minimal per-task adjustments. In our approach, we capitalize on\nthe inherent design of Vision Transformers, which are trained on image patches,\nthereby ensuring that the last hidden states retain a patch-based structure. We\npropose a novel self-supervised anomaly generation module that employs\nstraightforward operations like noise addition and shuffling to patch features\nto construct pseudo-abnormal samples. These features are fed to an\nattention-based discriminator, which is trained to score every patch in the\nimage. With this, our method can both accurately identify anomalies at the\nimage level and also generate interpretable anomaly maps. We extensively\nevaluated our approach on ten datasets, achieving state-of-the-art results in\nsix and on-par performance in the remaining for both localization and detection\ntasks.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.12474", "title": "Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI\n  Anomaly Detection", "abstract": "  Unsupervised Anomaly Detection (UAD) methods rely on healthy data\ndistributions to identify anomalies as outliers. In brain MRI, a common\napproach is reconstruction-based UAD, where generative models reconstruct\nhealthy brain MRIs, and anomalies are detected as deviations between input and\nreconstruction. However, this method is sensitive to imperfect reconstructions,\nleading to false positives that impede the segmentation. To address this\nlimitation, we construct multiple reconstructions with probabilistic diffusion\nmodels. We then analyze the resulting distribution of these reconstructions\nusing the Mahalanobis distance to identify anomalies as outliers. By leveraging\ninformation about normal variations and covariance of individual pixels within\nthis distribution, we effectively refine anomaly scoring, leading to improved\nsegmentation. Our experimental results demonstrate substantial performance\nimprovements across various data sets. Specifically, compared to relying solely\non single reconstructions, our approach achieves relative improvements of\n15.9%, 35.4%, 48.0%, and 4.7% in terms of AUPRC for the BRATS21, ATLAS, MSLUB\nand WMH data sets, respectively.\n", "date": "2024-07-17", "categories": []}
{"id": "2301.04257", "title": "ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models", "abstract": "  The unsupervised outlier detection (UOD) problem refers to a task to identify\ninliers given training data which contain outliers as well as inliers, without\nany labeled information about inliers and outliers. It has been widely\nrecognized that using fully-trained likelihood-based deep generative models\n(DGMs) often results in poor performance in distinguishing inliers from\noutliers. In this study, we claim that the likelihood itself could serve as\npowerful evidence for identifying inliers in UOD tasks, provided that DGMs are\ncarefully under-fitted. Our approach begins with a novel observation called the\ninlier-memorization (IM) effect-when training a deep generative model with data\nincluding outliers, the model initially memorizes inliers before outliers.\nBased on this finding, we develop a new method called the outlier detection via\nthe IM effect (ODIM). Remarkably, the ODIM requires only a few updates, making\nit computationally efficient-at least tens of times faster than other\ndeep-learning-based algorithms. Also, the ODIM filters out outliers\nexcellently, regardless of the data type, including tabular, image, and text\ndata. To validate the superiority and efficiency of our method, we provide\nextensive empirical analyses on close to 60 datasets.\n", "date": "2023-01-11", "categories": []}
{"id": "2403.11561", "title": "Learning Unified Reference Representation for Unsupervised Multi-class\n  Anomaly Detection", "abstract": "  In the field of multi-class anomaly detection, reconstruction-based methods\nderived from single-class anomaly detection face the well-known challenge of\n\"learning shortcuts\", wherein the model fails to learn the patterns of normal\nsamples as it should, opting instead for shortcuts such as identity mapping or\nartificial noise elimination. Consequently, the model becomes unable to\nreconstruct genuine anomalies as normal instances, resulting in a failure of\nanomaly detection. To counter this issue, we present a novel unified feature\nreconstruction-based anomaly detection framework termed RLR (Reconstruct\nfeatures from a Learnable Reference representation). Unlike previous methods,\nRLR utilizes learnable reference representations to compel the model to learn\nnormal feature patterns explicitly, thereby prevents the model from succumbing\nto the \"learning shortcuts\" issue. Additionally, RLR incorporates locality\nconstraints into the learnable reference to facilitate more effective normal\npattern capture and utilizes a masked learnable key attention mechanism to\nenhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and\nthe 12-category VisA dataset shows superior performance compared to\nstate-of-the-art methods under the unified setting. The code of RLR will be\npublicly available.\n", "date": "2024-03-18", "categories": []}
{"id": "2404.05231", "title": "PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly\n  Detection", "abstract": "  The vision-language model has brought great improvement to few-shot\nindustrial anomaly detection, which usually needs to design of hundreds of\nprompts through prompt engineering. For automated scenarios, we first use\nconventional prompt learning with many-class paradigm as the baseline to\nautomatically learn prompts but found that it can not work well in one-class\nanomaly detection. To address the above problem, this paper proposes a\none-class prompt learning method for few-shot anomaly detection, termed\nPromptAD. First, we propose semantic concatenation which can transpose normal\nprompts into anomaly prompts by concatenating normal prompts with anomaly\nsuffixes, thus constructing a large number of negative samples used to guide\nprompt learning in one-class setting. Furthermore, to mitigate the training\nchallenge caused by the absence of anomaly images, we introduce the concept of\nexplicit anomaly margin, which is used to explicitly control the margin between\nnormal prompt features and anomaly prompt features through a hyper-parameter.\nFor image-level/pixel-level anomaly detection, PromptAD achieves first place in\n11/12 few-shot settings on MVTec and VisA.\n", "date": "2024-04-08", "categories": []}
{"id": "2407.11082", "title": "Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation\n  and Feature Learning", "abstract": "  Graph-level anomaly detection (GLAD) has already gained significant\nimportance and has become a popular field of study, attracting considerable\nattention across numerous downstream works. The core focus of this domain is to\ncapture and highlight the anomalous information within given graph datasets. In\nmost existing studies, anomalies are often the instances of few. The stark\nimbalance misleads current GLAD methods to focus on learning the patterns of\nnormal graphs more, further impacting anomaly detection performance. Moreover,\nexisting methods predominantly utilize the inherent features of nodes to\nidentify anomalous graph patterns which is approved suboptimal according to our\nexperiments. In this work, we propose an imbalanced GLAD method via\ncounterfactual augmentation and feature learning. Specifically, we first\nconstruct anomalous samples based on counterfactual learning, aiming to expand\nand balance the datasets. Additionally, we construct a module based on Graph\nNeural Networks (GNNs), which allows us to utilize degree attributes to\ncomplement the inherent attribute features of nodes. Then, we design an\nadaptive weight learning module to integrate features tailored to different\ndatasets effectively to avoid indiscriminately treating all features as\nequivalent. Furthermore, extensive baseline experiments conducted on public\ndatasets substantiate the robustness and effectiveness. Besides, we apply the\nmodel to brain disease datasets, which can prove the generalization capability\nof our work. The source code of our work is available online.\n", "date": "2024-07-13", "categories": []}
{"id": "2407.11308", "title": "Detection of Global Anomalies on Distributed IoT Edges with\n  Device-to-Device Communication", "abstract": "  Anomaly detection is an important function in IoT applications for finding\noutliers caused by abnormal events. Anomaly detection sometimes comes with\nhigh-frequency data sampling which should be carried out at Edge devices rather\nthan Cloud. In this paper, we consider the case that multiple IoT devices are\ninstalled in a single remote site and that they collaboratively detect\nanomalies from the observations with device-to-device communications. For this,\nwe propose a fully distributed collaborative scheme for training distributed\nanomaly detectors with Wireless Ad Hoc Federated Learning, namely\n\"WAFL-Autoencoder\". We introduce the concept of Global Anomaly which sample is\nnot only rare to the local device but rare to all the devices in the target\ndomain. We also propose a distributed threshold-finding algorithm for Global\nAnomaly detection. With our standard benchmark-based evaluation, we have\nconfirmed that our scheme trained anomaly detectors perfectly across the\ndevices. We have also confirmed that the devices collaboratively found\nthresholds for Global Anomaly detection with low false positive rates while\nachieving high true positive rates with few exceptions.\n", "date": "2024-07-16", "categories": []}
{"id": "2407.05382", "title": "Rethinking Unsupervised Outlier Detection via Multiple Thresholding", "abstract": "  In the realm of unsupervised image outlier detection, assigning outlier\nscores holds greater significance than its subsequent task: thresholding for\npredicting labels. This is because determining the optimal threshold on\nnon-separable outlier score functions is an ill-posed problem. However, the\nlack of predicted labels not only hiders some real applications of current\noutlier detectors but also causes these methods not to be enhanced by\nleveraging the dataset's self-supervision. To advance existing scoring methods,\nwe propose a multiple thresholding (Multi-T) module. It generates two\nthresholds that isolate inliers and outliers from the unlabelled target\ndataset, whereas outliers are employed to obtain better feature representation\nwhile inliers provide an uncontaminated manifold. Extensive experiments verify\nthat Multi-T can significantly improve proposed outlier scoring methods.\nMoreover, Multi-T contributes to a naive distance-based method being\nstate-of-the-art.\n", "date": "2024-07-07", "categories": []}
{"id": "2407.10042", "title": "Harnessing Feature Clustering For Enhanced Anomaly Detection With\n  Variational Autoencoder And Dynamic Threshold", "abstract": "  We introduce an anomaly detection method for multivariate time series data\nwith the aim of identifying critical periods and features influencing extreme\nclimate events like snowmelt in the Arctic. This method leverages the\nVariational Autoencoder (VAE) integrated with dynamic thresholding and\ncorrelation-based feature clustering. This framework enhances the VAE's ability\nto identify localized dependencies and learn the temporal relationships in\nclimate data, thereby improving the detection of anomalies as demonstrated by\nits higher F1-score on benchmark datasets. The study's main contributions\ninclude the development of a robust anomaly detection method, improving feature\nrepresentation within VAEs through clustering, and creating a dynamic threshold\nalgorithm for localized anomaly detection. This method offers explainability of\nclimate anomalies across different regions.\n", "date": "2024-07-14", "categories": []}
{"id": "2407.10754", "title": "An Autonomous Drone Swarm for Detecting and Tracking Anomalies among\n  Dense Vegetation", "abstract": "  Swarms of drones offer an increased sensing aperture, and having them mimic\nbehaviors of natural swarms enhances sampling by adapting the aperture to local\nconditions. We demonstrate that such an approach makes detecting and tracking\nheavily occluded targets practically feasible. While object classification\napplied to conventional aerial images generalizes poorly the randomness of\nocclusion and is therefore inefficient even under lightly occluded conditions,\nanomaly detection applied to synthetic aperture integral images is robust for\ndense vegetation, such as forests, and is independent of pre-trained classes.\nOur autonomous swarm searches the environment for occurrences of the unknown or\nunexpected, tracking them while continuously adapting its sampling pattern to\noptimize for local viewing conditions. In our real-life field experiments with\na swarm of six drones, we achieved an average positional accuracy of 0.39 m\nwith an average precision of 93.2% and an average recall of 95.9%. Here,\nadapted particle swarm optimization considers detection confidences and\npredicted target appearance. We show that sensor noise can effectively be\nincluded in the synthetic aperture image integration process, removing the need\nfor a computationally costly optimization of high-dimensional parameter spaces.\nFinally, we present a complete hard- and software framework that supports\nlow-latency transmission (approx. 80 ms round-trip time) and fast processing\n(approx. 600 ms per formation step) of extensive (70-120 Mbit/s) video and\ntelemetry data, and swarm control for swarms of up to ten drones.\n", "date": "2024-07-15", "categories": []}
{"id": "2407.10862", "title": "R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection", "abstract": "  3D anomaly detection plays a crucial role in monitoring parts for localized\ninherent defects in precision manufacturing. Embedding-based and\nreconstruction-based approaches are among the most popular and successful\nmethods. However, there are two major challenges to the practical application\nof the current approaches: 1) the embedded models suffer the prohibitive\ncomputational and storage due to the memory bank structure; 2) the\nreconstructive models based on the MAE mechanism fail to detect anomalies in\nthe unmasked regions. In this paper, we propose R3D-AD, reconstructing\nanomalous point clouds by diffusion model for precise 3D anomaly detection. Our\napproach capitalizes on the data distribution conversion of the diffusion\nprocess to entirely obscure the input's anomalous geometry. It step-wisely\nlearns a strict point-level displacement behavior, which methodically corrects\nthe aberrant points. To increase the generalization of the model, we further\npresent a novel 3D anomaly simulation strategy named Patch-Gen to generate\nrealistic and diverse defect shapes, which narrows the domain gap between\ntraining and testing. Our R3D-AD ensures a uniform spatial transformation,\nwhich allows straightforwardly generating anomaly results by distance\ncomparison. Extensive experiments show that our R3D-AD outperforms previous\nstate-of-the-art methods, achieving 73.4% Image-level AUROC on the Real3D-AD\ndataset and 74.9% Image-level AUROC on the Anomaly-ShapeNet dataset with an\nexceptional efficiency.\n", "date": "2024-07-15", "categories": []}
{"id": "2407.08758", "title": "Credit Card Fraud Detection in the Nigerian Financial Sector: A\n  Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques,\n  Autoencoders and PCA Algorithm", "abstract": "  Credit card fraud is a major cause of national concern in the Nigerian\nfinancial sector, affecting hundreds of transactions per second and impacting\ninternational ecommerce negatively. Despite the rapid spread and adoption of\nonline marketing, millions of Nigerians are prevented from transacting in\nseveral countries with local credit cards due to bans and policies directed at\nrestricting credit card fraud. Presently, a myriad of technologies exist to\ndetect fraudulent transactions, a few of which are adopted by Nigerian\nfinancial institutions to proactively manage the situation. Fraud detection\nallows institutions to restrict offenders from networks and with a centralized\nbanking identity management system, such as the Bank Verification Number used\nby the Central Bank of Nigeria, offenders who may have stolen other identities\ncan be backtraced and their bank accounts frozen. This paper aims to compare\nthe effectiveness of two fraud detection technologies that are projected to\nwork fully independent of human intervention to possibly predict and detect\nfraudulent credit card transactions. Autoencoders as an unsupervised tensorflow\nbased anomaly detection technique generally offers greater performance in\ndimensionality reduction than the Principal Component Analysis, and this theory\nwas tested out on Nigerian credit card transaction data. Results demonstrate\nthat autoencoders are better suited to analyzing complex and extensive datasets\nand offer more reliable results with minimal mislabeling than the PCA\nalgorithm.\n", "date": "2024-03-08", "categories": []}
{"id": "2407.08933", "title": "Machine Learning in High Volume Media Manufacturing", "abstract": "  Errors or failures in a high-volume manufacturing environment can have\nsignificant impact that can result in both the loss of time and money.\nIdentifying such failures early has been a top priority for manufacturing\nindustries and various rule-based algorithms have been developed over the\nyears. However, catching these failures is time consuming and such algorithms\ncannot adapt well to changes in designs, and sometimes variations in everyday\nbehavior. More importantly, the number of units to monitor in a high-volume\nmanufacturing environment is too big for manual monitoring or for a simple\nprogram. Here we develop a novel program that combines both rule-based\ndecisions and machine learning models that can not only learn and adapt to such\nday-to-day variations or long-term design changes, but also can be applied at\nscale to the high number of manufacturing units in use today. Using the current\nstate-of-the-art technologies, we then deploy this program at-scale to handle\nthe needs of ever-increasing demand from the manufacturing environment.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.09359", "title": "A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial\n  Anomaly Detection and Localization", "abstract": "  Anomaly synthesis strategies can effectively enhance unsupervised anomaly\ndetection. However, existing strategies have limitations in the coverage and\ncontrollability of anomaly synthesis, particularly for weak defects that are\nvery similar to normal regions. In this paper, we propose Global and Local\nAnomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to\nsynthesize a broader coverage of anomalies under the manifold and hypersphere\ndistribution constraints of Global Anomaly Synthesis (GAS) at the feature level\nand Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes\nnear-in-distribution anomalies in a controllable way using Gaussian noise\nguided by gradient ascent and truncated projection. GLASS achieves\nstate-of-the-art results on the MVTec AD (detection AUROC of 99.9\\%), VisA, and\nMPDD datasets and excels in weak defect detection. The effectiveness and\nefficiency have been further validated in industrial applications for woven\nfabric defect detection. The code and dataset are available at:\n\\url{https://github.com/cqylunlun/GLASS}.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.03961", "title": "Leveraging Latent Diffusion Models for Training-Free In-Distribution\n  Data Augmentation for Surface Defect Detection", "abstract": "  Defect detection is the task of identifying defects in production samples.\nUsually, defect detection classifiers are trained on ground-truth data formed\nby normal samples (negative data) and samples with defects (positive data),\nwhere the latter are consistently fewer than normal samples. State-of-the-art\ndata augmentation procedures add synthetic defect data by superimposing\nartifacts to normal samples to mitigate problems related to unbalanced training\ndata. These techniques often produce out-of-distribution images, resulting in\nsystems that learn what is not a normal sample but cannot accurately identify\nwhat a defect looks like. In this work, we introduce DIAG, a training-free\nDiffusion-based In-distribution Anomaly Generation pipeline for data\naugmentation. Unlike conventional image generation techniques, we implement a\nhuman-in-the-loop pipeline, where domain experts provide multimodal guidance to\nthe model through text descriptions and region localization of the possible\nanomalies. This strategic shift enhances the interpretability of results and\nfosters a more robust human feedback loop, facilitating iterative improvements\nof the generated outputs. Remarkably, our approach operates in a zero-shot\nmanner, avoiding time-consuming fine-tuning procedures while achieving superior\nperformance. We demonstrate the efficacy and versatility of DIAG with respect\nto state-of-the-art data augmentation approaches on the challenging KSDD2\ndataset, with an improvement in AP of approximately 18% when positive samples\nare available and 28% when they are missing. The source code is available at\nhttps://github.com/intelligolabs/DIAG.\n", "date": "2024-07-04", "categories": []}
{"id": "2407.08735", "title": "Real-Time Anomaly Detection and Reactive Planning with Large Language\n  Models", "abstract": "  Foundation models, e.g., large language models (LLMs), trained on\ninternet-scale data possess zero-shot generalization capabilities that make\nthem a promising technology towards detecting and mitigating\nout-of-distribution failure modes of robotic systems. Fully realizing this\npromise, however, poses two challenges: (i) mitigating the considerable\ncomputational expense of these models such that they may be applied online, and\n(ii) incorporating their judgement regarding potential anomalies into a safe\ncontrol framework. In this work, we present a two-stage reasoning framework:\nFirst is a fast binary anomaly classifier that analyzes observations in an LLM\nembedding space, which may then trigger a slower fallback selection stage that\nutilizes the reasoning capabilities of generative LLMs. These stages correspond\nto branch points in a model predictive control strategy that maintains the\njoint feasibility of continuing along various fallback plans to account for the\nslow reasoner's latency as soon as an anomaly is detected, thus ensuring\nsafety. We show that our fast anomaly classifier outperforms autoregressive\nreasoning with state-of-the-art GPT models, even when instantiated with\nrelatively small language models. This enables our runtime monitor to improve\nthe trustworthiness of dynamic robotic systems, such as quadrotors or\nautonomous vehicles, under resource and time constraints. Videos illustrating\nour approach in both simulation and real-world experiments are available on\nthis project page: https://sites.google.com/view/aesop-llm.\n", "date": "2024-07-11", "categories": []}
{"id": "2210.02851", "title": "Anomaly detection using data depth: multivariate case", "abstract": "  Anomaly detection is a branch of data analysis and machine learning which\naims at identifying observations that exhibit abnormal behaviour. Be it\nmeasurement errors, disease development, severe weather, production quality\ndefault(s) (items) or failed equipment, financial frauds or crisis events,\ntheir on-time identification, isolation and explanation constitute an important\ntask in almost any branch of science and industry. By providing a robust\nordering, data depth - statistical function that measures belongingness of any\npoint of the space to a data set - becomes a particularly useful tool for\ndetection of anomalies. Already known for its theoretical properties, data\ndepth has undergone substantial computational developments in the last decade\nand particularly recent years, which has made it applicable for\ncontemporary-sized problems of data analysis and machine learning.\n  In this article, data depth is studied as an efficient anomaly detection\ntool, assigning abnormality labels to observations with lower depth values, in\na multivariate setting. Practical questions of necessity and reasonability of\ninvariances and shape of the depth function, its robustness and computational\ncomplexity, choice of the threshold are discussed. Illustrations include\nuse-cases that underline advantageous behaviour of data depth in various\nsettings.\n", "date": "2022-10-06", "categories": []}
{"id": "2311.09999", "title": "TransFusion -- A Transparency-Based Diffusion Model for Anomaly\n  Detection", "abstract": "  Surface anomaly detection is a vital component in manufacturing inspection.\nCurrent discriminative methods follow a two-stage architecture composed of a\nreconstructive network followed by a discriminative network that relies on the\nreconstruction output. Currently used reconstructive networks often produce\npoor reconstructions that either still contain anomalies or lack details in\nanomaly-free regions. Discriminative methods are robust to some reconstructive\nnetwork failures, suggesting that the discriminative network learns a strong\nnormal appearance signal that the reconstructive networks miss. We reformulate\nthe two-stage architecture into a single-stage iterative process that allows\nthe exchange of information between the reconstruction and localization. We\npropose a novel transparency-based diffusion process where the transparency of\nanomalous regions is progressively increased, restoring their normal appearance\naccurately while maintaining the appearance of anomaly-free regions using\nlocalization cues of previous steps. We implement the proposed process as\nTRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection\nmethod that achieves state-of-the-art performance on both the VisA and the\nMVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively.\nCode: https://github.com/MaticFuc/ECCV_TransFusion\n", "date": "2023-11-16", "categories": []}
{"id": "2407.07421", "title": "Federated PCA on Grassmann Manifold for IoT Anomaly Detection", "abstract": "  With the proliferation of the Internet of Things (IoT) and the rising\ninterconnectedness of devices, network security faces significant challenges,\nespecially from anomalous activities. While traditional machine learning-based\nintrusion detection systems (ML-IDS) effectively employ supervised learning\nmethods, they possess limitations such as the requirement for labeled data and\nchallenges with high dimensionality. Recent unsupervised ML-IDS approaches such\nas AutoEncoders and Generative Adversarial Networks (GAN) offer alternative\nsolutions but pose challenges in deployment onto resource-constrained IoT\ndevices and in interpretability. To address these concerns, this paper proposes\na novel federated unsupervised anomaly detection framework, FedPCA, that\nleverages Principal Component Analysis (PCA) and the Alternating Directions\nMethod Multipliers (ADMM) to learn common representations of distributed\nnon-i.i.d. datasets. Building on the FedPCA framework, we propose two\nalgorithms, FEDPE in Euclidean space and FEDPG on Grassmann manifolds. Our\napproach enables real-time threat detection and mitigation at the device level,\nenhancing network resilience while ensuring privacy. Moreover, the proposed\nalgorithms are accompanied by theoretical convergence rates even under a\nsubsampling scheme, a novel result. Experimental results on the UNSW-NB15 and\nTON-IoT datasets show that our proposed methods offer performance in anomaly\ndetection comparable to nonlinear baselines, while providing significant\nimprovements in communication and memory efficiency, underscoring their\npotential for securing IoT networks.\n", "date": "2024-07-10", "categories": []}
{"id": "2304.00709", "title": "MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected\n  Reconstruction", "abstract": "  AutoEncoders (AEs) are commonly used for machine learning tasks due to their\nintrinsic learning ability. This unique characteristic can be capitalized for\nOutlier Detection (OD). However conventional AE-based methods face the issue of\noverconfident decisions and unexpected reconstruction results of outliers,\nlimiting their performance in OD. To mitigate these issues, the Mean Squared\nError (MSE) and Negative Logarithmic Likelihood (NLL) were firstly analyzed,\nand the importance of incorporating aleatoric uncertainty to AE-based OD was\nelucidated. Then the Weighted Negative Logarithmic Likelihood (WNLL) was\nproposed to adjust for the effect of uncertainty for different OD scenarios.\nMoreover, the Mean-Shift Scoring (MSS) method was proposed to utilize the local\nrelationship of data to reduce the issue of false inliers caused by AE.\nExperiments on 32 real-world OD datasets proved the effectiveness of the\nproposed methods. The combination of WNLL and MSS achieved 41% relative\nperformance improvement compared to the best baseline. In addition, MSS\nimproved the detection performance of multiple AE-based outlier detectors by an\naverage of 20%. The proposed methods have the potential to advance AE's\ndevelopment in OD.\n", "date": "2023-04-03", "categories": []}
{"id": "2403.09303", "title": "Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical\n  Perspective", "abstract": "  Medical anomaly detection aims to identify abnormal findings using only\nnormal training data, playing a crucial role in health screening and\nrecognizing rare diseases. Reconstruction-based methods, particularly those\nutilizing autoencoders (AEs), are dominant in this field. They work under the\nassumption that AEs trained on only normal data cannot reconstruct unseen\nabnormal regions well, thereby enabling the anomaly detection based on\nreconstruction errors. However, this assumption does not always hold due to the\nmismatch between the reconstruction training objective and the anomaly\ndetection task objective, rendering these methods theoretically unsound. This\nstudy focuses on providing a theoretical foundation for AE-based reconstruction\nmethods in anomaly detection. By leveraging information theory, we elucidate\nthe principles of these methods and reveal that the key to improving AE in\nanomaly detection lies in minimizing the information entropy of latent vectors.\nExperiments on four datasets with two image modalities validate the\neffectiveness of our theory. To the best of our knowledge, this is the first\neffort to theoretically clarify the principles and design philosophy of AE for\nanomaly detection. The code is available at\n\\url{https://github.com/caiyu6666/AE4AD}.\n", "date": "2024-03-14", "categories": []}
{"id": "2407.06303", "title": "Unsupervised Fault Detection using SAM with a Moving Window Approach", "abstract": "  Automated f ault detection and monitoring in engineering are critical but\nfrequently difficult owing to the necessity for collecting and labeling large\namounts of defective samples . We present an unsupervised method that uses the\nhigh end Segment Anything Model (SAM) and a moving window approach. SAM has\ngained recognition in AI image segmentation communities for its accuracy and\nversatility. However, its performance can be inconsistent when dealing with\ncertain unexpected shapes , such as shadows and subtle surface irregularities.\nThis limitation raise s concerns about its applicability for fault detection in\nreal world scenarios We aim to overcome these challenges without requiring fine\ntun ing or labeled data. Our technique divides pictures into smaller windows,\nwhich are subsequently processed using SAM. This increases the accuracy of\nfault identification by focusing on localized details. We compute the sizes of\nthe segmented sections and then us e a clustering technique to discover\nconsistent fault areas while filtering out noise. To further improve the\nmethod's robustness , we propose adding the Exponentially Weighted Moving\nAverage (EWMA) technique for continuous monitoring in industrial settings,\nwhich would improve the method's capacity to trace faults over time. We compare\nour method to various well established methods u sing a real case study where\nour model achieve s 0.96 accuracy compared to 0. 8 5 for the second best\nmethod. W e also compare our method us ing two open source datasets where our\nmodel attains a consistent 0. 86 accuracy across the datasets compared to 0.53\nand 0.54 for second best model s.\n", "date": "2024-07-08", "categories": []}
{"id": "2407.06635", "title": "Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection", "abstract": "  Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in\ntest samples comparing them with a normative distribution learned from a\ndataset known to be anomaly-free. Approaches based on generative models offer\ninterpretability by generating anomaly-free versions of test images, but are\ntypically unable to identify subtle anomalies. Alternatively, approaches using\nfeature modelling or self-supervised methods, such as the ones relying on\nsynthetically generated anomalies, do not provide out-of-the-box\ninterpretability. In this work, we present a novel method that combines the\nstrengths of both strategies: a generative cold-diffusion pipeline (i.e., a\ndiffusion-like pipeline which uses corruptions not based on noise) that is\ntrained with the objective of turning synthetically-corrupted images back to\ntheir normal, original appearance. To support our pipeline we introduce a novel\nsynthetic anomaly generation procedure, called DAG, and a novel anomaly score\nwhich ensembles restorations conditioned with different degrees of abnormality.\nOur method surpasses the prior state-of-the art for unsupervised anomaly\ndetection in three different Brain MRI datasets.\n", "date": "2024-07-09", "categories": []}
{"id": "2302.14696", "title": "Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection", "abstract": "  Medical imaging often contains critical fine-grained features, such as tumors\nor hemorrhages, crucial for diagnosis yet potentially too subtle for detection\nwith conventional methods. In this paper, we introduce \\textit{DIA}, dissolving\nis amplifying. DIA is a fine-grained anomaly detection framework for medical\nimages. First, we introduce \\textit{dissolving transformations}. We employ\ndiffusion with a generative diffusion model as a dedicated feature-aware\ndenoiser. Applying diffusion to medical images in a certain manner can remove\nor diminish fine-grained discriminative features. Second, we introduce an\n\\textit{amplifying framework} based on contrastive learning to learn a\nsemantically meaningful representation of medical images in a self-supervised\nmanner, with a focus on fine-grained features. The amplifying framework\ncontrasts additional pairs of images with and without dissolving\ntransformations applied and thereby emphasizes the dissolved fine-grained\nfeatures. DIA significantly improves the medical anomaly detection performance\nwith around 18.40\\% AUC boost against the baseline method and achieves an\noverall SOTA against other benchmark methods. Our code is available at\n\\url{https://github.com/shijianjian/DIA.git}.\n", "date": "2023-02-28", "categories": []}
{"id": "2312.04521", "title": "Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping", "abstract": "  The paper explores the industrial multimodal Anomaly Detection (AD) task,\nwhich exploits point clouds and RGB images to localize anomalies. We introduce\na novel light and fast framework that learns to map features from one modality\nto the other on nominal samples. At test time, anomalies are detected by\npinpointing inconsistencies between observed and mapped features. Extensive\nexperiments show that our approach achieves state-of-the-art detection and\nsegmentation performance in both the standard and few-shot settings on the\nMVTec 3D-AD dataset while achieving faster inference and occupying less memory\nthan previous multimodal AD methods. Moreover, we propose a layer-pruning\ntechnique to improve memory and time efficiency with a marginal sacrifice in\nperformance.\n", "date": "2023-12-07", "categories": []}
{"id": "2407.04730", "title": "The OPS-SAT benchmark for detecting anomalies in satellite telemetry", "abstract": "  Detecting anomalous events in satellite telemetry is a critical task in space\noperations. This task, however, is extremely time-consuming, error-prone and\nhuman dependent, thus automated data-driven anomaly detection algorithms have\nbeen emerging at a steady pace. However, there are no publicly available\ndatasets of real satellite telemetry accompanied with the ground-truth\nannotations that could be used to train and verify anomaly detection supervised\nmodels. In this article, we address this research gap and introduce the\nAI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired\non board OPS-SAT -- a CubeSat mission which has been operated by the European\nSpace Agency which has come to an end during the night of 22--23 May 2024\n(CEST). The dataset is accompanied with the baseline results obtained using 30\nsupervised and unsupervised classic and deep machine learning algorithms for\nanomaly detection. They were trained and validated using the training-test\ndataset split introduced in this work, and we present a suggested set of\nquality metrics which should be always calculated to confront the new\nalgorithms for anomaly detection while exploiting OPSSAT-AD. We believe that\nthis work may become an important step toward building a fair, reproducible and\nobjective validation procedure that can be used to quantify the capabilities of\nthe emerging anomaly detection techniques in an unbiased and fully transparent\nway.\n", "date": "2024-06-29", "categories": []}
{"id": "2407.04760", "title": "SPINEX: Similarity-based Predictions with Explainable Neighbors\n  Exploration for Anomaly and Outlier Detection", "abstract": "  This paper presents a novel anomaly and outlier detection algorithm from the\nSPINEX (Similarity-based Predictions with Explainable Neighbors Exploration)\nfamily. This algorithm leverages the concept of similarity and higher-order\ninteractions across multiple subspaces to identify outliers. A comprehensive\nset of experiments was conducted to evaluate the performance of SPINEX. This\nalgorithm was examined against 21 commonly used anomaly detection algorithms,\nnamely, namely, Angle-Based Outlier Detection (ABOD), Connectivity-Based\nOutlier Factor (COF), Copula-Based Outlier Detection (COPOD), ECOD, Elliptic\nEnvelope (EE), Feature Bagging with KNN, Gaussian Mixture Models (GMM),\nHistogram-based Outlier Score (HBOS), Isolation Forest (IF), Isolation Neural\nNetwork Ensemble (INNE), Kernel Density Estimation (KDE), K-Nearest Neighbors\n(KNN), Lightweight Online Detector of Anomalies (LODA), Linear Model\nDeviation-based Detector (LMDD), Local Outlier Factor (LOF), Minimum Covariance\nDeterminant (MCD), One-Class SVM (OCSVM), Quadratic MCD (QMCD), Robust\nCovariance (RC), Stochastic Outlier Selection (SOS), and Subspace Outlier\nDetection (SOD), and across 39 synthetic and real datasets from various domains\nand of a variety of dimensions and complexities. Furthermore, a complexity\nanalysis was carried out to examine the complexity of the proposed algorithm.\nOur results demonstrate that SPINEX achieves superior performance, outperforms\ncommonly used anomaly detection algorithms, and has moderate complexity (e.g.,\nO(n log n d)). More specifically, SPINEX was found to rank at the top of\nalgorithms on the synthetic datasets and the 7th on the real datasets. Finally,\na demonstration of the explainability capabilities of SPINEX, along with future\nresearch needs, is presented.\n", "date": "2024-07-05", "categories": []}
{"id": "2407.06085", "title": "LLMcap: Large Language Model for Unsupervised PCAP Failure Detection", "abstract": "  The integration of advanced technologies into telecommunication networks\ncomplicates troubleshooting, posing challenges for manual error identification\nin Packet Capture (PCAP) data. This manual approach, requiring substantial\nresources, becomes impractical at larger scales. Machine learning (ML) methods\noffer alternatives, but the scarcity of labeled data limits accuracy. In this\nstudy, we propose a self-supervised, large language model-based (LLMcap) method\nfor PCAP failure detection. LLMcap leverages language-learning abilities and\nemploys masked language modeling to learn grammar, context, and structure.\nTested rigorously on various PCAPs, it demonstrates high accuracy despite the\nabsence of labeled data during training, presenting a promising solution for\nefficient network analysis. Index Terms: Network troubleshooting, Packet\nCapture Analysis, Self-Supervised Learning, Large Language Model, Network\nQuality of Service, Network Performance.\n", "date": "2024-07-03", "categories": []}
{"id": "2306.13595", "title": "Autoencoders for Real-Time SUEP Detection", "abstract": "  Confining dark sectors with pseudo-conformal dynamics can produce Soft\nUnclustered Energy Patterns (SUEP), at the Large Hadron Collider: the\nproduction of dark quarks in proton-proton collisions leading to a dark shower\nand the high-multiplicity production of dark hadrons. The final experimental\nsignature is spherically-symmetric energy deposits by an anomalously large\nnumber of soft Standard Model particles with a transverse energy of O(100) MeV.\nAssuming Yukawa-like couplings of the scalar portal state, the dominant\nproduction mode is gluon fusion, and the dominant background comes from\nmulti-jet QCD events. We have developed a deep learning-based Anomaly Detection\ntechnique to reject QCD jets and identify any anomalous signature, including\nSUEP, in real-time in the High-Level Trigger system of the Compact Muon\nSolenoid experiment at the Large Hadron Collider. A deep convolutional neural\nautoencoder network has been trained using QCD events by taking transverse\nenergy deposits in the inner tracker, electromagnetic calorimeter, and hadron\ncalorimeter sub-detectors as 3-channel image data. Due to the sparse nature of\nthe data, only ~0.5% of the total ~300 k image pixels have non-zero values. To\ntackle this challenge, a non-standard loss function, the inverse of the\nso-called Dice Loss, is exploited. The trained autoencoder with learned spatial\nfeatures of QCD jets can detect 40% of the SUEP events, with a QCD event\nmistagging rate as low as 2%. The model inference time has been measured using\nthe Intel CoreTM i5-9600KF processor and found to be ~20 ms, which perfectly\nsatisfies the High-Level Trigger system's latency of O(100) ms. Given the\nvirtue of the unsupervised learning of the autoencoders, the trained model can\nbe applied to any new physics model that predicts an experimental signature\nanomalous to QCD jets.\n", "date": "2023-06-23", "categories": []}
{"id": "2310.05330", "title": "A Lightweight Video Anomaly Detection Model with Weak Supervision and\n  Adaptive Instance Selection", "abstract": "  Video anomaly detection is to determine whether there are any abnormal\nevents, behaviors or objects in a given video, which enables effective and\nintelligent public safety management. As video anomaly labeling is both\ntime-consuming and expensive, most existing works employ unsupervised or weakly\nsupervised learning methods. This paper focuses on weakly supervised video\nanomaly detection, in which the training videos are labeled whether or not they\ncontain any anomalies, but there is no information about which frames the\nanomalies are located. However, the uncertainty of weakly labeled data and the\nlarge model size prevent existing methods from wide deployment in real\nscenarios, especially the resource-limit situations such as edge-computing. In\nthis paper, we develop a lightweight video anomaly detection model. On the one\nhand, we propose an adaptive instance selection strategy, which is based on the\nmodel's current status to select confident instances, thereby mitigating the\nuncertainty of weakly labeled data and subsequently promoting the model's\nperformance. On the other hand, we design a lightweight multi-level temporal\ncorrelation attention module and an hourglass-shaped fully connected layer to\nconstruct the model, which can reduce the model parameters to only 0.56\\% of\nthe existing methods (e.g. RTFM). Our extensive experiments on two public\ndatasets UCF-Crime and ShanghaiTech show that our model can achieve comparable\nor even superior AUC score compared to the state-of-the-art methods, with a\nsignificantly reduced number of model parameters.\n", "date": "2023-10-09", "categories": []}
{"id": "2403.13349", "title": "Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified\n  Anomaly Detection", "abstract": "  Unified anomaly detection (AD) is one of the most challenges for anomaly\ndetection, where one unified model is trained with normal samples from multiple\nclasses with the objective to detect anomalies in these classes. For such a\nchallenging task, popular normalizing flow (NF) based AD methods may fall into\na \"homogeneous mapping\" issue,where the NF-based AD models are biased to\ngenerate similar latent representations for both normal and abnormal features,\nand thereby lead to a high missing rate of anomalies. In this paper, we propose\na novel Hierarchical Gaussian mixture normalizing flow modeling method for\naccomplishing unified Anomaly Detection, which we call HGAD. Our HGAD consists\nof two key components: inter-class Gaussian mixture modeling and intra-class\nmixed class centers learning. Compared to the previous NF-based AD methods, the\nhierarchical Gaussian mixture modeling approach can bring stronger\nrepresentation capability to the latent space of normalizing flows, so that\neven complex multi-class distribution can be well represented and learned in\nthe latent space. In this way, we can avoid mapping different class\ndistributions into the same single Gaussian prior, thus effectively avoiding or\nmitigating the \"homogeneous mapping\" issue. We further indicate that the more\ndistinguishable different class centers, the more conducive to avoiding the\nbias issue. Thus, we further propose a mutual information maximization loss for\nbetter structuring the latent feature space. We evaluate our method on four\nreal-world AD benchmarks, where we can significantly improve the previous\nNF-based AD methods and also outperform the SOTA unified AD methods.\n", "date": "2024-03-20", "categories": []}
{"id": "2406.02318", "title": "PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly\n  Detection", "abstract": "  With the proliferation of mobile sensing techniques, huge amounts of time\nseries data are generated and accumulated in various domains, fueling plenty of\nreal-world applications. In this setting, time series anomaly detection is\npractically important. It endeavors to identify deviant samples from the normal\nsample distribution in time series. Existing approaches generally assume that\nall the time series is available at a central location. However, we are\nwitnessing the decentralized collection of time series due to the deployment of\nvarious edge devices. To bridge the gap between the decentralized time series\ndata and the centralized anomaly detection algorithms, we propose a\nParameter-efficient Federated Anomaly Detection framework named PeFAD with the\nincreasing privacy concerns. PeFAD for the first time employs the pre-trained\nlanguage model (PLM) as the body of the client's local model, which can benefit\nfrom its cross-modality knowledge transfer capability. To reduce the\ncommunication overhead and local model adaptation cost, we propose a\nparameter-efficient federated training module such that clients only need to\nfine-tune small-scale parameters and transmit them to the server for update.\nPeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the\nimpact of neglected anomalies during training. A knowledge distillation\noperation on a synthetic privacy-preserving dataset that is shared by all the\nclients is also proposed to address the data heterogeneity issue across\nclients. We conduct extensive evaluations on four real datasets, where PeFAD\noutperforms existing state-of-the-art baselines by up to 28.74%.\n", "date": "2024-06-04", "categories": []}
{"id": "2407.03130", "title": "Towards Efficient Pixel Labeling for Industrial Anomaly Detection and\n  Localization", "abstract": "  In the realm of practical Anomaly Detection (AD) tasks, manual labeling of\nanomalous pixels proves to be a costly endeavor. Consequently, many AD methods\nare crafted as one-class classifiers, tailored for training sets completely\ndevoid of anomalies, ensuring a more cost-effective approach. While some\npioneering work has demonstrated heightened AD accuracy by incorporating real\nanomaly samples in training, this enhancement comes at the price of\nlabor-intensive labeling processes. This paper strikes the balance between AD\naccuracy and labeling expenses by introducing ADClick, a novel Interactive\nImage Segmentation (IIS) algorithm. ADClick efficiently generates\n\"ground-truth\" anomaly masks for real defective images, leveraging innovative\nresidual features and meticulously crafted language prompts. Notably, ADClick\nshowcases a significantly elevated generalization capacity compared to existing\nstate-of-the-art IIS approaches. Functioning as an anomaly labeling tool,\nADClick generates high-quality anomaly labels (AP $= 94.1\\%$ on MVTec AD) based\non only $3$ to $5$ manual click annotations per training image. Furthermore, we\nextend the capabilities of ADClick into ADClick-Seg, an enhanced model designed\nfor anomaly detection and localization. By fine-tuning the ADClick-Seg model\nusing the weak labels inferred by ADClick, we establish the state-of-the-art\nperformances in supervised AD tasks (AP $= 86.4\\%$ on MVTec AD and AP $=\n78.4\\%$, PRO $= 98.6\\%$ on KSDD2).\n", "date": "2024-07-03", "categories": []}
{"id": "2407.03332", "title": "DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection\n  with Generative and Contrastive Learning", "abstract": "  The task of industrial detection based on deep learning often involves\nsolving two problems: (1) obtaining sufficient and effective data samples, (2)\nand using efficient and convenient model training methods. In this paper, we\nintroduce a novel defect-generation method, named DDPM-MoCo, to address these\nissues. Firstly, we utilize the Denoising Diffusion Probabilistic Model (DDPM)\nto generate high-quality defect data samples, overcoming the problem of\ninsufficient sample data for model learning. Furthermore, we utilize the\nunsupervised learning Momentum Contrast model (MoCo) with an enhanced batch\ncontrastive loss function for training the model on unlabeled data, addressing\nthe efficiency and consistency challenges in large-scale negative sample\nencoding during diffusion model training. The experimental results showcase an\nenhanced visual detection method for identifying defects on metal surfaces,\ncovering the entire process, starting from generating unlabeled sample data for\ntraining the diffusion model, to utilizing the same labeled sample data for\ndownstream detection tasks. This study offers valuable practical insights and\napplication potential for visual detection in the metal processing industry.\n", "date": "2024-05-09", "categories": []}
{"id": "2407.03920", "title": "Support Vector Based Anomaly Detection in Federated Learning", "abstract": "  Anomaly detection plays a crucial role in various domains, from cybersecurity\nto industrial systems. However, traditional centralized approaches often\nencounter challenges related to data privacy. In this context, Federated\nLearning emerges as a promising solution. This work introduces two innovative\nalgorithms--Ensemble SVDD and Support Vector Election--that leverage Support\nVector Machines for anomaly detection in a federated setting. In comparison\nwith the Neural Networks typically used in within Federated Learning, these new\nalgorithms emerge as potential alternatives, as they can operate effectively\nwith small datasets and incur lower computational costs. The novel algorithms\nare tested in various distributed system configurations, yielding promising\ninitial results that pave the way for further investigation.\n", "date": "2024-07-04", "categories": []}
{"id": "2407.04597", "title": "Feature Attenuation of Defective Representation Can Resolve Incomplete\n  Masking on Anomaly Detection", "abstract": "  In unsupervised anomaly detection (UAD) research, while state-of-the-art\nmodels have reached a saturation point with extensive studies on public\nbenchmark datasets, they adopt large-scale tailor-made neural networks (NN) for\ndetection performance or pursued unified models for various tasks. Towards edge\ncomputing, it is necessary to develop a computationally efficient and scalable\nsolution that avoids large-scale complex NNs. Motivated by this, we aim to\noptimize the UAD performance with minimal changes to NN settings. Thus, we\nrevisit the reconstruction-by-inpainting approach and rethink to improve it by\nanalyzing strengths and weaknesses. The strength of the SOTA methods is a\nsingle deterministic masking approach that addresses the challenges of random\nmultiple masking that is inference latency and output inconsistency.\nNevertheless, the issue of failure to provide a mask to completely cover\nanomalous regions is a remaining weakness. To mitigate this issue, we propose\nFeature Attenuation of Defective Representation (FADeR) that only employs two\nMLP layers which attenuates feature information of anomaly reconstruction\nduring decoding. By leveraging FADeR, features of unseen anomaly patterns are\nreconstructed into seen normal patterns, reducing false alarms. Experimental\nresults demonstrate that FADeR achieves enhanced performance compared to\nsimilar-scale NNs. Furthermore, our approach exhibits scalability in\nperformance enhancement when integrated with other single deterministic masking\nmethods in a plug-and-play manner.\n", "date": "2024-07-05", "categories": []}
{"id": "2401.16843", "title": "Evaluating ML-Based Anomaly Detection Across Datasets of Varied\n  Integrity: A Case Study", "abstract": "  Cybersecurity remains a critical challenge in the digital age, with network\ntraffic flow anomaly detection being a key pivotal instrument in the fight\nagainst cyber threats. In this study, we address the prevalent issue of data\nintegrity in network traffic datasets, which are instrumental in developing\nmachine learning (ML) models for anomaly detection. We introduce two refined\nversions of the CICIDS-2017 dataset, NFS-2023-nTE and NFS-2023-TE, processed\nusing NFStream to ensure methodologically sound flow expiration and labeling.\nOur research contrasts the performance of the Random Forest (RF) algorithm\nacross the original CICIDS-2017, its refined counterparts WTMC-2021 and\nCRiSIS-2022, and our NFStream-generated datasets, in both binary and\nmulti-class classification contexts. We observe that the RF model exhibits\nexceptional robustness, achieving consistent high-performance metrics\nirrespective of the underlying dataset quality, which prompts a critical\ndiscussion on the actual impact of data integrity on ML efficacy. Our study\nunderscores the importance of continual refinement and methodological rigor in\ndataset generation for network security research. As the landscape of network\nthreats evolves, so must the tools and techniques used to detect and analyze\nthem.\n", "date": "2024-01-30", "categories": []}
{"id": "2406.00734", "title": "GLADformer: A Mixed Perspective for Graph-level Anomaly Detection", "abstract": "  Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs\nwithin a graph dataset. However, current methods are constrained by their\nreceptive fields, struggling to learn global features within the graphs.\nMoreover, most contemporary methods are based on spatial domain and lack\nexploration of spectral characteristics. In this paper, we propose a\nmulti-perspective hybrid graph-level anomaly detector namely GLADformer,\nconsisting of two key modules. Specifically, we first design a Graph\nTransformer module with global spectrum enhancement, which ensures balanced and\nresilient parameter distributions by fusing global features and spectral\ndistribution characteristics. Furthermore, to uncover local anomalous\nattributes, we customize a band-pass spectral GNN message passing module that\nfurther enhances the model's generalization capability. Through comprehensive\nexperiments on ten real-world datasets from multiple domains, we validate the\neffectiveness and robustness of GLADformer. This demonstrates that GLADformer\noutperforms current state-of-the-art models in graph-level anomaly detection,\nparticularly in effectively capturing global anomaly representations and\nspectral characteristics.\n", "date": "2024-06-02", "categories": []}
{"id": "2407.02910", "title": "Domain-independent detection of known anomalies", "abstract": "  One persistent obstacle in industrial quality inspection is the detection of\nanomalies. In real-world use cases, two problems must be addressed: anomalous\ndata is sparse and the same types of anomalies need to be detected on\npreviously unseen objects. Current anomaly detection approaches can be trained\nwith sparse nominal data, whereas domain generalization approaches enable\ndetecting objects in previously unseen domains. Utilizing those two\nobservations, we introduce the hybrid task of domain generalization on sparse\nclasses. To introduce an accompanying dataset for this task, we present a\nmodification of the well-established MVTec AD dataset by generating three new\ndatasets. In addition to applying existing methods for benchmark, we design two\nembedding-based approaches, Spatial Embedding MLP (SEMLP) and Labeled\nPatchCore. Overall, SEMLP achieves the best performance with an average\nimage-level AUROC of 87.2 % vs. 80.4 % by MIRO. The new and openly available\ndatasets allow for further research to improve industrial anomaly detection.\n", "date": "2024-07-03", "categories": []}
{"id": "2407.02968", "title": "Unified Anomaly Detection methods on Edge Device using Knowledge\n  Distillation and Quantization", "abstract": "  With the rapid advances in deep learning and smart manufacturing in Industry\n4.0, there is an imperative for high-throughput, high-performance, and fully\nintegrated visual inspection systems. Most anomaly detection approaches using\ndefect detection datasets, such as MVTec AD, employ one-class models that\nrequire fitting separate models for each class. On the contrary, unified models\neliminate the need for fitting separate models for each class and significantly\nreduce cost and memory requirements. Thus, in this work, we experiment with\nconsidering a unified multi-class setup. Our experimental study shows that\nmulti-class models perform at par with one-class models for the standard MVTec\nAD dataset. Hence, this indicates that there may not be a need to learn\nseparate object/class-wise models when the object classes are significantly\ndifferent from each other, as is the case of the dataset considered.\nFurthermore, we have deployed three different unified lightweight architectures\non the CPU and an edge device (NVIDIA Jetson Xavier NX). We analyze the\nquantized multi-class anomaly detection models in terms of latency and memory\nrequirements for deployment on the edge device while comparing\nquantization-aware training (QAT) and post-training quantization (PTQ) for\nperformance at different precision widths. In addition, we explored two\ndifferent methods of calibration required in post-training scenarios and show\nthat one of them performs notably better, highlighting its importance for\nunsupervised tasks. Due to quantization, the performance drop in PTQ is further\ncompensated by QAT, which yields at par performance with the original 32-bit\nFloating point in two of the models considered.\n", "date": "2024-07-03", "categories": []}
{"id": "2407.01905", "title": "Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with\n  Dual Conditioning", "abstract": "  Anomaly detection, the technique of identifying abnormal samples using only\nnormal samples, has attracted widespread interest in industry. Existing\none-model-per-category methods often struggle with limited generalization\ncapabilities due to their focus on a single category, and can fail when\nencountering variations in product. Recent feature reconstruction methods, as\nrepresentatives in one-model-all-categories schemes, face challenges including\nreconstructing anomalous samples and blurry reconstructions. In this paper, we\ncreatively combine a diffusion model and a transformer for multi-class anomaly\ndetection. This approach leverages diffusion to obtain high-frequency\ninformation for refinement, greatly alleviating the blurry reconstruction\nproblem while maintaining the sampling efficiency of the reverse diffusion\nprocess. The task is transformed into image inpainting to disconnect the\ninput-output correlation, thereby mitigating the \"identical shortcuts\" problem\nand avoiding the model from reconstructing anomalous samples. Besides, we\nintroduce category-awareness using dual conditioning to ensure the accuracy of\nprediction and reconstruction in the reverse diffusion process, preventing\nexcessive deviation from the target category, thus effectively enabling\nmulti-class anomaly detection. Futhermore, Spatio-temporal fusion is also\nemployed to fuse heatmaps predicted at different timesteps and scales,\nenhancing the performance of multi-class anomaly detection. Extensive\nexperiments on benchmark datasets demonstrate the superior performance and\nexceptional multi-class anomaly detection capabilities of our proposed method\ncompared to others.\n", "date": "2024-07-02", "categories": []}
{"id": "2407.02143", "title": "Counterfactual Data Augmentation with Denoising Diffusion for Graph\n  Anomaly Detection", "abstract": "  A critical aspect of Graph Neural Networks (GNNs) is to enhance the node\nrepresentations by aggregating node neighborhood information. However, when\ndetecting anomalies, the representations of abnormal nodes are prone to be\naveraged by normal neighbors, making the learned anomaly representations less\ndistinguishable. To tackle this issue, we propose CAGAD -- an unsupervised\nCounterfactual data Augmentation method for Graph Anomaly Detection -- which\nintroduces a graph pointer neural network as the heterophilic node detector to\nidentify potential anomalies whose neighborhoods are normal-node-dominant. For\neach identified potential anomaly, we design a graph-specific diffusion model\nto translate a part of its neighbors, which are probably normal, into anomalous\nones. At last, we involve these translated neighbors in GNN neighborhood\naggregation to produce counterfactual representations of anomalies. Through\naggregating the translated anomalous neighbors, counterfactual representations\nbecome more distinguishable and further advocate detection performance. The\nexperimental results on four datasets demonstrate that CAGAD significantly\noutperforms strong baselines, with an average improvement of 2.35% on F1, 2.53%\non AUC-ROC, and 2.79% on AUC-PR.\n", "date": "2024-07-02", "categories": []}
{"id": "2211.13314", "title": "CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD", "abstract": "  Unsupervised learning methods are well established in the area of anomaly\ndetection and achieve state of the art performances on outlier datasets.\nOutliers play a significant role, since they bear the potential to distort the\npredictions of a machine learning algorithm on a given dataset. Especially\namong PCA-based methods, outliers have an additional destructive potential\nregarding the result: they may not only distort the orientation and translation\nof the principal components, they also make it more complicated to detect\noutliers. To address this problem, we propose the robust outlier detection\nalgorithm CoMadOut, which satisfies two required properties: (1) being robust\ntowards outliers and (2) detecting them. Our CoMadOut outlier detection\nvariants using comedian PCA define, dependent on its variant, an inlier region\nwith a robust noise margin by measures of in-distribution (variant CMO) and\noptimized scores by measures of out-of-distribution (variants CMO*), e.g.\nkurtosis-weighting by CMO+k. These measures allow distribution based outlier\nscoring for each principal component, and thus, an appropriate alignment of the\ndegree of outlierness between normal and abnormal instances. Experiments\ncomparing CoMadOut with traditional, deep and other comparable robust outlier\ndetection methods showed that the performance of the introduced CoMadOut\napproach is competitive to well established methods related to average\nprecision (AP), area under the precision recall curve (AUPRC) and area under\nthe receiver operating characteristic (AUROC) curve. In summary our approach\ncan be seen as a robust alternative for outlier detection tasks.\n", "date": "2022-11-23", "categories": []}
{"id": "2405.03180", "title": "Braced Fourier Continuation and Regression for Anomaly Detection", "abstract": "  In this work, the concept of Braced Fourier Continuation and Regression\n(BFCR) is introduced. BFCR is a novel and computationally efficient means of\nfinding nonlinear regressions or trend lines in arbitrary one-dimensional data\nsets. The Braced Fourier Continuation (BFC) and BFCR algorithms are first\noutlined, followed by a discussion of the properties of BFCR as well as\ndemonstrations of how BFCR trend lines may be used effectively for anomaly\ndetection both within and at the edges of arbitrary one-dimensional data sets.\nFinally, potential issues which may arise while using BFCR for anomaly\ndetection as well as possible mitigation techniques are outlined and discussed.\nAll source code and example data sets are either referenced or available via\nGitHub, and all associated code is written entirely in Python.\n", "date": "2024-05-06", "categories": []}
{"id": "2405.12502", "title": "EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy", "abstract": "  Unsupervised Outlier Detection (UOD) is an important data mining task. With\nthe advance of deep learning, deep Outlier Detection (OD) has received broad\ninterest. Most deep UOD models are trained exclusively on clean datasets to\nlearn the distribution of the normal data, which requires huge manual efforts\nto clean the real-world data if possible. Instead of relying on clean datasets,\nsome approaches directly train and detect on unlabeled contaminated datasets,\nleading to the need for methods that are robust to such conditions. Ensemble\nmethods emerged as a superior solution to enhance model robustness against\ncontaminated training sets. However, the training time is greatly increased by\nthe ensemble.\n  In this study, we investigate the impact of outliers on the training phase,\naiming to halt training on unlabeled contaminated datasets before performance\ndegradation. Initially, we noted that blending normal and anomalous data causes\nAUC fluctuations, a label-dependent measure of detection accuracy. To\ncircumvent the need for labels, we propose a zero-label entropy metric named\nLoss Entropy for loss distribution, enabling us to infer optimal stopping\npoints for training without labels. Meanwhile, we theoretically demonstrate\nnegative correlation between entropy metric and the label-based AUC. Based on\nthis, we develop an automated early-stopping algorithm, EntropyStop, which\nhalts training when loss entropy suggests the maximum model detection\ncapability. We conduct extensive experiments on ADBench (including 47 real\ndatasets), and the overall results indicate that AutoEncoder (AE) enhanced by\nour approach not only achieves better performance than ensemble AEs but also\nrequires under 2\\% of training time. Lastly, our proposed metric and\nearly-stopping approach are evaluated on other deep OD models, exhibiting their\nbroad potential applicability.\n", "date": "2024-05-21", "categories": []}
{"id": "2405.19387", "title": "Video Anomaly Detection in 10 Years: A Survey and Outlook", "abstract": "  Video anomaly detection (VAD) holds immense importance across diverse domains\nsuch as surveillance, healthcare, and environmental monitoring. While numerous\nsurveys focus on conventional VAD methods, they often lack depth in exploring\nspecific approaches and emerging trends. This survey explores deep\nlearning-based VAD, expanding beyond traditional supervised training paradigms\nto encompass emerging weakly supervised, self-supervised, and unsupervised\napproaches. A prominent feature of this review is the investigation of core\nchallenges within the VAD paradigms including large-scale datasets, features\nextraction, learning methods, loss functions, regularization, and anomaly score\nprediction. Moreover, this review also investigates the vision language models\n(VLMs) as potent feature extractors for VAD. VLMs integrate visual data with\ntextual descriptions or spoken language from videos, enabling a nuanced\nunderstanding of scenes crucial for anomaly detection. By addressing these\nchallenges and proposing future research directions, this review aims to foster\nthe development of robust and efficient VAD systems leveraging the capabilities\nof VLMs for enhanced anomaly detection in complex real-world scenarios. This\ncomprehensive analysis seeks to bridge existing knowledge gaps, provide\nresearchers with valuable insights, and contribute to shaping the future of VAD\nresearch.\n", "date": "2024-05-29", "categories": []}
{"id": "2406.12235", "title": "Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via\n  Multi-modal LLM", "abstract": "  Towards open-ended Video Anomaly Detection (VAD), existing methods often\nexhibit biased detection when faced with challenging or unseen events and lack\ninterpretability. To address these drawbacks, we propose Holmes-VAD, a novel\nframework that leverages precise temporal supervision and rich multimodal\ninstructions to enable accurate anomaly localization and comprehensive\nexplanations. Firstly, towards unbiased and explainable VAD system, we\nconstruct the first large-scale multimodal VAD instruction-tuning benchmark,\ni.e., VAD-Instruct50k. This dataset is created using a carefully designed\nsemi-automatic labeling paradigm. Efficient single-frame annotations are\napplied to the collected untrimmed videos, which are then synthesized into\nhigh-quality analyses of both abnormal and normal video clips using a robust\noff-the-shelf video captioner and a large language model (LLM). Building upon\nthe VAD-Instruct50k dataset, we develop a customized solution for interpretable\nvideo anomaly detection. We train a lightweight temporal sampler to select\nframes with high anomaly response and fine-tune a multimodal large language\nmodel (LLM) to generate explanatory content. Extensive experimental results\nvalidate the generality and interpretability of the proposed Holmes-VAD,\nestablishing it as a novel interpretable technique for real-world video anomaly\nanalysis. To support the community, our benchmark and model will be publicly\navailable at https://holmesvad.github.io.\n", "date": "2024-06-18", "categories": []}
{"id": "2407.00140", "title": "ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal\n  Structural Behavior", "abstract": "  External influences such as traffic and environmental factors induce\nvibrations in structures, leading to material degradation over time. These\nvibrations result in cracks due to the material's lack of plasticity\ncompromising structural integrity. Detecting such damage requires the\ninstallation of vibration sensors to capture the internal dynamics. However,\ndistinguishing relevant eigenmodes from external noise necessitates the use of\nDeep Learning models. The detection of changes in eigenmodes can be used to\nanticipate these shifts in material properties and to discern between normal\nand anomalous structural behavior. Eigenmodes, representing characteristic\nvibration patterns, provide insights into structural dynamics and deviations\nfrom expected states. Thus, we propose ModeConv to automatically capture and\nanalyze changes in eigenmodes, facilitating effective anomaly detection in\nstructures and material properties. In the conducted experiments, ModeConv\ndemonstrates computational efficiency improvements, resulting in reduced\nruntime for model calculations. The novel ModeConv neural network layer is\ntailored for temporal graph neural networks, in which every node represents one\nsensor. ModeConv employs a singular value decomposition based convolutional\nfilter design for complex numbers and leverages modal transformation in lieu of\nFourier or Laplace transformations in spectral graph convolutions. We include a\nmathematical complexity analysis illustrating the runtime reduction.\n", "date": "2024-06-28", "categories": []}
{"id": "2407.00383", "title": "FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for\n  Unsupervised Graph-Level Anomaly Detection", "abstract": "  Unsupervised graph-level anomaly detection (UGAD) has attracted increasing\ninterest due to its widespread application. In recent studies, knowledge\ndistillation-based methods have been widely used in unsupervised anomaly\ndetection to improve model efficiency and generalization. However, the inherent\nsymmetry between the source (teacher) and target (student) networks typically\nresults in consistent outputs across both architectures, making it difficult to\ndistinguish abnormal graphs from normal graphs. Also, existing methods mainly\nrely on graph features to distinguish anomalies, which may be unstable with\ncomplex and diverse data and fail to capture the essence that differentiates\nnormal graphs from abnormal ones. In this work, we propose a Graph Normalizing\nFlows-driven Asymmetric Network For Unsupervised Graph-Level Anomaly Detection\n(FANFOLD in short). We introduce normalizing flows to unsupervised graph-level\nanomaly detection due to their successful application and superior quality in\nlearning the underlying distribution of samples. Specifically, we adopt the\nknowledge distillation technique and apply normalizing flows on the source\nnetwork, achieving the asymmetric network. In the training stage, FANFOLD\ntransforms the original distribution of normal graphs to a standard normal\ndistribution. During inference, FANFOLD computes the anomaly score using the\nsource-target loss to discriminate between normal and anomalous graphs. We\nconduct extensive experiments on 15 datasets of different fields with 9\nbaseline methods to validate the superiority of FANFOLD.\n", "date": "2024-06-29", "categories": []}
{"id": "2407.01312", "title": "ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection", "abstract": "  Current unsupervised anomaly detection approaches perform well on public\ndatasets but struggle with specific anomaly types due to the domain gap between\npre-trained feature extractors and target-specific domains. To tackle this\nissue, this paper presents a two-stage training strategy, called\n\\textbf{ToCoAD}. In the first stage, a discriminative network is trained by\nusing synthetic anomalies in a self-supervised learning manner. This network is\nthen utilized in the second stage to provide a negative feature guide, aiding\nin the training of the feature extractor through bootstrap contrastive\nlearning. This approach enables the model to progressively learn the\ndistribution of anomalies specific to industrial datasets, effectively\nenhancing its generalizability to various types of anomalies. Extensive\nexperiments are conducted to demonstrate the effectiveness of our proposed\ntwo-stage training strategy, and our model produces competitive performance,\nachieving pixel-level AUROC scores of 98.21\\%, 98.43\\% and 97.70\\% on MVTec AD,\nVisA and BTAD respectively.\n", "date": "2024-07-01", "categories": []}
{"id": "2406.19770", "title": "Self-Supervised Spatial-Temporal Normality Learning for Time Series\n  Anomaly Detection", "abstract": "  Time Series Anomaly Detection (TSAD) finds widespread applications across\nvarious domains such as financial markets, industrial production, and\nhealthcare. Its primary objective is to learn the normal patterns of time\nseries data, thereby identifying deviations in test samples. Most existing TSAD\nmethods focus on modeling data from the temporal dimension, while ignoring the\nsemantic information in the spatial dimension. To address this issue, we\nintroduce a novel approach, called Spatial-Temporal Normality learning (STEN).\nSTEN is composed of a sequence Order prediction-based Temporal Normality\nlearning (OTN) module that captures the temporal correlations within sequences,\nand a Distance prediction-based Spatial Normality learning (DSN) module that\nlearns the relative spatial relations between sequences in a feature space. By\nsynthesizing these two modules, STEN learns expressive spatial-temporal\nrepresentations for the normal patterns hidden in the time series data.\nExtensive experiments on five popular TSAD benchmarks show that STEN\nsubstantially outperforms state-of-the-art competing methods. Our code is\navailable at https://github.com/mala-lab/STEN.\n", "date": "2024-06-28", "categories": []}
{"id": "2406.19840", "title": "AnomaLLMy -- Detecting anomalous tokens in black-box LLMs through\n  low-confidence single-token predictions", "abstract": "  This paper introduces AnomaLLMy, a novel technique for the automatic\ndetection of anomalous tokens in black-box Large Language Models (LLMs) with\nAPI-only access. Utilizing low-confidence single-token predictions as a\ncost-effective indicator, AnomaLLMy identifies irregularities in model\nbehavior, addressing the issue of anomalous tokens degrading the quality and\nreliability of models. Validated on the cl100k_base dataset, the token set of\nGPT-4, AnomaLLMy detected 413 major and 65 minor anomalies, demonstrating the\nmethod's efficiency with just \\$24.39 spent in API credits. The insights from\nthis research are expected to be beneficial for enhancing the robustness of and\naccuracy of LLMs, particularly in the development and assessment of tokenizers.\n", "date": "2024-06-28", "categories": []}
{"id": "2311.06153", "title": "GRAM: An Interpretable Approach for Graph Anomaly Detection using\n  Gradient Attention Maps", "abstract": "  Detecting unusual patterns in graph data is a crucial task in data mining.\nHowever, existing methods face challenges in consistently achieving\nsatisfactory performance and often lack interpretability, which hinders our\nunderstanding of anomaly detection decisions. In this paper, we propose a novel\napproach to graph anomaly detection that leverages the power of\ninterpretability to enhance performance. Specifically, our method extracts an\nattention map derived from gradients of graph neural networks, which serves as\na basis for scoring anomalies. Notably, our approach is flexible and can be\nused in various anomaly detection settings. In addition, we conduct theoretical\nanalysis using synthetic data to validate our method and gain insights into its\ndecision-making process. To demonstrate the effectiveness of our method, we\nextensively evaluate our approach against state-of-the-art graph anomaly\ndetection techniques on real-world graph classification and wireless network\ndatasets. The results consistently demonstrate the superior performance of our\nmethod compared to the baselines.\n", "date": "2023-11-10", "categories": []}
{"id": "2406.12260", "title": "Self-Supervised Time-Series Anomaly Detection Using Learnable Data\n  Augmentation", "abstract": "  Continuous efforts are being made to advance anomaly detection in various\nmanufacturing processes to increase the productivity and safety of industrial\nsites. Deep learning replaced rule-based methods and recently emerged as a\npromising method for anomaly detection in diverse industries. However, in the\nreal world, the scarcity of abnormal data and difficulties in obtaining labeled\ndata create limitations in the training of detection models. In this study, we\naddressed these shortcomings by proposing a learnable data augmentation-based\ntime-series anomaly detection (LATAD) technique that is trained in a\nself-supervised manner. LATAD extracts discriminative features from time-series\ndata through contrastive learning. At the same time, learnable data\naugmentation produces challenging negative samples to enhance learning\nefficiency. We measured anomaly scores of the proposed technique based on\nlatent feature similarities. As per the results, LATAD exhibited comparable or\nimproved performance to the state-of-the-art anomaly detection assessments on\nseveral benchmark datasets and provided a gradient-based diagnosis technique to\nhelp identify root causes.\n", "date": "2024-06-18", "categories": []}
{"id": "2406.18941", "title": "CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with\n  Multi-View Images Generation", "abstract": "  Few-shot anomaly detection methods can effectively address data collecting\ndifficulty in industrial scenarios. Compared to 2D few-shot anomaly detection\n(2D-FSAD), 3D few-shot anomaly detection (3D-FSAD) is still an unexplored but\nessential task. In this paper, we propose CLIP3D-AD, an efficient 3D-FSAD\nmethod extended on CLIP. We successfully transfer strong generalization ability\nof CLIP into 3D-FSAD. Specifically, we synthesize anomalous images on given\nnormal images as sample pairs to adapt CLIP for 3D anomaly classification and\nsegmentation. For classification, we introduce an image adapter and a text\nadapter to fine-tune global visual features and text features. Meanwhile, we\npropose a coarse-to-fine decoder to fuse and facilitate intermediate\nmulti-layer visual representations of CLIP. To benefit from geometry\ninformation of point cloud and eliminate modality and data discrepancy when\nprocessed by CLIP, we project and render point cloud to multi-view normal and\nanomalous images. Then we design multi-view fusion module to fuse features of\nmulti-view images extracted by CLIP which are used to facilitate visual\nrepresentations for further enhancing vision-language correlation. Extensive\nexperiments demonstrate that our method has a competitive performance of 3D\nfew-shot anomaly classification and segmentation on MVTec-3D AD dataset.\n", "date": "2024-06-27", "categories": []}
{"id": "2406.19220", "title": "Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent\n  Access Threats Within Highly Imbalanced Data", "abstract": "  Advanced Persistent Threats (APTs) are sophisticated, targeted cyberattacks\ndesigned to gain unauthorized access to systems and remain undetected for\nextended periods. To evade detection, APT cyberattacks deceive defense layers\nwith breaches and exploits, thereby complicating exposure by traditional\nanomaly detection-based security methods. The challenge of detecting APTs with\nmachine learning is compounded by the rarity of relevant datasets and the\nsignificant imbalance in the data, which makes the detection process highly\nburdensome. We present AE-APT, a deep learning-based tool for APT detection\nthat features a family of AutoEncoder methods ranging from a basic one to a\nTransformer-based one. We evaluated our tool on a suite of provenance trace\ndatabases produced by the DARPA Transparent Computing program, where APT-like\nattacks constitute as little as 0.004% of the data. The datasets span multiple\noperating systems, including Android, Linux, BSD, and Windows, and cover two\nattack scenarios. The outcomes showed that AE-APT has significantly higher\ndetection rates compared to its competitors, indicating superior performance in\ndetecting and ranking anomalies.\n", "date": "2024-06-27", "categories": []}
{"id": "2406.19393", "title": "Looking 3D: Anomaly Detection with 2D-3D Alignment", "abstract": "  Automatic anomaly detection based on visual cues holds practical significance\nin various domains, such as manufacturing and product quality assessment. This\npaper introduces a new conditional anomaly detection problem, which involves\nidentifying anomalies in a query image by comparing it to a reference shape. To\naddress this challenge, we have created a large dataset, BrokenChairs-180K,\nconsisting of around 180K images, with diverse anomalies, geometries, and\ntextures paired with 8,143 reference 3D shapes. To tackle this task, we have\nproposed a novel transformer-based approach that explicitly learns the\ncorrespondence between the query image and reference 3D shape via feature\nalignment and leverages a customized attention mechanism for anomaly detection.\nOur approach has been rigorously evaluated through comprehensive experiments,\nserving as a benchmark for future research in this domain.\n", "date": "2024-06-27", "categories": []}
{"id": "2309.10157", "title": "Autoencoder-based Anomaly Detection System for Online Data Quality\n  Monitoring of the CMS Electromagnetic Calorimeter", "abstract": "  The CMS detector is a general-purpose apparatus that detects high-energy\ncollisions produced at the LHC. Online Data Quality Monitoring of the CMS\nelectromagnetic calorimeter is a vital operational tool that allows detector\nexperts to quickly identify, localize, and diagnose a broad range of detector\nissues that could affect the quality of physics data. A real-time\nautoencoder-based anomaly detection system using semi-supervised machine\nlearning is presented enabling the detection of anomalies in the CMS\nelectromagnetic calorimeter data. A novel method is introduced which maximizes\nthe anomaly detection performance by exploiting the time-dependent evolution of\nanomalies as well as spatial variations in the detector response. The\nautoencoder-based system is able to efficiently detect anomalies, while\nmaintaining a very low false discovery rate. The performance of the system is\nvalidated with anomalies found in 2018 and 2022 LHC collision data.\nAdditionally, the first results from deploying the autoencoder-based system in\nthe CMS online Data Quality Monitoring workflow during the beginning of Run 3\nof the LHC are presented, showing its ability to detect issues missed by the\nexisting system.\n", "date": "2023-09-18", "categories": []}
{"id": "2401.04463", "title": "Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection", "abstract": "  Diffusion models have found valuable applications in anomaly detection by\ncapturing the nominal data distribution and identifying anomalies via\nreconstruction. Despite their merits, they struggle to localize anomalies of\nvarying scales, especially larger anomalies such as entire missing components.\nAddressing this, we present a novel framework that enhances the capability of\ndiffusion models, by extending the previous introduced implicit conditioning\napproach Meng et al. (2022) in three significant ways. First, we incorporate a\ndynamic step size computation that allows for variable noising steps in the\nforward process guided by an initial anomaly prediction. Second, we demonstrate\nthat denoising an only scaled input, without any added noise, outperforms\nconventional denoising process. Third, we project images in a latent space to\nabstract away from fine details that interfere with reconstruction of large\nmissing components. Additionally, we propose a fine-tuning mechanism that\nfacilitates the model to effectively grasp the nuances of the target domain.\nOur method undergoes rigorous evaluation on prominent anomaly detection\ndatasets VisA, BTAD and MVTec yielding strong performance. Importantly, our\nframework effectively localizes anomalies regardless of their scale, marking a\npivotal advancement in diffusion-based anomaly detection.\n", "date": "2024-01-09", "categories": []}
{"id": "2406.15283", "title": "FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event\n  Detection", "abstract": "  Early and accurate detection of anomalous events on the freeway, such as\naccidents, can improve emergency response and clearance. However, existing\ndelays and errors in event identification and reporting make it a difficult\nproblem to solve. Current large-scale freeway traffic datasets are not designed\nfor anomaly detection and ignore these challenges. In this paper, we introduce\nthe first large-scale lane-level freeway traffic dataset for anomaly detection.\nOur dataset consists of a month of weekday radar detection sensor data\ncollected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward\nNashville, TN, comprising over 3.7 million sensor measurements. We also collect\nofficial crash reports from the Nashville Traffic Management Center and\nmanually label all other potential anomalies in the dataset. To show the\npotential for our dataset to be used in future machine learning and traffic\nresearch, we benchmark numerous deep learning anomaly detection models on our\ndataset. We find that unsupervised graph neural network autoencoders are a\npromising solution for this problem and that ignoring spatial relationships\nleads to decreased performance. We demonstrate that our methods can reduce\nreporting delays by over 10 minutes on average while detecting 75% of crashes.\nOur dataset and all preprocessing code needed to get started are publicly\nreleased at https://vu.edu/ft-aed/ to facilitate future research.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.15395", "title": "An Exploratory Study on Human-Centric Video Anomaly Detection through\n  Variational Autoencoders and Trajectory Prediction", "abstract": "  Video Anomaly Detection (VAD) represents a challenging and prominent research\ntask within computer vision. In recent years, Pose-based Video Anomaly\nDetection (PAD) has drawn considerable attention from the research community\ndue to several inherent advantages over pixel-based approaches despite the\noccasional suboptimal performance. Specifically, PAD is characterized by\nreduced computational complexity, intrinsic privacy preservation, and the\nmitigation of concerns related to discrimination and bias against specific\ndemographic groups. This paper introduces TSGAD, a novel human-centric\nTwo-Stream Graph-Improved Anomaly Detection leveraging Variational Autoencoders\n(VAEs) and trajectory prediction. TSGAD aims to explore the possibility of\nutilizing VAEs as a new approach for pose-based human-centric VAD alongside the\nbenefits of trajectory prediction. We demonstrate TSGAD's effectiveness through\ncomprehensive experimentation on benchmark datasets. TSGAD demonstrates\ncomparable results with state-of-the-art methods showcasing the potential of\nadopting variational autoencoders. This suggests a promising direction for\nfuture research endeavors. The code base for this work is available at\nhttps://github.com/TeCSAR-UNCC/TSGAD.\n", "date": "2024-04-29", "categories": []}
{"id": "2406.15396", "title": "Feature Purified Transformer With Cross-level Feature Guiding Decoder\n  For Multi-class OOD and Anomaly Deteciton", "abstract": "  Reconstruction networks are prevalently used in unsupervised anomaly and\nOut-of-Distribution (OOD) detection due to their independence from labeled\nanomaly data. However, in multi-class datasets, the effectiveness of anomaly\ndetection is often compromised by the models' generalized reconstruction\ncapabilities, which allow anomalies to blend within the expanded boundaries of\nnormality resulting from the added categories, thereby reducing detection\naccuracy. We introduce the FUTUREG framework, which incorporates two innovative\nmodules: the Feature Purification Module (FPM) and the CFG Decoder. The FPM\nconstrains the normality boundary within the latent space to effectively filter\nout anomalous features, while the CFG Decoder uses layer-wise encoder\nrepresentations to guide the reconstruction of filtered features, preserving\nfine-grained details. Together, these modules enhance the reconstruction error\nfor anomalies, ensuring high-quality reconstructions for normal samples. Our\nresults demonstrate that FUTUREG achieves state-of-the-art performance in\nmulti-class OOD settings and remains competitive in industrial anomaly\ndetection scenarios.\n", "date": "2024-04-30", "categories": []}
{"id": "2406.15638", "title": "Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network\n  and Transformer", "abstract": "  The emergence of 5G technology marks a significant milestone in developing\ntelecommunication networks, enabling exciting new applications such as\naugmented reality and self-driving vehicles. However, these improvements bring\nan increased management complexity and a special concern in dealing with\nfailures, as the applications 5G intends to support heavily rely on high\nnetwork performance and low latency. Thus, automatic self-healing solutions\nhave become effective in dealing with this requirement, allowing a\nlearning-based system to automatically detect anomalies and perform Root Cause\nAnalysis (RCA). However, there are inherent challenges to the implementation of\nsuch intelligent systems. First, there is a lack of suitable data for anomaly\ndetection and RCA, as labelled data for failure scenarios is uncommon.\nSecondly, current intelligent solutions are tailored to LTE networks and do not\nfully capture the spatio-temporal characteristics present in the data.\nConsidering this, we utilize a calibrated simulator, Simu5G, and generate\nopen-source data for normal and failure scenarios. Using this data, we propose\nSimba, a state-of-the-art approach for anomaly detection and root cause\nanalysis in 5G Radio Access Networks (RANs). We leverage Graph Neural Networks\nto capture spatial relationships while a Transformer model is used to learn the\ntemporal dependencies of the data. We implement a prototype of Simba and\nevaluate it over multiple failures. The outcomes are compared against existing\nsolutions to confirm the superiority of Simba.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.16077", "title": "Detecting Abnormal Operations in Concentrated Solar Power Plants from\n  Irregular Sequences of Thermal Images", "abstract": "  Concentrated Solar Power (CSP) plants store energy by heating a storage\nmedium with an array of mirrors that focus sunlight onto solar receivers atop a\ncentral tower. Operating at high temperatures these receivers face risks such\nas freezing, deformation, and corrosion, leading to operational failures,\ndowntime, or costly equipment damage. We study the problem of anomaly detection\n(AD) in sequences of thermal images collected over a year from an operational\nCSP plant. These images are captured at irregular intervals ranging from one to\nfive minutes throughout the day by infrared cameras mounted on solar receivers.\nOur goal is to develop a method to extract useful representations from\nhigh-dimensional thermal images for AD. It should be able to handle temporal\nfeatures of the data, which include irregularity, temporal dependency between\nimages and non-stationarity due to a strong daily seasonal pattern. The\nco-occurrence of low-temperature anomalies that resemble normal images from the\nstart and the end of the operational cycle with high-temperature anomalies\nposes an additional challenge. We first evaluate state-of-the-art deep\nimage-based AD methods, which have been shown to be effective in deriving\nmeaningful image representations for the detection of anomalies. Then, we\nintroduce a forecasting-based AD method that predicts future thermal images\nfrom past sequences and timestamps via a deep sequence model. This method\neffectively captures specific temporal data features and distinguishes between\ndifficult-to-detect temperature-based anomalies. Our experiments demonstrate\nthe effectiveness of our approach compared to multiple SOTA baselines across\nmultiple evaluation metrics. We have also successfully deployed our solution on\nfive months of unseen data, providing critical insights for the maintenance of\nthe CSP plant. Our code is available at: https://tinyurl.com/ForecastAD\n", "date": "2024-06-23", "categories": []}
{"id": "2406.16308", "title": "Anomaly Detection of Tabular Data Using LLMs", "abstract": "  Large language models (LLMs) have shown their potential in long-context\nunderstanding and mathematical reasoning. In this paper, we study the problem\nof using LLMs to detect tabular anomalies and show that pre-trained LLMs are\nzero-shot batch-level anomaly detectors. That is, without extra\ndistribution-specific model fitting, they can discover hidden outliers in a\nbatch of data, demonstrating their ability to identify low-density data\nregions. For LLMs that are not well aligned with anomaly detection and\nfrequently output factual errors, we apply simple yet effective data-generating\nprocesses to simulate synthetic batch-level anomaly detection datasets and\npropose an end-to-end fine-tuning strategy to bring out the potential of LLMs\nin detecting real anomalies. Experiments on a large anomaly detection benchmark\n(ODDS) showcase i) GPT-4 has on-par performance with the state-of-the-art\ntransductive learning-based anomaly detection methods and ii) the efficacy of\nour synthetic dataset and fine-tuning strategy in aligning LLMs to this task.\n", "date": "2024-06-24", "categories": []}
{"id": "2306.10857", "title": "Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in\n  Public Procurement", "abstract": "  In the context of public procurement, several indicators called red flags are\nused to estimate fraud risk. They are computed according to certain contract\nattributes and are therefore dependent on the proper filling of the contract\nand award notices. However, these attributes are very often missing in\npractice, which prohibits red flags computation. Traditional fraud detection\napproaches focus on tabular data only, considering each contract separately,\nand are therefore very sensitive to this issue. In this work, we adopt a\ngraph-based method allowing leveraging relations between contracts, to\ncompensate for the missing attributes. We propose PANG (Pattern-Based Anomaly\nDetection in Graphs), a general supervised framework relying on pattern\nextraction to detect anomalous graphs in a collection of attributed graphs.\nNotably, it is able to identify induced subgraphs, a type of pattern widely\noverlooked in the literature. When benchmarked on standard datasets, its\npredictive performance is on par with state-of-the-art methods, with the\nadditional advantage of being explainable. These experiments also reveal that\ninduced patterns are more discriminative on certain datasets. When applying\nPANG to public procurement data, the prediction is superior to other methods,\nand it identifies subgraph patterns that are characteristic of fraud-prone\nsituations, thereby making it possible to better understand fraudulent\nbehavior.\n", "date": "2023-06-19", "categories": []}
{"id": "2406.14866", "title": "AI-based Anomaly Detection for Clinical-Grade Histopathological\n  Diagnostics", "abstract": "  While previous studies have demonstrated the potential of AI to diagnose\ndiseases in imaging data, clinical implementation is still lagging behind. This\nis partly because AI models require training with large numbers of examples\nonly available for common diseases. In clinical reality, however, only few\ndiseases are common, whereas the majority of diseases are less frequent\n(long-tail distribution). Current AI models overlook or misclassify these\ndiseases. We propose a deep anomaly detection approach that only requires\ntraining data from common diseases to detect also all less frequent diseases.\nWe collected two large real-world datasets of gastrointestinal biopsies, which\nare prototypical of the problem. Herein, the ten most common findings account\nfor approximately 90% of cases, whereas the remaining 10% contained 56 disease\nentities, including many cancers. 17 million histological images from 5,423\ncases were used for training and evaluation. Without any specific training for\nthe diseases, our best-performing model reliably detected a broad spectrum of\ninfrequent (\"anomalous\") pathologies with 95.0% (stomach) and 91.0% (colon)\nAUROC and generalized across scanners and hospitals. By design, the proposed\nanomaly detection can be expected to detect any pathological alteration in the\ndiagnostic tail of gastrointestinal biopsies, including rare primary or\nmetastatic cancers. This study establishes the first effective clinical\napplication of AI-based anomaly detection in histopathology that can flag\nanomalous cases, facilitate case prioritization, reduce missed diagnoses and\nenhance the general safety of AI models, thereby driving AI adoption and\nautomation in routine diagnostics and beyond.\n", "date": "2024-06-21", "categories": []}
{"id": "2312.01541", "title": "Revisiting Non-separable Binary Classification and its Applications in\n  Anomaly Detection", "abstract": "  The inability to linearly classify XOR has motivated much of deep learning.\nWe revisit this age-old problem and show that linear classification of XOR is\nindeed possible. Instead of separating data between halfspaces, we propose a\nslightly different paradigm, equality separation, that adapts the SVM objective\nto distinguish data within or outside the margin. Our classifier can then be\nintegrated into neural network pipelines with a smooth approximation. From its\nproperties, we intuit that equality separation is suitable for anomaly\ndetection. To formalize this notion, we introduce closing numbers, a\nquantitative measure on the capacity for classifiers to form closed decision\nregions for anomaly detection. Springboarding from this theoretical connection\nbetween binary classification and anomaly detection, we test our hypothesis on\nsupervised anomaly detection experiments, showing that equality separation can\ndetect both seen and unseen anomalies.\n", "date": "2023-12-03", "categories": []}
{"id": "2405.11976", "title": "Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays", "abstract": "  Anomaly detection in chest X-rays is a critical task. Most methods mainly\nmodel the distribution of normal images, and then regard significant deviation\nfrom normal distribution as anomaly. Recently, CLIP-based methods, pre-trained\non a large number of medical images, have shown impressive performance on\nzero/few-shot downstream tasks. In this paper, we aim to explore the potential\nof CLIP-based methods for anomaly detection in chest X-rays. Considering the\ndiscrepancy between the CLIP pre-training data and the task-specific data, we\npropose a position-guided prompt learning method. Specifically, inspired by the\nfact that experts diagnose chest X-rays by carefully examining distinct lung\nregions, we propose learnable position-guided text and image prompts to adapt\nthe task data to the frozen pre-trained CLIP-based model. To enhance the\nmodel's discriminative capability, we propose a novel structure-preserving\nanomaly synthesis method within chest x-rays during the training process.\nExtensive experiments on three datasets demonstrate that our proposed method\noutperforms some state-of-the-art methods. The code of our implementation is\navailable at https://github.com/sunzc-sunny/PPAD.\n", "date": "2024-05-20", "categories": []}
{"id": "2311.01367", "title": "Respiratory Anomaly Detection using Reflected Infrared Light-wave\n  Signals", "abstract": "  In this study, we present a non-contact respiratory anomaly detection method\nusing incoherent light-wave signals reflected from the chest of a mechanical\nrobot that can breathe like human beings. In comparison to existing radar and\ncamera-based sensing systems for vitals monitoring, this technology uses only a\nlow-cost ubiquitous infrared light source and sensor. This light-wave sensing\nsystem recognizes different breathing anomalies from the variations of light\nintensity reflected from the chest of the robot within a 0.5m-1.5m range with\nan average classification accuracy of up to 96.6% using machine learning.\n", "date": "2023-11-02", "categories": []}
{"id": "2406.12698", "title": "Online-Adaptive Anomaly Detection for Defect Identification in Aircraft\n  Assembly", "abstract": "  Anomaly detection deals with detecting deviations from established patterns\nwithin data. It has various applications like autonomous driving, predictive\nmaintenance, and medical diagnosis. To improve anomaly detection accuracy,\ntransfer learning can be applied to large, pre-trained models and adapt them to\nthe specific application context. In this paper, we propose a novel framework\nfor online-adaptive anomaly detection using transfer learning. The approach\nadapts to different environments by selecting visually similar training images\nand online fitting a normality model to EfficientNet features extracted from\nthe training subset. Anomaly detection is then performed by computing the\nMahalanobis distance between the normality model and the test image features.\nDifferent similarity measures (SIFT/FLANN, Cosine) and normality models (MVG,\nOCSVM) are employed and compared with each other. We evaluate the approach on\ndifferent anomaly detection benchmarks and data collected in controlled\nlaboratory settings. Experimental results showcase a detection accuracy\nexceeding 0.975, outperforming the state-of-the-art ET-NET approach.\n", "date": "2024-06-18", "categories": []}
{"id": "2207.10324", "title": "Enhancing Generative Networks for Chest Anomaly Localization through\n  Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data\n  Translation", "abstract": "  Image translation based on a generative adversarial network (GAN-IT) is a\npromising method for the precise localization of abnormal regions in chest\nX-ray images (AL-CXR) even without the pixel-level annotation. However,\nheterogeneous unpaired datasets undermine existing methods to extract key\nfeatures and distinguish normal from abnormal cases, resulting in inaccurate\nand unstable AL-CXR. To address this problem, we propose an improved two-stage\nGAN-IT involving registration and data augmentation. For the first stage, we\nintroduce an advanced deep-learning-based registration technique that virtually\nand reasonably converts unpaired data into paired data for learning\nregistration maps, by sequentially utilizing linear-based global and uniform\ncoordinate transformation and AI-based non-linear coordinate fine-tuning. This\napproach enables independent and complex coordinate transformation of each\ndetailed location of the lung while recognizing the entire lung structure,\nthereby achieving higher registration performance with resolving inherent\nartifacts caused by unpaired conditions. For the second stage, we apply data\naugmentation to diversify anomaly locations by swapping the left and right lung\nregions on the uniform registered frames, further improving the performance by\nalleviating imbalance in data distribution showing left and right lung lesions.\nThe proposed method is model agnostic and shows consistent AL-CXR performance\nimprovement in representative AI models. Therefore, we believe GAN-IT for\nAL-CXR can be clinically implemented by using our basis framework, even if\nlearning data are scarce or difficult for the pixel-level disease annotation.\n", "date": "2022-07-21", "categories": []}
{"id": "2406.05645", "title": "Anomaly Multi-classification in Industrial Scenarios: Transferring\n  Few-shot Learning to a New Task", "abstract": "  In industrial scenarios, it is crucial not only to identify anomalous items\nbut also to classify the type of anomaly. However, research on anomaly\nmulti-classification remains largely unexplored. This paper proposes a novel\nand valuable research task called anomaly multi-classification. Given the\nchallenges in applying few-shot learning to this task, due to limited training\ndata and unique characteristics of anomaly images, we introduce a baseline\nmodel that combines RelationNet and PatchCore. We propose a data generation\nmethod that creates pseudo classes and a corresponding proxy task, aiming to\nbridge the gap in transferring few-shot learning to industrial scenarios.\nFurthermore, we utilize contrastive learning to improve the vanilla baseline,\nachieving much better performance than directly fine-tune a ResNet. Experiments\nconducted on MvTec AD and MvTec3D AD demonstrate that our approach shows\nsuperior performance in this novel task.\n", "date": "2024-06-09", "categories": []}
{"id": "2406.10617", "title": "Enhancing Anomaly Detection Generalization through Knowledge Exposure:\n  The Dual Effects of Augmentation", "abstract": "  Anomaly detection involves identifying instances within a dataset that\ndeviate from the norm and occur infrequently. Current benchmarks tend to favor\nmethods biased towards low diversity in normal data, which does not align with\nreal-world scenarios. Despite advancements in these benchmarks, contemporary\nanomaly detection methods often struggle with out-of-distribution\ngeneralization, particularly in classifying samples with subtle transformations\nduring testing. These methods typically assume that normal samples during test\ntime have distributions very similar to those in the training set, while\nanomalies are distributed much further away. However, real-world test samples\noften exhibit various levels of distribution shift while maintaining semantic\nconsistency. Therefore, effectively generalizing to samples that have undergone\nsemantic-preserving transformations, while accurately detecting normal samples\nwhose semantic meaning has changed after transformation as anomalies, is\ncrucial for the trustworthiness and reliability of a model. For example,\nalthough it is clear that rotation shifts the meaning for a car in the context\nof anomaly detection but preserves the meaning for a bird, current methods are\nlikely to detect both as abnormal. This complexity underscores the necessity\nfor dynamic learning procedures rooted in the intrinsic concept of outliers. To\naddress this issue, we propose new testing protocols and a novel method called\nKnowledge Exposure (KE), which integrates external knowledge to comprehend\nconcept dynamics and differentiate transformations that induce semantic shifts.\nThis approach enhances generalization by utilizing insights from a pre-trained\nCLIP model to evaluate the significance of anomalies for each concept.\nEvaluation on CIFAR-10, CIFAR-100, and SVHN with the new protocols demonstrates\nsuperior performance compared to previous methods.\n", "date": "2024-06-15", "categories": []}
{"id": "2406.11507", "title": "Prior Normality Prompt Transformer for Multi-class Industrial Image\n  Anomaly Detection", "abstract": "  Image anomaly detection plays a pivotal role in industrial inspection.\nTraditional approaches often demand distinct models for specific categories,\nresulting in substantial deployment costs. This raises concerns about\nmulti-class anomaly detection, where a unified model is developed for multiple\nclasses. However, applying conventional methods, particularly\nreconstruction-based models, directly to multi-class scenarios encounters\nchallenges such as identical shortcut learning, hindering effective\ndiscrimination between normal and abnormal instances. To tackle this issue, our\nstudy introduces the Prior Normality Prompt Transformer (PNPT) method for\nmulti-class image anomaly detection. PNPT strategically incorporates normal\nsemantics prompting to mitigate the \"identical mapping\" problem. This entails\nintegrating a prior normality prompt into the reconstruction process, yielding\na dual-stream model. This innovative architecture combines normal prior\nsemantics with abnormal samples, enabling dual-stream reconstruction grounded\nin both prior knowledge and intrinsic sample characteristics. PNPT comprises\nfour essential modules: Class-Specific Normality Prompting Pool (CS-NPP),\nHierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding\n(SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental\nvalidation on diverse benchmark datasets and real-world industrial applications\nhighlights PNPT's superior performance in multi-class industrial anomaly\ndetection.\n", "date": "2024-06-17", "categories": []}
{"id": "2406.09966", "title": "Outlier detection in maritime environments using AIS data and deep\n  recurrent architectures", "abstract": "  A methodology based on deep recurrent models for maritime surveillance, over\npublicly available Automatic Identification System (AIS) data, is presented in\nthis paper. The setup employs a deep Recurrent Neural Network (RNN)-based\nmodel, for encoding and reconstructing the observed ships' motion patterns. Our\napproach is based on a thresholding mechanism, over the calculated errors\nbetween observed and reconstructed motion patterns of maritime vessels.\nSpecifically, a deep-learning framework, i.e. an encoder-decoder architecture,\nis trained using the observed motion patterns, enabling the models to learn and\npredict the expected trajectory, which will be compared to the effective ones.\nOur models, particularly the bidirectional GRU with recurrent dropouts,\nshowcased superior performance in capturing the temporal dynamics of maritime\ndata, illustrating the potential of deep learning to enhance maritime\nsurveillance capabilities. Our work lays a solid foundation for future research\nin this domain, highlighting a path toward improved maritime safety through the\ninnovative application of technology.\n", "date": "2024-06-14", "categories": []}
{"id": "2406.08516", "title": "Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical\n  Aggregated Anomaly Detection", "abstract": "  This paper presents a novel anomaly detection methodology termed Statistical\nAggregated Anomaly Detection (SAAD). The SAAD approach integrates advanced\nstatistical techniques with machine learning, and its efficacy is demonstrated\nthrough validation on real sensor data from a Hardware-in-the-Loop (HIL)\nenvironment within the automotive domain. The key innovation of SAAD lies in\nits ability to significantly enhance the accuracy and robustness of anomaly\ndetection when combined with Fully Connected Networks (FCNs) augmented by\ndropout layers. Comprehensive experimental evaluations indicate that the\nstandalone statistical method achieves an accuracy of 72.1%, whereas the deep\nlearning model alone attains an accuracy of 71.5%. In contrast, the aggregated\nmethod achieves a superior accuracy of 88.3% and an F1 score of 0.921, thereby\noutperforming the individual models. These results underscore the effectiveness\nof SAAD, demonstrating its potential for broad application in various domains,\nincluding automotive systems.\n", "date": "2024-06-11", "categories": []}
{"id": "2406.09147", "title": "Weakly-supervised anomaly detection for multimodal data distributions", "abstract": "  Weakly-supervised anomaly detection can outperform existing unsupervised\nmethods with the assistance of a very small number of labeled anomalies, which\nattracts increasing attention from researchers. However, existing\nweakly-supervised anomaly detection methods are limited as these methods do not\nfactor in the multimodel nature of the real-world data distribution. To\nmitigate this, we propose the Weakly-supervised Variational-mixture-model-based\nAnomaly Detector (WVAD). WVAD excels in multimodal datasets. It consists of two\ncomponents: a deep variational mixture model, and an anomaly score estimator.\nThe deep variational mixture model captures various features of the data from\ndifferent clusters, then these features are delivered to the anomaly score\nestimator to assess the anomaly levels. Experimental results on three\nreal-world datasets demonstrate WVAD's superiority.\n", "date": "2024-06-13", "categories": []}
{"id": "2311.11961", "title": "NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly\n  Generation", "abstract": "  Anomaly detection (AD) is essential in identifying rare and often critical\nevents in complex systems, finding applications in fields such as network\nintrusion detection, financial fraud detection, and fault detection in\ninfrastructure and industrial systems. While AD is typically treated as an\nunsupervised learning task due to the high cost of label annotation, it is more\npractical to assume access to a small set of labeled anomaly samples from\ndomain experts, as is the case for semi-supervised anomaly detection.\nSemi-supervised and supervised approaches can leverage such labeled data,\nresulting in improved performance. In this paper, rather than proposing a new\nsemi-supervised or supervised approach for AD, we introduce a novel algorithm\nfor generating additional pseudo-anomalies on the basis of the limited labeled\nanomalies and a large volume of unlabeled data. This serves as an augmentation\nto facilitate the detection of new anomalies. Our proposed algorithm, named\nNearest Neighbor Gaussian Mixup (NNG-Mix), efficiently integrates information\nfrom both labeled and unlabeled data to generate pseudo-anomalies. We compare\nthe performance of this novel algorithm with commonly applied augmentation\ntechniques, such as Mixup and Cutout. We evaluate NNG-Mix by training various\nexisting semi-supervised and supervised anomaly detection algorithms on the\noriginal training data along with the generated pseudo-anomalies. Through\nextensive experiments on 57 benchmark datasets in ADBench, reflecting different\ndata types, we demonstrate that NNG-Mix outperforms other data augmentation\nmethods. It yields significant performance improvements compared to the\nbaselines trained exclusively on the original training data. Notably, NNG-Mix\nyields up to 16.4%, 8.8%, and 8.0% improvements on Classical, CV, and NLP\ndatasets in ADBench. Our source code is available at\nhttps://github.com/donghao51/NNG-Mix.\n", "date": "2023-11-20", "categories": []}
{"id": "2406.07250", "title": "Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot\n  Unsupervised Anomalous Sound Detection for Machine Condition Monitoring", "abstract": "  We present the task description of the Detection and Classification of\nAcoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot\nunsupervised anomalous sound detection (ASD) for machine condition monitoring.\nContinuing from last year's DCASE 2023 Challenge Task 2, we organize the task\nas a first-shot problem under domain generalization required settings. The main\ngoal of the first-shot problem is to enable rapid deployment of ASD systems for\nnew kinds of machines without the need for machine-specific hyperparameter\ntunings. This problem setting was realized by (1) giving only one section for\neach machine type and (2) having completely different machine types for the\ndevelopment and evaluation datasets. For the DCASE 2024 Challenge Task 2, data\nof completely new machine types were newly collected and provided as the\nevaluation dataset. In addition, attribute information such as the machine\noperation conditions were concealed for several machine types to mimic\nsituations where such information are unavailable. We will add challenge\nresults and analysis of the submissions after the challenge submission\ndeadline.\n", "date": "2024-06-11", "categories": []}
{"id": "2406.07333", "title": "Global-Regularized Neighborhood Regression for Efficient Zero-Shot\n  Texture Anomaly Detection", "abstract": "  Texture surface anomaly detection finds widespread applications in industrial\nsettings. However, existing methods often necessitate gathering numerous\nsamples for model training. Moreover, they predominantly operate within a\nclose-set detection framework, limiting their ability to identify anomalies\nbeyond the training dataset. To tackle these challenges, this paper introduces\na novel zero-shot texture anomaly detection method named Global-Regularized\nNeighborhood Regression (GRNR). Unlike conventional approaches, GRNR can detect\nanomalies on arbitrary textured surfaces without any training data or cost.\nDrawing from human visual cognition, GRNR derives two intrinsic prior supports\ndirectly from the test texture image: local neighborhood priors characterized\nby coherent similarities and global normality priors featuring typical normal\npatterns. The fundamental principle of GRNR involves utilizing the two\nextracted intrinsic support priors for self-reconstructive regression of the\nquery sample. This process employs the transformation facilitated by local\nneighbor support while being regularized by global normality support, aiming to\nnot only achieve visually consistent reconstruction results but also preserve\nnormality properties. We validate the effectiveness of GRNR across various\nindustrial scenarios using eight benchmark datasets, demonstrating its superior\ndetection performance without the need for training data. Remarkably, our\nmethod is applicable for open-set texture defect detection and can even surpass\nexisting vanilla approaches that require extensive training.\n", "date": "2024-06-11", "categories": []}
{"id": "2312.04119", "title": "A brief introduction to a framework named Multilevel\n  Guidance-Exploration Network", "abstract": "  Human behavior anomaly detection aims to identify unusual human actions,\nplaying a crucial role in intelligent surveillance and other areas. The current\nmainstream methods still adopt reconstruction or future frame prediction\ntechniques. However, reconstructing or predicting low-level pixel features\neasily enables the network to achieve overly strong generalization ability,\nallowing anomalies to be reconstructed or predicted as effectively as normal\ndata. Different from their methods, inspired by the Student-Teacher Network, we\npropose a novel framework called the Multilevel Guidance-Exploration\nNetwork(MGENet), which detects anomalies through the difference in high-level\nrepresentation between the Guidance and Exploration network. Specifically, we\nfirst utilize the pre-trained Normalizing Flow that takes skeletal keypoints as\ninput to guide an RGB encoder, which takes unmasked RGB frames as input, to\nexplore motion latent features. Then, the RGB encoder guides the mask encoder,\nwhich takes masked RGB frames as input, to explore the latent appearance\nfeature. Additionally, we design a Behavior-Scene Matching Module(BSMM) to\ndetect scene-related behavioral anomalies. Extensive experiments demonstrate\nthat our proposed method achieves state-of-the-art performance on ShanghaiTech\nand UBnormal datasets.\n", "date": "2023-12-07", "categories": []}
{"id": "2406.04573", "title": "Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly\n  Detection", "abstract": "  This study targets Multi-Lighting Image Anomaly Detection (MLIAD), where\nmultiple lighting conditions are utilized to enhance imaging quality and\nanomaly detection performance. While numerous image anomaly detection methods\nhave been proposed, they lack the capacity to handle multiple inputs for a\nsingle sample, like multi-lighting images in MLIAD. Hence, this study proposes\nAttention Fusion Reverse Distillation (AFRD) to handle multiple inputs in\nMLIAD. For this purpose, AFRD utilizes a pre-trained teacher network to extract\nfeatures from multiple inputs. Then these features are aggregated into fused\nfeatures through an attention module. Subsequently, a corresponding student\nnet-work is utilized to regress the attention fused features. The regression\nerrors are denoted as anomaly scores during inference. Experiments on\nEyecandies demonstrates that AFRD achieves superior MLIAD performance than\nother MLIAD alternatives, also highlighting the benefit of using multiple\nlighting conditions for anomaly detection.\n", "date": "2024-06-07", "categories": []}
{"id": "2406.04608", "title": "A Recover-then-Discriminate Framework for Robust Anomaly Detection", "abstract": "  Anomaly detection (AD) has been extensively studied and applied in a wide\nrange of scenarios in the recent past. However, there are still gaps between\nachieved and desirable levels of recognition accuracy for making AD for\npractical applications. In this paper, we start from an insightful analysis of\ntwo types of fundamental yet representative failure cases in the baseline\nmodel, and reveal reasons that hinder current AD methods from achieving a\nhigher recognition accuracy. Specifically, by Case-1, we found that the main\nreasons detrimental to current AD methods is that the inputs to the recovery\nmodel contain a large number of detailed features to be recovered, which leads\nto the normal/abnormal area has-not/has been recovered into its original state.\nBy Case-2, we surprisingly found that the abnormal area that cannot be\nrecognized in image-level representations can be easily recognized in the\nfeature-level representation. Based on the above observations, we propose a\nnovel Recover-then-Discriminate (ReDi) framework for AD. ReDi takes a\nself-generated feature map and a selected prompted image as explicit input\ninformation to solve problems in case-1. Concurrently, a feature-level\ndiscriminative network is proposed to enhance abnormal differences between the\nrecovered representation and the input representation. Extensive experimental\nresults on two popular yet challenging AD datasets validate that ReDi achieves\nthe new state-of-the-art accuracy.\n", "date": "2024-06-07", "categories": []}
{"id": "2406.04687", "title": "LogiCode: an LLM-Driven Framework for Logical Anomaly Detection", "abstract": "  This paper presents LogiCode, a novel framework that leverages Large Language\nModels (LLMs) for identifying logical anomalies in industrial settings, moving\nbeyond traditional focus on structural inconsistencies. By harnessing LLMs for\nlogical reasoning, LogiCode autonomously generates Python codes to pinpoint\nanomalies such as incorrect component quantities or missing elements, marking a\nsignificant leap forward in anomaly detection technologies. A custom dataset\n\"LOCO-Annotations\" and a benchmark \"LogiBench\" are introduced to evaluate the\nLogiCode's performance across various metrics including binary classification\naccuracy, code generation success rate, and precision in reasoning. Findings\ndemonstrate LogiCode's enhanced interpretability, significantly improving the\naccuracy of logical anomaly detection and offering detailed explanations for\nidentified anomalies. This represents a notable shift towards more intelligent,\nLLM-driven approaches in industrial anomaly detection, promising substantial\nimpacts on industry-specific applications.\n", "date": "2024-06-07", "categories": []}
{"id": "2406.04690", "title": "Higher-order Structure Based Anomaly Detection on Attributed Networks", "abstract": "  Anomaly detection (such as telecom fraud detection and medical image\ndetection) has attracted the increasing attention of people. The complex\ninteraction between multiple entities widely exists in the network, which can\nreflect specific human behavior patterns. Such patterns can be modeled by\nhigher-order network structures, thus benefiting anomaly detection on\nattributed networks. However, due to the lack of an effective mechanism in most\nexisting graph learning methods, these complex interaction patterns fail to be\napplied in detecting anomalies, hindering the progress of anomaly detection to\nsome extent. In order to address the aforementioned issue, we present a\nhigher-order structure based anomaly detection (GUIDE) method. We exploit\nattribute autoencoder and structure autoencoder to reconstruct node attributes\nand higher-order structures, respectively. Moreover, we design a graph\nattention layer to evaluate the significance of neighbors to nodes through\ntheir higher-order structure differences. Finally, we leverage node attribute\nand higher-order structure reconstruction errors to find anomalies. Extensive\nexperiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and\nPubmed) are implemented to verify the effectiveness of GUIDE. Experimental\nresults in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly\noutperforms the state-of-art methods.\n", "date": "2024-06-07", "categories": []}
{"id": "2401.10338", "title": "MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online\n  Anomaly Detection with Multivariate Time Series", "abstract": "  In large IT systems, software deployment is a crucial process in online\nservices as their code is regularly updated. However, a faulty code change may\ndegrade the target service's performance and cause cascading outages in\ndownstream services. Thus, software deployments should be comprehensively\nmonitored, and their anomalies should be detected timely. In this paper, we\nstudy the problem of anomaly detection for deployments. We begin by identifying\nthe challenges unique to this anomaly detection problem, which is at\nentity-level (e.g., deployments), relative to the more typical problem of\nanomaly detection in multivariate time series (MTS). The unique challenges\ninclude the heterogeneity of deployments, the low latency tolerance, the\nambiguous anomaly definition, and the limited supervision. To address them, we\npropose a novel framework, semi-supervised hybrid Model for Entity-Level Online\nDetection of anomalY (MELODY). MELODY first transforms the MTS of different\nentities to the same feature space by an online feature extractor, then uses a\nnewly proposed semi-supervised deep one-class model for detecting anomalous\nentities. We evaluated MELODY on real data of cloud services with 1.2M+ time\nseries. The relative F1 score improvement of MELODY over the state-of-the-art\nmethods ranges from 7.6% to 56.5%. The user evaluation suggests MELODY is\nsuitable for monitoring deployments in large online systems.\n", "date": "2024-01-18", "categories": []}
{"id": "2305.10464", "title": "Reconstruction Error-based Anomaly Detection with Few Outlying Examples", "abstract": "  Reconstruction error-based neural architectures constitute a classical deep\nlearning approach to anomaly detection which has shown great performances. It\nconsists in training an Autoencoder to reconstruct a set of examples deemed to\nrepresent the normality and then to point out as anomalies those data that show\na sufficiently large reconstruction error. Unfortunately, these architectures\noften become able to well reconstruct also the anomalies in the data. This\nphenomenon is more evident when there are anomalies in the training set. In\nparticular when these anomalies are labeled, a setting called semi-supervised,\nthe best way to train Autoencoders is to ignore anomalies and minimize the\nreconstruction error on normal data. The goal of this work is to investigate\napproaches to allow reconstruction error-based architectures to instruct the\nmodel to put known anomalies outside of the domain description of the normal\ndata. Specifically, our strategy exploits a limited number of anomalous\nexamples to increase the contrast between the reconstruction error associated\nwith normal examples and those associated with both known and unknown\nanomalies, thus enhancing anomaly detection performances. The experiments show\nthat this new procedure achieves better performances than the standard\nAutoencoder approach and the main deep learning techniques for semi-supervised\nanomaly detection.\n", "date": "2023-05-17", "categories": []}
{"id": "2405.02678", "title": "Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?", "abstract": "  The current state of machine learning scholarship in Timeseries Anomaly\nDetection (TAD) is plagued by the persistent use of flawed evaluation metrics,\ninconsistent benchmarking practices, and a lack of proper justification for the\nchoices made in novel deep learning-based model designs. Our paper presents a\ncritical analysis of the status quo in TAD, revealing the misleading track of\ncurrent research and highlighting problematic methods, and evaluation\npractices. Our position advocates for a shift in focus from solely pursuing\nnovel model designs to improving benchmarking practices, creating non-trivial\ndatasets, and critically evaluating the utility of complex methods against\nsimpler baselines. Our findings demonstrate the need for rigorous evaluation\nprotocols, the creation of simple baselines, and the revelation that\nstate-of-the-art deep anomaly detection models effectively learn linear\nmappings. These findings suggest the need for more exploration and development\nof simple and interpretable TAD methods. The increment of model complexity in\nthe state-of-the-art deep-learning based models unfortunately offers very\nlittle improvement. We offer insights and suggestions for the field to move\nforward.\n  Code: https://github.com/ssarfraz/QuoVadisTAD\n", "date": "2024-05-04", "categories": []}
{"id": "2406.02775", "title": "Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind\n  Energy", "abstract": "  The demand for condition-based and predictive maintenance is rising across\nindustries, especially for remote, high-value, and high-risk assets. In this\narticle, the diagnostic digital twin concept is introduced, discussed, and\nimplemented for a floating offshore turbine. A diagnostic digital twin is a\nvirtual representation of an asset that combines real-time data and models to\nmonitor damage, detect anomalies, and diagnose failures, thereby enabling\ncondition-based and predictive maintenance. By applying diagnostic digital\ntwins to offshore assets, unexpected failures can be alleviated, but the\nimplementation can prove challenging. Here, a diagnostic digital twin is\nimplemented for an operational floating offshore wind turbine. The asset is\nmonitored through measurements. Unsupervised learning methods are employed to\nbuild a normal operation model, detect anomalies, and provide a fault\ndiagnosis. Warnings and diagnoses are sent through text messages, and a more\ndetailed diagnosis can be accessed in a virtual reality interface. The\ndiagnostic digital twin successfully detected an anomaly with high confidence\nhours before a failure occurred. The paper concludes by discussing diagnostic\ndigital twins in the broader context of offshore engineering. The presented\napproach can be generalized to other offshore assets to improve maintenance and\nincrease the lifetime, efficiency, and sustainability of offshore assets.\n", "date": "2024-06-04", "categories": []}
{"id": "2406.02976", "title": "DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video\n  Anomaly Detection", "abstract": "  Cooperation between temporal convolutional networks (TCN) and graph\nconvolutional networks (GCN) as a processing module has shown promising results\nin skeleton-based video anomaly detection (SVAD). However, to maintain a\nlightweight model with low computational and storage complexity, shallow GCN\nand TCN blocks are constrained by small receptive fields and a lack of\ncross-dimension interaction capture. To tackle this limitation, we propose a\nlightweight module called the Dual Attention Module (DAM) for capturing\ncross-dimension interaction relationships in spatio-temporal skeletal data. It\nemploys the frame attention mechanism to identify the most significant frames\nand the skeleton attention mechanism to capture broader relationships across\nfixed partitions with minimal parameters and flops. Furthermore, the proposed\nDual Attention Normalizing Flow (DA-Flow) integrates the DAM as a\npost-processing unit after GCN within the normalizing flow framework.\nSimulations show that the proposed model is robust against noise and negative\nsamples. Experimental results show that DA-Flow reaches competitive or better\nperformance than the existing state-of-the-art (SOTA) methods in terms of the\nmicro AUC metric with the fewest number of parameters. Moreover, we found that\neven without training, simply using random projection without dimensionality\nreduction on skeleton data enables substantial anomaly detection capabilities.\n", "date": "2024-06-05", "categories": []}
{"id": "2406.01975", "title": "Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS", "abstract": "  Recent advances in Out-of-Distribution (OOD) Detection is the driving force\nbehind safe and reliable deployment of Convolutional Neural Networks (CNNs) in\nreal world applications. However, existing studies focus on OOD detection\nthrough confidence score and deep generative model-based methods, without\nconsidering the impact of DNN structures, especially dense connectivity in\narchitecture fabrications. In addition, existing outlier detection approaches\nexhibit high variance in generalization performance, lacking stability and\nconfidence in evaluating and ranking different outlier detectors. In this work,\nwe propose a novel paradigm, Dense Connectivity Search of Outlier Detector\n(DCSOD), that automatically explore the dense connectivity of CNN architectures\non near-OOD detection task using Neural Architecture Search (NAS). We introduce\na hierarchical search space containing versatile convolution operators and\ndense connectivity, allowing a flexible exploration of CNN architectures with\ndiverse connectivity patterns. To improve the quality of evaluation on OOD\ndetection during search, we propose evolving distillation based on our\nmulti-view feature learning explanation. Evolving distillation stabilizes\ntraining for OOD detection evaluation, thus improves the quality of search. We\nthoroughly examine DCSOD on CIFAR benchmarks under OOD detection protocol.\nExperimental results show that DCSOD achieve remarkable performance over widely\nused architectures and previous NAS baselines. Notably, DCSOD achieves\nstate-of-the-art (SOTA) performance on CIFAR benchmark, with AUROC improvement\nof $\\sim$1.0%.\n", "date": "2024-06-04", "categories": []}
{"id": "2406.02263", "title": "M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via\n  Multimodal Denoising", "abstract": "  Existing industrial anomaly detection methods primarily concentrate on\nunsupervised learning with pristine RGB images. Yet, both RGB and 3D data are\ncrucial for anomaly detection, and the datasets are seldom completely clean in\npractical scenarios. To address above challenges, this paper initially delves\ninto the RGB-3D multi-modal noisy anomaly detection, proposing a novel\nnoise-resistant M3DM-NR framework to leveraging strong multi-modal\ndiscriminative capabilities of CLIP. M3DM-NR consists of three stages: Stage-I\nintroduces the Suspected References Selection module to filter a few normal\nsamples from the training dataset, using the multimodal features extracted by\nthe Initial Feature Extraction, and a Suspected Anomaly Map Computation module\nto generate a suspected anomaly map to focus on abnormal regions as reference.\nStage-II uses the suspected anomaly maps of the reference samples as reference,\nand inputs image, point cloud, and text information to achieve denoising of the\ntraining samples through intra-modal comparison and multi-scale aggregation\noperations. Finally, Stage-III proposes the Point Feature Alignment,\nUnsupervised Feature Fusion, Noise Discriminative Coreset Selection, and\nDecision Layer Fusion modules to learn the pattern of the training dataset,\nenabling anomaly detection and segmentation while filtering out noise.\nExtensive experiments show that M3DM-NR outperforms state-of-the-art methods in\n3D-RGB multi-modal noisy anomaly detection.\n", "date": "2024-06-04", "categories": []}
{"id": "2210.12941", "title": "Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and\n  Superior Method", "abstract": "  A large number of studies on Graph Outlier Detection (GOD) have emerged in\nrecent years due to its wide applications, in which Unsupervised Node Outlier\nDetection (UNOD) on attributed networks is an important area. UNOD focuses on\ndetecting two kinds of typical outliers in graphs: the structural outlier and\nthe contextual outlier. Most existing works conduct experiments based on\ndatasets with injected outliers. However, we find that the most widely-used\noutlier injection approach has a serious data leakage issue. By only utilizing\nsuch data leakage, a simple approach can achieve state-of-the-art performance\nin detecting outliers. In addition, we observe that existing algorithms have a\nperformance drop with the mitigated data leakage issue. The other major issue\nis on balanced detection performance between the two types of outliers, which\nhas not been considered by existing studies. In this paper, we analyze the\ncause of the data leakage issue in depth since the injection approach is a\nbuilding block to advance UNOD. Moreover, we devise a novel variance-based\nmodel to detect structural outliers, which outperforms existing algorithms\nsignificantly and is more robust at kinds of injection settings. On top of\nthis, we propose a new framework, Variance based Graph Outlier Detection\n(VGOD), which combines our variance-based model and attribute reconstruction\nmodel to detect outliers in a balanced way. Finally, we conduct extensive\nexperiments to demonstrate the effectiveness and efficiency of VGOD. The\nresults on 5 real-world datasets validate that VGOD achieves not only the best\nperformance in detecting outliers but also a balanced detection performance\nbetween structural and contextual outliers.\n", "date": "2022-10-24", "categories": []}
{"id": "2211.12353", "title": "U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with\n  Unsupervised Threshold", "abstract": "  In this work we propose a one-class self-supervised method for anomaly\nsegmentation in images that benefits both from a modern machine learning\napproach and a more classic statistical detection theory. The method consists\nof four phases. First, features are extracted using a multi-scale image\nTransformer architecture. Then, these features are fed into a U-shaped\nNormalizing Flow (NF) that lays the theoretical foundations for the subsequent\nphases. The third phase computes a pixel-level anomaly map from the NF\nembedding, and the last phase performs a segmentation based on the a contrario\nframework. This multiple hypothesis testing strategy permits the derivation of\nrobust unsupervised detection thresholds, which are crucial in real-world\napplications where an operational point is needed. The segmentation results are\nevaluated using the Mean Intersection over Union (mIoU) metric, and for\nassessing the generated anomaly maps we report the area under the Receiver\nOperating Characteristic curve (AUROC), as well as the Area Under the\nPer-Region-Overlap curve (AUPRO). Extensive experimentation in various datasets\nshows that the proposed approach produces state-of-the-art results for all\nmetrics and all datasets, ranking first in most MVTec-AD categories, with a\nmean pixel-level AUROC of 98.74%. Code and trained models are available at\nhttps:// github.com/mtailanian/uflow.\n", "date": "2022-11-22", "categories": []}
{"id": "2310.06085", "title": "Quantile-based Maximum Likelihood Training for Outlier Detection", "abstract": "  Discriminative learning effectively predicts true object class for image\nclassification. However, it often results in false positives for outliers,\nposing critical concerns in applications like autonomous driving and video\nsurveillance systems. Previous attempts to address this challenge involved\ntraining image classifiers through contrastive learning using actual outlier\ndata or synthesizing outliers for self-supervised learning. Furthermore,\nunsupervised generative modeling of inliers in pixel space has shown limited\nsuccess for outlier detection. In this work, we introduce a quantile-based\nmaximum likelihood objective for learning the inlier distribution to improve\nthe outlier separation during inference. Our approach fits a normalizing flow\nto pre-trained discriminative features and detects the outliers according to\nthe evaluated log-likelihood. The experimental evaluation demonstrates the\neffectiveness of our method as it surpasses the performance of the\nstate-of-the-art unsupervised methods for outlier detection. The results are\nalso competitive compared with a recent self-supervised approach for outlier\ndetection. Our work allows to reduce dependency on well-sampled negative\ntraining data, which is especially important for domains like medical\ndiagnostics or remote sensing.\n", "date": "2023-08-20", "categories": []}
{"id": "2402.03724", "title": "Statistical Test for Anomaly Detections by Variational Auto-Encoders", "abstract": "  In this study, we consider the reliability assessment of anomaly detection\n(AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD\nhas been actively studied in various perspective, from method development to\napplied research. However, when the results of ADs are used in high-stakes\ndecision-making, such as in medical diagnosis, it is necessary to ensure the\nreliability of the detected anomalies. In this study, we propose the VAE-AD\nTest as a method for quantifying the statistical reliability of VAE-based AD\nwithin the framework of statistical testing. Using the VAE-AD Test, the\nreliability of the anomaly regions detected by a VAE can be quantified in the\nform of p-values. This means that if an anomaly is declared when the p-value is\nbelow a certain threshold, it is possible to control the probability of false\ndetection to a desired level. Since the VAE-AD Test is constructed based on a\nnew statistical inference framework called selective inference, its validity is\ntheoretically guaranteed in finite samples. To demonstrate the validity and\neffectiveness of the proposed VAE-AD Test, numerical experiments on artificial\ndata and applications to brain image analysis are conducted.\n", "date": "2024-02-06", "categories": []}
{"id": "2406.00134", "title": "Anomaly Detection in Dynamic Graphs: A Comprehensive Survey", "abstract": "  This survey paper presents a comprehensive and conceptual overview of anomaly\ndetection using dynamic graphs. We focus on existing graph-based anomaly\ndetection (AD) techniques and their applications to dynamic networks. The\ncontributions of this survey paper include the following: i) a comparative\nstudy of existing surveys on anomaly detection; ii) a Dynamic Graph-based\nAnomaly Detection (DGAD) review framework in which approaches for detecting\nanomalies in dynamic graphs are grouped based on traditional machine-learning\nmodels, matrix transformations, probabilistic approaches, and deep-learning\napproaches; iii) a discussion of graphically representing both discrete and\ndynamic networks; and iv) a discussion of the advantages of graph-based\ntechniques for capturing the relational structure and complex interactions in\ndynamic graph data. Finally, this work identifies the potential challenges and\nfuture directions for detecting anomalies in dynamic networks. This DGAD survey\napproach aims to provide a valuable resource for researchers and practitioners\nby summarizing the strengths and limitations of each approach, highlighting\ncurrent research trends, and identifying open challenges. In doing so, it can\nguide future research efforts and promote advancements in anomaly detection in\ndynamic graphs.\n  Keywords: Graphs, Anomaly Detection, dynamic networks,Graph Neural Networks\n(GNN), Node anomaly, Graph mining.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.00452", "title": "Towards a Unified Framework of Clustering-based Anomaly Detection", "abstract": "  Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying\nabnormal patterns within data without labeled examples, holding significant\npractical implications across various domains. Although the individual\ncontributions of representation learning and clustering to anomaly detection\nare well-established, their interdependencies remain under-explored due to the\nabsence of a unified theoretical framework. Consequently, their collective\npotential to enhance anomaly detection performance remains largely untapped. To\nbridge this gap, in this paper, we propose a novel probabilistic mixture model\nfor anomaly detection to establish a theoretical connection among\nrepresentation learning, clustering, and anomaly detection. By maximizing a\nnovel anomaly-aware data likelihood, representation learning and clustering can\neffectively reduce the adverse impact of anomalous data and collaboratively\nbenefit anomaly detection. Meanwhile, a theoretically substantiated anomaly\nscore is naturally derived from this framework. Lastly, drawing inspiration\nfrom gravitational analysis in physics, we have devised an improved anomaly\nscore that more effectively harnesses the combined power of representation\nlearning and clustering. Extensive experiments, involving 17 baseline methods\nacross 30 diverse datasets, validate the effectiveness and generalization\ncapability of the proposed method, surpassing state-of-the-art methods.\n", "date": "2024-06-01", "categories": []}
{"id": "2405.20341", "title": "From Zero to Hero: Cold-Start Anomaly Detection", "abstract": "  When first deploying an anomaly detection system, e.g., to detect\nout-of-scope queries in chatbots, there are no observed data, making\ndata-driven approaches ineffective. Zero-shot anomaly detection methods offer a\nsolution to such \"cold-start\" cases, but unfortunately they are often not\naccurate enough. This paper studies the realistic but underexplored cold-start\nsetting where an anomaly detection model is initialized using zero-shot\nguidance, but subsequently receives a small number of contaminated observations\n(namely, that may include anomalies). The goal is to make efficient use of both\nthe zero-shot guidance and the observations. We propose ColdFusion, a method\nthat effectively adapts the zero-shot anomaly detector to contaminated\nobservations. To support future development of this new setting, we propose an\nevaluation suite consisting of evaluation protocols and metrics.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.18487", "title": "Anomaly detection for the identification of volcanic unrest in satellite\n  imagery", "abstract": "  Satellite images have the potential to detect volcanic deformation prior to\neruptions, but while a vast number of images are routinely acquired, only a\nsmall percentage contain volcanic deformation events. Manual inspection could\nmiss these anomalies, and an automatic system modelled with supervised learning\nrequires suitably labelled datasets. To tackle these issues, this paper\nexplores the use of unsupervised deep learning on satellite data for the\npurpose of identifying volcanic deformation as anomalies. Our detector is based\non Patch Distribution Modeling (PaDiM), and the detection performance is\nenhanced with a weighted distance, assigning greater importance to features\nfrom deeper layers. Additionally, we propose a preprocessing approach to handle\nnoisy and incomplete data points. The final framework was tested with five\nvolcanoes, which have different deformation characteristics and its performance\nwas compared against the supervised learning method for volcanic deformation\ndetection.\n", "date": "2024-05-28", "categories": []}
{"id": "2405.18932", "title": "A Mallows-like Criterion for Anomaly Detection with Random Forest\n  Implementation", "abstract": "  The effectiveness of anomaly signal detection can be significantly undermined\nby the inherent uncertainty of relying on one specified model. Under the\nframework of model average methods, this paper proposes a novel criterion to\nselect the weights on aggregation of multiple models, wherein the focal loss\nfunction accounts for the classification of extremely imbalanced data. This\nstrategy is further integrated into Random Forest algorithm by replacing the\nconventional voting method. We have evaluated the proposed method on benchmark\ndatasets across various domains, including network intrusion. The findings\nindicate that our proposed method not only surpasses the model averaging with\ntypical loss functions but also outstrips common anomaly detection algorithms\nin terms of accuracy and robustness.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.19247", "title": "Comparative Study of Neighbor-based Methods for Local Outlier Detection", "abstract": "  The neighbor-based method has become a powerful tool to handle the outlier\ndetection problem, which aims to infer the abnormal degree of the sample based\non the compactness of the sample and its neighbors. However, the existing\nmethods commonly focus on designing different processes to locate outliers in\nthe dataset, while the contributions of different types neighbors to outlier\ndetection has not been well discussed. To this end, this paper studies the\nneighbor in the existing outlier detection algorithms and a taxonomy is\nintroduced, which uses the three-level components of information, neighbor and\nmethodology to define hybrid methods. This taxonomy can serve as a paradigm\nwhere a novel neighbor-based outlier detection method can be proposed by\ncombining different components in this taxonomy. A large number of comparative\nexperiments were conducted on synthetic and real-world datasets in terms of\nperformance comparison and case study, and the results show that reverse\nK-nearest neighbor based methods achieve promising performance and dynamic\nselection method is suitable for working in high-dimensional space. Notably, it\nis verified that rationally selecting components from this taxonomy may create\nan algorithms superior to existing methods.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.17488", "title": "Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert\n  Filtering -- A Predictive Maintenance Case Study", "abstract": "  Fault detection is a key challenge in the management of complex systems. In\nthe context of SparkCognition's efforts towards predictive maintenance in large\nscale industrial systems, this problem is often framed in terms of anomaly\ndetection - identifying patterns of behavior in the data which deviate from\nnormal. Patterns of normal behavior aren't captured simply in the coarse\nstatistics of measured signals. Rather, the multivariate sequential pattern\nitself can be indicative of normal vs. abnormal behavior. For this reason,\nnormal behavior modeling that relies on snapshots of the data without taking\ninto account temporal relationships as they evolve would be lacking. However,\ncommon strategies for dealing with temporal dependence, such as Recurrent\nNeural Networks or attention mechanisms are oftentimes computationally\nexpensive and difficult to train. In this paper, we propose a fast and\nefficient approach to anomaly detection and alert filtering based on sequential\npattern similarities. In our empirical analysis section, we show how this\napproach can be leveraged for a variety of purposes involving anomaly detection\non a large scale real-world industrial system. Subsequently, we test our\napproach on a publicly-available dataset in order to establish its general\napplicability and robustness compared to a state-of-the-art baseline. We also\ndemonstrate an efficient way of optimizing the framework based on an alert\nrecall objective function.\n", "date": "2024-05-24", "categories": []}
{"id": "2305.00735", "title": "Unsupervised anomaly detection algorithms on real-world data: how many\n  do we need?", "abstract": "  In this study we evaluate 32 unsupervised anomaly detection algorithms on 52\nreal-world multivariate tabular datasets, performing the largest comparison of\nunsupervised anomaly detection algorithms to date. On this collection of\ndatasets, the $k$-thNN (distance to the $k$-nearest neighbor) algorithm\nsignificantly outperforms the most other algorithms. Visualizing and then\nclustering the relative performance of the considered algorithms on all\ndatasets, we identify two clear clusters: one with ``local'' datasets, and\nanother with ``global'' datasets. ``Local'' anomalies occupy a region with low\ndensity when compared to nearby samples, while ``global'' occupy an overall low\ndensity region in the feature space. On the local datasets the $k$NN\n($k$-nearest neighbor) algorithm comes out on top. On the global datasets, the\nEIF (extended isolation forest) algorithm performs the best. Also taking into\nconsideration the algorithms' computational complexity, a toolbox with these\nthree unsupervised anomaly detection algorithms suffices for finding anomalies\nin this representative collection of multivariate datasets. By providing access\nto code and datasets, our study can be easily reproduced and extended with more\nalgorithms and/or datasets.\n", "date": "2023-05-01", "categories": []}
{"id": "2310.02576", "title": "A Prototype-Based Neural Network for Image Anomaly Detection and\n  Localization", "abstract": "  Image anomaly detection and localization perform not only image-level anomaly\nclassification but also locate pixel-level anomaly regions. Recently, it has\nreceived much research attention due to its wide application in various fields.\nThis paper proposes ProtoAD, a prototype-based neural network for image anomaly\ndetection and localization. First, the patch features of normal images are\nextracted by a deep network pre-trained on nature images. Then, the prototypes\nof the normal patch features are learned by non-parametric clustering. Finally,\nwe construct an image anomaly localization network (ProtoAD) by appending the\nfeature extraction network with $L2$ feature normalization, a $1\\times1$\nconvolutional layer, a channel max-pooling, and a subtraction operation. We use\nthe prototypes as the kernels of the $1\\times1$ convolutional layer; therefore,\nour neural network does not need a training phase and can conduct anomaly\ndetection and localization in an end-to-end manner. Extensive experiments on\ntwo challenging industrial anomaly detection datasets, MVTec AD and BTAD,\ndemonstrate that ProtoAD achieves competitive performance compared to the\nstate-of-the-art methods with a higher inference speed. The source code is\navailable at: https://github.com/98chao/ProtoAD.\n", "date": "2023-10-04", "categories": []}
{"id": "2405.15832", "title": "DETECTA 2.0: Research into non-intrusive methodologies supported by\n  Industry 4.0 enabling technologies for predictive and cyber-secure\n  maintenance in SMEs", "abstract": "  The integration of predictive maintenance and cybersecurity represents a\ntransformative advancement for small and medium-sized enterprises (SMEs)\noperating within the Industry 4.0 paradigm. Despite their economic importance,\nSMEs often face significant challenges in adopting advanced technologies due to\nresource constraints and knowledge gaps. The DETECTA 2.0 project addresses\nthese hurdles by developing an innovative system that harmonizes real-time\nanomaly detection, sophisticated analytics, and predictive forecasting\ncapabilities.\n  The system employs a semi-supervised methodology, combining unsupervised\nanomaly detection with supervised learning techniques. This approach enables\nmore agile and cost-effective development of AI detection systems,\nsignificantly reducing the time required for manual case review.\n  At the core lies a Digital Twin interface, providing intuitive real-time\nvisualizations of machine states and detected anomalies. Leveraging\ncutting-edge AI engines, the system intelligently categorizes anomalies based\non observed patterns, differentiating between technical errors and potential\ncybersecurity incidents. This discernment is fortified by detailed analytics,\nincluding certainty levels that enhance alert reliability and minimize false\npositives.\n  The predictive engine uses advanced time series algorithms like N-HiTS to\nforecast future machine utilization trends. This proactive approach optimizes\nmaintenance planning, enhances cybersecurity measures, and minimizes unplanned\ndowntimes despite variable production processes.\n  With its modular architecture enabling seamless integration across industrial\nsetups and low implementation costs, DETECTA 2.0 presents an attractive\nsolution for SMEs to strengthen their predictive maintenance and cybersecurity\nstrategies.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.16580", "title": "A Study on Unsupervised Anomaly Detection and Defect Localization using\n  Generative Model in Ultrasonic Non-Destructive Testing", "abstract": "  In recent years, the deterioration of artificial materials used in structures\nhas become a serious social issue, increasing the importance of inspections.\nNon-destructive testing is gaining increased demand due to its capability to\ninspect for defects and deterioration in structures while preserving their\nfunctionality. Among these, Laser Ultrasonic Visualization Testing (LUVT)\nstands out because it allows the visualization of ultrasonic propagation. This\nmakes it visually straightforward to detect defects, thereby enhancing\ninspection efficiency. With the increasing number of the deterioration\nstructures, challenges such as a shortage of inspectors and increased workload\nin non-destructive testing have become more apparent. Efforts to address these\nchallenges include exploring automated inspection using machine learning.\nHowever, the lack of anomalous data with defects poses a barrier to improving\nthe accuracy of automated inspection through machine learning. Therefore, in\nthis study, we propose a method for automated LUVT inspection using an anomaly\ndetection approach with a diffusion model that can be trained solely on\nnegative examples (defect-free data). We experimentally confirmed that our\nproposed method improves defect detection and localization compared to general\nobject detection algorithms used previously.\n", "date": "2024-05-26", "categories": []}
{"id": "2405.16886", "title": "Hawk: Learning to Understand Open-World Video Anomalies", "abstract": "  Video Anomaly Detection (VAD) systems can autonomously monitor and identify\ndisturbances, reducing the need for manual labor and associated costs. However,\ncurrent VAD systems are often limited by their superficial semantic\nunderstanding of scenes and minimal user interaction. Additionally, the\nprevalent data scarcity in existing datasets restricts their applicability in\nopen-world scenarios. In this paper, we introduce Hawk, a novel framework that\nleverages interactive large Visual Language Models (VLM) to interpret video\nanomalies precisely. Recognizing the difference in motion information between\nabnormal and normal videos, Hawk explicitly integrates motion modality to\nenhance anomaly identification. To reinforce motion attention, we construct an\nauxiliary consistency loss within the motion and video space, guiding the video\nbranch to focus on the motion modality. Moreover, to improve the interpretation\nof motion-to-language, we establish a clear supervisory relationship between\nmotion and its linguistic representation. Furthermore, we have annotated over\n8,000 anomaly videos with language descriptions, enabling effective training\nacross diverse open-world scenarios, and also created 8,000 question-answering\npairs for users' open-world questions. The final results demonstrate that Hawk\nachieves SOTA performance, surpassing existing baselines in both video\ndescription generation and question-answering. Our codes/dataset/demo will be\nreleased at https://github.com/jqtangust/hawk.\n", "date": "2024-05-27", "categories": []}
{"id": "2405.15370", "title": "Large Language Models can Deliver Accurate and Interpretable Time Series\n  Anomaly Detection", "abstract": "  Time series anomaly detection (TSAD) plays a crucial role in various\nindustries by identifying atypical patterns that deviate from standard trends,\nthereby maintaining system integrity and enabling prompt response measures.\nTraditional TSAD models, which often rely on deep learning, require extensive\ntraining data and operate as black boxes, lacking interpretability for detected\nanomalies. To address these challenges, we propose LLMAD, a novel TSAD method\nthat employs Large Language Models (LLMs) to deliver accurate and interpretable\nTSAD results. LLMAD innovatively applies LLMs for in-context anomaly detection\nby retrieving both positive and negative similar time series segments,\nsignificantly enhancing LLMs' effectiveness. Furthermore, LLMAD employs the\nAnomaly Detection Chain-of-Thought (AnoCoT) approach to mimic expert logic for\nits decision-making process. This method further enhances its performance and\nenables LLMAD to provide explanations for their detections through versatile\nperspectives, which are particularly important for user decision-making.\nExperiments on three datasets indicate that our LLMAD achieves detection\nperformance comparable to state-of-the-art deep learning methods while offering\nremarkable interpretability for detections. To the best of our knowledge, this\nis the first work that directly employs LLMs for TSAD.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.13670", "title": "GNN-based Anomaly Detection for Encoded Network Traffic", "abstract": "  The early research report explores the possibility of using Graph Neural\nNetworks (GNNs) for anomaly detection in internet traffic data enriched with\ninformation. While recent studies have made significant progress in using GNNs\nfor anomaly detection in finance, multivariate time-series, and biochemistry\ndomains, there is limited research in the context of network flow data. In this\nreport, we explore the idea that leverages information-enriched features\nextracted from network flow packet data to improve the performance of GNN in\nanomaly detection. The idea is to utilize feature encoding (binary, numerical,\nand string) to capture the relationships between the network components,\nallowing the GNN to learn latent relationships and better identify anomalies.\n", "date": "2024-05-22", "categories": []}
{"id": "2405.13699", "title": "Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected\n  Anomaly Posterior", "abstract": "  Anomaly detection is the task of identifying examples that do not behave as\nexpected. Because anomalies are rare and unexpected events, collecting real\nanomalous examples is often challenging in several applications. In addition,\nlearning an anomaly detector with limited (or no) anomalies often yields poor\nprediction performance. One option is to employ auxiliary synthetic anomalies\nto improve the model training. However, synthetic anomalies may be of poor\nquality: anomalies that are unrealistic or indistinguishable from normal\nsamples may deteriorate the detector's performance. Unfortunately, no existing\nmethods quantify the quality of auxiliary anomalies. We fill in this gap and\npropose the expected anomaly posterior (EAP), an uncertainty-based score\nfunction that measures the quality of auxiliary anomalies by quantifying the\ntotal uncertainty of an anomaly detector. Experimentally on 40 benchmark\ndatasets of images and tabular data, we show that EAP outperforms 12 adapted\ndata quality estimators in the majority of cases.\n", "date": "2024-05-22", "categories": []}
{"id": "2405.14754", "title": "Applied Machine Learning to Anomaly Detection in Enterprise Purchase\n  Processes", "abstract": "  In a context of a continuous digitalisation of processes, organisations must\ndeal with the challenge of detecting anomalies that can reveal suspicious\nactivities upon an increasing volume of data. To pursue this goal, audit\nengagements are carried out regularly, and internal auditors and purchase\nspecialists are constantly looking for new methods to automate these processes.\nThis work proposes a methodology to prioritise the investigation of the cases\ndetected in two large purchase datasets from real data. The goal is to\ncontribute to the effectiveness of the companies' control efforts and to\nincrease the performance of carrying out such tasks. A comprehensive\nExploratory Data Analysis is carried out before using unsupervised Machine\nLearning techniques addressed to detect anomalies. A univariate approach has\nbeen applied through the z-Score index and the DBSCAN algorithm, while a\nmultivariate analysis is implemented with the k-Means and Isolation Forest\nalgorithms, and the Silhouette index, resulting in each method having a\ntransaction candidates' proposal to be reviewed. An ensemble prioritisation of\nthe candidates is provided jointly with a proposal of explicability methods\n(LIME, Shapley, SHAP) to help the company specialists in their understanding.\n", "date": "2024-05-23", "categories": []}
{"id": "2405.12391", "title": "Automated Anomaly Detection on European XFEL Klystrons", "abstract": "  High-power multi-beam klystrons represent a key component to amplify RF to\ngenerate the accelerating field of the superconducting radio frequency (SRF)\ncavities at European XFEL. Exchanging these high-power components takes time\nand effort, thus it is necessary to minimize maintenance and downtime and at\nthe same time maximize the device's operation. In an attempt to explore the\nbehavior of klystrons using machine learning, we completed a series of\nexperiments on our klystrons to determine various operational modes and conduct\nfeature extraction and dimensionality reduction to extract the most valuable\ninformation about a normal operation. To analyze recorded data we used\nstate-of-the-art data-driven learning techniques and recognized the most\npromising components that might help us better understand klystron operational\nstates and identify early on possible faults or anomalies.\n", "date": "2024-05-20", "categories": []}
{"id": "2405.12708", "title": "Multimodal video analysis for crowd anomaly detection using open access\n  tourism cameras", "abstract": "  In this article, we propose the detection of crowd anomalies through the\nextraction of information in the form of time series from video format using a\nmultimodal approach. Through pattern recognition algorithms and segmentation,\ninformative measures of the number of people and image occupancy are extracted\nat regular intervals, which are then analyzed to obtain trends and anomalous\nbehaviors. Specifically, through temporal decomposition and residual analysis,\nintervals or specific situations of unusual behaviors are identified, which can\nbe used in decision-making and improvement of actions in sectors related to\nhuman movement such as tourism or security.\n  The application of this methodology on the webcam of Turisme Comunitat\nValenciana in the town of Morella (Comunitat Valenciana, Spain) has provided\nexcellent results. It is shown to correctly detect specific anomalous\nsituations and unusual overall increases during the previous weekend and during\nthe festivities in October 2023. These results have been obtained while\npreserving the confidentiality of individuals at all times by using measures\nthat maximize anonymity, without trajectory recording or person recognition.\n", "date": "2024-05-21", "categories": []}
{"id": "2405.12872", "title": "Spatial-aware Attention Generative Adversarial Network for\n  Semi-supervised Anomaly Detection in Medical Image", "abstract": "  Medical anomaly detection is a critical research area aimed at recognizing\nabnormal images to aid in diagnosis.Most existing methods adopt synthetic\nanomalies and image restoration on normal samples to detect anomaly. The\nunlabeled data consisting of both normal and abnormal data is not well\nexplored. We introduce a novel Spatial-aware Attention Generative Adversarial\nNetwork (SAGAN) for one-class semi-supervised generation of health images.Our\ncore insight is the utilization of position encoding and attention to\naccurately focus on restoring abnormal regions and preserving normal regions.\nTo fully utilize the unlabelled data, SAGAN relaxes the cyclic consistency\nrequirement of the existing unpaired image-to-image conversion methods, and\ngenerates high-quality health images corresponding to unlabeled data, guided by\nthe reconstruction of normal images and restoration of pseudo-anomaly\nimages.Subsequently, the discrepancy between the generated healthy image and\nthe original image is utilized as an anomaly score.Extensive experiments on\nthree medical datasets demonstrate that the proposed SAGAN outperforms the\nstate-of-the-art methods.\n", "date": "2024-05-21", "categories": []}
{"id": "2405.11315", "title": "MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection", "abstract": "  In the field of medical decision-making, precise anomaly detection in medical\nimaging plays a pivotal role in aiding clinicians. However, previous work is\nreliant on large-scale datasets for training anomaly detection models, which\nincreases the development cost. This paper first focuses on the task of medical\nimage anomaly detection in the few-shot setting, which is critically\nsignificant for the medical field where data collection and annotation are both\nvery expensive. We propose an innovative approach, MediCLIP, which adapts the\nCLIP model to few-shot medical image anomaly detection through self-supervised\nfine-tuning. Although CLIP, as a vision-language model, demonstrates\noutstanding zero-/fewshot performance on various downstream tasks, it still\nfalls short in the anomaly detection of medical images. To address this, we\ndesign a series of medical image anomaly synthesis tasks to simulate common\ndisease patterns in medical imaging, transferring the powerful generalization\ncapabilities of CLIP to the task of medical image anomaly detection. When only\nfew-shot normal medical images are provided, MediCLIP achieves state-of-the-art\nperformance in anomaly detection and location compared to other methods.\nExtensive experiments on three distinct medical anomaly detection tasks have\ndemonstrated the superiority of our approach. The code is available at\nhttps://github.com/cnulab/MediCLIP.\n", "date": "2024-05-18", "categories": []}
{"id": "2405.12096", "title": "PATE: Proximity-Aware Time series anomaly Evaluation", "abstract": "  Evaluating anomaly detection algorithms in time series data is critical as\ninaccuracies can lead to flawed decision-making in various domains where\nreal-time analytics and data-driven strategies are essential. Traditional\nperformance metrics assume iid data and fail to capture the complex temporal\ndynamics and specific characteristics of time series anomalies, such as early\nand delayed detections. We introduce Proximity-Aware Time series anomaly\nEvaluation (PATE), a novel evaluation metric that incorporates the temporal\nrelationship between prediction and anomaly intervals. PATE uses\nproximity-based weighting considering buffer zones around anomaly intervals,\nenabling a more detailed and informed assessment of a detection. Using these\nweights, PATE computes a weighted version of the area under the Precision and\nRecall curve. Our experiments with synthetic and real-world datasets show the\nsuperiority of PATE in providing more sensible and accurate evaluations than\nother evaluation metrics. We also tested several state-of-the-art anomaly\ndetectors across various benchmark datasets using the PATE evaluation scheme.\nThe results show that a common metric like Point-Adjusted F1 Score fails to\ncharacterize the detection performances well, and that PATE is able to provide\na more fair model comparison. By introducing PATE, we redefine the\nunderstanding of model efficacy that steers future studies toward developing\nmore effective and accurate detection models.\n", "date": "2024-05-20", "categories": []}
{"id": "2308.12577", "title": "REB: Reducing Biases in Representation for Industrial Anomaly Detection", "abstract": "  Existing representation-based methods usually conduct industrial anomaly\ndetection in two stages: obtain feature representations with a pre-trained\nmodel and perform distance measures for anomaly detection. Among them,\nK-nearest neighbor (KNN) retrieval-based anomaly detection methods show\npromising results. However, the features are not fully exploited as these\nmethods ignore domain bias of pre-trained models and the difference of local\ndensity in feature space, which limits the detection performance. In this\npaper, we propose Reducing Biases (REB) in representation by considering the\ndomain bias and building a self-supervised learning task for better domain\nadaption with a defect generation strategy (DefectMaker) that ensures a strong\ndiversity in the synthetic defects. Additionally, we propose a local-density\nKNN (LDKNN) to reduce the local density bias in the feature space and obtain\neffective anomaly detection. The proposed REB method achieves a promising\nresult of 99.5\\% Im.AUROC on the widely used MVTec AD, with smaller backbone\nnetworks such as Vgg11 and Resnet18. The method also achieves an impressive\n88.8\\% Im.AUROC on the MVTec LOCO AD dataset and a remarkable 96.0\\% on the\nBTAD dataset, outperforming other representation-based approaches. These\nresults indicate the effectiveness and efficiency of REB for practical\nindustrial applications. Code:https://github.com/ShuaiLYU/REB.\n", "date": "2023-08-24", "categories": []}
{"id": "2403.12864", "title": "A Comparison of Deep Learning Architectures for Spacecraft Anomaly\n  Detection", "abstract": "  Spacecraft operations are highly critical, demanding impeccable reliability\nand safety. Ensuring the optimal performance of a spacecraft requires the early\ndetection and mitigation of anomalies, which could otherwise result in unit or\nmission failures. With the advent of deep learning, a surge of interest has\nbeen seen in leveraging these sophisticated algorithms for anomaly detection in\nspace operations. This study aims to compare the efficacy of various deep\nlearning architectures in detecting anomalies in spacecraft data. The deep\nlearning models under investigation include Convolutional Neural Networks\n(CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM)\nnetworks, and Transformer-based architectures. Each of these models was trained\nand validated using a comprehensive dataset sourced from multiple spacecraft\nmissions, encompassing diverse operational scenarios and anomaly types. Initial\nresults indicate that while CNNs excel in identifying spatial patterns and may\nbe effective for some classes of spacecraft data, LSTMs and RNNs show a marked\nproficiency in capturing temporal anomalies seen in time-series spacecraft\ntelemetry. The Transformer-based architectures, given their ability to focus on\nboth local and global contexts, have showcased promising results, especially in\nscenarios where anomalies are subtle and span over longer durations.\nAdditionally, considerations such as computational efficiency, ease of\ndeployment, and real-time processing capabilities were evaluated. While CNNs\nand LSTMs demonstrated a balance between accuracy and computational demands,\nTransformer architectures, though highly accurate, require significant\ncomputational resources. In conclusion, the choice of deep learning\narchitecture for spacecraft anomaly detection is highly contingent on the\nnature of the data, the type of anomalies, and operational constraints.\n", "date": "2024-03-19", "categories": []}
{"id": "2404.01933", "title": "PREGO: online mistake detection in PRocedural EGOcentric videos", "abstract": "  Promptly identifying procedural errors from egocentric videos in an online\nsetting is highly challenging and valuable for detecting mistakes as soon as\nthey happen. This capability has a wide range of applications across various\nfields, such as manufacturing and healthcare. The nature of procedural mistakes\nis open-set since novel types of failures might occur, which calls for\none-class classifiers trained on correctly executed procedures. However, no\ntechnique can currently detect open-set procedural mistakes online. We propose\nPREGO, the first online one-class classification model for mistake detection in\nPRocedural EGOcentric videos. PREGO is based on an online action recognition\ncomponent to model the current action, and a symbolic reasoning module to\npredict the next actions. Mistake detection is performed by comparing the\nrecognized current action with the expected future one. We evaluate PREGO on\ntwo procedural egocentric video datasets, Assembly101 and Epic-tent, which we\nadapt for online benchmarking of procedural mistake detection to establish\nsuitable benchmarks, thus defining the Assembly101-O and Epic-tent-O datasets,\nrespectively.\n", "date": "2024-04-02", "categories": []}
{"id": "2405.05886", "title": "Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies", "abstract": "  Due to the rare occurrence of anomalous events, a typical approach to anomaly\ndetection is to train an autoencoder (AE) with normal data only so that it\nlearns the patterns or representations of the normal training data. At test\ntime, the trained AE is expected to well reconstruct normal but to poorly\nreconstruct anomalous data. However, contrary to the expectation, anomalous\ndata is often well reconstructed as well. In order to further separate the\nreconstruction quality between normal and anomalous data, we propose creating\npseudo anomalies from learned adaptive noise by exploiting the aforementioned\nweakness of AE, i.e., reconstructing anomalies too well. The generated noise is\nadded to the normal data to create pseudo anomalies. Extensive experiments on\nPed2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the\neffectiveness and generic applicability of our approach in improving the\ndiscriminative capability of AEs for anomaly detection.\n", "date": "2024-05-09", "categories": []}
{"id": "2405.06925", "title": "Semi-supervised Anomaly Detection via Adaptive Reinforcement\n  Learning-Enabled Method with Causal Inference for Sensor Signals", "abstract": "  Semi-supervised anomaly detection for sensor signals is critical in ensuring\nsystem reliability in smart manufacturing. However, existing methods rely\nheavily on data correlation, neglecting causality and leading to potential\nmisinterpretations due to confounding factors. Moreover, while current\nreinforcement learning-based methods can effectively identify known and unknown\nanomalies with limited labeled samples, these methods still face several\nchallenges, such as under-utilization of priori knowledge, lack of model\nflexibility, and deficient reward feedback during environmental interactions.\nTo address the above problems, this paper innovatively constructs a\ncounterfactual causal reinforcement learning model, termed Triple-Assisted\nCausal Reinforcement Learning Anomaly Detector (Tri-CRLAD). The model leverages\ncausal inference to extract the intrinsic causal feature in data, enhancing the\nagent's utilization of prior knowledge and improving its generalization\ncapability. In addition, Tri-CRLAD features a triple decision support\nmechanism, including a sampling strategy based on historical similarity, an\nadaptive threshold smoothing adjustment strategy, and an adaptive decision\nreward mechanism. These mechanisms further enhance the flexibility and\ngeneralization ability of the model, enabling it to effectively respond to\nvarious complex and dynamically changing environments. Experimental results\nacross seven diverse sensor signal datasets demonstrate that Tri-CRLAD\noutperforms nine state-of-the-art baseline methods. Notably, Tri-CRLAD achieves\nup to a 23\\% improvement in anomaly detection stability with minimal known\nanomaly samples, highlighting its potential in semi-supervised anomaly\ndetection scenarios. Our code is available at\nhttps://github.com/Aoudsung/Tri-CRLAD.\n", "date": "2024-05-11", "categories": []}
{"id": "2405.09561", "title": "GAD: A Real-time Gait Anomaly Detection System with Online Adaptive\n  Learning", "abstract": "  Gait anomaly detection is a task that involves detecting deviations from a\nperson's normal gait pattern. These deviations can indicate health issues and\nmedical conditions in the healthcare domain, or fraudulent impersonation and\nunauthorized identity access in the security domain. A number of gait anomaly\ndetection approaches have been introduced, but many of them require offline\ndata preprocessing, offline model learning, setting parameters, and so on,\nwhich might restrict their effectiveness and applicability in real-world\nscenarios. To address these issues, this paper introduces GAD, a real-time gait\nanomaly detection system. GAD focuses on detecting anomalies within an\nindividual's three-dimensional accelerometer readings based on dimensionality\nreduction and Long Short-Term Memory (LSTM). Upon being launched, GAD begins\ncollecting a gait segment from the user and training an anomaly detector to\nlearn the user's walking pattern on the fly. If the subsequent model\nverification is successful, which involves validating the trained detector\nusing the user's subsequent steps, the detector is employed to identify\nabnormalities in the user's subsequent gait readings at the user's request. The\nanomaly detector will be retained online to adapt to minor pattern changes and\nwill undergo retraining as long as it cannot provide adequate prediction. We\nexplored two methods for capturing users' gait segments: a personalized method\ntailored to each individual's step length, and a uniform method utilizing a\nfixed step length. Experimental results using an open-source gait dataset show\nthat GAD achieves a higher detection accuracy ratio when combined with the\npersonalized method.\n", "date": "2024-05-04", "categories": []}
{"id": "2405.09903", "title": "Federated Learning for Misbehaviour Detection with Variational\n  Autoencoders and Gaussian Mixture Models", "abstract": "  Federated Learning (FL) has become an attractive approach to collaboratively\ntrain Machine Learning (ML) models while data sources' privacy is still\npreserved. However, most of existing FL approaches are based on supervised\ntechniques, which could require resource-intensive activities and human\nintervention to obtain labelled datasets. Furthermore, in the scope of\ncyberattack detection, such techniques are not able to identify previously\nunknown threats. In this direction, this work proposes a novel unsupervised FL\napproach for the identification of potential misbehavior in vehicular\nenvironments. We leverage the computing capabilities of public cloud services\nfor model aggregation purposes, and also as a central repository of misbehavior\nevents, enabling cross-vehicle learning and collective defense strategies. Our\nsolution integrates the use of Gaussian Mixture Models (GMM) and Variational\nAutoencoders (VAE) on the VeReMi dataset in a federated environment, where each\nvehicle is intended to train only with its own data. Furthermore, we use\nRestricted Boltzmann Machines (RBM) for pre-training purposes, and Fedplus as\naggregation function to enhance model's convergence. Our approach provides\nbetter performance (more than 80 percent) compared to recent proposals, which\nare usually based on supervised techniques and artificial divisions of the\nVeReMi dataset.\n", "date": "2024-05-16", "categories": []}
{"id": "2405.09148", "title": "A Hierarchically Feature Reconstructed Autoencoder for Unsupervised\n  Anomaly Detection", "abstract": "  Anomaly detection and localization without any manual annotations and prior\nknowledge is a challenging task under the setting of unsupervised learning. The\nexisting works achieve excellent performance in the anomaly detection, but with\ncomplex networks or cumbersome pipelines. To address this issue, this paper\nexplores a simple but effective architecture in the anomaly detection. It\nconsists of a well pre-trained encoder to extract hierarchical feature\nrepresentations and a decoder to reconstruct these intermediate features from\nthe encoder. In particular, it does not require any data augmentations and\nanomalous images for training. The anomalies can be detected when the decoder\nfails to reconstruct features well, and then errors of hierarchical feature\nreconstruction are aggregated into an anomaly map to achieve anomaly\nlocalization. The difference comparison between those features of encoder and\ndecode lead to more accurate and robust localization results than the\ncomparison in single feature or pixel-by-pixel comparison in the conventional\nworks. Experiment results show that the proposed method outperforms the\nstate-of-the-art methods on MNIST, Fashion-MNIST, CIFAR-10, and MVTec Anomaly\nDetection datasets on both anomaly detection and localization.\n", "date": "2024-05-15", "categories": []}
{"id": "2206.11723", "title": "Self-Supervised Training with Autoencoders for Visual Anomaly Detection", "abstract": "  We focus on a specific use case in anomaly detection where the distribution\nof normal samples is supported by a lower-dimensional manifold. Here,\nregularized autoencoders provide a popular approach by learning the identity\nmapping on the set of normal examples, while trying to prevent good\nreconstruction on points outside of the manifold. Typically, this goal is\nimplemented by controlling the capacity of the model, either directly by\nreducing the size of the bottleneck layer or implicitly by imposing some\nsparsity (or contraction) constraints on parts of the corresponding network.\nHowever, neither of these techniques does explicitly penalize the\nreconstruction of anomalous signals often resulting in poor detection. We\ntackle this problem by adapting a self-supervised learning regime that exploits\ndiscriminative information during training but focuses on the submanifold of\nnormal examples. Informally, our training objective regularizes the model to\nproduce locally consistent reconstructions, while replacing irregularities by\nacting as a filter that removes anomalous patterns. To support this intuition,\nwe perform a rigorous formal analysis of the proposed method and provide a\nnumber of interesting insights. In particular, we show that the resulting model\nresembles a non-linear orthogonal projection of partially corrupted images onto\nthe submanifold of uncorrupted samples. On the other hand, we identify the\northogonal projection as an optimal solution for a number of regularized\nautoencoders including the contractive and denoising variants. We support our\ntheoretical analysis by empirical evaluation of the resulting detection and\nlocalization performance of the proposed method. In particular, we achieve a\nnew state-of-the-art result on the MVTec AD dataset -- a challenging benchmark\nfor visual anomaly detection in the manufacturing domain.\n", "date": "2022-06-23", "categories": []}
{"id": "2403.10903", "title": "DTOR: Decision Tree Outlier Regressor to explain anomalies", "abstract": "  Explaining outliers occurrence and mechanism of their occurrence can be\nextremely important in a variety of domains. Malfunctions, frauds, threats, in\naddition to being correctly identified, oftentimes need a valid explanation in\norder to effectively perform actionable counteracts. The ever more widespread\nuse of sophisticated Machine Learning approach to identify anomalies make such\nexplanations more challenging. We present the Decision Tree Outlier Regressor\n(DTOR), a technique for producing rule-based explanations for individual data\npoints by estimating anomaly scores generated by an anomaly detection model.\nThis is accomplished by first applying a Decision Tree Regressor, which\ncomputes the estimation score, and then extracting the relative path associated\nwith the data point score. Our results demonstrate the robustness of DTOR even\nin datasets with a large number of features. Additionally, in contrast to other\nrule-based approaches, the generated rules are consistently satisfied by the\npoints to be explained. Furthermore, our evaluation metrics indicate comparable\nperformance to Anchors in outlier explanation tasks, with reduced execution\ntime.\n", "date": "2024-03-16", "categories": []}
{"id": "2405.04953", "title": "Supervised Anomaly Detection for Complex Industrial Images", "abstract": "  Automating visual inspection in industrial production lines is essential for\nincreasing product quality across various industries. Anomaly detection (AD)\nmethods serve as robust tools for this purpose. However, existing public\ndatasets primarily consist of images without anomalies, limiting the practical\napplication of AD methods in production settings. To address this challenge, we\npresent (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial\ndataset comprising 5000 images, including 2000 instances of challenging real\ndefects across more than 20 subclasses. Acknowledging that traditional AD\nmethods struggle with this dataset, we introduce (2) Segmentation-based Anomaly\nDetector (SegAD). First, SegAD leverages anomaly maps as well as segmentation\nmaps to compute local statistics. Next, SegAD uses these statistics and an\noptional supervised classifier score as input features for a Boosted Random\nForest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves\nstate-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset\n(+0.4% AUROC). The code and the models are publicly available.\n", "date": "2024-05-08", "categories": []}
{"id": "2405.06875", "title": "LogicAL: Towards logical anomaly synthesis for unsupervised anomaly\n  localization", "abstract": "  Anomaly localization is a practical technology for improving industrial\nproduction line efficiency. Due to anomalies are manifold and hard to be\ncollected, existing unsupervised researches are usually equipped with anomaly\nsynthesis methods. However, most of them are biased towards structural defects\nsynthesis while ignoring the underlying logical constraints. To fill the gap\nand boost anomaly localization performance, we propose an edge manipulation\nbased anomaly synthesis framework, named LogicAL, that produces photo-realistic\nboth logical and structural anomalies. We introduce a logical anomaly\ngeneration strategy that is adept at breaking logical constraints and a\nstructural anomaly generation strategy that complements to the structural\ndefects synthesis. We further improve the anomaly localization performance by\nintroducing edge reconstruction into the network structure. Extensive\nexperiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets\nverify the advantage of proposed LogicAL on both logical and structural anomaly\nlocalization.\n", "date": "2024-05-11", "categories": []}
{"id": "2405.06980", "title": "Fractals as Pre-training Datasets for Anomaly Detection and Localization", "abstract": "  Anomaly detection is crucial in large-scale industrial manufacturing as it\nhelps detect and localise defective parts. Pre-training feature extractors on\nlarge-scale datasets is a popular approach for this task. Stringent data\nsecurity and privacy regulations and high costs and acquisition time hinder the\navailability and creation of such large datasets. While recent work in anomaly\ndetection primarily focuses on the development of new methods built on such\nextractors, the importance of the data used for pre-training has not been\nstudied. Therefore, we evaluated the performance of eight state-of-the-art\nmethods pre-trained using dynamically generated fractal images on the famous\nbenchmark datasets MVTec and VisA. In contrast to existing literature, which\npredominantly examines the transfer-learning capabilities of fractals, in this\nstudy, we compare models pre-trained with fractal images against those\npre-trained with ImageNet, without subsequent fine-tuning. Although\npre-training with ImageNet remains a clear winner, the results of fractals are\npromising considering that the anomaly detection task required features capable\nof discerning even minor visual variations. This opens up the possibility for a\nnew research direction where feature extractors could be trained on\nsynthetically generated abstract datasets reconciling the ever-increasing\ndemand for data in machine learning while circumventing privacy and security\nconcerns.\n", "date": "2024-05-11", "categories": []}
{"id": "2405.07509", "title": "RESTAD: REconstruction and Similarity based Transformer for time series\n  Anomaly Detection", "abstract": "  Anomaly detection in time series data is crucial across various domains. The\nscarcity of labeled data for such tasks has increased the attention towards\nunsupervised learning methods. These approaches, often relying solely on\nreconstruction error, typically fail to detect subtle anomalies in complex\ndatasets. To address this, we introduce RESTAD, an adaptation of the\nTransformer model by incorporating a layer of Radial Basis Function (RBF)\nneurons within its architecture. This layer fits a non-parametric density in\nthe latent representation, such that a high RBF output indicates similarity\nwith predominantly normal training data. RESTAD integrates the RBF similarity\nscores with the reconstruction errors to increase sensitivity to anomalies. Our\nempirical evaluations demonstrate that RESTAD outperforms various established\nbaselines across multiple benchmark datasets.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.07749", "title": "DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in\n  Dynamically-Configured Systems", "abstract": "  Anomaly detection in distributed systems such as High-Performance Computing\n(HPC) clusters is vital for early fault detection, performance optimisation,\nsecurity monitoring, reliability in general but also operational insights. Deep\nNeural Networks have seen successful use in detecting long-term anomalies in\nmultidimensional data, originating for instance from industrial or medical\nsystems, or weather prediction. A downside of such methods is that they require\na static input size, or lose data through cropping, sampling, or other\ndimensionality reduction methods, making deployment on systems with variability\non monitored data channels, such as computing clusters difficult. To address\nthese problems, we present DeepHYDRA (Deep Hybrid DBSCAN/Reduction-Based\nAnomaly Detection) which combines DBSCAN and learning-based anomaly detection.\nDBSCAN clustering is used to find point anomalies in time-series data,\nmitigating the risk of missing outliers through loss of information when\nreducing input data to a fixed number of channels. A deep learning-based\ntime-series anomaly detection method is then applied to the reduced data in\norder to identify long-term outliers. This hybrid approach reduces the chances\nof missing anomalies that might be made indistinguishable from normal data by\nthe reduction process, and likewise enables the algorithm to be scalable and\ntolerate partial system failures while retaining its detection capabilities.\nUsing a subset of the well-known SMD dataset family, a modified variant of the\nEclipse dataset, as well as an in-house dataset with a large variability in\nactive data channels, made publicly available with this work, we furthermore\nanalyse computational intensity, memory footprint, and activation counts.\nDeepHYDRA is shown to reliably detect different types of anomalies in both\nlarge and complex datasets.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.07969", "title": "Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly\n  Segmentation", "abstract": "  Zero-shot anomaly segmentation using pre-trained foundation models is a\npromising approach that enables effective algorithms without expensive,\ndomain-specific training or fine-tuning. Ensuring that these methods work\nacross various environmental conditions and are robust to distribution shifts\nis an open problem. We investigate the performance of WinCLIP [14] zero-shot\nanomaly segmentation algorithm by perturbing test data using three semantic\ntransformations: bounded angular rotations, bounded saturation shifts, and hue\nshifts. We empirically measure a lower performance bound by aggregating across\nper-sample worst-case perturbations and find that average performance drops by\nup to 20% in area under the ROC curve and 40% in area under the per-region\noverlap curve. We find that performance is consistently lowered on three CLIP\nbackbones, regardless of model architecture or learning objective,\ndemonstrating a need for careful performance evaluation.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.06172", "title": "Anomaly Detection in Graph Structured Data: A Survey", "abstract": "  Real-world graphs are complex to process for performing effective analysis,\nsuch as anomaly detection. However, recently, there have been several research\nefforts addressing the issues surrounding graph-based anomaly detection. In\nthis paper, we discuss a comprehensive overview of anomaly detection techniques\non graph data. We also discuss the various application domains which use those\nanomaly detection techniques. We present a new taxonomy that categorizes the\ndifferent state-of-the-art anomaly detection methods based on assumptions and\ntechniques. Within each category, we discuss the fundamental research ideas\nthat have been done to improve anomaly detection. We further discuss the\nadvantages and disadvantages of current anomaly detection techniques. Finally,\nwe present potential future research directions in anomaly detection on\ngraph-structured data.\n", "date": "2024-05-10", "categories": []}
{"id": "2405.06467", "title": "Attend, Distill, Detect: Attention-aware Entropy Distillation for\n  Anomaly Detection", "abstract": "  Unsupervised anomaly detection encompasses diverse applications in industrial\nsettings where a high-throughput and precision is imperative. Early works were\ncentered around one-class-one-model paradigm, which poses significant\nchallenges in large-scale production environments. Knowledge-distillation based\nmulti-class anomaly detection promises a low latency with a reasonably good\nperformance but with a significant drop as compared to one-class version. We\npropose a DCAM (Distributed Convolutional Attention Module) which improves the\ndistillation process between teacher and student networks when there is a high\nvariance among multiple classes or objects. Integrated multi-scale feature\nmatching strategy to utilise a mixture of multi-level knowledge from the\nfeature pyramid of the two networks, intuitively helping in detecting anomalies\nof varying sizes which is also an inherent problem in the multi-class scenario.\nBriefly, our DCAM module consists of Convolutional Attention blocks distributed\nacross the feature maps of the student network, which essentially learns to\nmasks the irrelevant information during student learning alleviating the\n\"cross-class interference\" problem. This process is accompanied by minimizing\nthe relative entropy using KL-Divergence in Spatial dimension and a\nChannel-wise Cosine Similarity between the same feature maps of teacher and\nstudent. The losses enables to achieve scale-invariance and capture non-linear\nrelationships. We also highlight that the DCAM module would only be used during\ntraining and not during inference as we only need the learned feature maps and\nlosses for anomaly scoring and hence, gaining a performance gain of 3.92% than\nthe multi-class baseline with a preserved latency.\n", "date": "2024-05-10", "categories": []}
{"id": "2405.05277", "title": "An Attention-Based Deep Generative Model for Anomaly Detection in\n  Industrial Control Systems", "abstract": "  Anomaly detection is critical for the secure and reliable operation of\nindustrial control systems. As our reliance on such complex cyber-physical\nsystems grows, it becomes paramount to have automated methods for detecting\nanomalies, preventing attacks, and responding intelligently. {This paper\npresents a novel deep generative model to meet this need. The proposed model\nfollows a variational autoencoder architecture with a convolutional encoder and\ndecoder to extract features from both spatial and temporal dimensions.\nAdditionally, we incorporate an attention mechanism that directs focus towards\nspecific regions, enhancing the representation of relevant features and\nimproving anomaly detection accuracy. We also employ a dynamic threshold\napproach leveraging the reconstruction probability and make our source code\npublicly available to promote reproducibility and facilitate further research.\nComprehensive experimental analysis is conducted on data from all six stages of\nthe Secure Water Treatment (SWaT) testbed, and the experimental results\ndemonstrate the superior performance of our approach compared to several\nstate-of-the-art baseline techniques.\n", "date": "2024-05-03", "categories": []}
{"id": "2306.00582", "title": "Anomaly Detection with Variance Stabilized Density Estimation", "abstract": "  We propose a modified density estimation problem that is highly effective for\ndetecting anomalies in tabular data. Our approach assumes that the density\nfunction is relatively stable (with lower variance) around normal samples. We\nhave verified this hypothesis empirically using a wide range of real-world\ndata. Then, we present a variance-stabilized density estimation problem for\nmaximizing the likelihood of the observed samples while minimizing the variance\nof the density around normal samples. To obtain a reliable anomaly detector, we\nintroduce a spectral ensemble of autoregressive models for learning the\nvariance-stabilized distribution. We have conducted an extensive benchmark with\n52 datasets, demonstrating that our method leads to state-of-the-art results\nwhile alleviating the need for data-specific hyperparameter tuning. Finally, we\nhave used an ablation study to demonstrate the importance of each of the\nproposed components, followed by a stability analysis evaluating the robustness\nof our model.\n", "date": "2023-06-01", "categories": []}
{"id": "2405.04782", "title": "Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection", "abstract": "  Image Anomaly Detection has been a challenging task in Computer Vision field.\nThe advent of Vision-Language models, particularly the rise of CLIP-based\nframeworks, has opened new avenues for zero-shot anomaly detection. Recent\nstudies have explored the use of CLIP by aligning images with normal and prompt\ndescriptions. However, the exclusive dependence on textual guidance often falls\nshort, highlighting the critical importance of additional visual references. In\nthis work, we introduce a Dual-Image Enhanced CLIP approach, leveraging a joint\nvision-language scoring system. Our methods process pairs of images, utilizing\neach as a visual reference for the other, thereby enriching the inference\nprocess with visual context. This dual-image strategy markedly enhanced both\nanomaly classification and localization performances. Furthermore, we have\nstrengthened our model with a test-time adaptation module that incorporates\nsynthesized anomalies to refine localization capabilities. Our approach\nsignificantly exploits the potential of vision-language joint anomaly detection\nand demonstrates comparable performance with current SOTA methods across\nvarious datasets.\n", "date": "2024-05-08", "categories": []}
{"id": "2405.05206", "title": "Anomaly Detection in Certificate Transparency Logs", "abstract": "  We propose an anomaly detection technique for X.509 certificates utilizing\nIsolation Forest. This method can be beneficial when compliance testing with\nX.509 linters proves unsatisfactory, and we seek to identify anomalies beyond\nstandards compliance. The technique is validated on a sample of certificates\nfrom Certificate Transparency logs.\n", "date": "2024-05-08", "categories": []}
{"id": "2302.06430", "title": "Deep Orthogonal Hypersphere Compression for Anomaly Detection", "abstract": "  Many well-known and effective anomaly detection methods assume that a\nreasonable decision boundary has a hypersphere shape, which however is\ndifficult to obtain in practice and is not sufficiently compact, especially\nwhen the data are in high-dimensional spaces. In this paper, we first propose a\nnovel deep anomaly detection model that improves the original hypersphere\nlearning through an orthogonal projection layer, which ensures that the\ntraining data distribution is consistent with the hypersphere hypothesis,\nthereby increasing the true positive rate and decreasing the false negative\nrate. Moreover, we propose a bi-hypersphere compression method to obtain a\nhyperspherical shell that yields a more compact decision region than a\nhyperball, which is demonstrated theoretically and numerically. The proposed\nmethods are not confined to common datasets such as image and tabular data, but\nare also extended to a more challenging but promising scenario, graph-level\nanomaly detection, which learns graph representation with maximum mutual\ninformation between the substructure and global structure features while\nexploring orthogonal single- or bi-hypersphere anomaly decision boundaries. The\nnumerical and visualization results on benchmark datasets demonstrate the\nsuperiority of our methods in comparison to many baselines and state-of-the-art\nmethods.\n", "date": "2023-02-13", "categories": []}
{"id": "2401.15647", "title": "UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via\n  Adversarial Image Restoration", "abstract": "  Over the past decade, automated methods have been developed to detect cracks\nmore efficiently, accurately, and objectively, with the ultimate goal of\nreplacing conventional manual visual inspection techniques. Among these\nmethods, semantic segmentation algorithms have demonstrated promising results\nin pixel-wise crack detection tasks. However, training such networks requires a\nlarge amount of human-annotated datasets with pixel-level annotations, which is\na highly labor-intensive and time-consuming process. Moreover, supervised\nlearning-based methods often struggle with poor generalizability in unseen\ndatasets. Therefore, we propose an unsupervised pixel-wise road crack detection\nnetwork, known as UP-CrackNet. Our approach first generates multi-scale square\nmasks and randomly selects them to corrupt undamaged road images by removing\ncertain regions. Subsequently, a generative adversarial network is trained to\nrestore the corrupted regions by leveraging the semantic context learned from\nsurrounding uncorrupted regions. During the testing phase, an error map is\ngenerated by calculating the difference between the input and restored images,\nwhich allows for pixel-wise crack detection. Our comprehensive experimental\nresults demonstrate that UP-CrackNet outperforms other general-purpose\nunsupervised anomaly detection algorithms, and exhibits satisfactory\nperformance and superior generalizability when compared with state-of-the-art\nsupervised crack segmentation algorithms. Our source code is publicly available\nat mias.group/UP-CrackNet.\n", "date": "2024-01-28", "categories": []}
{"id": "2404.03011", "title": "Transfer learning applications for anomaly detection in wind turbines", "abstract": "  Anomaly detection in wind turbines typically involves using normal behaviour\nmodels to detect faults early. However, training autoencoder models for each\nturbine is time-consuming and resource intensive. Thus, transfer learning\nbecomes essential for wind turbines with limited data or applications with\nlimited computational resources. This study examines how cross-turbine transfer\nlearning can be applied to autoencoder-based anomaly detection. Here,\nautoencoders are combined with constant thresholds for the reconstruction error\nto determine if input data contains an anomaly. The models are initially\ntrained on one year's worth of data from one or more source wind turbines. They\nare then fine-tuned using smaller amounts of data from another turbine. Three\nmethods for fine-tuning are investigated: adjusting the entire autoencoder,\nonly the decoder, or only the threshold of the model. The performance of the\ntransfer learning models is compared to baseline models that were trained on\none year's worth of data from the target wind turbine. The results of the tests\nconducted in this study indicate that models trained on data of multiple wind\nturbines do not improve the anomaly detection capability compared to models\ntrained on data of one source wind turbine. In addition, modifying the model's\nthreshold can lead to comparable or even superior performance compared to the\nbaseline, whereas fine-tuning the decoder or autoencoder further enhances the\nmodels' performance.\n", "date": "2024-04-03", "categories": []}
{"id": "2405.02346", "title": "Temporal assessment of malicious behaviors: application to turnout field\n  data monitoring", "abstract": "  Monitored data collected from railway turnouts are vulnerable to\ncyberattacks: attackers may either conceal failures or trigger unnecessary\nmaintenance actions. To address this issue, a cyberattack investigation method\nis proposed based on predictions made from the temporal evolution of the\nturnout behavior. These predictions are then compared to the field acquired\ndata to detect any discrepancy. This method is illustrated on a collection of\nreal-life data.\n", "date": "2024-05-02", "categories": []}
{"id": "2405.02359", "title": "CVTGAD: Simplified Transformer with Cross-View Attention for\n  Unsupervised Graph-level Anomaly Detection", "abstract": "  Unsupervised graph-level anomaly detection (UGAD) has received remarkable\nperformance in various critical disciplines, such as chemistry analysis and\nbioinformatics. Existing UGAD paradigms often adopt data augmentation\ntechniques to construct multiple views, and then employ different strategies to\nobtain representations from different views for jointly conducting UGAD.\nHowever, most previous works only considered the relationship between\nnodes/graphs from a limited receptive field, resulting in some key structure\npatterns and feature information being neglected. In addition, most existing\nmethods consider different views separately in a parallel manner, which is not\nable to explore the inter-relationship across different views directly. Thus, a\nmethod with a larger receptive field that can explore the inter-relationship\nacross different views directly is in need. In this paper, we propose a novel\nSimplified Transformer with Cross-View Attention for Unsupervised Graph-level\nAnomaly Detection, namely, CVTGAD. To increase the receptive field, we\nconstruct a simplified transformer-based module, exploiting the relationship\nbetween nodes/graphs from both intra-graph and inter-graph perspectives.\nFurthermore, we design a cross-view attention mechanism to directly exploit the\nview co-occurrence between different views, bridging the inter-view gap at node\nlevel and graph level. To the best of our knowledge, this is the first work to\napply transformer and cross attention to UGAD, which realizes graph neural\nnetwork and transformer working collaboratively. Extensive experiments on 15\nreal-world datasets of 3 fields demonstrate the superiority of CVTGAD on the\nUGAD task. The code is available at\n\\url{https://github.com/jindongli-Ai/CVTGAD}.\n", "date": "2024-05-03", "categories": []}
{"id": "2405.02574", "title": "A Data Mining-Based Dynamical Anomaly Detection Method for Integrating\n  with an Advance Metering System", "abstract": "  Building operations consume 30% of total power consumption and contribute 26%\nof global power-related emissions. Therefore, monitoring, and early detection\nof anomalies at the meter level are essential for residential and commercial\nbuildings. This work investigates both supervised and unsupervised approaches\nand introduces a dynamic anomaly detection system. The system introduces a\nsupervised Light Gradient Boosting machine and an unsupervised autoencoder with\na dynamic threshold. This system is designed to provide real-time detection of\nanomalies at the meter level. The proposed dynamical system comes with a\ndynamic threshold based on the Mahalanobis distance and moving averages. This\napproach allows the system to adapt to changes in the data distribution over\ntime. The effectiveness of the proposed system is evaluated using real-life\npower consumption data collected from smart metering systems. This empirical\ntesting ensures that the system's performance is validated under real-world\nconditions. By detecting unusual data movements and providing early warnings,\nthe proposed system contributes significantly to visual analytics and decision\nscience. Early detection of anomalies enables timely troubleshooting,\npreventing financial losses and potential disasters such as fire incidents.\n", "date": "2024-05-04", "categories": []}
{"id": "2405.02731", "title": "Systematic Review: Anomaly Detection in Connected and Autonomous\n  Vehicles", "abstract": "  This systematic review focuses on anomaly detection for connected and\nautonomous vehicles. The initial database search identified 2160 articles, of\nwhich 203 were included in this review after rigorous screening and assessment.\nThis study revealed that the most commonly used Artificial Intelligence (AI)\nalgorithms employed in anomaly detection are neural networks like LSTM, CNN,\nand autoencoders, alongside one-class SVM. Most anomaly-based models were\ntrained using real-world operational vehicle data, although anomalies, such as\nattacks and faults, were often injected artificially into the datasets. These\nmodels were evaluated mostly using five key evaluation metrics: recall,\naccuracy, precision, F1-score, and false positive rate. The most frequently\nused selection of evaluation metrics used for anomaly detection models were\naccuracy, precision, recall, and F1-score. This systematic review presents\nseveral recommendations. First, there is a need to incorporate multiple\nevaluation metrics to provide a comprehensive assessment of the anomaly\ndetection models. Second, only a small proportion of the studies have made\ntheir models open source, indicating a need to share models publicly to\nfacilitate collaboration within the research community, and to validate and\ncompare findings effectively. Third, there is a need for benchmarking datasets\nwith predefined anomalies or cyberattacks to test and improve the effectiveness\nof the proposed anomaly-based detection models. Furthermore, there is a need\nfor future research to investigate the deployment of anomaly detection to a\nvehicle to assess its performance on the road. There is a notable lack of\nresearch done on intrusion detection systems using different protocols to CAN,\nsuch as Ethernet and FlexRay.\n", "date": "2024-05-04", "categories": []}
{"id": "2405.03075", "title": "AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection", "abstract": "  Anomaly detection, a critical facet in data analysis, involves identifying\npatterns that deviate from expected behavior. This research addresses the\ncomplexities inherent in anomaly detection, exploring challenges and adapting\nto sophisticated malicious activities. With applications spanning\ncybersecurity, healthcare, finance, and surveillance, anomalies often signify\ncritical information or potential threats. Inspired by the success of Anomaly\nGenerative Adversarial Network (AnoGAN) in image domains, our research extends\nits principles to tabular data. Our contributions include adapting AnoGAN's\nprinciples to a new domain and promising advancements in detecting previously\nundetectable anomalies. This paper delves into the multifaceted nature of\nanomaly detection, considering the dynamic evolution of normal behavior,\ncontext-dependent anomaly definitions, and data-related challenges like noise\nand imbalances.\n", "date": "2024-05-05", "categories": []}
{"id": "2301.07281", "title": "Detecting and Ranking Causal Anomalies in End-to-End Complex System", "abstract": "  With the rapid development of technology, the automated monitoring systems of\nlarge-scale factories are becoming more and more important. By collecting a\nlarge amount of machine sensor data, we can have many ways to find anomalies.\nWe believe that the real core value of an automated monitoring system is to\nidentify and track the cause of the problem. The most famous method for finding\ncausal anomalies is RCA, but there are many problems that cannot be ignored.\nThey used the AutoRegressive eXogenous (ARX) model to create a time-invariant\ncorrelation network as a machine profile, and then use this profile to track\nthe causal anomalies by means of a method called fault propagation. There are\ntwo major problems in describing the behavior of a machine by using the\ncorrelation network established by ARX: (1) It does not take into account the\ndiversity of states (2) It does not separately consider the correlations with\ndifferent time-lag. Based on these problems, we propose a framework called\nRanking Causal Anomalies in End-to-End System (RCAE2E), which completely solves\nthe problems mentioned above. In the experimental part, we use synthetic data\nand real-world large-scale photoelectric factory data to verify the correctness\nand existence of our method hypothesis.\n", "date": "2023-01-18", "categories": []}
{"id": "2305.15121", "title": "Beyond Individual Input for Deep Anomaly Detection on Tabular Data", "abstract": "  Anomaly detection is vital in many domains, such as finance, healthcare, and\ncybersecurity. In this paper, we propose a novel deep anomaly detection method\nfor tabular data that leverages Non-Parametric Transformers (NPTs), a model\ninitially proposed for supervised tasks, to capture both feature-feature and\nsample-sample dependencies. In a reconstruction-based framework, we train an\nNPT to reconstruct masked features of normal samples. In a non-parametric\nfashion, we leverage the whole training set during inference and use the\nmodel's ability to reconstruct the masked features to generate an anomaly\nscore. To the best of our knowledge, this is the first work to successfully\ncombine feature-feature and sample-sample dependencies for anomaly detection on\ntabular datasets. Through extensive experiments on 31 benchmark tabular\ndatasets, we demonstrate that our method achieves state-of-the-art performance,\noutperforming existing methods by 2.4% and 1.2% in terms of F1-score and AUROC,\nrespectively. Our ablation study further proves that modeling both types of\ndependencies is crucial for anomaly detection on tabular data.\n", "date": "2023-05-24", "categories": []}
{"id": "2404.19444", "title": "AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion", "abstract": "  Anomaly synthesis is one of the effective methods to augment abnormal samples\nfor training. However, current anomaly synthesis methods predominantly rely on\ntexture information as input, which limits the fidelity of synthesized abnormal\nsamples. Because texture information is insufficient to correctly depict the\npattern of anomalies, especially for logical anomalies. To surmount this\nobstacle, we present the AnomalyXFusion framework, designed to harness\nmulti-modality information to enhance the quality of synthesized abnormal\nsamples. The AnomalyXFusion framework comprises two distinct yet synergistic\nmodules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion\n(DDF) module. The MIF module refines modality alignment by aggregating and\nintegrating various modality features into a unified embedding space, termed\nX-embedding, which includes image, text, and mask features. Concurrently, the\nDDF module facilitates controlled generation through an adaptive adjustment of\nX-embedding conditioned on the diffusion steps. In addition, to reveal the\nmulti-modality representational power of AnomalyXFusion, we propose a new\ndataset, called MVTec Caption. More precisely, MVTec Caption extends 2.2k\naccurate image-mask-text annotations for the MVTec AD and LOCO datasets.\nComprehensive evaluations demonstrate the effectiveness of AnomalyXFusion,\nespecially regarding the fidelity and diversity for logical anomalies. Project\npage: http:github.com/hujiecpp/MVTec-Caption\n", "date": "2024-04-30", "categories": []}
{"id": "2302.10753", "title": "DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate\n  Time Series Data", "abstract": "  Anomaly detection techniques enable effective anomaly detection and diagnosis\nin multi-variate time series data, which are of major significance for today's\nindustrial applications. However, establishing an anomaly detection system that\ncan be rapidly and accurately located is a challenging problem due to the lack\nof anomaly labels, the high dimensional complexity of the data, memory\nbottlenecks in actual hardware, and the need for fast reasoning. In this paper,\nwe propose an anomaly detection and diagnosis model, DTAAD, based on\nTransformer and Dual Temporal Convolutional Network (TCN). Our overall model is\nan integrated design in which an autoregressive model (AR) combines with an\nautoencoder (AE) structure. Scaling methods and feedback mechanisms are\nintroduced to improve prediction accuracy and expand correlation differences.\nConstructed by us, the Dual TCN-Attention Network (DTA) uses only a single\nlayer of Transformer encoder in our baseline experiment, belonging to an\nultra-lightweight model. Our extensive experiments on seven public datasets\nvalidate that DTAAD exceeds the majority of currently advanced baseline methods\nin both detection and diagnostic performance. Specifically, DTAAD improved F1\nscores by $8.38\\%$ and reduced training time by $99\\%$ compared to the\nbaseline. The code and training scripts are publicly available on GitHub at\nhttps://github.com/Yu-Lingrui/DTAAD.\n", "date": "2023-02-17", "categories": []}
{"id": "2304.07769", "title": "Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly\n  Detector GAN", "abstract": "  This study presents an adversarial method for anomaly detection in real-world\napplications, leveraging the power of generative adversarial neural networks\n(GANs) through cycle consistency in reconstruction error. Previous methods\nsuffer from the high variance between class-wise accuracy which leads to not\nbeing applicable for all types of anomalies. The proposed method named RCALAD\ntries to solve this problem by introducing a novel discriminator to the\nstructure, which results in a more efficient training process. Additionally,\nRCALAD employs a supplementary distribution in the input space to steer\nreconstructions toward the normal data distribution, effectively separating\nanomalous samples from their reconstructions and facilitating more accurate\nanomaly detection. To further enhance the performance of the model, two novel\nanomaly scores are introduced. The proposed model has been thoroughly evaluated\nthrough extensive experiments on six various datasets, yielding results that\ndemonstrate its superiority over existing state-of-the-art models. The code is\nreadily available to the research community at\nhttps://github.com/zahraDehghanian97/RCALAD.\n", "date": "2023-04-16", "categories": []}
{"id": "2404.17454", "title": "Domain Adaptive and Fine-grained Anomaly Detection for Single-cell\n  Sequencing Data and Beyond", "abstract": "  Fined-grained anomalous cell detection from affected tissues is critical for\nclinical diagnosis and pathological research. Single-cell sequencing data\nprovide unprecedented opportunities for this task. However, current anomaly\ndetection methods struggle to handle domain shifts prevalent in multi-sample\nand multi-domain single-cell sequencing data, leading to suboptimal\nperformance. Moreover, these methods fall short of distinguishing anomalous\ncells into pathologically distinct subtypes. In response, we propose ACSleuth,\na novel, reconstruction deviation-guided generative framework that integrates\nthe detection, domain adaptation, and fine-grained annotating of anomalous\ncells into a methodologically cohesive workflow. Notably, we present the first\ntheoretical analysis of using reconstruction deviations output by generative\nmodels for anomaly detection in lieu of domain shifts. This analysis informs us\nto develop a novel and superior maximum mean discrepancy-based anomaly scorer\nin ACSleuth. Extensive benchmarks over various single-cell data and other types\nof tabular data demonstrate ACSleuth's superiority over the state-of-the-art\nmethods in identifying and subtyping anomalies in multi-sample and multi-domain\ncontexts. Our code is available at https://github.com/Catchxu/ACsleuth.\n", "date": "2024-04-26", "categories": []}
{"id": "2404.18948", "title": "Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with\n  Reconstruction Error from Sub-Adjacent Neighborhoods", "abstract": "  In this paper, we present the Sub-Adjacent Transformer with a novel attention\nmechanism for unsupervised time series anomaly detection. Unlike previous\napproaches that rely on all the points within some neighborhood for time point\nreconstruction, our method restricts the attention to regions not immediately\nadjacent to the target points, termed sub-adjacent neighborhoods. Our key\nobservation is that owing to the rarity of anomalies, they typically exhibit\nmore pronounced differences from their sub-adjacent neighborhoods than from\ntheir immediate vicinities. By focusing the attention on the sub-adjacent\nareas, we make the reconstruction of anomalies more challenging, thereby\nenhancing their detectability. Technically, our approach concentrates attention\non the non-diagonal areas of the attention matrix by enlarging the\ncorresponding elements in the training stage. To facilitate the implementation\nof the desired attention matrix pattern, we adopt linear attention because of\nits flexibility and adaptability. Moreover, a learnable mapping function is\nproposed to improve the performance of linear attention. Empirically, the\nSub-Adjacent Transformer achieves state-of-the-art performance across six\nreal-world anomaly detection benchmarks, covering diverse fields such as server\nmonitoring, space exploration, and water treatment.\n", "date": "2024-04-27", "categories": []}
{"id": "2306.11876", "title": "BMAD: Benchmarks for Medical Anomaly Detection", "abstract": "  Anomaly detection (AD) is a fundamental research problem in machine learning\nand computer vision, with practical applications in industrial inspection,\nvideo surveillance, and medical diagnosis. In medical imaging, AD is especially\nvital for detecting and diagnosing anomalies that may indicate rare diseases or\nconditions. However, there is a lack of a universal and fair benchmark for\nevaluating AD methods on medical images, which hinders the development of more\ngeneralized and robust AD methods in this specific domain. To bridge this gap,\nwe introduce a comprehensive evaluation benchmark for assessing anomaly\ndetection methods on medical images. This benchmark encompasses six reorganized\ndatasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT,\nchest X-ray, and digital histopathology) and three key evaluation metrics, and\nincludes a total of fourteen state-of-the-art AD algorithms. This standardized\nand well-curated medical benchmark with the well-structured codebase enables\ncomprehensive comparisons among recently proposed anomaly detection methods. It\nwill facilitate the community to conduct a fair comparison and advance the\nfield of AD on medical imaging. More information on BMAD is available in our\nGitHub repository: https://github.com/DorisBao/BMAD\n", "date": "2023-06-20", "categories": []}
{"id": "2404.17900", "title": "Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling", "abstract": "  Reconstruction-based methods have been commonly used for unsupervised anomaly\ndetection, in which a normal image is reconstructed and compared with the given\ntest image to detect and locate anomalies. Recently, diffusion models have\nshown promising applications for anomaly detection due to their powerful\ngenerative ability. However, these models lack strict mathematical support for\nnormal image reconstruction and unexpectedly suffer from low reconstruction\nquality. To address these issues, this paper proposes a novel and\nhighly-interpretable method named Masked Diffusion Posterior Sampling (MDPS).\nIn MDPS, the problem of normal image reconstruction is mathematically modeled\nas multiple diffusion posterior sampling for normal images based on the devised\nmasked noisy observation model and the diffusion-based normal image prior under\nBayesian framework. Using a metric designed from pixel-level and\nperceptual-level perspectives, MDPS can effectively compute the difference map\nbetween each normal posterior sample and the given test image. Anomaly scores\nare obtained by averaging all difference maps for multiple posterior samples.\nExhaustive experiments on MVTec and BTAD datasets demonstrate that MDPS can\nachieve state-of-the-art performance in normal image reconstruction quality as\nwell as anomaly detection and localization.\n", "date": "2024-04-27", "categories": []}
{"id": "2404.17925", "title": "Accurate and fast anomaly detection in industrial processes and IoT\n  environments", "abstract": "  We present a novel, simple and widely applicable semi-supervised procedure\nfor anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly\nDetection). SAnD comprises 5 steps, each leveraging well-known statistical\ntools, namely; smoothing filters, variance inflation factors, the Mahalanobis\ndistance, threshold selection algorithms and feature importance techniques. To\nour knowledge, SAnD is the first procedure that integrates these tools to\nidentify anomalies and help decipher their putative causes. We show how each\nstep contributes to tackling technical challenges that practitioners face when\ndetecting anomalies in industrial contexts, where signals can be highly\nmulticollinear, have unknown distributions, and intertwine short-lived noise\nwith the long(er)-lived actual anomalies. The development of SAnD was motivated\nby a concrete case study from our industrial partner, which we use here to show\nits effectiveness. We also evaluate the performance of SAnD by comparing it\nwith a selection of semi-supervised methods on public datasets from the\nliterature on anomaly detection. We conclude that SAnD is effective, broadly\napplicable, and outperforms existing approaches in both anomaly detection and\nruntime.\n", "date": "2024-04-27", "categories": []}
{"id": "2404.18599", "title": "Self-supervised learning for classifying paranasal anomalies in the\n  maxillary sinus", "abstract": "  Purpose: Paranasal anomalies, frequently identified in routine radiological\nscreenings, exhibit diverse morphological characteristics. Due to the diversity\nof anomalies, supervised learning methods require large labelled dataset\nexhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be\nused to learn representations from unlabelled data. However, there are no SSL\nmethods designed for the downstream task of classifying paranasal anomalies in\nthe maxillary sinus (MS).\n  Methods: Our approach uses a 3D Convolutional Autoencoder (CAE) trained in an\nunsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE\nto reduce reconstruction errors when reconstructing normal maxillary sinus (MS)\nimage. Then, this CAE is applied to an unlabelled dataset to generate coarse\nanomaly locations by creating residual MS images. Following this, a 3D\nConvolutional Neural Network (CNN) reconstructs these residual images, which\nforms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a\nlabelled dataset of normal and anomalous MS images.\n  Results: The proposed SSL technique exhibits superior performance compared to\nexisting generic self-supervised methods, especially in scenarios with limited\nannotated data. When trained on just 10% of the annotated dataset, our method\nachieves an Area Under the Precision-Recall Curve (AUPRC) of 0.79 for the\ndownstream classification task. This performance surpasses other methods, with\nBYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and Masked\nAutoencoding using SparK at 0.75.\n  Conclusion: A self-supervised learning approach that inherently focuses on\nlocalizing paranasal anomalies proves to be advantageous, particularly when the\nsubsequent task involves differentiating normal from anomalous maxillary\nsinuses. Access our code at\nhttps://github.com/mtec-tuhh/self-supervised-paranasal-anomaly\n", "date": "2024-04-29", "categories": []}
{"id": "2404.18747", "title": "Evaluating the Effectiveness of Video Anomaly Detection in the Wild:\n  Online Learning and Inference for Real-world Deployment", "abstract": "  Video Anomaly Detection (VAD) identifies unusual activities in video streams,\na key technology with broad applications ranging from surveillance to\nhealthcare. Tackling VAD in real-life settings poses significant challenges due\nto the dynamic nature of human actions, environmental variations, and domain\nshifts. Many research initiatives neglect these complexities, often\nconcentrating on traditional testing methods that fail to account for\nperformance on unseen datasets, creating a gap between theoretical models and\ntheir real-world utility. Online learning is a potential strategy to mitigate\nthis issue by allowing models to adapt to new information continuously. This\npaper assesses how well current VAD algorithms can adjust to real-life\nconditions through an online learning framework, particularly those based on\npose analysis, for their efficiency and privacy advantages. Our proposed\nframework enables continuous model updates with streaming data from novel\nenvironments, thus mirroring actual world challenges and evaluating the models'\nability to adapt in real-time while maintaining accuracy. We investigate three\nstate-of-the-art models in this setting, focusing on their adaptability across\ndifferent domains. Our findings indicate that, even under the most challenging\nconditions, our online learning approach allows a model to preserve 89.39% of\nits original effectiveness compared to its offline-trained counterpart in a\nspecific target domain.\n", "date": "2024-04-29", "categories": []}
{"id": "2404.16887", "title": "Anomaly Detection for Incident Response at Scale", "abstract": "  We present a machine learning-based anomaly detection product, AI Detect and\nRespond (AIDR), that monitors Walmart's business and system health in\nreal-time. During the validation over 3 months, the product served predictions\nfrom over 3000 models to more than 25 application, platform, and operation\nteams, covering 63\\% of major incidents and reducing the mean-time-to-detect\n(MTTD) by more than 7 minutes. Unlike previous anomaly detection methods, our\nsolution leverages statistical, ML and deep learning models while continuing to\nincorporate rule-based static thresholds to incorporate domain-specific\nknowledge. Both univariate and multivariate ML models are deployed and\nmaintained through distributed services for scalability and high availability.\nAIDR has a feedback loop that assesses model quality with a combination of\ndrift detection algorithms and customer feedback. It also offers\nself-onboarding capabilities and customizability. AIDR has achieved success\nwith various internal teams with lower time to detection and fewer false\npositives than previous methods. As we move forward, we aim to expand incident\ncoverage and prevention, reduce noise, and integrate further with root cause\nrecommendation (RCR) to enable an end-to-end AIDR experience.\n", "date": "2024-04-24", "categories": []}
{"id": "2404.17023", "title": "Out-of-Distribution Detection using Maximum Entropy Coding", "abstract": "  Given a default distribution $P$ and a set of test data\n$x^M=\\{x_1,x_2,\\ldots,x_M\\}$ this paper seeks to answer the question if it was\nlikely that $x^M$ was generated by $P$. For discrete distributions, the\ndefinitive answer is in principle given by Kolmogorov-Martin-L\\\"{o}f\nrandomness. In this paper we seek to generalize this to continuous\ndistributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\\ldots$. To\neach statistic we associate its maximum entropy distribution and with this a\nuniversal source coder. The maximum entropy distributions are subsequently\ncombined to give a total codelength, which is compared with $-\\log P(x^M)$. We\nshow that this approach satisfied a number of theoretical properties.\n  For real world data $P$ usually is unknown. We transform data into a standard\ndistribution in the latent space using a bidirectional generate network and use\nmaximum entropy coding there. We compare the resulting method to other methods\nthat also used generative neural networks to detect anomalies. In most cases,\nour results show better performance.\n", "date": "2024-04-25", "categories": []}
{"id": "2404.17381", "title": "Frequency-Guided Multi-Level Human Action Anomaly Detection with\n  Normalizing Flows", "abstract": "  We introduce the task of human action anomaly detection (HAAD), which aims to\nidentify anomalous motions in an unsupervised manner given only the\npre-determined normal category of training action samples. Compared to prior\nhuman-related anomaly detection tasks which primarily focus on unusual events\nfrom videos, HAAD involves the learning of specific action labels to recognize\nsemantically anomalous human behaviors. To address this task, we propose a\nnormalizing flow (NF)-based detection framework where the sample likelihood is\neffectively leveraged to indicate anomalies. As action anomalies often occur in\nsome specific body parts, in addition to the full-body action feature learning,\nwe incorporate extra encoding streams into our framework for a finer modeling\nof body subsets. Our framework is thus multi-level to jointly discover global\nand local motion anomalies. Furthermore, to show awareness of the potentially\njittery data during recording, we resort to discrete cosine transformation by\nconverting the action samples from the temporal to the frequency domain to\nmitigate the issue of data instability. Extensive experimental results on two\nhuman action datasets demonstrate that our method outperforms the baselines\nformed by adapting state-of-the-art human activity AD approaches to our task of\nHAAD.\n", "date": "2024-04-26", "categories": []}
{"id": "2303.09841", "title": "GADformer: A Transparent Transformer Model for Group Anomaly Detection\n  on Trajectories", "abstract": "  Group Anomaly Detection (GAD) identifies unusual pattern in groups where\nindividual members might not be anomalous. This task is of major importance\nacross multiple disciplines, in which also sequences like trajectories can be\nconsidered as a group. As groups become more diverse in heterogeneity and size,\ndetecting group anomalies becomes challenging, especially without supervision.\nThough Recurrent Neural Networks are well established deep sequence models,\ntheir performance can decrease with increasing sequence lengths. Hence, this\npaper introduces GADformer, a BERT-based model for attention-driven GAD on\ntrajectories in unsupervised and semi-supervised settings. We demonstrate how\ngroup anomalies can be detected by attention-based GAD. We also introduce the\nBlock-Attention-anomaly-Score (BAS) to enhance model transparency by scoring\nattention patterns. In addition to that, synthetic trajectory generation allows\nvarious ablation studies. In extensive experiments we investigate our approach\nversus related works in their robustness for trajectory noise and novelties on\nsynthetic data and three real world datasets.\n", "date": "2023-03-17", "categories": []}
{"id": "2404.16179", "title": "S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting\n  Fusion for Maximizing Anomaly Prediction in Timeseries", "abstract": "  Anomaly detection plays a crucial role in industrial settings, particularly\nin maintaining the reliability and optimal performance of cooling systems.\nTraditional anomaly detection methods often face challenges in handling diverse\ndata characteristics and variations in noise levels, resulting in limited\neffectiveness. And yet traditional anomaly detection often relies on\napplication of single models. This work proposes a novel, robust approach using\nfive heterogeneous independent models combined with a dual ensemble fusion of\nvoting techniques. Diverse models capture various system behaviors, while the\nfusion strategy maximizes detection effectiveness and minimizes false alarms.\nEach base autoencoder model learns a unique representation of the data,\nleveraging their complementary strengths to improve anomaly detection\nperformance. To increase the effectiveness and reliability of final anomaly\nprediction, dual ensemble technique is applied. This approach outperforms in\nmaximizing the coverage of identifying anomalies. Experimental results on a\nreal-world dataset of industrial cooling system data demonstrate the\neffectiveness of the proposed approach. This approach can be extended to other\nindustrial applications where anomaly detection is critical for ensuring system\nreliability and preventing potential malfunctions.\n", "date": "2024-04-24", "categories": []}
{"id": "2404.16183", "title": "ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk\n  Assessment", "abstract": "  Anomaly detection in industrial systems is crucial for preventing equipment\nfailures, ensuring risk identification, and maintaining overall system\nefficiency. Traditional monitoring methods often rely on fixed thresholds and\nempirical rules, which may not be sensitive enough to detect subtle changes in\nsystem health and predict impending failures. To address this limitation, this\npaper proposes, a novel Attention-based convolutional autoencoder (ABCD) for\nrisk detection and map the risk value derive to the maintenance planning. ABCD\nlearns the normal behavior of conductivity from historical data of a real-world\nindustrial cooling system and reconstructs the input data, identifying\nanomalies that deviate from the expected patterns. The framework also employs\ncalibration techniques to ensure the reliability of its predictions. Evaluation\nresults demonstrate that with the attention mechanism in ABCD a 57.4% increase\nin performance and a reduction of false alarms by 9.37% is seen compared to\nwithout attention. The approach can effectively detect risks, the risk priority\nrank mapped to maintenance, providing valuable insights for cooling system\ndesigners and service personnel. Calibration error of 0.03% indicates that the\nmodel is well-calibrated and enhances model's trustworthiness, enabling\ninformed decisions about maintenance strategies\n", "date": "2024-04-24", "categories": []}
{"id": "2207.12201", "title": "Calibrated One-class Classification for Unsupervised Time Series Anomaly\n  Detection", "abstract": "  Time series anomaly detection is instrumental in maintaining system\navailability in various domains. Current work in this research line mainly\nfocuses on learning data normality deeply and comprehensively by devising\nadvanced neural network structures and new reconstruction/prediction learning\nobjectives. However, their one-class learning process can be misled by latent\nanomalies in training data (i.e., anomaly contamination) under the unsupervised\nparadigm. Their learning process also lacks knowledge about the anomalies.\nConsequently, they often learn a biased, inaccurate normality boundary. To\ntackle these problems, this paper proposes calibrated one-class classification\nfor anomaly detection, realizing contamination-tolerant, anomaly-informed\nlearning of data normality via uncertainty modeling-based calibration and\nnative anomaly-based calibration. Specifically, our approach adaptively\npenalizes uncertain predictions to restrain irregular samples in anomaly\ncontamination during optimization, while simultaneously encouraging confident\npredictions on regular samples to ensure effective normality learning. This\nlargely alleviates the negative impact of anomaly contamination. Our approach\nalso creates native anomaly examples via perturbation to simulate time series\nabnormal behaviors. Through discriminating these dummy anomalies, our one-class\nlearning is further calibrated to form a more precise normality boundary.\nExtensive experiments on ten real-world datasets show that our model achieves\nsubstantial improvement over sixteen state-of-the-art contenders.\n", "date": "2022-07-25", "categories": []}
{"id": "2404.10800", "title": "Integrating Graph Neural Networks with Scattering Transform for Anomaly\n  Detection", "abstract": "  In this paper, we present two novel methods in Network Intrusion Detection\nSystems (NIDS) using Graph Neural Networks (GNNs). The first approach,\nScattering Transform with E-GraphSAGE (STEG), utilizes the scattering transform\nto conduct multi-resolution analysis of edge feature vectors. This provides a\ndetailed representation that is essential for identifying subtle anomalies in\nnetwork traffic. The second approach improves node representation by initiating\nwith Node2Vec, diverging from standard methods of using uniform values, thereby\ncapturing a more accurate and holistic network picture. Our methods have shown\nsignificant improvements in performance compared to existing state-of-the-art\nmethods in benchmark NIDS datasets.\n", "date": "2024-04-16", "categories": []}
{"id": "2404.15333", "title": "EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection", "abstract": "  Cardiologists use electrocardiograms (ECG) for the detection of arrhythmias.\nHowever, continuous monitoring of ECG signals to detect cardiac abnormal-ities\nrequires significant time and human resources. As a result, several deep\nlearning studies have been conducted in advance for the automatic detection of\narrhythmia. These models show relatively high performance in supervised\nlearning, but are not applicable in cases with few training examples. This is\nbecause abnormal ECG data is scarce compared to normal data in most real-world\nclinical settings. Therefore, in this study, GAN-based anomaly detec-tion,\ni.e., unsupervised learning, was employed to address the issue of data\nimbalance. This paper focuses on detecting abnormal signals in\nelectrocardi-ograms (ECGs) using only labels from normal signals as training\ndata. In-spired by self-supervised vision transformers, which learn by dividing\nimages into patches, and masked auto-encoders, known for their effectiveness in\npatch reconstruction and solving information redundancy, we introduce the ECG\nHeartbeat Anomaly Detection model, EB-GAME. EB-GAME was trained and validated\non the MIT-BIH Arrhythmia Dataset, where it achieved state-of-the-art\nperformance on this benchmark.\n", "date": "2024-04-08", "categories": []}
{"id": "2404.14451", "title": "Generative Subspace Adversarial Active Learning for Outlier Detection in\n  Multiple Views of High-dimensional Data", "abstract": "  Outlier detection in high-dimensional tabular data is an important task in\ndata mining, essential for many downstream tasks and applications. Existing\nunsupervised outlier detection algorithms face one or more problems, including\ninlier assumption (IA), curse of dimensionality (CD), and multiple views (MV).\nTo address these issues, we introduce Generative Subspace Adversarial Active\nLearning (GSAAL), a novel approach that uses a Generative Adversarial Network\nwith multiple adversaries. These adversaries learn the marginal class\nprobability functions over different data subspaces, while a single generator\nin the full space models the entire distribution of the inlier class. GSAAL is\nspecifically designed to address the MV limitation while also handling the IA\nand CD, being the only method to do so. We provide a comprehensive mathematical\nformulation of MV, convergence guarantees for the discriminators, and\nscalability results for GSAAL. Our extensive experiments demonstrate the\neffectiveness and scalability of GSAAL, highlighting its superior performance\ncompared to other popular OD methods, especially in MV scenarios.\n", "date": "2024-04-20", "categories": []}
{"id": "2404.14933", "title": "Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data", "abstract": "  Anomaly detection in real-world scenarios poses challenges due to dynamic and\noften unknown anomaly distributions, requiring robust methods that operate\nunder an open-world assumption. This challenge is exacerbated in practical\nsettings, where models are employed by private organizations, precluding data\nsharing due to privacy and competitive concerns. Despite potential benefits,\nthe sharing of anomaly information across organizations is restricted. This\npaper addresses the question of enhancing outlier detection within individual\norganizations without compromising data confidentiality. We propose a novel\nmethod leveraging representation learning and federated learning techniques to\nimprove the detection of unknown anomalies. Specifically, our approach utilizes\nlatent representations obtained from client-owned autoencoders to refine the\ndecision boundary of inliers. Notably, only model parameters are shared between\norganizations, preserving data privacy. The efficacy of our proposed method is\nevaluated on two standard financial tabular datasets and an image dataset for\nanomaly detection in a distributed setting. The results demonstrate a strong\nimprovement in the classification of unknown outliers during the inference\nphase for each organization's model.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.15033", "title": "IPAD: Industrial Process Anomaly Detection Dataset", "abstract": "  Video anomaly detection (VAD) is a challenging task aiming to recognize\nanomalies in video frames, and existing large-scale VAD researches primarily\nfocus on road traffic and human activity scenes. In industrial scenes, there\nare often a variety of unpredictable anomalies, and the VAD method can play a\nsignificant role in these scenarios. However, there is a lack of applicable\ndatasets and methods specifically tailored for industrial production scenarios\ndue to concerns regarding privacy and security. To bridge this gap, we propose\na new dataset, IPAD, specifically designed for VAD in industrial scenarios. The\nindustrial processes in our dataset are chosen through on-site factory research\nand discussions with engineers. This dataset covers 16 different industrial\ndevices and contains over 6 hours of both synthetic and real-world video\nfootage. Moreover, we annotate the key feature of the industrial process, ie,\nperiodicity. Based on the proposed dataset, we introduce a period memory module\nand a sliding window inspection mechanism to effectively investigate the\nperiodic information in a basic reconstruction model. Our framework leverages\nLoRA adapter to explore the effective migration of pretrained models, which are\ninitially trained using synthetic data, into real-world scenarios. Our proposed\ndataset and method will fill the gap in the field of industrial video anomaly\ndetection and drive the process of video understanding tasks as well as smart\nfactory deployment.\n", "date": "2024-04-23", "categories": []}
{"id": "2401.05453", "title": "Dimensionality-Aware Outlier Detection: Theoretical and Experimental\n  Analysis", "abstract": "  We present a nonparametric method for outlier detection that takes full\naccount of local variations in intrinsic dimensionality within the dataset.\nUsing the theory of Local Intrinsic Dimensionality (LID), our\n'dimensionality-aware' outlier detection method, DAO, is derived as an\nestimator of an asymptotic local expected density ratio involving the query\npoint and a close neighbor drawn at random. The dimensionality-aware behavior\nof DAO is due to its use of local estimation of LID values in a\ntheoretically-justified way. Through comprehensive experimentation on more than\n800 synthetic and real datasets, we show that DAO significantly outperforms\nthree popular and important benchmark outlier detection methods: Local Outlier\nFactor (LOF), Simplified LOF, and kNN.\n", "date": "2024-01-10", "categories": []}
{"id": "2404.13342", "title": "Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior", "abstract": "  The majority of existing hyperspectral anomaly detection (HAD) methods use\nthe low-rank representation (LRR) model to separate the background and anomaly\ncomponents, where the anomaly component is optimized by handcrafted sparse\npriors (e.g., $\\ell_{2,1}$-norm). However, this may not be ideal since they\noverlook the spatial structure present in anomalies and make the detection\nresult largely dependent on manually set sparsity. To tackle these problems, we\nredefine the optimization criterion for the anomaly component in the LRR model\nwith a self-supervised network called self-supervised anomaly prior (SAP). This\nprior is obtained by the pretext task of self-supervised learning, which is\ncustomized to learn the characteristics of hyperspectral anomalies.\nSpecifically, this pretext task is a classification task to distinguish the\noriginal hyperspectral image (HSI) and the pseudo-anomaly HSI, where the\npseudo-anomaly is generated from the original HSI and designed as a prism with\narbitrary polygon bases and arbitrary spectral bands. In addition, a\ndual-purified strategy is proposed to provide a more refined background\nrepresentation with an enriched background dictionary, facilitating the\nseparation of anomalies from complex backgrounds. Extensive experiments on\nvarious hyperspectral datasets demonstrate that the proposed SAP offers a more\naccurate and interpretable solution than other advanced HAD methods.\n", "date": "2024-04-20", "categories": []}
{"id": "2404.13690", "title": "Detecting Compromised IoT Devices Using Autoencoders with Sequential\n  Hypothesis Testing", "abstract": "  IoT devices fundamentally lack built-in security mechanisms to protect\nthemselves from security attacks. Existing works on improving IoT security\nmostly focus on detecting anomalous behaviors of IoT devices. However, these\nexisting anomaly detection schemes may trigger an overwhelmingly large number\nof false alerts, rendering them unusable in detecting compromised IoT devices.\nIn this paper we develop an effective and efficient framework, named CUMAD, to\ndetect compromised IoT devices. Instead of directly relying on individual\nanomalous events, CUMAD aims to accumulate sufficient evidence in detecting\ncompromised IoT devices, by integrating an autoencoder-based anomaly detection\nsubsystem with a sequential probability ratio test (SPRT)-based sequential\nhypothesis testing subsystem. CUMAD can effectively reduce the number of false\nalerts in detecting compromised IoT devices, and moreover, it can detect\ncompromised IoT devices quickly. Our evaluation studies based on the\npublic-domain N-BaIoT dataset show that CUMAD can on average reduce the false\npositive rate from about 3.57% using only the autoencoder-based anomaly\ndetection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT\ndevices quickly, with less than 5 observations on average.\n", "date": "2024-04-21", "categories": []}
{"id": "2303.12342", "title": "One-Step Detection Paradigm for Hyperspectral Anomaly Detection via\n  Spectral Deviation Relationship Learning", "abstract": "  Hyperspectral anomaly detection (HAD) involves identifying the targets that\ndeviate spectrally from their surroundings, without prior knowledge. Recently,\ndeep learning based methods have become the mainstream HAD methods, due to\ntheir powerful spatial-spectral feature extraction ability. However, the\ncurrent deep detection models are optimized to complete a proxy task (two-step\nparadigm), such as background reconstruction or generation, rather than\nachieving anomaly detection directly. This leads to suboptimal results and poor\ntransferability, which means that the deep model is trained and tested on the\nsame image. In this paper, an unsupervised transferred direct detection (TDD)\nmodel is proposed, which is optimized directly for the anomaly detection task\n(one-step paradigm) and has transferability. Specially, the TDD model is\noptimized to identify the spectral deviation relationship according to the\nanomaly definition. Compared to learning the specific background distribution\nas most models do, the spectral deviation relationship is universal for\ndifferent images and guarantees the model transferability. To train the TDD\nmodel in an unsupervised manner, an anomaly sample simulation strategy is\nproposed to generate numerous pairs of anomaly samples. Furthermore, a global\nself-attention module and a local self-attention module are designed to help\nthe model focus on the \"spectrally deviating\" relationship. The TDD model was\nvalidated on four public HAD datasets. The results show that the proposed TDD\nmodel can successfully overcome the limitation of traditional model training\nand testing on a single image, and the model has a powerful detection ability\nand excellent transferability.\n", "date": "2023-03-22", "categories": []}
{"id": "2404.12667", "title": "Detecting Out-Of-Distribution Earth Observation Images with Diffusion\n  Models", "abstract": "  Earth Observation imagery can capture rare and unusual events, such as\ndisasters and major landscape changes, whose visual appearance contrasts with\nthe usual observations. Deep models trained on common remote sensing data will\noutput drastically different features for these out-of-distribution samples,\ncompared to those closer to their training dataset. Detecting them could\ntherefore help anticipate changes in the observations, either geographical or\nenvironmental. In this work, we show that the reconstruction error of diffusion\nmodels can effectively serve as unsupervised out-of-distribution detectors for\nremote sensing images, using them as a plausibility score. Moreover, we\nintroduce ODEED, a novel reconstruction-based scorer using the probability-flow\nODE of diffusion models. We validate it experimentally on SpaceNet 8 with\nvarious scenarios, such as classical OOD detection with geographical shift and\nnear-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We\nshow that our ODEED scorer significantly outperforms other diffusion-based and\ndiscriminative baselines on the more challenging near-OOD scenarios of flood\nimage detection, where OOD images are close to the distribution tail. We aim to\npave the way towards better use of generative models for anomaly detection in\nremote sensing.\n", "date": "2024-04-19", "categories": []}
{"id": "2404.12712", "title": "uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories", "abstract": "  Deep learning-based approaches have achieved significant improvements on\npublic video anomaly datasets, but often do not perform well in real-world\napplications. This paper addresses two issues: the lack of labeled data and the\ndifficulty of explaining the predictions of a neural network. To this end, we\npresent a framework called uTRAND, that shifts the problem of anomalous\ntrajectory prediction from the pixel space to a semantic-topological domain.\nThe framework detects and tracks all types of traffic agents in bird's-eye-view\nvideos of traffic cameras mounted at an intersection. By conceptualizing the\nintersection as a patch-based graph, it is shown that the framework learns and\nmodels the normal behaviour of traffic agents without costly manual labeling.\nFurthermore, uTRAND allows to formulate simple rules to classify anomalous\ntrajectories in a way suited for human interpretation. We show that uTRAND\noutperforms other state-of-the-art approaches on a dataset of anomalous\ntrajectories collected in a real-world setting, while producing explainable\ndetection results.\n", "date": "2024-04-19", "categories": []}
{"id": "2404.08224", "title": "HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate\n  Detection of Industrial Multivariate Time Series Anomalies", "abstract": "  Multivariate Time Series (MTS) anomaly detection focuses on pinpointing\nsamples that diverge from standard operational patterns, which is crucial for\nensuring the safety and security of industrial applications. The primary\nchallenge in this domain is to develop representations capable of discerning\nanomalies effectively. The prevalent methods for anomaly detection in the\nliterature are predominantly reconstruction-based and predictive in nature.\nHowever, they typically concentrate on a single-dimensional instance level,\nthereby not fully harnessing the complex associations inherent in industrial\nMTS. To address this issue, we propose a novel self-supervised hierarchical\ncontrastive consistency learning method for detecting anomalies in MTS, named\nHCL-MTSAD. It innovatively leverages data consistency at multiple levels\ninherent in industrial MTS, systematically capturing consistent associations\nacross four latent levels-measurement, sample, channel, and process. By\ndeveloping a multi-layer contrastive loss, HCL-MTSAD can extensively mine data\nconsistency and spatio-temporal association, resulting in more informative\nrepresentations. Subsequently, an anomaly discrimination module, grounded in\nself-supervised hierarchical contrastive learning, is designed to detect\ntimestamp-level anomalies by calculating multi-scale data consistency.\nExtensive experiments conducted on six diverse MTS datasets retrieved from real\ncyber-physical systems and server machines, in comparison with 20 baselines,\nindicate that HCL-MTSAD's anomaly detection capability outperforms the\nstate-of-the-art benchmark models by an average of 1.8\\% in terms of F1 score.\n", "date": "2024-04-12", "categories": []}
{"id": "2404.12246", "title": "Blind Localization and Clustering of Anomalies in Textures", "abstract": "  Anomaly detection and localization in images is a growing field in computer\nvision. In this area, a seemingly understudied problem is anomaly clustering,\ni.e., identifying and grouping different types of anomalies in a fully\nunsupervised manner. In this work, we propose a novel method for clustering\nanomalies in largely stationary images (textures) in a blind setting. That is,\nthe input consists of normal and anomalous images without distinction and\nwithout labels. What contributes to the difficulty of the task is that\nanomalous regions are often small and may present only subtle changes in\nappearance, which can be easily overshadowed by the genuine variance in the\ntexture. Moreover, each anomaly type may have a complex appearance\ndistribution. We introduce a novel scheme for solving this task using a\ncombination of blind anomaly localization and contrastive learning. By\nidentifying the anomalous regions with high fidelity, we can restrict our focus\nto those regions of interest; then, contrastive learning is employed to\nincrease the separability of different anomaly types and reduce the intra-class\nvariation. Our experiments show that the proposed solution yields significantly\nbetter results compared to prior work, setting a new state of the art. Project\npage: https://reality.tf.fau.de/pub/ardelean2024blind.html.\n", "date": "2024-04-18", "categories": []}
{"id": "2212.12092", "title": "Anomaly Detection using Ensemble Classification and Evidence Theory", "abstract": "  Multi-class ensemble classification remains a popular focus of investigation\nwithin the research community. The popularization of cloud services has sped up\ntheir adoption due to the ease of deploying large-scale machine-learning\nmodels. It has also drawn the attention of the industrial sector because of its\nability to identify common problems in production. However, there are\nchallenges to conform an ensemble classifier, namely a proper selection and\neffective training of the pool of classifiers, the definition of a proper\narchitecture for multi-class classification, and uncertainty quantification of\nthe ensemble classifier. The robustness and effectiveness of the ensemble\nclassifier lie in the selection of the pool of classifiers, as well as in the\nlearning process. Hence, the selection and the training procedure of the pool\nof classifiers play a crucial role. An (ensemble) classifier learns to detect\nthe classes that were used during the supervised training. However, when\ninjecting data with unknown conditions, the trained classifier will intend to\npredict the classes learned during the training. To this end, the uncertainty\nof the individual and ensemble classifier could be used to assess the learning\ncapability. We present a novel approach for novel detection using ensemble\nclassification and evidence theory. A pool selection strategy is presented to\nbuild a solid ensemble classifier. We present an architecture for multi-class\nensemble classification and an approach to quantify the uncertainty of the\nindividual classifiers and the ensemble classifier. We use uncertainty for the\nanomaly detection approach. Finally, we use the benchmark Tennessee Eastman to\nperform experiments to test the ensemble classifier's prediction and anomaly\ndetection capabilities.\n", "date": "2022-12-23", "categories": []}
{"id": "2301.03713", "title": "Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave\n  Sensing", "abstract": "  Human respiratory rate and its pattern convey essential information about the\nphysical and psychological states of the subject. Abnormal breathing can\nindicate fatal health issues leading to further diagnosis and treatment.\nWireless light-wave sensing (LWS) using incoherent infrared light shows promise\nin safe, discreet, efficient, and non-invasive human breathing monitoring\nwithout raising privacy concerns. The respiration monitoring system needs to be\ntrained on different types of breathing patterns to identify breathing\nanomalies.The system must also validate the collected data as a breathing\nwaveform, discarding any faulty data caused by external interruption, user\nmovement, or system malfunction. To address these needs, this study simulated\nnormal and different types of abnormal respiration using a robot that mimics\nhuman breathing patterns. Then, time-series respiration data were collected\nusing infrared light-wave sensing technology. Three machine learning\nalgorithms, decision tree, random forest and XGBoost, were applied to detect\nbreathing anomalies and faulty data. Model performances were evaluated through\ncross-validation, assessing classification accuracy, precision and recall\nscores. The random forest model achieved the highest classification accuracy of\n96.75% with data collected at a 0.5m distance. In general, ensemble models like\nrandom forest and XGBoost performed better than a single model in classifying\nthe data collected at multiple distances from the light-wave sensing setup.\n", "date": "2023-01-09", "categories": []}
{"id": "2311.02612", "title": "GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for\n  Zero-shot Anomaly Detection", "abstract": "  Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding\ncapabilities, making it possible to handle certain tasks through the Visual\nQuestion Answering (VQA) paradigm. This paper explores the potential of\nVQA-oriented GPT-4V in the recently popular visual Anomaly Detection (AD) and\nis the first to conduct qualitative and quantitative evaluations on the popular\nMVTec AD and VisA datasets. Considering that this task requires both\nimage-/pixel-level evaluations, the proposed GPT-4V-AD framework contains three\ncomponents: \\textbf{\\textit{1)}} Granular Region Division, \\textbf{\\textit{2)}}\nPrompt Designing, \\textbf{\\textit{3)}} Text2Segmentation for easy quantitative\nevaluation, and have made some different attempts for comparative analysis. The\nresults show that GPT-4V can achieve certain results in the zero-shot AD task\nthrough a VQA paradigm, such as achieving image-level 77.1/88.0 and pixel-level\n68.0/76.6 AU-ROCs on MVTec AD and VisA datasets, respectively. However, its\nperformance still has a certain gap compared to the state-of-the-art zero-shot\nmethod, \\eg, WinCLIP and CLIP-AD, and further researches are needed. This study\nprovides a baseline reference for the research of VQA-oriented LMM in the\nzero-shot AD task, and we also post several possible future works. Code is\navailable at \\url{https://github.com/zhangzjn/GPT-4V-AD}.\n", "date": "2023-11-05", "categories": []}
{"id": "2404.00724", "title": "Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic\n  Distribution Alignment", "abstract": "  Conventional unsupervised anomaly detection (UAD) methods build separate\nmodels for each object category. Recent studies have proposed to train a\nunified model for multiple classes, namely model-unified UAD. However, such\nmethods still implement the unified model separately on each class during\ninference with respective anomaly decision thresholds, which hinders their\napplication when the image categories are entirely unavailable. In this work,\nwe present a simple yet powerful method to address multi-class anomaly\ndetection without any class information, namely \\textit{absolute-unified} UAD.\nWe target the crux of prior works in this challenging setting: different\nobjects have mismatched anomaly score distributions. We propose Class-Agnostic\nDistribution Alignment (CADA) to align the mismatched score distribution of\neach implicit class without knowing class information, which enables unified\nanomaly detection for all classes and samples. The essence of CADA is to\npredict each class's score distribution of normal samples given any image,\nnormal or anomalous, of this class. As a general component, CADA can activate\nthe potential of nearly all UAD methods under absolute-unified setting. Our\napproach is extensively evaluated under the proposed setting on two popular UAD\nbenchmark datasets, MVTec AD and VisA, where we exceed previous\nstate-of-the-art by a large margin.\n", "date": "2024-03-31", "categories": []}
{"id": "2404.10760", "title": "Learning Feature Inversion for Multi-class Anomaly Detection under\n  General-purpose COCO-AD Benchmark", "abstract": "  Anomaly detection (AD) is often focused on detecting anomaly areas for\nindustrial quality inspection and medical lesion examination. However, due to\nthe specific scenario targets, the data scale for AD is relatively small, and\nevaluation metrics are still deficient compared to classic vision tasks, such\nas object detection and semantic segmentation. To fill these gaps, this work\nfirst constructs a large-scale and general-purpose COCO-AD dataset by extending\nCOCO to the AD field. This enables fair evaluation and sustainable development\nfor different methods on this challenging benchmark. Moreover, current metrics\nsuch as AU-ROC have nearly reached saturation on simple datasets, which\nprevents a comprehensive evaluation of different methods. Inspired by the\nmetrics in the segmentation field, we further propose several more practical\nthreshold-dependent AD-specific metrics, ie, m$F_1$$^{.2}_{.8}$,\nmAcc$^{.2}_{.8}$, mIoU$^{.2}_{.8}$, and mIoU-max. Motivated by GAN inversion's\nhigh-quality reconstruction capability, we propose a simple but more powerful\nInvAD framework to achieve high-quality feature reconstruction. Our method\nimproves the effectiveness of reconstruction-based methods on popular MVTec AD,\nVisA, and our newly proposed COCO-AD datasets under a multi-class unsupervised\nsetting, where only a single detection model is trained to detect anomalies\nfrom different classes. Extensive ablation experiments have demonstrated the\neffectiveness of each component of our InvAD. Full codes and models are\navailable at https://github.com/zhangzjn/ader.\n", "date": "2024-04-16", "categories": []}
{"id": "2312.13783", "title": "Few Shot Part Segmentation Reveals Compositional Logic for Industrial\n  Anomaly Detection", "abstract": "  Logical anomalies (LA) refer to data violating underlying logical constraints\ne.g., the quantity, arrangement, or composition of components within an image.\nDetecting accurately such anomalies requires models to reason about various\ncomponent types through segmentation. However, curation of pixel-level\nannotations for semantic segmentation is both time-consuming and expensive.\nAlthough there are some prior few-shot or unsupervised co-part segmentation\nalgorithms, they often fail on images with industrial object. These images have\ncomponents with similar textures and shapes, and a precise differentiation\nproves challenging. In this study, we introduce a novel component segmentation\nmodel for LA detection that leverages a few labeled samples and unlabeled\nimages sharing logical constraints. To ensure consistent segmentation across\nunlabeled images, we employ a histogram matching loss in conjunction with an\nentropy loss. As segmentation predictions play a crucial role, we propose to\nenhance both local and global sample validity detection by capturing key\naspects from visual semantics via three memory banks: class histograms,\ncomponent composition embeddings and patch-level representations. For effective\nLA detection, we propose an adaptive scaling strategy to standardize anomaly\nscores from different memory banks in inference. Extensive experiments on the\npublic benchmark MVTec LOCO AD reveal our method achieves 98.1% AUROC in LA\ndetection vs. 89.6% from competing methods.\n", "date": "2023-12-21", "categories": []}
{"id": "2312.16831", "title": "METER: A Dynamic Concept Adaptation Framework for Online Anomaly\n  Detection", "abstract": "  Real-time analytics and decision-making require online anomaly detection\n(OAD) to handle drifts in data streams efficiently and effectively.\nUnfortunately, existing approaches are often constrained by their limited\ndetection capacity and slow adaptation to evolving data streams, inhibiting\ntheir efficacy and efficiency in handling concept drift, which is a major\nchallenge in evolving data streams. In this paper, we introduce METER, a novel\ndynamic concept adaptation framework that introduces a new paradigm for OAD.\nMETER addresses concept drift by first training a base detection model on\nhistorical data to capture recurring central concepts, and then learning to\ndynamically adapt to new concepts in data streams upon detecting concept drift.\nParticularly, METER employs a novel dynamic concept adaptation technique that\nleverages a hypernetwork to dynamically generate the parameter shift of the\nbase detection model, providing a more effective and efficient solution than\nconventional retraining or fine-tuning approaches. Further, METER incorporates\na lightweight drift detection controller, underpinned by evidential deep\nlearning, to support robust and interpretable concept drift detection. We\nconduct an extensive experimental evaluation, and the results show that METER\nsignificantly outperforms existing OAD approaches in various application\nscenarios.\n", "date": "2023-12-28", "categories": []}
{"id": "2401.03522", "title": "Text-Driven Traffic Anomaly Detection with Temporal High-Frequency\n  Modeling in Driving Videos", "abstract": "  Traffic anomaly detection (TAD) in driving videos is critical for ensuring\nthe safety of autonomous driving and advanced driver assistance systems.\nPrevious single-stage TAD methods primarily rely on frame prediction, making\nthem vulnerable to interference from dynamic backgrounds induced by the rapid\nmovement of the dashboard camera. While two-stage TAD methods appear to be a\nnatural solution to mitigate such interference by pre-extracting\nbackground-independent features (such as bounding boxes and optical flow) using\nperceptual algorithms, they are susceptible to the performance of first-stage\nperceptual algorithms and may result in error propagation. In this paper, we\nintroduce TTHF, a novel single-stage method aligning video clips with text\nprompts, offering a new perspective on traffic anomaly detection. Unlike\nprevious approaches, the supervised signal of our method is derived from\nlanguages rather than orthogonal one-hot vectors, providing a more\ncomprehensive representation. Further, concerning visual representation, we\npropose to model the high frequency of driving videos in the temporal domain.\nThis modeling captures the dynamic changes of driving scenes, enhances the\nperception of driving behavior, and significantly improves the detection of\ntraffic anomalies. In addition, to better perceive various types of traffic\nanomalies, we carefully design an attentive anomaly focusing mechanism that\nvisually and linguistically guides the model to adaptively focus on the visual\ncontext of interest, thereby facilitating the detection of traffic anomalies.\nIt is shown that our proposed TTHF achieves promising performance,\noutperforming state-of-the-art competitors by +5.4% AUC on the DoTA dataset and\nachieving high generalization on the DADA dataset.\n", "date": "2024-01-07", "categories": []}
{"id": "2404.08750", "title": "FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly\n  Generation and Discrimination", "abstract": "  Nowadays large computers extensively output logs to record the runtime status\nand it has become crucial to identify any suspicious or malicious activities\nfrom the information provided by the realtime logs. Thus, fast log anomaly\ndetection is a necessary task to be implemented for automating the infeasible\nmanual detection. Most of the existing unsupervised methods are trained only on\nnormal log data, but they usually require either additional abnormal data for\nhyperparameter selection or auxiliary datasets for discriminative model\noptimization. In this paper, aiming for a highly effective discriminative model\nthat enables rapid anomaly detection,we propose FastLogAD, a\ngenerator-discriminator framework trained to exhibit the capability of\ngenerating pseudo-abnormal logs through the Mask-Guided Anomaly Generation\n(MGAG) model and efficiently identifying the anomalous logs via the\nDiscriminative Abnormality Separation (DAS) model. Particularly,\npseudo-abnormal logs are generated by replacing randomly masked tokens in a\nnormal sequence with unlikely candidates. During the discriminative stage,\nFastLogAD learns a distinct separation between normal and pseudoabnormal\nsamples based on their embedding norms, allowing the selection of a threshold\nwithout exposure to any test data and achieving competitive performance.\nExtensive experiments on several common benchmarks show that our proposed\nFastLogAD outperforms existing anomaly detection approaches. Furthermore,\ncompared to previous methods, FastLogAD achieves at least x10 speed increase in\nanomaly detection over prior work. Our implementation is available at\nhttps://github.com/YifeiLin0226/FastLogAD.\n", "date": "2024-04-12", "categories": []}
{"id": "2404.08931", "title": "Label-free Anomaly Detection in Aerial Agricultural Images with Masked\n  Image Modeling", "abstract": "  Detecting various types of stresses (nutritional, water, nitrogen, etc.) in\nagricultural fields is critical for farmers to ensure maximum productivity.\nHowever, stresses show up in different shapes and sizes across different crop\ntypes and varieties. Hence, this is posed as an anomaly detection task in\nagricultural images. Accurate anomaly detection in agricultural UAV images is\nvital for early identification of field irregularities. Traditional supervised\nlearning faces challenges in adapting to diverse anomalies, necessitating\nextensive annotated data. In this work, we overcome this limitation with\nself-supervised learning using a masked image modeling approach. Masked\nAutoencoders (MAE) extract meaningful normal features from unlabeled image\nsamples which produces high reconstruction error for the abnormal pixels during\nreconstruction. To remove the need of using only ``normal\" data while training,\nwe use an anomaly suppression loss mechanism that effectively minimizes the\nreconstruction of anomalous pixels and allows the model to learn anomalous\nareas without explicitly separating ``normal\" images for training. Evaluation\non the Agriculture-Vision data challenge shows a mIOU score improvement in\ncomparison to prior state of the art in unsupervised and self-supervised\nmethods. A single model generalizes across all the anomaly categories in the\nAgri-Vision Challenge Dataset\n", "date": "2024-04-13", "categories": []}
{"id": "2404.09240", "title": "Fault Detection in Mobile Networks Using Diffusion Models", "abstract": "  In today's hyper-connected world, ensuring the reliability of telecom\nnetworks becomes increasingly crucial. Telecom networks encompass numerous\nunderlying and intertwined software and hardware components, each providing\ndifferent functionalities. To ensure the stability of telecom networks, telecom\nsoftware, and hardware vendors developed several methods to detect any aberrant\nbehavior in telecom networks and enable instant feedback and alerts. These\napproaches, although powerful, struggle to generalize due to the unsteady\nnature of the software-intensive embedded system and the complexity and\ndiversity of multi-standard mobile networks. In this paper, we present a system\nto detect anomalies in telecom networks using a generative AI model. We\nevaluate several strategies using diffusion models to train the model for\nanomaly detection using multivariate time-series data. The contributions of\nthis paper are threefold: (i) A proposal of a framework for utilizing diffusion\nmodels for time-series anomaly detection in telecom networks, (ii) A proposal\nof a particular Diffusion model architecture that outperforms other\nstate-of-the-art techniques, (iii) Experiments on a real-world dataset to\ndemonstrate that our model effectively provides explainable results, exposing\nsome of its limitations and suggesting future research avenues to enhance its\ncapabilities further.\n", "date": "2024-04-14", "categories": []}
{"id": "2308.10918", "title": "Label-based Graph Augmentation with Metapath for Graph Anomaly Detection", "abstract": "  Graph anomaly detection has attracted considerable attention from various\ndomain ranging from network security to finance in recent years. Due to the\nfact that labeling is very costly, existing methods are predominately developed\nin an unsupervised manner. However, the detected anomalies may be found out\nuninteresting instances due to the absence of prior knowledge regarding the\nanomalies looking for. This issue may be solved by using few labeled anomalies\nas prior knowledge. In real-world scenarios, we can easily obtain few labeled\nanomalies. Efficiently leveraging labelled anomalies as prior knowledge is\ncrucial for graph anomaly detection; however, this process remains challenging\ndue to the inherently limited number of anomalies available. To address the\nproblem, we propose a novel approach that leverages metapath to embed actual\nconnectivity patterns between anomalous and normal nodes. To further\nefficiently exploit context information from metapath-based anomaly subgraph,\nwe present a new framework, Metapath-based Graph Anomaly Detection (MGAD),\nincorporating GCN layers in both the dual-encoders and decoders to efficiently\npropagate context information between abnormal and normal nodes. Specifically,\nMGAD employs GNN-based graph autoencoder as its backbone network. Moreover,\ndual encoders capture the complex interactions and metapath-based context\ninformation between labeled and unlabeled nodes both globally and locally.\nThrough a comprehensive set of experiments conducted on seven real-world\nnetworks, this paper demonstrates the superiority of the MGAD method compared\nto state-of-the-art techniques. The code is available at\nhttps://github.com/missinghwan/MGAD.\n", "date": "2023-08-21", "categories": []}
{"id": "2404.08531", "title": "Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly\n  Detection", "abstract": "  Weakly supervised video anomaly detection (WSVAD) is a challenging task.\nGenerating fine-grained pseudo-labels based on weak-label and then\nself-training a classifier is currently a promising solution. However, since\nthe existing methods use only RGB visual modality and the utilization of\ncategory text information is neglected, thus limiting the generation of more\naccurate pseudo-labels and affecting the performance of self-training. Inspired\nby the manual labeling process based on the event description, in this paper,\nwe propose a novel pseudo-label generation and self-training framework based on\nText Prompt with Normality Guidance (TPWNG) for WSVAD. Our idea is to transfer\nthe rich language-visual knowledge of the contrastive language-image\npre-training (CLIP) model for aligning the video event description text and\ncorresponding video frames to generate pseudo-labels. Specifically, We first\nfine-tune the CLIP for domain adaptation by designing two ranking losses and a\ndistributional inconsistency loss. Further, we propose a learnable text prompt\nmechanism with the assist of a normality visual prompt to further improve the\nmatching accuracy of video event description text and video frames. Then, we\ndesign a pseudo-label generation module based on the normality guidance to\ninfer reliable frame-level pseudo-labels. Finally, we introduce a temporal\ncontext self-adaptive learning module to learn the temporal dependencies of\ndifferent video events more flexibly and accurately. Extensive experiments show\nthat our method achieves state-of-the-art performance on two benchmark\ndatasets, UCF-Crime and XD-Viole\n", "date": "2024-04-12", "categories": []}
{"id": "2311.10421", "title": "Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to\n  the Real World", "abstract": "  Anomaly detection techniques are essential in automating the monitoring of IT\nsystems and operations. These techniques imply that machine learning algorithms\nare trained on operational data corresponding to a specific period of time and\nthat they are continuously evaluated on newly emerging data. Operational data\nis constantly changing over time, which affects the performance of deployed\nanomaly detection models. Therefore, continuous model maintenance is required\nto preserve the performance of anomaly detectors over time. In this work, we\nanalyze two different anomaly detection model maintenance techniques in terms\nof the model update frequency, namely blind model retraining and informed model\nretraining. We further investigate the effects of updating the model by\nretraining it on all the available data (full-history approach) and only the\nnewest data (sliding window approach). Moreover, we investigate whether a data\nchange monitoring tool is capable of determining when the anomaly detection\nmodel needs to be updated through retraining.\n", "date": "2023-11-17", "categories": []}
{"id": "2404.07748", "title": "3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing\n  Surfaces", "abstract": "  The surface quality inspection of manufacturing parts based on 3D point cloud\ndata has attracted increasing attention in recent years. The reason is that the\n3D point cloud can capture the entire surface of manufacturing parts, unlike\nthe previous practices that focus on some key product characteristics. However,\nachieving accurate 3D anomaly detection is challenging, due to the complex\nsurfaces of manufacturing parts and the difficulty of collecting sufficient\nanomaly samples. To address these challenges, we propose a novel untrained\nanomaly detection method based on 3D point cloud data for complex manufacturing\nparts, which can achieve accurate anomaly detection in a single sample without\ntraining data. In the proposed framework, we transform an input sample into two\nsets of profiles along different directions. Based on one set of the profiles,\na novel segmentation module is devised to segment the complex surface into\nmultiple basic and simple components. In each component, another set of\nprofiles, which have the nature of similar shapes, can be modeled as a low-rank\nmatrix. Thus, accurate 3D anomaly detection can be achieved by using Robust\nPrincipal Component Analysis (RPCA) on these low-rank matrices. Extensive\nnumerical experiments on different types of parts show that our method achieves\npromising results compared with the benchmark methods.\n", "date": "2024-04-11", "categories": []}
{"id": "2404.07887", "title": "Context-aware Video Anomaly Detection in Long-Term Datasets", "abstract": "  Video anomaly detection research is generally evaluated on short, isolated\nbenchmark videos only a few minutes long. However, in real-world environments,\nsecurity cameras observe the same scene for months or years at a time, and the\nnotion of anomalous behavior critically depends on context, such as the time of\nday, day of week, or schedule of events. Here, we propose a context-aware video\nanomaly detection algorithm, Trinity, specifically targeted to these scenarios.\nTrinity is especially well-suited to crowded scenes in which individuals cannot\nbe easily tracked, and anomalies are due to speed, direction, or absence of\ngroup motion. Trinity is a contrastive learning framework that aims to learn\nalignments between context, appearance, and motion, and uses alignment quality\nto classify videos as normal or anomalous. We evaluate our algorithm on both\nconventional benchmarks and a public webcam-based dataset we collected that\nspans more than three months of activity.\n", "date": "2024-04-11", "categories": []}
{"id": "2404.07898", "title": "Anomaly Detection in Power Grids via Context-Agnostic Learning", "abstract": "  An important tool grid operators use to safeguard against failures, whether\nnaturally occurring or malicious, involves detecting anomalies in the power\nsystem SCADA data. In this paper, we aim to solve a real-time anomaly detection\nproblem. Given time-series measurement values coming from a fixed set of\nsensors on the grid, can we identify anomalies in the network topology or\nmeasurement data? Existing methods, primarily optimization-based, mostly use\nonly a single snapshot of the measurement values and do not scale well with the\nnetwork size. Recent data-driven ML techniques have shown promise by using a\ncombination of current and historical data for anomaly detection but generally\ndo not consider physical attributes like the impact of topology or\nload/generation changes on sensor measurements and thus cannot accommodate\nregular context-variability in the historical data. To address this gap, we\npropose a novel context-aware anomaly detection algorithm, GridCAL, that\nconsiders the effect of regular topology and load/generation changes. This\nalgorithm converts the real-time power flow measurements to context-agnostic\nvalues, which allows us to analyze measurement coming from different grid\ncontexts in an aggregate fashion, enabling us to derive a unified statistical\nmodel that becomes the basis of anomaly detection. Through numerical\nsimulations on networks up to 2383 nodes, we show that our approach is\naccurate, outperforming state-of-the-art approaches, and is computationally\nefficient.\n", "date": "2024-04-11", "categories": []}
{"id": "2404.07968", "title": "AD-NEv++ : The multi-architecture neuroevolution-based multivariate\n  anomaly detection framework", "abstract": "  Anomaly detection tools and methods enable key analytical capabilities in\nmodern cyberphysical and sensor-based systems. Despite the fast-paced\ndevelopment in deep learning architectures for anomaly detection, model\noptimization for a given dataset is a cumbersome and time-consuming process.\nNeuroevolution could be an effective and efficient solution to this problem, as\na fully automated search method for learning optimal neural networks,\nsupporting both gradient and non-gradient fine tuning. However, existing\nframeworks incorporating neuroevolution lack of support for new layers and\narchitectures and are typically limited to convolutional and LSTM layers. In\nthis paper we propose AD-NEv++, a three-stage neuroevolution-based method that\nsynergically combines subspace evolution, model evolution, and fine-tuning. Our\nmethod overcomes the limitations of existing approaches by optimizing the\nmutation operator in the neuroevolution process, while supporting a wide\nspectrum of neural layers, including attention, dense, and graph convolutional\nlayers. Our extensive experimental evaluation was conducted with widely adopted\nmultivariate anomaly detection benchmark datasets, and showed that the models\ngenerated by AD-NEv++ outperform well-known deep learning architectures and\nneuroevolution-based approaches for anomaly detection. Moreover, results show\nthat AD-NEv++ can improve and outperform the state-of-the-art GNN (Graph Neural\nNetworks) model architecture in all anomaly detection benchmarks.\n", "date": "2024-03-25", "categories": []}
{"id": "2404.06832", "title": "SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection", "abstract": "  Detecting anomalies in images has become a well-explored problem in both\nacademia and industry. State-of-the-art algorithms are able to detect defects\nin increasingly difficult settings and data modalities. However, most current\nmethods are not suited to address 3D objects captured from differing poses.\nWhile solutions using Neural Radiance Fields (NeRFs) have been proposed, they\nsuffer from excessive computation requirements, which hinder real-world\nusability. For this reason, we propose the novel 3D Gaussian splatting-based\nframework SplatPose which, given multi-view images of a 3D object, accurately\nestimates the pose of unseen views in a differentiable manner, and detects\nanomalies in them. We achieve state-of-the-art results in both training and\ninference speed, and detection performance, even when using less training data\nthan competing methods. We thoroughly evaluate our framework using the recently\nproposed Pose-agnostic Anomaly Detection benchmark and its multi-pose anomaly\ndetection (MAD) data set.\n", "date": "2024-04-10", "categories": []}
{"id": "2403.04932", "title": "Divide and Conquer: High-Resolution Industrial Anomaly Detection via\n  Memory Efficient Tiled Ensemble", "abstract": "  Industrial anomaly detection is an important task within computer vision with\na wide range of practical use cases. The small size of anomalous regions in\nmany real-world datasets necessitates processing the images at a high\nresolution. This frequently poses significant challenges concerning memory\nconsumption during the model training and inference stages, leaving some\nexisting methods impractical for widespread adoption. To overcome this\nchallenge, we present the tiled ensemble approach, which reduces memory\nconsumption by dividing the input images into a grid of tiles and training a\ndedicated model for each tile location. The tiled ensemble is compatible with\nany existing anomaly detection model without the need for any modification of\nthe underlying architecture. By introducing overlapping tiles, we utilize the\nbenefits of traditional stacking ensembles, leading to further improvements in\nanomaly detection capabilities beyond high resolution alone. We perform a\ncomprehensive analysis using diverse underlying architectures, including Padim,\nPatchCore, FastFlow, and Reverse Distillation, on two standard anomaly\ndetection datasets: MVTec and VisA. Our method demonstrates a notable\nimprovement across setups while remaining within GPU memory constraints,\nconsuming only as much GPU memory as a single model needs to process a single\ntile.\n", "date": "2024-03-07", "categories": []}
{"id": "2404.06144", "title": "Differential Privacy for Anomaly Detection: Analyzing the Trade-off\n  Between Privacy and Explainability", "abstract": "  Anomaly detection (AD), also referred to as outlier detection, is a\nstatistical process aimed at identifying observations within a dataset that\nsignificantly deviate from the expected pattern of the majority of the data.\nSuch a process finds wide application in various fields, such as finance and\nhealthcare. While the primary objective of AD is to yield high detection\naccuracy, the requirements of explainability and privacy are also paramount.\nThe first ensures the transparency of the AD process, while the second\nguarantees that no sensitive information is leaked to untrusted parties. In\nthis work, we exploit the trade-off of applying Explainable AI (XAI) through\nSHapley Additive exPlanations (SHAP) and differential privacy (DP). We perform\nAD with different models and on various datasets, and we thoroughly evaluate\nthe cost of privacy in terms of decreased accuracy and explainability. Our\nresults show that the enforcement of privacy through DP has a significant\nimpact on detection accuracy and explainability, which depends on both the\ndataset and the considered AD model. We further show that the visual\ninterpretation of explanations is also influenced by the choice of the AD\nalgorithm.\n", "date": "2024-04-09", "categories": []}
{"id": "2308.08736", "title": "On the Effectiveness of Log Representation for Log-based Anomaly\n  Detection", "abstract": "  Logs are an essential source of information for people to understand the\nrunning status of a software system. Due to the evolving modern software\narchitecture and maintenance methods, more research efforts have been devoted\nto automated log analysis. In particular, machine learning (ML) has been widely\nused in log analysis tasks. In ML-based log analysis tasks, converting textual\nlog data into numerical feature vectors is a critical and indispensable step.\nHowever, the impact of using different log representation techniques on the\nperformance of the downstream models is not clear, which limits researchers and\npractitioners' opportunities of choosing the optimal log representation\ntechniques in their automated log analysis workflows. Therefore, this work\ninvestigates and compares the commonly adopted log representation techniques\nfrom previous log analysis research. Particularly, we select six log\nrepresentation techniques and evaluate them with seven ML models and four\npublic log datasets (i.e., HDFS, BGL, Spirit and Thunderbird) in the context of\nlog-based anomaly detection. We also examine the impacts of the log parsing\nprocess and the different feature aggregation approaches when they are employed\nwith log representation techniques. From the experiments, we provide some\nheuristic guidelines for future researchers and developers to follow when\ndesigning an automated log analysis workflow. We believe our comprehensive\ncomparison of log representation techniques can help researchers and\npractitioners better understand the characteristics of different log\nrepresentation techniques and provide them with guidance for selecting the most\nsuitable ones for their ML-based log analysis workflow.\n", "date": "2023-08-17", "categories": []}
{"id": "2311.16514", "title": "Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation :\n  A Unified Approach", "abstract": "  Video Anomaly Detection (VAD) is an open-set recognition task, which is\nusually formulated as a one-class classification (OCC) problem, where training\ndata is comprised of videos with normal instances while test data contains both\nnormal and anomalous instances. Recent works have investigated the creation of\npseudo-anomalies (PAs) using only the normal data and making strong assumptions\nabout real-world anomalies with regards to abnormality of objects and speed of\nmotion to inject prior information about anomalies in an autoencoder (AE) based\nreconstruction model during training. This work proposes a novel method for\ngenerating generic spatio-temporal PAs by inpainting a masked out region of an\nimage using a pre-trained Latent Diffusion Model and further perturbing the\noptical flow using mixup to emulate spatio-temporal distortions in the data. In\naddition, we present a simple unified framework to detect real-world anomalies\nunder the OCC setting by learning three types of anomaly indicators, namely\nreconstruction quality, temporal irregularity and semantic inconsistency.\nExtensive experiments on four VAD benchmark datasets namely Ped2, Avenue,\nShanghaiTech and UBnormal demonstrate that our method performs on par with\nother existing state-of-the-art PAs generation and reconstruction based methods\nunder the OCC setting. Our analysis also examines the transferability and\ngeneralisation of PAs across these datasets, offering valuable insights by\nidentifying real-world anomalies through PAs.\n", "date": "2023-11-27", "categories": []}
{"id": "2312.15288", "title": "Understanding normalization in contrastive representation learning and\n  out-of-distribution detection", "abstract": "  Contrastive representation learning has emerged as an outstanding approach\nfor anomaly detection. In this work, we explore the $\\ell_2$-norm of\ncontrastive features and its applications in out-of-distribution detection. We\npropose a simple method based on contrastive learning, which incorporates\nout-of-distribution data by discriminating against normal samples in the\ncontrastive layer space. Our approach can be applied flexibly as an outlier\nexposure (OE) approach, where the out-of-distribution data is a huge collective\nof random images, or as a fully self-supervised learning approach, where the\nout-of-distribution data is self-generated by applying distribution-shifting\ntransformations. The ability to incorporate additional out-of-distribution\nsamples enables a feasible solution for datasets where AD methods based on\ncontrastive learning generally underperform, such as aerial images or\nmicroscopy images. Furthermore, the high-quality features learned through\ncontrastive learning consistently enhance performance in OE scenarios, even\nwhen the available out-of-distribution dataset is not diverse enough. Our\nextensive experiments demonstrate the superiority of our proposed method under\nvarious scenarios, including unimodal and multimodal settings, with various\nimage datasets.\n", "date": "2023-12-23", "categories": []}
{"id": "2404.04311", "title": "A Real-time Anomaly Detection Using Convolutional Autoencoder with\n  Dynamic Threshold", "abstract": "  The majority of modern consumer-level energy is generated by real-time smart\nmetering systems. These frequently contain anomalies, which prevent reliable\nestimates of the series' evolution. This work introduces a hybrid modeling\napproach combining statistics and a Convolutional Autoencoder with a dynamic\nthreshold. The threshold is determined based on Mahalanobis distance and moving\naverages. It has been tested using real-life energy consumption data collected\nfrom smart metering systems. The solution includes a real-time, meter-level\nanomaly detection system that connects to an advanced monitoring system. This\nmakes a substantial contribution by detecting unusual data movements and\ndelivering an early warning. Early detection and subsequent troubleshooting can\nfinancially benefit organizations and consumers and prevent disasters from\noccurring.\n", "date": "2024-04-05", "categories": []}
{"id": "2404.04456", "title": "Beyond the Known: Adversarial Autoencoders in Novelty Detection", "abstract": "  In novelty detection, the goal is to decide if a new data point should be\ncategorized as an inlier or an outlier, given a training dataset that primarily\ncaptures the inlier distribution. Recent approaches typically use deep encoder\nand decoder network frameworks to derive a reconstruction error, and employ\nthis error either to determine a novelty score, or as the basis for a one-class\nclassifier. In this research, we use a similar framework but with a lightweight\ndeep network, and we adopt a probabilistic score with reconstruction error. Our\nmethodology calculates the probability of whether the sample comes from the\ninlier distribution or not. This work makes two key contributions. The first is\nthat we compute the novelty probability by linearizing the manifold that holds\nthe structure of the inlier distribution. This allows us to interpret how the\nprobability is distributed and can be determined in relation to the local\ncoordinates of the manifold tangent space. The second contribution is that we\nimprove the training protocol for the network. Our results indicate that our\napproach is effective at learning the target class, and it outperforms recent\nstate-of-the-art methods on several benchmark datasets.\n", "date": "2024-04-06", "categories": []}
{"id": "2404.04935", "title": "Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis\n  Through Self-Supervised Learning", "abstract": "  The electrocardiogram (ECG) is an essential tool for diagnosing heart\ndisease, with computer-aided systems improving diagnostic accuracy and reducing\nhealthcare costs. Despite advancements, existing systems often miss rare\ncardiac anomalies that could be precursors to serious, life-threatening issues\nor alterations in the cardiac macro/microstructure. We address this gap by\nfocusing on self-supervised anomaly detection (AD), training exclusively on\nnormal ECGs to recognize deviations indicating anomalies. We introduce a novel\nself-supervised learning framework for ECG AD, utilizing a vast dataset of\nnormal ECGs to autonomously detect and localize cardiac anomalies. It proposes\na novel masking and restoration technique alongside a multi-scale\ncross-attention module, enhancing the model's ability to integrate global and\nlocal signal features. The framework emphasizes accurate localization of\nanomalies within ECG signals, ensuring the method's clinical relevance and\nreliability. To reduce the impact of individual variability, the approach\nfurther incorporates crucial patient-specific information from ECG reports,\nsuch as age and gender, thus enabling accurate identification of a broad\nspectrum of cardiac anomalies, including rare ones. Utilizing an extensive\ndataset of 478,803 ECG graphic reports from real-world clinical practice, our\nmethod has demonstrated exceptional effectiveness in AD across all tested\nconditions, regardless of their frequency of occurrence, significantly\noutperforming existing models. It achieved superior performance metrics,\nincluding an AUROC of 91.2%, an F1 score of 83.7%, a sensitivity rate of 84.2%,\na specificity of 83.0%, and a precision of 75.6% with a fixed recall rate of\n90%. It has also demonstrated robust localization capabilities, with an AUROC\nof 76.5% and a Dice coefficient of 65.3% for anomaly localization.\n", "date": "2024-04-07", "categories": []}
{"id": "2404.04986", "title": "Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video\n  Anomaly Detection", "abstract": "  We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection,\na novel video anomaly detection methodology that combines pseudo-anomalies,\ndynamic anomaly weighting, and a distinction loss function to improve detection\naccuracy. By training on pseudo-anomalies, our approach adapts to the\nvariability of normal and anomalous behaviors without fixed anomaly thresholds.\nOur model showcases superior performance on the Ped2, Avenue and ShanghaiTech\ndatasets, where individual models are tailored for each scene. These\nachievements highlight DDL's effectiveness in advancing anomaly detection,\noffering a scalable and adaptable solution for video surveillance challenges.\n", "date": "2024-04-07", "categories": []}
{"id": "2404.03743", "title": "Test Time Training for Industrial Anomaly Segmentation", "abstract": "  Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality\ncontrol. While existing methods excel in generating anomaly scores for each\npixel, practical applications require producing a binary segmentation to\nidentify anomalies. Due to the absence of labeled anomalies in many real\nscenarios, standard practices binarize these maps based on some statistics\nderived from a validation set containing only nominal samples, resulting in\npoor segmentation performance. This paper addresses this problem by proposing a\ntest time training strategy to improve the segmentation performance. Indeed, at\ntest time, we can extract rich features directly from anomalous samples to\ntrain a classifier that can discriminate defects effectively. Our general\napproach can work downstream to any AD&S method that provides an anomaly score\nmap as output, even in multimodal settings. We demonstrate the effectiveness of\nour approach over baselines through extensive experimentation and evaluation on\nMVTec AD and MVTec 3D-AD.\n", "date": "2024-04-04", "categories": []}
{"id": "2404.04064", "title": "Fusing Dictionary Learning and Support Vector Machines for Unsupervised\n  Anomaly Detection", "abstract": "  We study in this paper the improvement of one-class support vector machines\n(OC-SVM) through sparse representation techniques for unsupervised anomaly\ndetection. As Dictionary Learning (DL) became recently a common analysis\ntechnique that reveals hidden sparse patterns of data, our approach uses this\ninsight to endow unsupervised detection with more control on pattern finding\nand dimensions. We introduce a new anomaly detection model that unifies the\nOC-SVM and DL residual functions into a single composite objective,\nsubsequently solved through K-SVD-type iterative algorithms. A closed-form of\nthe alternating K-SVD iteration is explicitly derived for the new composite\nmodel and practical implementable schemes are discussed. The standard DL model\nis adapted for the Dictionary Pair Learning (DPL) context, where the usual\nsparsity constraints are naturally eliminated. Finally, we extend both\nobjectives to the more general setting that allows the use of kernel functions.\nThe empirical convergence properties of the resulting algorithms are provided\nand an in-depth analysis of their parametrization is performed while also\ndemonstrating their numerical performance in comparison with existing methods.\n", "date": "2024-04-05", "categories": []}
{"id": "2306.00006", "title": "Truncated Affinity Maximization: One-class Homophily Modeling for Graph\n  Anomaly Detection", "abstract": "  We reveal a one-class homophily phenomenon, which is one prevalent property\nwe find empirically in real-world graph anomaly detection (GAD) datasets, i.e.,\nnormal nodes tend to have strong connection/affinity with each other, while the\nhomophily in abnormal nodes is significantly weaker than normal nodes. However,\nthis anomaly-discriminative property is ignored by existing GAD methods that\nare typically built using a conventional anomaly detection objective, such as\ndata reconstruction. In this work, we explore this property to introduce a\nnovel unsupervised anomaly scoring measure for GAD, local node affinity, that\nassigns a larger anomaly score to nodes that are less affiliated with their\nneighbors, with the affinity defined as similarity on node\nattributes/representations. We further propose Truncated Affinity Maximization\n(TAM) that learns tailored node representations for our anomaly measure by\nmaximizing the local affinity of nodes to their neighbors. Optimizing on the\noriginal graph structure can be biased by nonhomophily edges (i.e., edges\nconnecting normal and abnormal nodes). Thus, TAM is instead optimized on\ntruncated graphs where non-homophily edges are removed iteratively to mitigate\nthis bias. The learned representations result in significantly stronger local\naffinity for normal nodes than abnormal nodes. Extensive empirical results on\n10 real-world GAD datasets show that TAM substantially outperforms seven\ncompeting models, achieving over 10% increase in AUROC/AUPRC compared to the\nbest contenders on challenging datasets. Our code is available at\nhttps://github.com/mala-lab/TAM-master/.\n", "date": "2023-05-29", "categories": []}
{"id": "2307.12301", "title": "Image Outlier Detection Without Training using RANSAC", "abstract": "  Image outlier detection (OD) is an essential tool to ensure the quality of\nimages used in computer vision tasks. Existing algorithms often involve\ntraining a model to represent the inlier distribution, and outliers are\ndetermined by some deviation measure. Although existing methods proved\neffective when trained on strictly inlier samples, their performance remains\nquestionable when undesired outliers are included during training. As a result\nof this limitation, it is necessary to carefully examine the data when\ndeveloping OD models for new domains. In this work, we present a novel image OD\nalgorithm called RANSAC-NN that eliminates the need of data examination and\nmodel training altogether. Unlike existing approaches, RANSAC-NN can be\ndirectly applied on datasets containing outliers by sampling and comparing\nsubsets of the data. Our algorithm maintains favorable performance compared to\nexisting methods on a range of benchmarks. Furthermore, we show that RANSAC-NN\ncan enhance the robustness of existing methods by incorporating our algorithm\nas part of the data preparation process.\n", "date": "2023-07-23", "categories": []}
{"id": "2303.07557", "title": "Lifelong Continual Learning for Anomaly Detection: New Challenges,\n  Perspectives, and Insights", "abstract": "  Anomaly detection is of paramount importance in many real-world domains,\ncharacterized by evolving behavior. Lifelong learning represents an emerging\ntrend, answering the need for machine learning models that continuously adapt\nto new challenges in dynamic environments while retaining past knowledge.\nHowever, limited efforts are dedicated to building foundations for lifelong\nanomaly detection, which provides intrinsically different challenges compared\nto the more widely explored classification setting. In this paper, we face this\nissue by exploring, motivating, and discussing lifelong anomaly detection,\ntrying to build foundations for its wider adoption. First, we explain why\nlifelong anomaly detection is relevant, defining challenges and opportunities\nto design anomaly detection methods that deal with lifelong learning\ncomplexities. Second, we characterize learning settings and a scenario\ngeneration procedure that enables researchers to experiment with lifelong\nanomaly detection using existing datasets. Third, we perform experiments with\npopular anomaly detection methods on proposed lifelong scenarios, emphasizing\nthe gap in performance that could be gained with the adoption of lifelong\nlearning. Overall, we conclude that the adoption of lifelong anomaly detection\nis important to design more robust models that provide a comprehensive view of\nthe environment, as well as simultaneous adaptation and knowledge retention.\n", "date": "2023-03-14", "categories": []}
{"id": "2301.11514", "title": "Deep Industrial Image Anomaly Detection: A Survey", "abstract": "  The recent rapid development of deep learning has laid a milestone in\nindustrial Image Anomaly Detection (IAD). In this paper, we provide a\ncomprehensive review of deep learning-based image anomaly detection techniques,\nfrom the perspectives of neural network architectures, levels of supervision,\nloss functions, metrics and datasets. In addition, we extract the new setting\nfrom industrial manufacturing and review the current IAD approaches under our\nproposed our new setting. Moreover, we highlight several opening challenges for\nimage anomaly detection. The merits and downsides of representative network\narchitectures under varying supervision are discussed. Finally, we summarize\nthe research findings and point out future research directions. More resources\nare available at\nhttps://github.com/M-3LAB/awesome-industrial-anomaly-detection.\n", "date": "2023-01-27", "categories": []}
{"id": "2404.00060", "title": "Temporal Graph Networks for Graph Anomaly Detection in Financial\n  Networks", "abstract": "  This paper explores the utilization of Temporal Graph Networks (TGN) for\nfinancial anomaly detection, a pressing need in the era of fintech and\ndigitized financial transactions. We present a comprehensive framework that\nleverages TGN, capable of capturing dynamic changes in edges within financial\nnetworks, for fraud detection. Our study compares TGN's performance against\nstatic Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph\nneural network baselines using DGraph dataset for a realistic financial\ncontext. Our results demonstrate that TGN significantly outperforms other\nmodels in terms of AUC metrics. This superior performance underlines TGN's\npotential as an effective tool for detecting financial fraud, showcasing its\nability to adapt to the dynamic and complex nature of modern financial systems.\nWe also experimented with various graph embedding modules within the TGN\nframework and compared the effectiveness of each module. In conclusion, we\ndemonstrated that, even with variations within TGN, it is possible to achieve\ngood performance in the anomaly detection task.\n", "date": "2024-03-27", "categories": []}
{"id": "2404.01014", "title": "Harnessing Large Language Models for Training-free Video Anomaly\n  Detection", "abstract": "  Video anomaly detection (VAD) aims to temporally locate abnormal events in a\nvideo. Existing works mostly rely on training deep models to learn the\ndistribution of normality with either video-level supervision, one-class\nsupervision, or in an unsupervised setting. Training-based methods are prone to\nbe domain-specific, thus being costly for practical deployment as any domain\nchange will involve data collection and model training. In this paper, we\nradically depart from previous efforts and propose LAnguage-based VAD (LAVAD),\na method tackling VAD in a novel, training-free paradigm, exploiting the\ncapabilities of pre-trained large language models (LLMs) and existing\nvision-language models (VLMs). We leverage VLM-based captioning models to\ngenerate textual descriptions for each frame of any test video. With the\ntextual scene description, we then devise a prompting mechanism to unlock the\ncapability of LLMs in terms of temporal aggregation and anomaly score\nestimation, turning LLMs into an effective video anomaly detector. We further\nleverage modality-aligned VLMs and propose effective techniques based on\ncross-modal similarity for cleaning noisy captions and refining the LLM-based\nanomaly scores. We evaluate LAVAD on two large datasets featuring real-world\nsurveillance scenarios (UCF-Crime and XD-Violence), showing that it outperforms\nboth unsupervised and one-class methods without requiring any training or data\ncollection.\n", "date": "2024-04-01", "categories": []}
{"id": "2403.20236", "title": "Long-Tailed Anomaly Detection with Learnable Class Names", "abstract": "  Anomaly detection (AD) aims to identify defective images and localize their\ndefects (if any). Ideally, AD models should be able to detect defects over many\nimage classes; without relying on hard-coded class names that can be\nuninformative or inconsistent across datasets; learn without anomaly\nsupervision; and be robust to the long-tailed distributions of real-world\napplications. To address these challenges, we formulate the problem of\nlong-tailed AD by introducing several datasets with different levels of class\nimbalance and metrics for performance evaluation. We then propose a novel\nmethod, LTAD, to detect defects from multiple and long-tailed classes, without\nrelying on dataset class names. LTAD combines AD by reconstruction and semantic\nAD modules. AD by reconstruction is implemented with a transformer-based\nreconstruction module. Semantic AD is implemented with a binary classifier,\nwhich relies on learned pseudo class names and a pretrained foundation model.\nThese modules are learned over two phases. Phase 1 learns the pseudo-class\nnames and a variational autoencoder (VAE) for feature synthesis that augments\nthe training data to combat long-tails. Phase 2 then learns the parameters of\nthe reconstruction and classification modules of LTAD. Extensive experiments\nusing the proposed long-tailed datasets show that LTAD substantially\noutperforms the state-of-the-art methods for most forms of dataset imbalance.\nThe long-tailed dataset split is available at\nhttps://zenodo.org/records/10854201 .\n", "date": "2024-03-29", "categories": []}
{"id": "2306.12627", "title": "Targeted collapse regularized autoencoder for anomaly detection: black\n  hole at the center", "abstract": "  Autoencoders have been extensively used in the development of recent anomaly\ndetection techniques. The premise of their application is based on the notion\nthat after training the autoencoder on normal training data, anomalous inputs\nwill exhibit a significant reconstruction error. Consequently, this enables a\nclear differentiation between normal and anomalous samples. In practice,\nhowever, it is observed that autoencoders can generalize beyond the normal\nclass and achieve a small reconstruction error on some of the anomalous\nsamples. To improve the performance, various techniques propose additional\ncomponents and more sophisticated training procedures. In this work, we propose\na remarkably straightforward alternative: instead of adding neural network\ncomponents, involved computations, and cumbersome training, we complement the\nreconstruction loss with a computationally light term that regulates the norm\nof representations in the latent space. The simplicity of our approach\nminimizes the requirement for hyperparameter tuning and customization for new\napplications which, paired with its permissive data modality constraint,\nenhances the potential for successful adoption across a broad range of\napplications. We test the method on various visual and tabular benchmarks and\ndemonstrate that the technique matches and frequently outperforms more complex\nalternatives. We further demonstrate that implementing this idea in the context\nof state-of-the-art methods can further improve their performance. We also\nprovide a theoretical analysis and numerical simulations that help demonstrate\nthe underlying process that unfolds during training and how it helps with\nanomaly detection. This mitigates the black-box nature of autoencoder-based\nanomaly detection algorithms and offers an avenue for further investigation of\nadvantages, fail cases, and potential new directions.\n", "date": "2023-06-22", "categories": []}
{"id": "2310.02861", "title": "Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly\n  Detection", "abstract": "  Graph-level anomaly detection has gained significant attention as it finds\napplications in various domains, such as cancer diagnosis and enzyme\nprediction. However, existing methods fail to capture the spectral properties\nof graph anomalies, resulting in unexplainable framework design and\nunsatisfying performance. In this paper, we re-investigate the spectral\ndifferences between anomalous and normal graphs. Our main observation shows a\nsignificant disparity in the accumulated spectral energy between these two\nclasses. Moreover, we prove that the accumulated spectral energy of the graph\nsignal can be represented by its Rayleigh Quotient, indicating that the\nRayleigh Quotient is a driving factor behind the anomalous properties of\ngraphs. Motivated by this, we propose Rayleigh Quotient Graph Neural Network\n(RQGNN), the first spectral GNN that explores the inherent spectral features of\nanomalous graphs for graph-level anomaly detection. Specifically, we introduce\na novel framework with two components: the Rayleigh Quotient learning component\n(RQL) and Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly\ncaptures the Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the\nspectral space of graphs. Extensive experiments on 10 real-world datasets show\nthat RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in\nAUC, demonstrating the effectiveness of our framework. Our code is available at\nhttps://github.com/xydong127/RQGNN.\n", "date": "2023-10-04", "categories": []}
{"id": "2403.19111", "title": "Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection", "abstract": "  Video Anomaly Detection (VAD), aiming to identify abnormalities within a\nspecific context and timeframe, is crucial for intelligent Video Surveillance\nSystems. While recent deep learning-based VAD models have shown promising\nresults by generating high-resolution frames, they often lack competence in\npreserving detailed spatial and temporal coherence in video frames. To tackle\nthis issue, we propose a self-supervised learning approach for VAD through an\ninter-patch relationship prediction task. Specifically, we introduce a\ntwo-branch vision transformer network designed to capture deep visual features\nof video frames, addressing spatial and temporal dimensions responsible for\nmodeling appearance and motion patterns, respectively. The inter-patch\nrelationship in each dimension is decoupled into inter-patch similarity and the\norder information of each patch. To mitigate memory consumption, we convert the\norder information prediction task into a multi-label learning problem, and the\ninter-patch similarity prediction task into a distance matrix regression\nproblem. Comprehensive experiments demonstrate the effectiveness of our method,\nsurpassing pixel-generation-based methods by a significant margin across three\npublic benchmarks. Additionally, our approach outperforms other self-supervised\nlearning-based methods.\n", "date": "2024-03-28", "categories": []}
{"id": "2308.11138", "title": "NLP-based detection of systematic anomalies among the narratives of\n  consumer complaints", "abstract": "  We develop an NLP-based procedure for detecting systematic nonmeritorious\nconsumer complaints, simply called systematic anomalies, among complaint\nnarratives. While classification algorithms are used to detect pronounced\nanomalies, in the case of smaller and frequent systematic anomalies, the\nalgorithms may falter due to a variety of reasons, including technical ones as\nwell as natural limitations of human analysts. Therefore, as the next step\nafter classification, we convert the complaint narratives into quantitative\ndata, which are then analyzed using an algorithm for detecting systematic\nanomalies. We illustrate the entire procedure using complaint narratives from\nthe Consumer Complaint Database of the Consumer Financial Protection Bureau.\n", "date": "2023-08-22", "categories": []}
{"id": "2309.11427", "title": "Generative Pre-Training of Time-Series Data for Unsupervised Fault\n  Detection in Semiconductor Manufacturing", "abstract": "  This paper introduces TRACE-GPT, which stands for Time-seRies\nAnomaly-detection with Convolutional Embedding and Generative Pre-trained\nTransformers. TRACE-GPT is designed to pre-train univariate time-series sensor\ndata and detect faults on unlabeled datasets in semiconductor manufacturing. In\nsemiconductor industry, classifying abnormal time-series sensor data from\nnormal data is important because it is directly related to wafer defect.\nHowever, small, unlabeled, and even mixed training data without enough\nanomalies make classification tasks difficult. In this research, we capture\nfeatures of time-series data with temporal convolutional embedding and\nGenerative Pre-trained Transformer (GPT) to classify abnormal sequences from\nnormal sequences using cross entropy loss. We prove that our model shows better\nperformance than previous unsupervised models with both an open dataset, the\nUniversity of California Riverside (UCR) time-series classification archive,\nand the process log of our Chemical Vapor Deposition (CVD) equipment. Our model\nhas the highest F1 score at Equal Error Rate (EER) across all datasets and is\nonly 0.026 below the supervised state-of-the-art baseline on the open dataset.\n", "date": "2023-09-20", "categories": []}
{"id": "2403.18514", "title": "CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection\n  of Pathological Pulmonary CT scans", "abstract": "  Unsupervised pathology detection can be implemented by training a model on\nhealthy data only and measuring the deviation from the training set upon\ninference, for example with CNN-based feature extraction and one-class\nclassifiers, or reconstruction-score-based methods such as AEs, GANs and\nDiffusion models. Normalizing Flows (NF) have the ability to directly learn the\nprobability distribution of training examples through an invertible\narchitecture. We leverage this property in a novel 3D NF-based model named\nCT-3DFlow, specifically tailored for patient-level pulmonary pathology\ndetection in chest CT data. Our model is trained unsupervised on healthy 3D\npulmonary CT patches, and detects deviations from its log-likelihood\ndistribution as anomalies. We aggregate patches-level likelihood values from a\npatient's CT scan to provide a patient-level 'normal'/'abnormal' prediction.\nOut-of-distribution detection performance is evaluated using expert annotations\non a separate chest CT test dataset, outperforming other state-of-the-art\nmethods.\n", "date": "2024-03-27", "categories": []}
{"id": "2402.19330", "title": "A Novel Approach to Industrial Defect Generation through Blended Latent\n  Diffusion Model with Online Adaptation", "abstract": "  Effectively addressing the challenge of industrial Anomaly Detection (AD)\nnecessitates an ample supply of defective samples, a constraint often hindered\nby their scarcity in industrial contexts. This paper introduces a novel\nalgorithm designed to augment defective samples, thereby enhancing AD\nperformance. The proposed method tailors the blended latent diffusion model for\ndefect sample generation, employing a diffusion model to generate defective\nsamples in the latent space. A feature editing process, controlled by a\n``trimap\" mask and text prompts, refines the generated samples. The image\ngeneration inference process is structured into three stages: a free diffusion\nstage, an editing diffusion stage, and an online decoder adaptation stage. This\nsophisticated inference strategy yields high-quality synthetic defective\nsamples with diverse pattern variations, leading to significantly improved AD\naccuracies based on the augmented training set. Specifically, on the widely\nrecognized MVTec AD dataset, the proposed method elevates the state-of-the-art\n(SOTA) performance of AD with augmented data by 1.5%, 1.9%, and 3.1% for AD\nmetrics AP, IAP, and IAP90, respectively. The implementation code of this work\ncan be found at the GitHub repository\nhttps://github.com/GrandpaXun242/AdaBLDM.git\n", "date": "2024-02-29", "categories": []}
{"id": "2403.06247", "title": "Text-Guided Variational Image Generation for Industrial Anomaly\n  Detection and Segmentation", "abstract": "  We propose a text-guided variational image generation method to address the\nchallenge of getting clean data for anomaly detection in industrial\nmanufacturing. Our method utilizes text information about the target object,\nlearned from extensive text library documents, to generate non-defective data\nimages resembling the input image. The proposed framework ensures that the\ngenerated non-defective images align with anticipated distributions derived\nfrom textual and image-based knowledge, ensuring stability and generality.\nExperimental results demonstrate the effectiveness of our approach, surpassing\nprevious methods even with limited non-defective data. Our approach is\nvalidated through generalization tests across four baseline models and three\ndistinct datasets. We present an additional analysis to enhance the\neffectiveness of anomaly detection models by utilizing the generated images.\n", "date": "2024-03-10", "categories": []}
{"id": "2403.17891", "title": "Image-based Novel Fault Detection with Deep Learning Classifiers using\n  Hierarchical Labels", "abstract": "  One important characteristic of modern fault classification systems is the\nability to flag the system when faced with previously unseen fault types. This\nwork considers the unknown fault detection capabilities of deep neural\nnetwork-based fault classifiers. Specifically, we propose a methodology on how,\nwhen available, labels regarding the fault taxonomy can be used to increase\nunknown fault detection performance without sacrificing model performance. To\nachieve this, we propose to utilize soft label techniques to improve the\nstate-of-the-art deep novel fault detection techniques during the training\nprocess and novel hierarchically consistent detection statistics for online\nnovel fault detection. Finally, we demonstrated increased detection performance\non novel fault detection in inspection images from the hot steel rolling\nprocess, with results well replicated across multiple scenarios and baseline\ndetection methods.\n", "date": "2024-03-26", "categories": []}
{"id": "2403.15463", "title": "Unveiling the Anomalies in an Ever-Changing World: A Benchmark for\n  Pixel-Level Anomaly Detection in Continual Learning", "abstract": "  Anomaly Detection is a relevant problem in numerous real-world applications,\nespecially when dealing with images. However, little attention has been paid to\nthe issue of changes over time in the input data distribution, which may cause\na significant decrease in performance. In this study, we investigate the\nproblem of Pixel-Level Anomaly Detection in the Continual Learning setting,\nwhere new data arrives over time and the goal is to perform well on new and old\ndata. We implement several state-of-the-art techniques to solve the Anomaly\nDetection problem in the classic setting and adapt them to work in the\nContinual Learning setting. To validate the approaches, we use a real-world\ndataset of images with pixel-based anomalies to provide a reliable benchmark\nand serve as a foundation for further advancements in the field. We provide a\ncomprehensive analysis, discussing which Anomaly Detection methods and which\nfamilies of approaches seem more suitable for the Continual Learning setting.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.15497", "title": "On the Detection of Anomalous or Out-Of-Distribution Data in Vision\n  Models Using Statistical Techniques", "abstract": "  Out-of-distribution data and anomalous inputs are vulnerabilities of machine\nlearning systems today, often causing systems to make incorrect predictions.\nThe diverse range of data on which these models are used makes detecting\natypical inputs a difficult and important task. We assess a tool, Benford's\nlaw, as a method used to quantify the difference between real and corrupted\ninputs. We believe that in many settings, it could function as a filter for\nanomalous data points and for signalling out-of-distribution data. We hope to\nopen a discussion on these applications and further areas where this technique\nis underexplored.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.16270", "title": "Constricting Normal Latent Space for Anomaly Detection with Normal-only\n  Training Data", "abstract": "  In order to devise an anomaly detection model using only normal training\ndata, an autoencoder (AE) is typically trained to reconstruct the data. As a\nresult, the AE can extract normal representations in its latent space. During\ntest time, since AE is not trained using real anomalies, it is expected to\npoorly reconstruct the anomalous data. However, several researchers have\nobserved that it is not the case. In this work, we propose to limit the\nreconstruction capability of AE by introducing a novel latent constriction\nloss, which is added to the existing reconstruction loss. By using our method,\nno extra computational cost is added to the AE during test time. Evaluations\nusing three video anomaly detection benchmark datasets, i.e., Ped2, Avenue, and\nShanghaiTech, demonstrate the effectiveness of our method in limiting the\nreconstruction capability of AE, which leads to a better anomaly detection\nmodel.\n", "date": "2024-03-24", "categories": []}
{"id": "2403.16610", "title": "Distributed collaborative anomalous sound detection by embedding sharing", "abstract": "  To develop a machine sound monitoring system, a method for detecting\nanomalous sound is proposed. In this paper, we explore a method for multiple\nclients to collaboratively learn an anomalous sound detection model while\nkeeping their raw data private from each other. In the context of industrial\nmachine anomalous sound detection, each client possesses data from different\nmachines or different operational states, making it challenging to learn\nthrough federated learning or split learning. In our proposed method, each\nclient calculates embeddings using a common pre-trained model developed for\nsound data classification, and these calculated embeddings are aggregated on\nthe server to perform anomalous sound detection through outlier exposure.\nExperiments showed that our proposed method improves the AUC of anomalous sound\ndetection by an average of 6.8%.\n", "date": "2024-03-25", "categories": []}
{"id": "2403.14738", "title": "A task of anomaly detection for a smart satellite Internet of things\n  system", "abstract": "  When the equipment is working, real-time collection of environmental sensor\ndata for anomaly detection is one of the key links to prevent industrial\nprocess accidents and network attacks and ensure system security. However,\nunder the environment with specific real-time requirements, the anomaly\ndetection for environmental sensors still faces the following difficulties: (1)\nThe complex nonlinear correlation characteristics between environmental sensor\ndata variables lack effective expression methods, and the distribution between\nthe data is difficult to be captured. (2) it is difficult to ensure the\nreal-time monitoring requirements by using complex machine learning models, and\nthe equipment cost is too high. (3) Too little sample data leads to less\nlabeled data in supervised learning. This paper proposes an unsupervised deep\nlearning anomaly detection system. Based on the generative adversarial network\nand self-attention mechanism, considering the different feature information\ncontained in the local subsequences, it automatically learns the complex linear\nand nonlinear dependencies between environmental sensor variables, and uses the\nanomaly score calculation method combining reconstruction error and\ndiscrimination error. It can monitor the abnormal points of real sensor data\nwith high real-time performance and can run on the intelligent satellite\nInternet of things system, which is suitable for the real working environment.\nAnomaly detection outperforms baseline methods in most cases and has good\ninterpretability, which can be used to prevent industrial accidents and\ncyber-attacks for monitoring environmental sensors.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.14213", "title": "Toward Multi-class Anomaly Detection: Exploring Class-aware Unified\n  Model against Inter-class Interference", "abstract": "  In the context of high usability in single-class anomaly detection models,\nrecent academic research has become concerned about the more complex\nmulti-class anomaly detection. Although several papers have designed unified\nmodels for this task, they often overlook the utility of class labels, a potent\ntool for mitigating inter-class interference. To address this issue, we\nintroduce a Multi-class Implicit Neural representation Transformer for unified\nAnomaly Detection (MINT-AD), which leverages the fine-grained category\ninformation in the training stage. By learning the multi-class distributions,\nthe model generates class-aware query embeddings for the transformer decoder,\nmitigating inter-class interference within the reconstruction model. Utilizing\nsuch an implicit neural representation network, MINT-AD can project category\nand position information into a feature embedding space, further supervised by\nclassification and prior probability loss functions. Experimental results on\nmultiple datasets demonstrate that MINT-AD outperforms existing unified\ntraining models.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.14233", "title": "SoftPatch: Unsupervised Anomaly Detection with Noisy Data", "abstract": "  Although mainstream unsupervised anomaly detection (AD) algorithms perform\nwell in academic datasets, their performance is limited in practical\napplication due to the ideal experimental setting of clean training data.\nTraining with noisy data is an inevitable problem in real-world anomaly\ndetection but is seldom discussed. This paper considers label-level noise in\nimage sensory anomaly detection for the first time. To solve this problem, we\nproposed a memory-based unsupervised AD method, SoftPatch, which efficiently\ndenoises the data at the patch level. Noise discriminators are utilized to\ngenerate outlier scores for patch-level noise elimination before coreset\nconstruction. The scores are then stored in the memory bank to soften the\nanomaly detection boundary. Compared with existing methods, SoftPatch maintains\na strong modeling ability of normal data and alleviates the overconfidence\nproblem in coreset. Comprehensive experiments in various noise scenes\ndemonstrate that SoftPatch outperforms the state-of-the-art AD methods on the\nMVTecAD and BTAD benchmarks and is comparable to those methods under the\nsetting without noise.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.14262", "title": "Diffusion Models with Ensembled Structure-Based Anomaly Scoring for\n  Unsupervised Anomaly Detection", "abstract": "  Supervised deep learning techniques show promise in medical image analysis.\nHowever, they require comprehensive annotated data sets, which poses\nchallenges, particularly for rare diseases. Consequently, unsupervised anomaly\ndetection (UAD) emerges as a viable alternative for pathology segmentation, as\nonly healthy data is required for training. However, recent UAD anomaly scoring\nfunctions often focus on intensity only and neglect structural differences,\nwhich impedes the segmentation performance. This work investigates the\npotential of Structural Similarity (SSIM) to bridge this gap. SSIM captures\nboth intensity and structural disparities and can be advantageous over the\nclassical $l1$ error. However, we show that there is more than one optimal\nkernel size for the SSIM calculation for different pathologies. Therefore, we\ninvestigate an adaptive ensembling strategy for various kernel sizes to offer a\nmore pathology-agnostic scoring mechanism. We demonstrate that this ensembling\nstrategy can enhance the performance of DMs and mitigate the sensitivity to\ndifferent kernel sizes across varying pathologies, highlighting its promise for\nbrain MRI anomaly detection.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.14497", "title": "MULDE: Multiscale Log-Density Estimation via Denoising Score Matching\n  for Video Anomaly Detection", "abstract": "  We propose a novel approach to video anomaly detection: we treat feature\nvectors extracted from videos as realizations of a random variable with a fixed\ndistribution and model this distribution with a neural network. This lets us\nestimate the likelihood of test videos and detect video anomalies by\nthresholding the likelihood estimates. We train our video anomaly detector\nusing a modification of denoising score matching, a method that injects\ntraining data with noise to facilitate modeling its distribution. To eliminate\nhyperparameter selection, we model the distribution of noisy video features\nacross a range of noise levels and introduce a regularizer that tends to align\nthe models for different levels of noise. At test time, we combine anomaly\nindications at multiple noise scales with a Gaussian mixture model. Running our\nvideo anomaly detector induces minimal delays as inference requires merely\nextracting the features and forward-propagating them through a shallow neural\nnetwork and a Gaussian mixture model. Our experiments on five popular video\nanomaly detection benchmarks demonstrate state-of-the-art performance, both in\nthe object-centric and in the frame-centric setup.\n", "date": "2024-03-21", "categories": []}
{"id": "2311.16191", "title": "Learning Multi-Pattern Normalities in the Frequency Domain for Efficient\n  Time Series Anomaly Detection", "abstract": "  Anomaly detection significantly enhances the robustness of cloud systems.\nWhile neural network-based methods have recently demonstrated strong\nadvantages, they encounter practical challenges in cloud environments: the\ncontradiction between the impracticality of maintaining a unique model for each\nservice and the limited ability to deal with diverse normal patterns by a\nunified model, as well as issues with handling heavy traffic in real time and\nshort-term anomaly detection sensitivity.\n  Thus, we propose MACE, a multi-normal-pattern accommodated and efficient\nanomaly detection method in the frequency domain for time series anomaly\ndetection. There are three novel characteristics of it: (i) a pattern\nextraction mechanism excelling at handling diverse normal patterns with a\nunified model, which enables the model to identify anomalies by examining the\ncorrelation between the data sample and its service normal pattern, instead of\nsolely focusing on the data sample itself; (ii) a dualistic convolution\nmechanism that amplifies short-term anomalies in the time domain and hinders\nthe reconstruction of anomalies in the frequency domain, which enlarges the\nreconstruction error disparity between anomaly and normality and facilitates\nanomaly detection; (iii) leveraging the sparsity and parallelism of frequency\ndomain to enhance model efficiency. We theoretically and experimentally prove\nthat using a strategically selected subset of Fourier bases can not only reduce\ncomputational overhead but is also profitable to distinguish anomalies,\ncompared to using the complete spectrum. Moreover, extensive experiments\ndemonstrate MACE's effectiveness in handling diverse normal patterns with a\nunified model and it achieves state-of-the-art performance with high\nefficiency.\n", "date": "2023-11-26", "categories": []}
{"id": "2403.12362", "title": "DMAD: Dual Memory Bank for Real-World Anomaly Detection", "abstract": "  Training a unified model is considered to be more suitable for practical\nindustrial anomaly detection scenarios due to its generalization ability and\nstorage efficiency. However, this multi-class setting, which exclusively uses\nnormal data, overlooks the few but important accessible annotated anomalies in\nthe real world. To address the challenge of real-world anomaly detection, we\npropose a new framework named Dual Memory bank enhanced representation learning\nfor Anomaly Detection (DMAD). This framework handles both unsupervised and\nsemi-supervised scenarios in a unified (multi-class) setting. DMAD employs a\ndual memory bank to calculate feature distance and feature attention between\nnormal and abnormal patterns, thereby encapsulating knowledge about normal and\nabnormal instances. This knowledge is then used to construct an enhanced\nrepresentation for anomaly score learning. We evaluated DMAD on the MVTec-AD\nand VisA datasets. The results show that DMAD surpasses current\nstate-of-the-art methods, highlighting DMAD's capability in handling the\ncomplexities of real-world anomaly detection scenarios.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12415", "title": "VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual\n  Navigation", "abstract": "  This paper explores the potential of Large Language Models(LLMs) in zero-shot\nanomaly detection for safe visual navigation. With the assistance of the\nstate-of-the-art real-time open-world object detection model Yolo-World and\nspecialized prompts, the proposed framework can identify anomalies within\ncamera-captured frames that include any possible obstacles, then generate\nconcise, audio-delivered descriptions emphasizing abnormalities, assist in safe\nvisual navigation in complex circumstances. Moreover, our proposed framework\nleverages the advantages of LLMs and the open-vocabulary object detection model\nto achieve the dynamic scenario switch, which allows users to transition\nsmoothly from scene to scene, which addresses the limitation of traditional\nvisual navigation. Furthermore, this paper explored the performance\ncontribution of different prompt components, provided the vision for future\nimprovement in visual accessibility, and paved the way for LLMs in video\nanomaly detection and vision-language understanding.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12570", "title": "Adapting Visual-Language Models for Generalizable Anomaly Detection in\n  Medical Images", "abstract": "  Recent advancements in large-scale visual-language pre-trained models have\nled to significant progress in zero-/few-shot anomaly detection within natural\nimage domains. However, the substantial domain divergence between natural and\nmedical images limits the effectiveness of these methodologies in medical\nanomaly detection. This paper introduces a novel lightweight multi-level\nadaptation and comparison framework to repurpose the CLIP model for medical\nanomaly detection. Our approach integrates multiple residual adapters into the\npre-trained visual encoder, enabling a stepwise enhancement of visual features\nacross different levels. This multi-level adaptation is guided by multi-level,\npixel-wise visual-language feature alignment loss functions, which recalibrate\nthe model's focus from object semantics in natural imagery to anomaly\nidentification in medical images. The adapted features exhibit improved\ngeneralization across various medical data types, even in zero-shot scenarios\nwhere the model encounters unseen medical modalities and anatomical regions\nduring training. Our experiments on medical anomaly detection benchmarks\ndemonstrate that our method significantly surpasses current state-of-the-art\nmodels, with an average AUC improvement of 6.24% and 7.33% for anomaly\nclassification, 2.03% and 2.37% for anomaly segmentation, under the zero-shot\nand few-shot settings, respectively. Source code is available at:\nhttps://github.com/MediaBrain-SJTU/MVFA-AD\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12580", "title": "Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile\n  Industrial Anomaly Detection", "abstract": "  Industrial anomaly detection (IAD) has garnered significant attention and\nexperienced rapid development. However, the recent development of IAD approach\nhas encountered certain difficulties due to dataset limitations. On the one\nhand, most of the state-of-the-art methods have achieved saturation (over 99%\nin AUROC) on mainstream datasets such as MVTec, and the differences of methods\ncannot be well distinguished, leading to a significant gap between public\ndatasets and actual application scenarios. On the other hand, the research on\nvarious new practical anomaly detection settings is limited by the scale of the\ndataset, posing a risk of overfitting in evaluation results. Therefore, we\npropose a large-scale, Real-world, and multi-view Industrial Anomaly Detection\ndataset, named Real-IAD, which contains 150K high-resolution images of 30\ndifferent objects, an order of magnitude larger than existing datasets. It has\na larger range of defect area and ratio proportions, making it more challenging\nthan previous datasets. To make the dataset closer to real application\nscenarios, we adopted a multi-view shooting method and proposed sample-level\nevaluation metrics. In addition, beyond the general unsupervised anomaly\ndetection setting, we propose a new setting for Fully Unsupervised Industrial\nAnomaly Detection (FUIAD) based on the observation that the yield rate in\nindustrial production is usually greater than 60%, which has more practical\napplication value. Finally, we report the results of popular IAD methods on the\nReal-IAD dataset, providing a highly challenging benchmark to promote the\ndevelopment of the IAD field.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12672", "title": "Improving Interpretability of Scores in Anomaly Detection Based on\n  Gaussian-Bernoulli Restricted Boltzmann Machine", "abstract": "  Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs) are often used for\nsemi-supervised anomaly detection, where they are trained using only normal\ndata points. In GBRBM-based anomaly detection, normal and anomalous data are\nclassified based on a score that is identical to an energy function of the\nmarginal GBRBM. However, the classification threshold is difficult to set to an\nappropriate value, as this score cannot be interpreted. In this study, we\npropose a measure that improves score's interpretability based on its\ncumulative distribution, and establish a guideline for setting the threshold\nusing the interpretable measure. The results of numerical experiments show that\nthe guideline is reasonable when setting the threshold solely using normal data\npoints. Moreover, because identifying the measure involves computationally\ninfeasible evaluation of the minimum score value, we also propose an evaluation\nmethod for the minimum score based on simulated annealing, which is widely used\nfor optimization problems. The proposed evaluation method was also validated\nusing numerical experiments.\n", "date": "2024-03-19", "categories": []}
{"id": "2310.12790", "title": "Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection", "abstract": "  Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly\ndetection area - aims at utilizing a few samples of anomaly classes seen during\ntraining to detect unseen anomalies (i.e., samples from open-set anomaly\nclasses), while effectively identifying the seen anomalies. Benefiting from the\nprior knowledge illustrated by the seen anomalies, current OSAD methods can\noften largely reduce false positive errors. However, these methods are trained\nin a closed-set setting and treat the anomaly examples as from a homogeneous\ndistribution, rendering them less effective in generalizing to unseen anomalies\nthat can be drawn from any distribution. This paper proposes to learn\nheterogeneous anomaly distributions using the limited anomaly examples to\naddress this issue. To this end, we introduce a novel approach, namely Anomaly\nHeterogeneity Learning (AHL), that simulates a diverse set of heterogeneous\nanomaly distributions and then utilizes them to learn a unified heterogeneous\nabnormality model in surrogate open-set environments. Further, AHL is a generic\nframework that existing OSAD models can plug and play for enhancing their\nabnormality modeling. Extensive experiments on nine real-world anomaly\ndetection datasets show that AHL can 1) substantially enhance different\nstate-of-the-art OSAD models in detecting seen and unseen anomalies, and 2)\neffectively generalize to unseen anomalies in new domains. Code is available at\nhttps://github.com/mala-lab/AHL.\n", "date": "2023-10-19", "categories": []}
{"id": "2403.06495", "title": "Toward Generalist Anomaly Detection via In-context Residual Learning\n  with Few-shot Sample Prompts", "abstract": "  This paper explores the problem of Generalist Anomaly Detection (GAD), aiming\nto train one single detection model that can generalize to detect anomalies in\ndiverse datasets from different application domains without any further\ntraining on the target data. Some recent studies have shown that large\npre-trained Visual-Language Models (VLMs) like CLIP have strong generalization\ncapabilities on detecting industrial defects from various datasets, but their\nmethods rely heavily on handcrafted text prompts about defects, making them\ndifficult to generalize to anomalies in other applications, e.g., medical image\nanomalies or semantic anomalies in natural images. In this work, we propose to\ntrain a GAD model with few-shot normal images as sample prompts for AD on\ndiverse datasets on the fly. To this end, we introduce a novel approach that\nlearns an in-context residual learning model for GAD, termed InCTRL. It is\ntrained on an auxiliary dataset to discriminate anomalies from normal samples\nbased on a holistic evaluation of the residuals between query images and\nfew-shot normal sample prompts. Regardless of the datasets, per definition of\nanomaly, larger residuals are expected for anomalies than normal samples,\nthereby enabling InCTRL to generalize across different domains without further\ntraining. Comprehensive experiments on nine AD datasets are performed to\nestablish a GAD benchmark that encapsulate the detection of industrial defect\nanomalies, medical anomalies, and semantic anomalies in both one-vs-all and\nmulti-class setting, on which InCTRL is the best performer and significantly\noutperforms state-of-the-art competing methods. Code is available at\nhttps://github.com/mala-lab/InCTRL.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.10550", "title": "Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional\n  Normalizing Flows", "abstract": "  With the rapid development of the Internet, various types of anomaly traffic\nare threatening network security. We consider the problem of anomaly network\ntraffic detection and propose a three-stage anomaly detection framework using\nonly normal traffic. Our framework can generate pseudo anomaly samples without\nprior knowledge of anomalies to achieve the detection of anomaly data. Firstly,\nwe employ a reconstruction method to learn the deep representation of normal\nsamples. Secondly, these representations are normalized to a standard normal\ndistribution using a bidirectional flow module. To simulate anomaly samples, we\nadd noises to the normalized representations which are then passed through the\ngeneration direction of the bidirectional flow module. Finally, a simple\nclassifier is trained to differentiate the normal samples and pseudo anomaly\nsamples in the latent space. During inference, our framework requires only two\nmodules to detect anomalous samples, leading to a considerable reduction in\nmodel size. According to the experiments, our method achieves the state\nof-the-art results on the common benchmarking datasets of anomaly network\ntraffic detection. The code is given in the\nhttps://github.com/ZxuanDang/ATD-via-Flows.git\n", "date": "2024-03-13", "categories": []}
{"id": "2403.11536", "title": "OCR is All you need: Importing Multi-Modality into Image-based Defect\n  Detection System", "abstract": "  Automatic optical inspection (AOI) plays a pivotal role in the manufacturing\nprocess, predominantly leveraging high-resolution imaging instruments for\nscanning purposes. It detects anomalies by analyzing image textures or\npatterns, making it an essential tool in industrial manufacturing and quality\ncontrol. Despite its importance, the deployment of models for AOI often faces\nchallenges. These include limited sample sizes, which hinder effective feature\nlearning, variations among source domains, and sensitivities to changes in\nlighting and camera positions during imaging. These factors collectively\ncompromise the accuracy of model predictions. Traditional AOI often fails to\ncapitalize on the rich mechanism-parameter information from machines or inside\nimages, including statistical parameters, which typically benefit AOI\nclassification. To address this, we introduce an external modality-guided data\nmining framework, primarily rooted in optical character recognition (OCR), to\nextract statistical features from images as a second modality to enhance\nperformance, termed OANet (Ocr-Aoi-Net). A key aspect of our approach is the\nalignment of external modality features, extracted using a single\nmodality-aware model, with image features encoded by a convolutional neural\nnetwork. This synergy enables a more refined fusion of semantic representations\nfrom different modalities. We further introduce feature refinement and a gating\nfunction in our OANet to optimize the combination of these features, enhancing\ninference and decision-making capabilities. Experimental outcomes show that our\nmethodology considerably boosts the recall rate of the defect detection model\nand maintains high robustness even in challenging scenarios.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.11667", "title": "Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for\n  Unsupervised Anomaly Detection", "abstract": "  The high performance of denoising diffusion models for image generation has\npaved the way for their application in unsupervised medical anomaly detection.\nAs diffusion-based methods require a lot of GPU memory and have long sampling\ntimes, we present a novel and fast unsupervised anomaly detection approach\nbased on latent Bernoulli diffusion models. We first apply an autoencoder to\ncompress the input images into a binary latent representation. Next, a\ndiffusion model that follows a Bernoulli noise schedule is employed to this\nlatent space and trained to restore binary latent representations from\nperturbed ones. The binary nature of this diffusion model allows us to identify\nentries in the latent space that have a high probability of flipping their\nbinary code during the denoising process, which indicates out-of-distribution\ndata. We propose a masking algorithm based on these probabilities, which\nimproves the anomaly detection scores. We achieve state-of-the-art performance\ncompared to other diffusion-based unsupervised anomaly detection algorithms\nwhile significantly reducing sampling time and memory consumption. The code is\navailable at https://github.com/JuliaWolleb/Anomaly_berdiff.\n", "date": "2024-03-18", "categories": []}
{"id": "2309.08374", "title": "Understanding the limitations of self-supervised learning for tabular\n  anomaly detection", "abstract": "  While self-supervised learning has improved anomaly detection in computer\nvision and natural language processing, it is unclear whether tabular data can\nbenefit from it. This paper explores the limitations of self-supervision for\ntabular anomaly detection. We conduct several experiments spanning various\npretext tasks on 26 benchmark datasets to understand why this is the case. Our\nresults confirm representations derived from self-supervision do not improve\ntabular anomaly detection performance compared to using the raw representations\nof the data. We show this is due to neural networks introducing irrelevant\nfeatures, which reduces the effectiveness of anomaly detectors. However, we\ndemonstrate that using a subspace of the neural network's representation can\nrecover performance.\n", "date": "2023-09-15", "categories": []}
{"id": "2403.10220", "title": "From Chaos to Clarity: Time Series Anomaly Detection in Astronomical\n  Observations", "abstract": "  With the development of astronomical facilities, large-scale time series data\nobserved by these facilities is being collected. Analyzing anomalies in these\nastronomical observations is crucial for uncovering potential celestial events\nand physical phenomena, thus advancing the scientific research process.\nHowever, existing time series anomaly detection methods fall short in tackling\nthe unique characteristics of astronomical observations where each star is\ninherently independent but interfered by random concurrent noise, resulting in\na high rate of false alarms. To overcome the challenges, we propose AERO, a\nnovel two-stage framework tailored for unsupervised anomaly detection in\nastronomical observations. In the first stage, we employ a Transformer-based\nencoder-decoder architecture to learn the normal temporal patterns on each\nvariate (i.e., star) in alignment with the characteristic of variate\nindependence. In the second stage, we enhance the graph neural network with a\nwindow-wise graph structure learning to tackle the occurrence of concurrent\nnoise characterized by spatial and temporal randomness. In this way, AERO is\nnot only capable of distinguishing normal temporal patterns from potential\nanomalies but also effectively differentiating concurrent noise, thus\ndecreasing the number of false alarms. We conducted extensive experiments on\nthree synthetic datasets and three real-world datasets. The results demonstrate\nthat AERO outperforms the compared baselines. Notably, compared to the\nstate-of-the-art model, AERO improves the F1-score by up to 8.76% and 2.63% on\nsynthetic and real-world datasets respectively.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.10327", "title": "Unsupervised Threat Hunting using Continuous Bag-of-Terms-and-Time\n  (CBoTT)", "abstract": "  Threat hunting is sifting through system logs to detect malicious activities\nthat might have bypassed existing security measures. It can be performed in\nseveral ways, one of which is based on detecting anomalies. We propose an\nunsupervised framework, called continuous bag-of-terms-and-time (CBoTT), and\npublish its application programming interface (API) to help researchers and\ncybersecurity analysts perform anomaly-based threat hunting among SIEM logs\ngeared toward process auditing on endpoint devices. Analyses show that our\nframework consistently outperforms benchmark approaches. When logs are sorted\nby likelihood of being an anomaly (from most likely to least), our approach\nidentifies anomalies at higher percentiles (between 1.82-6.46) while benchmark\napproaches identify the same anomalies at lower percentiles (between\n3.25-80.92). This framework can be used by other researchers to conduct\nbenchmark analyses and cybersecurity analysts to find anomalies in SIEM logs.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.10339", "title": "Generation is better than Modification: Combating High Class Homophily\n  Variance in Graph Anomaly Detection", "abstract": "  Graph-based anomaly detection is currently an important research topic in the\nfield of graph neural networks (GNNs). We find that in graph anomaly detection,\nthe homophily distribution differences between different classes are\nsignificantly greater than those in homophilic and heterophilic graphs. For the\nfirst time, we introduce a new metric called Class Homophily Variance, which\nquantitatively describes this phenomenon. To mitigate its impact, we propose a\nnovel GNN model named Homophily Edge Generation Graph Neural Network (HedGe).\nPrevious works typically focused on pruning, selecting or connecting on\noriginal relationships, and we refer to these methods as modifications.\nDifferent from these works, our method emphasizes generating new relationships\nwith low class homophily variance, using the original relationships as an\nauxiliary. HedGe samples homophily adjacency matrices from scratch using a\nself-attention mechanism, and leverages nodes that are relevant in the feature\nspace but not directly connected in the original graph. Additionally, we modify\nthe loss function to punish the generation of unnecessary heterophilic edges by\nthe model. Extensive comparison experiments demonstrate that HedGe achieved the\nbest performance across multiple benchmark datasets, including anomaly\ndetection and edgeless node classification. The proposed model also improves\nthe robustness under the novel Heterophily Attack with increased class\nhomophily variance on other graph classification tasks.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.09493", "title": "Anomaly Detection by Adapting a pre-trained Vision Language Model", "abstract": "  Recently, large vision and language models have shown their success when\nadapting them to many downstream tasks. In this paper, we present a unified\nframework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP\nmodel. To this end, we make two important improvements: 1) To acquire unified\nanomaly detection across industrial images of multiple categories, we introduce\nthe learnable prompt and propose to associate it with abnormal patterns through\nself-supervised learning. 2) To fully exploit the representation power of CLIP,\nwe introduce an anomaly region refinement strategy to refine the localization\nquality. During testing, the anomalies are localized by directly calculating\nthe similarity between the representation of the learnable prompt and the\nimage. Comprehensive experiments demonstrate the superiority of our framework,\ne.g., we achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and\nVisA for anomaly detection and localization. In addition, the proposed method\nalso achieves encouraging performance with marginal training data, which is\nmore challenging.\n", "date": "2024-03-14", "categories": []}
{"id": "2311.07042", "title": "Open-Vocabulary Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) with weak supervision has achieved remarkable\nperformance in utilizing video-level labels to discriminate whether a video\nframe is normal or abnormal. However, current approaches are inherently limited\nto a closed-set setting and may struggle in open-world applications where there\ncan be anomaly categories in the test data unseen during training. A few recent\nstudies attempt to tackle a more realistic setting, open-set VAD, which aims to\ndetect unseen anomalies given seen anomalies and normal videos. However, such a\nsetting focuses on predicting frame anomaly scores, having no ability to\nrecognize the specific categories of anomalies, despite the fact that this\nability is essential for building more informed video surveillance systems.\nThis paper takes a step further and explores open-vocabulary video anomaly\ndetection (OVVAD), in which we aim to leverage pre-trained large models to\ndetect and categorize seen and unseen anomalies. To this end, we propose a\nmodel that decouples OVVAD into two mutually complementary tasks --\nclass-agnostic detection and class-specific classification -- and jointly\noptimizes both tasks. Particularly, we devise a semantic knowledge injection\nmodule to introduce semantic knowledge from large language models for the\ndetection task, and design a novel anomaly synthesis module to generate pseudo\nunseen anomaly videos with the help of large vision generation models for the\nclassification task. These semantic knowledge and synthesis anomalies\nsubstantially extend our model's capability in detecting and categorizing a\nvariety of seen and unseen anomalies. Extensive experiments on three\nwidely-used benchmarks demonstrate our model achieves state-of-the-art\nperformance on OVVAD task.\n", "date": "2023-11-13", "categories": []}
{"id": "2403.08013", "title": "Supervised Time Series Classification for Anomaly Detection in Subsea\n  Engineering", "abstract": "  Time series classification is of significant importance in monitoring\nstructural systems. In this work, we investigate the use of supervised machine\nlearning classification algorithms on simulated data based on a physical system\nwith two states: Intact and Broken. We provide a comprehensive discussion of\nthe preprocessing of temporal data, using measures of statistical dispersion\nand dimension reduction techniques. We present an intuitive baseline method and\ndiscuss its efficiency. We conclude with a comparison of the various methods\nbased on different performance metrics, showing the advantage of using machine\nlearning techniques as a tool in decision making.\n", "date": "2024-03-12", "categories": []}
{"id": "2403.08027", "title": "McCatch: Scalable Microcluster Detection in Dimensional and\n  Nondimensional Datasets", "abstract": "  How could we have an outlier detector that works even with nondimensional\ndata, and ranks together both singleton microclusters ('one-off' outliers) and\nnonsingleton microclusters by their anomaly scores? How to obtain scores that\nare principled in one scalable and 'hands-off' manner? Microclusters of\noutliers indicate coalition or repetition in fraud activities, etc.; their\nidentification is thus highly desirable. This paper presents McCatch: a new\nalgorithm that detects microclusters by leveraging our proposed 'Oracle' plot\n(1NN Distance versus Group 1NN Distance). We study 31 real and synthetic\ndatasets with up to 1M data elements to show that McCatch is the only method\nthat answers both of the questions above; and, it outperforms 11 other methods,\nespecially when the data has nonsingleton microclusters or is nondimensional.\nWe also showcase McCatch's ability to detect meaningful microclusters in\ngraphs, fingerprints, logs of network connections, text data, and satellite\nimagery. For example, it found a 30-elements microcluster of confirmed 'Denial\nof Service' attacks in the network logs, taking only ~3 minutes for 222K data\nelements on a stock desktop.\n", "date": "2024-03-12", "categories": []}
{"id": "2403.08464", "title": "Diffusion Models with Implicit Guidance for Medical Anomaly Detection", "abstract": "  Diffusion models have advanced unsupervised anomaly detection by improving\nthe transformation of pathological images into pseudo-healthy equivalents.\nNonetheless, standard approaches may compromise critical information during\npathology removal, leading to restorations that do not align with unaffected\nregions in the original scans. Such discrepancies can inadvertently increase\nfalse positive rates and reduce specificity, complicating radiological\nevaluations. This paper introduces Temporal Harmonization for Optimal\nRestoration (THOR), which refines the de-noising process by integrating\nimplicit guidance through temporal anomaly maps. THOR aims to preserve the\nintegrity of healthy tissue in areas unaffected by pathology. Comparative\nevaluations show that THOR surpasses existing diffusion-based methods in\ndetecting and segmenting anomalies in brain MRIs and wrist X-rays. Code:\nhttps://github.com/ci-ber/THOR_DDPM.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.08689", "title": "Exploiting Structural Consistency of Chest Anatomy for Unsupervised\n  Anomaly Detection in Radiography Images", "abstract": "  Radiography imaging protocols focus on particular body regions, therefore\nproducing images of great similarity and yielding recurrent anatomical\nstructures across patients. Exploiting this structured information could\npotentially ease the detection of anomalies from radiography images. To this\nend, we propose a Simple Space-Aware Memory Matrix for In-painting and\nDetecting anomalies from radiography images (abbreviated as SimSID). We\nformulate anomaly detection as an image reconstruction task, consisting of a\nspace-aware memory matrix and an in-painting block in the feature space. During\nthe training, SimSID can taxonomize the ingrained anatomical structures into\nrecurrent visual patterns, and in the inference, it can identify anomalies\n(unseen/modified visual patterns) from the test image. Our SimSID surpasses the\nstate of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9%\nAUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively.\nCode: https://github.com/MrGiovanni/SimSID\n", "date": "2024-03-13", "categories": []}
{"id": "2402.14892", "title": "Novelty Detection on Radio Astronomy Data using Signatures", "abstract": "  We introduce SigNova, a new semi-supervised framework for detecting anomalies\nin streamed data. While our initial examples focus on detecting radio-frequency\ninterference (RFI) in digitized signals within the field of radio astronomy, it\nis important to note that SigNova's applicability extends to any type of\nstreamed data. The framework comprises three primary components. Firstly, we\nuse the signature transform to extract a canonical collection of summary\nstatistics from observational sequences. This allows us to represent\nvariable-length visibility samples as finite-dimensional feature vectors.\nSecondly, each feature vector is assigned a novelty score, calculated as the\nMahalanobis distance to its nearest neighbor in an RFI-free training set. By\nthresholding these scores we identify observation ranges that deviate from the\nexpected behavior of RFI-free visibility samples without relying on stringent\ndistributional assumptions. Thirdly, we integrate this anomaly detector with\nPysegments, a segmentation algorithm, to localize consecutive observations\ncontaminated with RFI, if any. This approach provides a compelling alternative\nto classical windowing techniques commonly used for RFI detection. Importantly,\nthe complexity of our algorithm depends on the RFI pattern rather than on the\nsize of the observation window. We demonstrate how SigNova improves the\ndetection of various types of RFI (e.g., broadband and narrowband) in\ntime-frequency visibility data. We validate our framework on the Murchison\nWidefield Array (MWA) telescope and simulated data and the Hydrogen Epoch of\nReionization Array (HERA).\n", "date": "2024-02-22", "categories": []}
{"id": "2403.06154", "title": "GlanceVAD: Exploring Glance Supervision for Label-efficient Video\n  Anomaly Detection", "abstract": "  In recent years, video anomaly detection has been extensively investigated in\nboth unsupervised and weakly supervised settings to alleviate costly temporal\nlabeling. Despite significant progress, these methods still suffer from\nunsatisfactory results such as numerous false alarms, primarily due to the\nabsence of precise temporal anomaly annotation. In this paper, we present a\nnovel labeling paradigm, termed \"glance annotation\", to achieve a better\nbalance between anomaly detection accuracy and annotation cost. Specifically,\nglance annotation is a random frame within each abnormal event, which can be\neasily accessed and is cost-effective. To assess its effectiveness, we manually\nannotate the glance annotations for two standard video anomaly detection\ndatasets: UCF-Crime and XD-Violence. Additionally, we propose a customized\nGlanceVAD method, that leverages gaussian kernels as the basic unit to compose\nthe temporal anomaly distribution, enabling the learning of diverse and robust\nanomaly representations from the glance annotations. Through comprehensive\nanalysis and experiments, we verify that the proposed labeling paradigm can\nachieve an excellent trade-off between annotation cost and model performance.\nExtensive experimental results also demonstrate the effectiveness of our\nGlanceVAD approach, which significantly outperforms existing advanced\nunsupervised and weakly supervised methods. Code and annotations will be\npublicly available at https://github.com/pipixin321/GlanceVAD.\n", "date": "2024-03-10", "categories": []}
{"id": "2306.12041", "title": "Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly\n  Detectors", "abstract": "  We propose an efficient abnormal event detection model based on a lightweight\nmasked auto-encoder (AE) applied at the video frame level. The novelty of the\nproposed model is threefold. First, we introduce an approach to weight tokens\nbased on motion gradients, thus shifting the focus from the static background\nscene to the foreground objects. Second, we integrate a teacher decoder and a\nstudent decoder into our architecture, leveraging the discrepancy between the\noutputs given by the two decoders to improve anomaly detection. Third, we\ngenerate synthetic abnormal events to augment the training videos, and task the\nmasked AE model to jointly reconstruct the original frames (without anomalies)\nand the corresponding pixel-level anomaly maps. Our design leads to an\nefficient and effective model, as demonstrated by the extensive experiments\ncarried out on four benchmarks: Avenue, ShanghaiTech, UBnormal and UCSD Ped2.\nThe empirical results show that our model achieves an excellent trade-off\nbetween speed and accuracy, obtaining competitive AUC scores, while processing\n1655 FPS. Hence, our model is between 8 and 70 times faster than competing\nmethods. We also conduct an ablation study to justify our design. Our code is\nfreely available at: https://github.com/ristea/aed-mae.\n", "date": "2023-06-21", "categories": []}
{"id": "2403.05897", "title": "RealNet: A Feature Selection Network with Realistic Synthetic Anomaly\n  for Anomaly Detection", "abstract": "  Self-supervised feature reconstruction methods have shown promising advances\nin industrial image anomaly detection and localization. Despite this progress,\nthese methods still face challenges in synthesizing realistic and diverse\nanomaly samples, as well as addressing the feature redundancy and pre-training\nbias of pre-trained feature. In this work, we introduce RealNet, a feature\nreconstruction network with realistic synthetic anomaly and adaptive feature\nselection. It is incorporated with three key innovations: First, we propose\nStrength-controllable Diffusion Anomaly Synthesis (SDAS), a diffusion\nprocess-based synthesis strategy capable of generating samples with varying\nanomaly strengths that mimic the distribution of real anomalous samples.\nSecond, we develop Anomaly-aware Features Selection (AFS), a method for\nselecting representative and discriminative pre-trained feature subsets to\nimprove anomaly detection performance while controlling computational costs.\nThird, we introduce Reconstruction Residuals Selection (RRS), a strategy that\nadaptively selects discriminative residuals for comprehensive identification of\nanomalous regions across multiple levels of granularity. We assess RealNet on\nfour benchmark datasets, and our results demonstrate significant improvements\nin both Image AUROC and Pixel AUROC compared to the current state-o-the-art\nmethods. The code, data, and models are available at\nhttps://github.com/cnulab/RealNet.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.06552", "title": "Detection of Object Throwing Behavior in Surveillance Videos", "abstract": "  Anomalous behavior detection is a challenging research area within computer\nvision. Progress in this area enables automated detection of dangerous behavior\nusing surveillance camera feeds. A dangerous behavior that is often overlooked\nin other research is the throwing action in traffic flow, which is one of the\nunique requirements of our Smart City project to enhance public safety. This\npaper proposes a solution for throwing action detection in surveillance videos\nusing deep learning. At present, datasets for throwing actions are not publicly\navailable. To address the use-case of our Smart City project, we first generate\nthe novel public 'Throwing Action' dataset, consisting of 271 videos of\nthrowing actions performed by traffic participants, such as pedestrians,\nbicyclists, and car drivers, and 130 normal videos without throwing actions.\nSecond, we compare the performance of different feature extractors for our\nanomaly detection method on the UCF-Crime and Throwing-Action datasets. The\nexplored feature extractors are the Convolutional 3D (C3D) network, the\nInflated 3D ConvNet (I3D) network, and the Multi-Fiber Network (MFNet).\nFinally, the performance of the anomaly detection algorithm is improved by\napplying the Adam optimizer instead of Adadelta, and proposing a mean normal\nloss function that covers the multitude of normal situations in traffic. Both\naspects yield better anomaly detection performance. Besides this, the proposed\nmean normal loss function lowers the false alarm rate on the combined dataset.\nThe experimental results reach an area under the ROC curve of 86.10 for the\nThrowing-Action dataset, and 80.13 on the combined dataset, respectively.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.06874", "title": "COOD: Combined out-of-distribution detection using multiple measures for\n  anomaly & novel class detection in large-scale hierarchical classification", "abstract": "  High-performing out-of-distribution (OOD) detection, both anomaly and novel\nclass, is an important prerequisite for the practical use of classification\nmodels. In this paper, we focus on the species recognition task in images\nconcerned with large databases, a large number of fine-grained hierarchical\nclasses, severe class imbalance, and varying image quality. We propose a\nframework for combining individual OOD measures into one combined OOD (COOD)\nmeasure using a supervised model. The individual measures are several existing\nstate-of-the-art measures and several novel OOD measures developed with novel\nclass detection and hierarchical class structure in mind. COOD was extensively\nevaluated on three large-scale (500k+ images) biodiversity datasets in the\ncontext of anomaly and novel class detection. We show that COOD outperforms\nindividual, including state-of-the-art, OOD measures by a large margin in terms\nof TPR@1% FPR in the majority of experiments, e.g., improving detecting\nImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset.\nSHAP (feature contribution) analysis shows that different individual OOD\nmeasures are essential for various tasks, indicating that multiple OOD measures\nand combinations are needed to generalize. Additionally, we show that\nexplicitly considering ID images that are incorrectly classified for the\noriginal (species) recognition task is important for constructing\nhigh-performing OOD detection methods and for practical applicability. The\nframework can easily be extended or adapted to other tasks and media\nmodalities.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.04010", "title": "Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message\n  Passing and Hyperbolic Neural Networks", "abstract": "  Graph anomaly detection plays a vital role for identifying abnormal instances\nin complex networks. Despite advancements of methodology based on deep learning\nin recent years, existing benchmarking approaches exhibit limitations that\nhinder a comprehensive comparison. In this paper, we revisit datasets and\napproaches for unsupervised node-level graph anomaly detection tasks from three\naspects. Firstly, we introduce outlier injection methods that create more\ndiverse and graph-based anomalies in graph datasets. Secondly, we compare\nmethods employing message passing against those without, uncovering the\nunexpected decline in performance associated with message passing. Thirdly, we\nexplore the use of hyperbolic neural networks, specifying crucial architecture\nand loss design that contribute to enhanced performance. Through rigorous\nexperiments and evaluations, our study sheds light on general strategies for\nimproving node-level graph anomaly detection methods.\n", "date": "2024-03-06", "categories": []}
{"id": "2403.04429", "title": "Exploring the Influence of Dimensionality Reduction on Anomaly Detection\n  Performance in Multivariate Time Series", "abstract": "  This paper presents an extensive empirical study on the integration of\ndimensionality reduction techniques with advanced unsupervised time series\nanomaly detection models, focusing on the MUTANT and Anomaly-Transformer\nmodels. The study involves a comprehensive evaluation across three different\ndatasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing\nfor a robust assessment of the models' capabilities in varied contexts. The\ndimensionality reduction techniques examined include PCA, UMAP, Random\nProjection, and t-SNE, each offering distinct advantages in simplifying\nhigh-dimensional data. Our findings reveal that dimensionality reduction not\nonly aids in reducing computational complexity but also significantly enhances\nanomaly detection performance in certain scenarios. Moreover, a remarkable\nreduction in training times was observed, with reductions by approximately\n300\\% and 650\\% when dimensionality was halved and minimized to the lowest\ndimensions, respectively. This efficiency gain underscores the dual benefit of\ndimensionality reduction in both performance enhancement and operational\nefficiency. The MUTANT model exhibits notable adaptability, especially with\nUMAP reduction, while the Anomaly-Transformer demonstrates versatility across\nvarious reduction techniques. These insights provide a deeper understanding of\nthe synergistic effects of dimensionality reduction and anomaly detection,\ncontributing valuable perspectives to the field of time series analysis. The\nstudy underscores the importance of selecting appropriate dimensionality\nreduction strategies based on specific model requirements and dataset\ncharacteristics, paving the way for more efficient, accurate, and scalable\nsolutions in anomaly detection.\n", "date": "2024-03-07", "categories": []}
{"id": "2307.07893", "title": "Anomaly Detection in Automated Fibre Placement: Learning with Data\n  Limitations", "abstract": "  Conventional defect detection systems in Automated Fibre Placement (AFP)\ntypically rely on end-to-end supervised learning, necessitating a substantial\nnumber of labelled defective samples for effective training. However, the\nscarcity of such labelled data poses a challenge. To overcome this limitation,\nwe present a comprehensive framework for defect detection and localization in\nAutomated Fibre Placement. Our approach combines unsupervised deep learning and\nclassical computer vision algorithms, eliminating the need for labelled data or\nmanufacturing defect samples. It efficiently detects various surface issues\nwhile requiring fewer images of composite parts for training. Our framework\nemploys an innovative sample extraction method leveraging AFP's inherent\nsymmetry to expand the dataset. By inputting a depth map of the fibre layup\nsurface, we extract local samples aligned with each composite strip (tow).\nThese samples are processed through an autoencoder, trained on normal samples\nfor precise reconstructions, highlighting anomalies through reconstruction\nerrors. Aggregated values form an anomaly map for insightful visualization. The\nframework employs blob detection on this map to locate manufacturing defects.\nThe experimental findings reveal that despite training the autoencoder with a\nlimited number of images, our proposed method exhibits satisfactory detection\naccuracy and accurately identifies defect locations. Our framework demonstrates\ncomparable performance to existing methods, while also offering the advantage\nof detecting all types of anomalies without relying on an extensive labelled\ndataset of defects.\n", "date": "2023-07-15", "categories": []}
{"id": "2312.10187", "title": "TSRNet: Simple Framework for Real-time ECG Anomaly Detection with\n  Multimodal Time and Spectrogram Restoration Network", "abstract": "  The electrocardiogram (ECG) is a valuable signal used to assess various\naspects of heart health, such as heart rate and rhythm. It plays a crucial role\nin identifying cardiac conditions and detecting anomalies in ECG data. However,\ndistinguishing between normal and abnormal ECG signals can be a challenging\ntask. In this paper, we propose an approach that leverages anomaly detection to\nidentify unhealthy conditions using solely normal ECG data for training.\nFurthermore, to enhance the information available and build a robust system, we\nsuggest considering both the time series and time-frequency domain aspects of\nthe ECG signal. As a result, we introduce a specialized network called the\nMultimodal Time and Spectrogram Restoration Network (TSRNet) designed\nspecifically for detecting anomalies in ECG signals. TSRNet falls into the\ncategory of restoration-based anomaly detection and draws inspiration from both\nthe time series and spectrogram domains. By extracting representations from\nboth domains, TSRNet effectively captures the comprehensive characteristics of\nthe ECG signal. This approach enables the network to learn robust\nrepresentations with superior discrimination abilities, allowing it to\ndistinguish between normal and abnormal ECG patterns more effectively.\nFurthermore, we introduce a novel inference method, termed Peak-based Error,\nthat specifically focuses on ECG peaks, a critical component in detecting\nabnormalities. The experimental result on the large-scale dataset PTB-XL has\ndemonstrated the effectiveness of our approach in ECG anomaly detection, while\nalso prioritizing efficiency by minimizing the number of trainable parameters.\nOur code is available at https://github.com/UARK-AICV/TSRNet.\n", "date": "2023-12-15", "categories": []}
{"id": "2403.03563", "title": "Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip\n  Perception of Mobile Manipulation Robots", "abstract": "  Object slip perception is essential for mobile manipulation robots to perform\nmanipulation tasks reliably in the dynamic real-world. Traditional approaches\nto robot arms' slip perception use tactile or vision sensors. However, mobile\nrobots still have to deal with noise in their sensor signals caused by the\nrobot's movement in a changing environment. To solve this problem, we present\nan anomaly detection method that utilizes multisensory data based on a deep\nautoencoder model. The proposed framework integrates heterogeneous data streams\ncollected from various robot sensors, including RGB and depth cameras, a\nmicrophone, and a force-torque sensor. The integrated data is used to train a\ndeep autoencoder to construct latent representations of the multisensory data\nthat indicate the normal status. Anomalies can then be identified by error\nscores measured by the difference between the trained encoder's latent values\nand the latent values of reconstructed input data. In order to evaluate the\nproposed framework, we conducted an experiment that mimics an object slip by a\nmobile service robot operating in a real-world environment with diverse\nhousehold objects and different moving patterns. The experimental results\nverified that the proposed framework reliably detects anomalies in object slip\nsituations despite various object types and robot behaviors, and visual and\nauditory noise in the environment.\n", "date": "2024-03-06", "categories": []}
{"id": "2306.03933", "title": "High-dimensional and Permutation Invariant Anomaly Detection", "abstract": "  Methods for anomaly detection of new physics processes are often limited to\nlow-dimensional spaces due to the difficulty of learning high-dimensional\nprobability densities. Particularly at the constituent level, incorporating\ndesirable properties such as permutation invariance and variable-length inputs\nbecomes difficult within popular density estimation methods. In this work, we\nintroduce a permutation-invariant density estimator for particle physics data\nbased on diffusion models, specifically designed to handle variable-length\ninputs. We demonstrate the efficacy of our methodology by utilizing the learned\ndensity as a permutation-invariant anomaly detection score, effectively\nidentifying jets with low likelihood under the background-only hypothesis. To\nvalidate our density estimation method, we investigate the ratio of learned\ndensities and compare to those obtained by a supervised classification\nalgorithm.\n", "date": "2023-06-06", "categories": []}
{"id": "2311.15453", "title": "DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised\n  Anomaly Detection", "abstract": "  Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize\nanomalies without relying on annotations, only leveraging a model trained on a\ndataset known to be free of anomalies. Diffusion models learn to modify inputs\n$x$ to increase the probability of it belonging to a desired distribution,\ni.e., they model the score function $\\nabla_x \\log p(x)$. Such a score function\nis potentially relevant for UAD, since $\\nabla_x \\log p(x)$ is itself a\npixel-wise anomaly score. However, diffusion models are trained to invert a\ncorruption process based on Gaussian noise and the learned score function is\nunlikely to generalize to medical anomalies. This work addresses the problem of\nhow to learn a score function relevant for UAD and proposes DISYRE:\nDiffusion-Inspired SYnthetic REstoration. We retain the diffusion-like pipeline\nbut replace the Gaussian noise corruption with a gradual, synthetic anomaly\ncorruption so the learned score function generalizes to medical, naturally\noccurring anomalies. We evaluate DISYRE on three common Brain MRI UAD\nbenchmarks and substantially outperform other methods in two out of the three\ntasks.\n", "date": "2023-11-26", "categories": []}
{"id": "2311.18061", "title": "TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural\n  Architecture Search in Time Series Anomaly Detection", "abstract": "  The surge in real-time data collection across various industries has\nunderscored the need for advanced anomaly detection in both univariate and\nmultivariate time series data. This paper introduces TransNAS-TSAD, a framework\nthat synergizes the transformer architecture with neural architecture search\n(NAS), enhanced through NSGA-II algorithm optimization. This approach\neffectively tackles the complexities of time series data, balancing\ncomputational efficiency with detection accuracy. Our evaluation reveals that\nTransNAS-TSAD surpasses conventional anomaly detection models due to its\ntailored architectural adaptability and the efficient exploration of complex\nsearch spaces, leading to marked improvements in diverse data scenarios. We\nalso introduce the Efficiency-Accuracy-Complexity Score (EACS) as a new metric\nfor assessing model performance, emphasizing the balance between accuracy and\ncomputational resources. TransNAS-TSAD sets a new benchmark in time series\nanomaly detection, offering a versatile, efficient solution for complex\nreal-world applications. This research highlights the TransNAS-TSAD potential\nacross a wide range of industry applications and paves the way for future\ndevelopments in the field.\n", "date": "2023-11-29", "categories": []}
{"id": "2403.02429", "title": "Towards efficient deep autoencoders for multivariate time series anomaly\n  detection", "abstract": "  Multivariate time series anomaly detection is a crucial problem in many\nindustrial and research applications. Timely detection of anomalies allows, for\ninstance, to prevent defects in manufacturing processes and failures in\ncyberphysical systems. Deep learning methods are preferred among others for\ntheir accuracy and robustness for the analysis of complex multivariate data.\nHowever, a key aspect is being able to extract predictions in a timely manner,\nto accommodate real-time requirements in different applications. In the case of\ndeep learning models, model reduction is extremely important to achieve optimal\nresults in real-time systems with limited time and memory constraints. In this\npaper, we address this issue by proposing a novel compression method for deep\nautoencoders that involves three key factors. First, pruning reduces the number\nof weights, while preventing catastrophic drops in accuracy by means of a fast\nsearch process that identifies high sparsity levels. Second, linear and\nnon-linear quantization reduces model complexity by reducing the number of bits\nfor every single weight. The combined contribution of these three aspects allow\nthe model size to be reduced, by removing a subset of the weights (pruning),\nand decreasing their bit-width (quantization). As a result, the compressed\nmodel is faster and easier to adopt in highly constrained hardware\nenvironments. Experiments performed on popular multivariate anomaly detection\nbenchmarks, show that our method is capable of achieving significant model\ncompression ratio (between 80% and 95%) without a significant reduction in the\nanomaly detection performance.\n", "date": "2024-03-04", "categories": []}
{"id": "2403.02616", "title": "Unsupervised Spatio-Temporal State Estimation for Fine-grained Adaptive\n  Anomaly Diagnosis of Industrial Cyber-physical Systems", "abstract": "  Accurate detection and diagnosis of abnormal behaviors such as network\nattacks from multivariate time series (MTS) are crucial for ensuring the stable\nand effective operation of industrial cyber-physical systems (CPS). However,\nexisting researches pay little attention to the logical dependencies among\nsystem working states, and have difficulties in explaining the evolution\nmechanisms of abnormal signals. To reveal the spatio-temporal association\nrelationships and evolution mechanisms of the working states of industrial CPS,\nthis paper proposes a fine-grained adaptive anomaly diagnosis method (i.e.\nMAD-Transformer) to identify and diagnose anomalies in MTS. MAD-Transformer\nfirst constructs a temporal state matrix to characterize and estimate the\nchange patterns of the system states in the temporal dimension. Then, to better\nlocate the anomalies, a spatial state matrix is also constructed to capture the\ninter-sensor state correlation relationships within the system. Subsequently,\nbased on these two types of state matrices, a three-branch structure of\nseries-temporal-spatial attention module is designed to simultaneously capture\nthe series, temporal, and space dependencies among MTS. Afterwards, three\nassociated alignment loss functions and a reconstruction loss are constructed\nto jointly optimize the model. Finally, anomalies are determined and diagnosed\nby comparing the residual matrices with the original matrices. We conducted\ncomparative experiments on five publicly datasets spanning three application\ndomains (service monitoring, spatial and earth exploration, and water\ntreatment), along with a petroleum refining simulation dataset collected by\nourselves. The results demonstrate that MAD-Transformer can adaptively detect\nfine-grained anomalies with short duration, and outperforms the\nstate-of-the-art baselines in terms of noise robustness and localization\nperformance.\n", "date": "2024-03-05", "categories": []}
{"id": "2311.00453", "title": "CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly\n  Detection", "abstract": "  This paper considers zero-shot Anomaly Detection (AD), performing AD without\nreference images of the test objects. We propose a framework called CLIP-AD to\nleverage the zero-shot capabilities of the large vision-language model CLIP.\nFirstly, we reinterpret the text prompts design from a distributional\nperspective and propose a Representative Vector Selection (RVS) paradigm to\nobtain improved text features. Secondly, we note opposite predictions and\nirrelevant highlights in the direct computation of the anomaly maps. To address\nthese issues, we introduce a Staged Dual-Path model (SDP) that leverages\nfeatures from various levels and applies architecture and feature surgery.\nLastly, delving deeply into the two phenomena, we point out that the image and\ntext features are not aligned in the joint embedding space. Thus, we introduce\na fine-tuning strategy by adding linear layers and construct an extended model\nSDP+, further enhancing the performance. Abundant experiments demonstrate the\neffectiveness of our approach, e.g., on MVTec-AD, SDP outperforms the SOTA\nWinCLIP by +4.2/+10.7 in segmentation metrics F1-max/PRO, while SDP+ achieves\n+8.3/+20.5 improvements.\n", "date": "2023-11-01", "categories": []}
{"id": "2403.01245", "title": "AcME-AD: Accelerated Model Explanations for Anomaly Detection", "abstract": "  Pursuing fast and robust interpretability in Anomaly Detection is crucial,\nespecially due to its significance in practical applications. Traditional\nAnomaly Detection methods excel in outlier identification but are often\nblack-boxes, providing scant insights into their decision-making process. This\nlack of transparency compromises their reliability and hampers their adoption\nin scenarios where comprehending the reasons behind anomaly detection is vital.\nAt the same time, getting explanations quickly is paramount in practical\nscenarios. To bridge this gap, we present AcME-AD, a novel approach rooted in\nExplainable Artificial Intelligence principles, designed to clarify Anomaly\nDetection models for tabular data. AcME-AD transcends the constraints of\nmodel-specific or resource-heavy explainability techniques by delivering a\nmodel-agnostic, efficient solution for interoperability. It offers local\nfeature importance scores and a what-if analysis tool, shedding light on the\nfactors contributing to each anomaly, thus aiding root cause analysis and\ndecision-making. This paper elucidates AcME-AD's foundation, its benefits over\nexisting methods, and validates its effectiveness with tests on both synthetic\nand real datasets. AcME-AD's implementation and experiment replication code is\naccessible in a public repository.\n", "date": "2024-03-02", "categories": []}
{"id": "2403.01804", "title": "PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using\n  Local-Global Features", "abstract": "  Three-dimensional point cloud anomaly detection that aims to detect anomaly\ndata points from a training set serves as the foundation for a variety of\napplications, including industrial inspection and autonomous driving. However,\nexisting point cloud anomaly detection methods often incorporate multiple\nfeature memory banks to fully preserve local and global representations, which\ncomes at the high cost of computational complexity and mismatches between\nfeatures. To address that, we propose an unsupervised point cloud anomaly\ndetection framework based on joint local-global features, termed PointCore. To\nbe specific, PointCore only requires a single memory bank to store local\n(coordinate) and global (PointMAE) representations and different priorities are\nassigned to these local-global features, thereby reducing the computational\ncost and mismatching disturbance in inference. Furthermore, to robust against\nthe outliers, a normalization ranking method is introduced to not only adjust\nvalues of different scales to a notionally common scale, but also transform\ndensely-distributed data into a uniform distribution. Extensive experiments on\nReal3D-AD dataset demonstrate that PointCore achieves competitive inference\ntime and the best performance in both detection and localization as compared to\nthe state-of-the-art Reg3D-AD approach and several competitors.\n", "date": "2024-03-04", "categories": []}
{"id": "2403.01859", "title": "CSE: Surface Anomaly Detection with Contrastively Selected Embedding", "abstract": "  Detecting surface anomalies of industrial materials poses a significant\nchallenge within a myriad of industrial manufacturing processes. In recent\ntimes, various methodologies have emerged, capitalizing on the advantages of\nemploying a network pre-trained on natural images for the extraction of\nrepresentative features. Subsequently, these features are subjected to\nprocessing through a diverse range of techniques including memory banks,\nnormalizing flow, and knowledge distillation, which have exhibited exceptional\naccuracy. This paper revisits approaches based on pre-trained features by\nintroducing a novel method centered on target-specific embedding. To capture\nthe most representative features of the texture under consideration, we employ\na variant of a contrastive training procedure that incorporates both\nartificially generated defective samples and anomaly-free samples during\ntraining. Exploiting the intrinsic properties of surfaces, we derived a\nmeaningful representation from the defect-free samples during training,\nfacilitating a straightforward yet effective calculation of anomaly scores. The\nexperiments conducted on the MVTEC AD and TILDA datasets demonstrate the\ncompetitiveness of our approach compared to state-of-the-art methods.\n", "date": "2024-03-04", "categories": []}
{"id": "2403.01895", "title": "Unsupervised Distance Metric Learning for Anomaly Detection Over\n  Multivariate Time Series", "abstract": "  Distance-based time series anomaly detection methods are prevalent due to\ntheir relative non-parametric nature and interpretability. However, the\ncommonly used Euclidean distance is sensitive to noise. While existing works\nhave explored dynamic time warping (DTW) for its robustness, they only support\nsupervised tasks over multivariate time series (MTS), leaving a scarcity of\nunsupervised methods. In this work, we propose FCM-wDTW, an unsupervised\ndistance metric learning method for anomaly detection over MTS, which encodes\nraw data into latent space and reveals normal dimension relationships through\ncluster centers. FCM-wDTW introduces locally weighted DTW into fuzzy C-means\nclustering and learns the optimal latent space efficiently, enabling anomaly\nidentification via data reconstruction. Experiments with 11 different types of\nbenchmarks demonstrate our method's competitive accuracy and efficiency.\n", "date": "2024-03-04", "categories": []}
{"id": "2402.18998", "title": "COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection", "abstract": "  Existing approaches towards anomaly detection~(AD) often rely on a\nsubstantial amount of anomaly-free data to train representation and density\nmodels. However, large anomaly-free datasets may not always be available before\nthe inference stage; in which case an anomaly detection model must be trained\nwith only a handful of normal samples, a.k.a. few-shot anomaly detection\n(FSAD). In this paper, we propose a novel methodology to address the challenge\nof FSAD which incorporates two important techniques. Firstly, we employ a model\npre-trained on a large source dataset to initialize model weights. Secondly, to\nameliorate the covariate shift between source and target domains, we adopt\ncontrastive training to fine-tune on the few-shot target domain data. To learn\nsuitable representations for the downstream AD task, we additionally\nincorporate cross-instance positive pairs to encourage a tight cluster of the\nnormal samples, and negative pairs for better separation between normal and\nsynthesized negative samples. We evaluate few-shot anomaly detection on on 3\ncontrolled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness\nof the proposed method.\n", "date": "2024-02-29", "categories": []}
{"id": "2307.12545", "title": "Towards Video Anomaly Retrieval from Video Anomaly Detection: New\n  Benchmarks and Model", "abstract": "  Video anomaly detection (VAD) has been paid increasing attention due to its\npotential applications, its current dominant tasks focus on online detecting\nanomalies% at the frame level, which can be roughly interpreted as the binary\nor multiple event classification. However, such a setup that builds\nrelationships between complicated anomalous events and single labels, e.g.,\n``vandalism'', is superficial, since single labels are deficient to\ncharacterize anomalous events. In reality, users tend to search a specific\nvideo rather than a series of approximate videos. Therefore, retrieving\nanomalous events using detailed descriptions is practical and positive but few\nresearches focus on this. In this context, we propose a novel task called Video\nAnomaly Retrieval (VAR), which aims to pragmatically retrieve relevant\nanomalous videos by cross-modalities, e.g., language descriptions and\nsynchronous audios. Unlike the current video retrieval where videos are assumed\nto be temporally well-trimmed with short duration, VAR is devised to retrieve\nlong untrimmed videos which may be partially relevant to the given query. To\nachieve this, we present two large-scale VAR benchmarks, UCFCrime-AR and\nXDViolence-AR, constructed on top of prevalent anomaly datasets. Meanwhile, we\ndesign a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we\npropose an anomaly-led sampling to focus on key segments in long untrimmed\nvideos. Then, we introduce an efficient pretext task to enhance semantic\nassociations between video-text fine-grained representations. Besides, we\nleverage two complementary alignments to further match cross-modal contents.\nExperimental results on two benchmarks reveal the challenges of VAR task and\nalso demonstrate the advantages of our tailored method. Captions are publicly\nreleased at https://github.com/Roc-Ng/VAR.\n", "date": "2023-07-24", "categories": []}
{"id": "2311.04948", "title": "Explained anomaly detection in text reviews: Can subjective scenarios be\n  correctly evaluated?", "abstract": "  This paper presents a pipeline to detect and explain anomalous reviews in\nonline platforms. The pipeline is made up of three modules and allows the\ndetection of reviews that do not generate value for users due to either\nworthless or malicious composition. The classifications are accompanied by a\nnormality score and an explanation that justifies the decision made. The\npipeline's ability to solve the anomaly detection task was evaluated using\ndifferent datasets created from a large Amazon database. Additionally, a study\ncomparing three explainability techniques involving 241 participants was\nconducted to assess the explainability module. The study aimed to measure the\nimpact of explanations on the respondents' ability to reproduce the\nclassification model and their perceived usefulness. This work can be useful to\nautomate tasks in review online platforms, such as those for electronic\ncommerce, and offers inspiration for addressing similar problems in the field\nof anomaly detection in textual data. We also consider it interesting to have\ncarried out a human evaluation of the capacity of different explainability\ntechniques in a real and infrequent scenario such as the detection of anomalous\nreviews, as well as to reflect on whether it is possible to explain tasks as\nhumanly subjective as this one.\n", "date": "2023-11-08", "categories": []}
{"id": "2402.18362", "title": "Objective and Interpretable Breast Cosmesis Evaluation with Attention\n  Guided Denoising Diffusion Anomaly Detection Model", "abstract": "  As advancements in the field of breast cancer treatment continue to progress,\nthe assessment of post-surgical cosmetic outcomes has gained increasing\nsignificance due to its substantial impact on patients' quality of life.\nHowever, evaluating breast cosmesis presents challenges due to the inherently\nsubjective nature of expert labeling. In this study, we present a novel\nautomated approach, Attention-Guided Denoising Diffusion Anomaly Detection\n(AG-DDAD), designed to assess breast cosmesis following surgery, addressing the\nlimitations of conventional supervised learning and existing anomaly detection\nmodels. Our approach leverages the attention mechanism of the distillation with\nno label (DINO) self-supervised Vision Transformer (ViT) in combination with a\ndiffusion model to achieve high-quality image reconstruction and precise\ntransformation of discriminative regions. By training the diffusion model on\nunlabeled data predominantly with normal cosmesis, we adopt an unsupervised\nanomaly detection perspective to automatically score the cosmesis. Real-world\ndata experiments demonstrate the effectiveness of our method, providing\nvisually appealing representations and quantifiable scores for cosmesis\nevaluation. Compared to commonly used rule-based programs, our fully automated\napproach eliminates the need for manual annotations and offers objective\nevaluation. Moreover, our anomaly detection model exhibits state-of-the-art\nperformance, surpassing existing models in accuracy. Going beyond the scope of\nbreast cosmesis, our research represents a significant advancement in\nunsupervised anomaly detection within the medical domain, thereby paving the\nway for future investigations.\n", "date": "2024-02-28", "categories": []}
{"id": "2308.15939", "title": "Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot\n  Anomaly Localization", "abstract": "  Contrastive Language-Image Pre-training (CLIP) models have shown promising\nperformance on zero-shot visual recognition tasks by learning visual\nrepresentations under natural language supervision. Recent studies attempt the\nuse of CLIP to tackle zero-shot anomaly detection by matching images with\nnormal and abnormal state prompts. However, since CLIP focuses on building\ncorrespondence between paired text prompts and global image-level\nrepresentations, the lack of fine-grained patch-level vision to text alignment\nlimits its capability on precise visual anomaly localization. In this work, we\npropose AnoCLIP for zero-shot anomaly localization. In the visual encoder, we\nintroduce a training-free value-wise attention mechanism to extract intrinsic\nlocal tokens of CLIP for patch-level local description. From the perspective of\ntext supervision, we particularly design a unified domain-aware contrastive\nstate prompting template for fine-grained vision-language matching. On top of\nthe proposed AnoCLIP, we further introduce a test-time adaptation (TTA)\nmechanism to refine visual anomaly localization results, where we optimize a\nlightweight adapter in the visual encoder using AnoCLIP's pseudo-labels and\nnoise-corrupted tokens. With both AnoCLIP and TTA, we significantly exploit the\npotential of CLIP for zero-shot anomaly localization and demonstrate the\neffectiveness of AnoCLIP on various datasets.\n", "date": "2023-08-30", "categories": []}
{"id": "2402.17091", "title": "Structural Teacher-Student Normality Learning for Multi-Class Anomaly\n  Detection and Localization", "abstract": "  Visual anomaly detection is a challenging open-set task aimed at identifying\nunknown anomalous patterns while modeling normal data. The knowledge\ndistillation paradigm has shown remarkable performance in one-class anomaly\ndetection by leveraging teacher-student network feature comparisons. However,\nextending this paradigm to multi-class anomaly detection introduces novel\nscalability challenges. In this study, we address the significant performance\ndegradation observed in previous teacher-student models when applied to\nmulti-class anomaly detection, which we identify as resulting from cross-class\ninterference. To tackle this issue, we introduce a novel approach known as\nStructural Teacher-Student Normality Learning (SNL): (1) We propose\nspatial-channel distillation and intra-&inter-affinity distillation techniques\nto measure structural distance between the teacher and student networks. (2) We\nintroduce a central residual aggregation module (CRAM) to encapsulate the\nnormal representation space of the student network. We evaluate our proposed\napproach on two anomaly detection datasets, MVTecAD and VisA. Our method\nsurpasses the state-of-the-art distillation-based algorithms by a significant\nmargin of 3.9% and 1.5% on MVTecAD and 1.2% and 2.5% on VisA in the multi-class\nanomaly detection and localization tasks, respectively. Furthermore, our\nalgorithm outperforms the current state-of-the-art unified models on both\nMVTecAD and VisA.\n", "date": "2024-02-27", "categories": []}
{"id": "2310.00797", "title": "Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly\n  Detection", "abstract": "  Anomaly Detection (AD) is a critical task that involves identifying\nobservations that do not conform to a learned model of normality. Prior work in\ndeep AD is predominantly based on a familiarity hypothesis, where familiar\nfeatures serve as the reference in a pre-trained embedding space. While this\nstrategy has proven highly successful, it turns out that it causes consistent\nfalse negatives when anomalies consist of truly novel features that are not\nwell captured by the pre-trained encoding. We propose a novel approach to AD\nusing explainability to capture such novel features as unexplained observations\nin the input space. We achieve strong performance across a wide range of\nanomaly benchmarks by combining familiarity and novelty in a hybrid approach.\nOur approach establishes a new state-of-the-art across multiple benchmarks,\nhandling diverse anomaly types while eliminating the need for expensive\nbackground models and dense matching. In particular, we show that by taking\naccount of novel features, we reduce false negative anomalies by up to 40% on\nchallenging benchmarks compared to the state-of-the-art. Our method gives\nvisually inspectable explanations for pixel-level anomalies.\n", "date": "2023-10-01", "categories": []}
{"id": "2310.05668", "title": "LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised\n  Time Series Anomaly Detection", "abstract": "  Most of current anomaly detection models assume that the normal pattern\nremains same all the time. However, the normal patterns of Web services change\ndramatically and frequently. The model trained on old-distribution data is\noutdated after such changes. Retraining the whole model every time is\nexpensive. Besides, at the beginning of normal pattern changes, there is not\nenough observation data from the new distribution. Retraining a large neural\nnetwork model with limited data is vulnerable to overfitting. Thus, we propose\na Light and Anti-overfitting Retraining Approach (LARA) for deep variational\nauto-encoder based time series anomaly detection methods (VAEs). This work aims\nto make three novel contributions: 1) the retraining process is formulated as a\nconvex problem and can converge at a fast rate as well as prevent overfitting;\n2) designing a ruminate block, which leverages the historical data without the\nneed to store them; 3) mathematically proving that when fine-tuning the latent\nvector and reconstructed data, the linear formations can achieve the least\nadjusting errors between the ground truths and the fine-tuned ones.\n  Moreover, we have performed many experiments to verify that retraining LARA\nwith even 43 time slots of data from new distribution can result in its\ncompetitive F1 Score in comparison with the state-of-the-art anomaly detection\nmodels trained with sufficient data. Besides, we verify its light overhead.\n", "date": "2023-10-09", "categories": []}
{"id": "2402.15335", "title": "Low-Rank Representations Meets Deep Unfolding: A Generalized and\n  Interpretable Network for Hyperspectral Anomaly Detection", "abstract": "  Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from\nlow resolution, simple background, and small size of the detection data. These\nfactors also limit the performance of the well-known low-rank representation\n(LRR) models in terms of robustness on the separation of background and target\nfeatures and the reliance on manual parameter selection. To this end, we build\na new set of HAD benchmark datasets for improving the robustness of the HAD\nalgorithm in complex scenarios, AIR-HAD for short. Accordingly, we propose a\ngeneralized and interpretable HAD network by deeply unfolding a\ndictionary-learnable LLR model, named LRR-Net$^+$, which is capable of\nspectrally decoupling the background structure and object properties in a more\ngeneralized fashion and eliminating the bias introduced by vital interference\ntargets concurrently. In addition, LRR-Net$^+$ integrates the solution process\nof the Alternating Direction Method of Multipliers (ADMM) optimizer with the\ndeep network, guiding its search process and imparting a level of\ninterpretability to parameter optimization. Additionally, the integration of\nphysical models with DL techniques eliminates the need for manual parameter\ntuning. The manually tuned parameters are seamlessly transformed into trainable\nparameters for deep neural networks, facilitating a more efficient and\nautomated optimization process. Extensive experiments conducted on the AIR-HAD\ndataset show the superiority of our LRR-Net$^+$ in terms of detection\nperformance and generalization ability, compared to top-performing rivals.\nFurthermore, the compilable codes and our AIR-HAD benchmark datasets in this\npaper will be made available freely and openly at\n\\url{https://sites.google.com/view/danfeng-hong}.\n", "date": "2024-02-23", "categories": []}
{"id": "2312.05767", "title": "AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model", "abstract": "  Anomaly inspection plays an important role in industrial manufacture.\nExisting anomaly inspection methods are limited in their performance due to\ninsufficient anomaly data. Although anomaly generation methods have been\nproposed to augment the anomaly data, they either suffer from poor generation\nauthenticity or inaccurate alignment between the generated anomalies and masks.\nTo address the above problems, we propose AnomalyDiffusion, a novel\ndiffusion-based few-shot anomaly generation model, which utilizes the strong\nprior information of latent diffusion model learned from large-scale dataset to\nenhance the generation authenticity under few-shot training data. Firstly, we\npropose Spatial Anomaly Embedding, which consists of a learnable anomaly\nembedding and a spatial embedding encoded from an anomaly mask, disentangling\nthe anomaly information into anomaly appearance and location information.\nMoreover, to improve the alignment between the generated anomalies and the\nanomaly masks, we introduce a novel Adaptive Attention Re-weighting Mechanism.\nBased on the disparities between the generated anomaly image and normal sample,\nit dynamically guides the model to focus more on the areas with less noticeable\ngenerated anomalies, enabling generation of accurately-matched anomalous\nimage-mask pairs. Extensive experiments demonstrate that our model\nsignificantly outperforms the state-of-the-art methods in generation\nauthenticity and diversity, and effectively improves the performance of\ndownstream anomaly inspection tasks. The code and data are available in\nhttps://github.com/sjtuplayer/anomalydiffusion.\n", "date": "2023-12-10", "categories": []}
{"id": "2402.14022", "title": "Statistical validation of a deep learning algorithm for dental anomaly\n  detection in intraoral radiographs using paired data", "abstract": "  This article describes the clinical validation study setup, statistical\nanalysis and results for a deep learning algorithm which detects dental\nanomalies in intraoral radiographic images, more specifically caries, apical\nlesions, root canal treatment defects, marginal defects at crown restorations,\nperiodontal bone loss and calculus. The study compares the detection\nperformance of dentists using the deep learning algorithm to the prior\nperformance of these dentists evaluating the images without algorithmic\nassistance. Calculating the marginal profit and loss of performance from the\nannotated paired image data allows for a quantification of the hypothesized\nchange in sensitivity and specificity. The statistical significance of these\nresults is extensively proven using both McNemar's test and the binomial\nhypothesis test. The average sensitivity increases from $60.7\\%$ to $85.9\\%$,\nwhile the average specificity slightly decreases from $94.5\\%$ to $92.7\\%$. We\nprove that the increase of the area under the localization ROC curve (AUC) is\nsignificant (from $0.60$ to $0.86$ on average), while the average AUC is\nbounded by the $95\\%$ confidence intervals ${[}0.54, 0.65{]}$ and ${[}0.82,\n0.90{]}$. When using the deep learning algorithm for diagnostic guidance, the\ndentist can be $95\\%$ confident that the average true population sensitivity is\nbounded by the range $79.6\\%$ to $91.9\\%$. The proposed paired data setup and\nstatistical analysis can be used as a blueprint to thoroughly test the effect\nof a modality change, like a deep learning based detection and/or segmentation,\non radiographic images.\n", "date": "2024-02-01", "categories": []}
{"id": "2402.14246", "title": "Reconstruction-Based Anomaly Localization via Knowledge-Informed\n  Self-Training", "abstract": "  Anomaly localization, which involves localizing anomalous regions within\nimages, is a significant industrial task. Reconstruction-based methods are\nwidely adopted for anomaly localization because of their low complexity and\nhigh interpretability. Most existing reconstruction-based methods only use\nnormal samples to construct model. If anomalous samples are appropriately\nutilized in the process of anomaly localization, the localization performance\ncan be improved. However, usually only weakly labeled anomalous samples are\navailable, which limits the improvement. In many cases, we can obtain some\nknowledge of anomalies summarized by domain experts. Taking advantage of such\nknowledge can help us better utilize the anomalous samples and thus further\nimprove the localization performance. In this paper, we propose a novel\nreconstruction-based method named knowledge-informed self-training (KIST) which\nintegrates knowledge into reconstruction model through self-training.\nSpecifically, KIST utilizes weakly labeled anomalous samples in addition to the\nnormal ones and exploits knowledge to yield pixel-level pseudo-labels of the\nanomalous samples. Based on the pseudo labels, a novel loss which promotes the\nreconstruction of normal pixels while suppressing the reconstruction of\nanomalous pixels is used. We conduct experiments on different datasets and\ndemonstrate the advantages of KIST over the existing reconstruction-based\nmethods.\n", "date": "2024-02-22", "categories": []}
{"id": "2402.14384", "title": "Generative Adversarial Network with Soft-Dynamic Time Warping and\n  Parallel Reconstruction for Energy Time Series Anomaly Detection", "abstract": "  In this paper, we employ a 1D deep convolutional generative adversarial\nnetwork (DCGAN) for sequential anomaly detection in energy time series data.\nAnomaly detection involves gradient descent to reconstruct energy\nsub-sequences, identifying the noise vector that closely generates them through\nthe generator network. Soft-DTW is used as a differentiable alternative for the\nreconstruction loss and is found to be superior to Euclidean distance.\nCombining reconstruction loss and the latent space's prior probability\ndistribution serves as the anomaly score. Our novel method accelerates\ndetection by parallel computation of reconstruction of multiple points and\nshows promise in identifying anomalous energy consumption in buildings, as\nevidenced by performing experiments on hourly energy time series from 15\nbuildings.\n", "date": "2024-02-22", "categories": []}
{"id": "2402.12761", "title": "FGAD: Self-boosted Knowledge Distillation for An Effective Federated\n  Graph Anomaly Detection Framework", "abstract": "  Graph anomaly detection (GAD) aims to identify anomalous graphs that\nsignificantly deviate from other ones, which has raised growing attention due\nto the broad existence and complexity of graph-structured data in many\nreal-world scenarios. However, existing GAD methods usually execute with\ncentralized training, which may lead to privacy leakage risk in some sensitive\ncases, thereby impeding collaboration among organizations seeking to\ncollectively develop robust GAD models. Although federated learning offers a\npromising solution, the prevalent non-IID problems and high communication costs\npresent significant challenges, particularly pronounced in collaborations with\ngraph data distributed among different participants. To tackle these\nchallenges, we propose an effective federated graph anomaly detection framework\n(FGAD). We first introduce an anomaly generator to perturb the normal graphs to\nbe anomalous, and train a powerful anomaly detector by distinguishing generated\nanomalous graphs from normal ones. Then, we leverage a student model to distill\nknowledge from the trained anomaly detector (teacher model), which aims to\nmaintain the personality of local models and alleviate the adverse impact of\nnon-IID problems. Moreover, we design an effective collaborative learning\nmechanism that facilitates the personalization preservation of local models and\nsignificantly reduces communication costs among clients. Empirical results of\nthe GAD tasks on non-IID graphs compared with state-of-the-art baselines\ndemonstrate the superiority and efficiency of the proposed FGAD method.\n", "date": "2024-02-20", "categories": []}
{"id": "2402.10397", "title": "LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs", "abstract": "  System logs are some of the most important information for the maintenance of\nsoftware systems, which have become larger and more complex in recent years.\nThe goal of log-based anomaly detection is to automatically detect system\nanomalies by analyzing the large number of logs generated in a short period of\ntime, which is a critical challenge in the real world. Previous studies have\nused a log parser to extract templates from unstructured log data and detect\nanomalies on the basis of patterns of the template occurrences. These methods\nhave limitations for logs with unknown templates. Furthermore, since most log\nanomalies are known to be point anomalies rather than contextual anomalies,\ndetection methods based on occurrence patterns can cause unnecessary delays in\ndetection. In this paper, we propose LogELECTRA, a new log anomaly detection\nmodel that analyzes a single line of log messages more deeply on the basis of\nself-supervised anomaly detection. LogELECTRA specializes in detecting log\nanomalies as point anomalies by applying ELECTRA, a natural language processing\nmodel, to analyze the semantics of a single line of log messages. LogELECTRA\noutperformed existing state-of-the-art methods in experiments on the public\nbenchmark log datasets BGL, Sprit, and Thunderbird.\n", "date": "2024-02-16", "categories": []}
{"id": "2211.14307", "title": "MAEDAY: MAE for few and zero shot AnomalY-Detection", "abstract": "  We propose using Masked Auto-Encoder (MAE), a transformer model\nself-supervisedly trained on image inpainting, for anomaly detection (AD).\nAssuming anomalous regions are harder to reconstruct compared with normal\nregions. MAEDAY is the first image-reconstruction-based anomaly detection\nmethod that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly\nDetection (FSAD). We also show the same method works surprisingly well for the\nnovel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection\n(ZSFOD), where no normal samples are available. Code is available at\nhttps://github.com/EliSchwartz/MAEDAY .\n", "date": "2022-11-25", "categories": []}
{"id": "2301.00462", "title": "A Latent Space Correlation-Aware Autoencoder for Anomaly Detection in\n  Skewed Data", "abstract": "  Unsupervised learning-based anomaly detection in latent space has gained\nimportance since discriminating anomalies from normal data becomes difficult in\nhigh-dimensional space. Both density estimation and distance-based methods to\ndetect anomalies in latent space have been explored in the past. These methods\nprove that retaining valuable properties of input data in latent space helps in\nthe better reconstruction of test data. Moreover, real-world sensor data is\nskewed and non-Gaussian in nature, making mean-based estimators unreliable for\nskewed data. Again, anomaly detection methods based on reconstruction error\nrely on Euclidean distance, which does not consider useful correlation\ninformation in the feature space and also fails to accurately reconstruct the\ndata when it deviates from the training distribution. In this work, we address\nthe limitations of reconstruction error-based autoencoders and propose a\nkernelized autoencoder that leverages a robust form of Mahalanobis distance\n(MD) to measure latent dimension correlation to effectively detect both near\nand far anomalies. This hybrid loss is aided by the principle of maximizing the\nmutual information gain between the latent dimension and the high-dimensional\nprior data space by maximizing the entropy of the latent space while preserving\nuseful correlation information of the original data in the low-dimensional\nlatent space. The multi-objective function has two goals -- it measures\ncorrelation information in the latent feature space in the form of robust MD\ndistance and simultaneously tries to preserve useful correlation information\nfrom the original data space in the latent space by maximizing mutual\ninformation between the prior and latent space.\n", "date": "2023-01-01", "categories": []}
{"id": "2402.09830", "title": "Utilizing GANs for Fraud Detection: Model Training with Synthetic\n  Transaction Data", "abstract": "  Anomaly detection is a critical challenge across various research domains,\naiming to identify instances that deviate from normal data distributions. This\npaper explores the application of Generative Adversarial Networks (GANs) in\nfraud detection, comparing their advantages with traditional methods. GANs, a\ntype of Artificial Neural Network (ANN), have shown promise in modeling complex\ndata distributions, making them effective tools for anomaly detection. The\npaper systematically describes the principles of GANs and their derivative\nmodels, emphasizing their application in fraud detection across different\ndatasets. And by building a collection of adversarial verification graphs, we\nwill effectively prevent fraud caused by bots or automated systems and ensure\nthat the users in the transaction are real. The objective of the experiment is\nto design and implement a fake face verification code and fraud detection\nsystem based on Generative Adversarial network (GANs) algorithm to enhance the\nsecurity of the transaction process.The study demonstrates the potential of\nGANs in enhancing transaction security through deep learning techniques.\n", "date": "2024-02-15", "categories": []}
{"id": "2402.10085", "title": "Develop End-to-End Anomaly Detection System", "abstract": "  Anomaly detection plays a crucial role in ensuring network robustness.\nHowever, implementing intelligent alerting systems becomes a challenge when\nconsidering scenarios in which anomalies can be caused by both malicious and\nnon-malicious events, leading to the difficulty of determining anomaly\npatterns. The lack of labeled data in the computer networking domain further\nexacerbates this issue, impeding the development of robust models capable of\nhandling real-world scenarios. To address this challenge, in this paper, we\npropose an end-to-end anomaly detection model development pipeline. This\nframework makes it possible to consume user feedback and enable continuous\nuser-centric model performance evaluation and optimization. We demonstrate the\nefficacy of the framework by way of introducing and bench-marking a new\nforecasting model -- named \\emph{Lachesis} -- on a real-world networking\nproblem. Experiments have demonstrated the robustness and effectiveness of the\ntwo proposed versions of \\emph{Lachesis} compared with other models proposed in\nthe literature. Our findings underscore the potential for improving the\nperformance of data-driven products over their life cycles through a harmonized\nintegration of user feedback and iterative development.\n", "date": "2024-02-01", "categories": []}
{"id": "2402.08742", "title": "Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize\n  Energy Management in Sports Facilities", "abstract": "  Anomaly detection in sport facilities has gained significant attention due to\nits potential to promote energy saving and optimizing operational efficiency.\nIn this research article, we investigate the role of machine learning,\nparticularly deep learning, in anomaly detection for sport facilities. We\nexplore the challenges and perspectives of utilizing deep learning methods for\nthis task, aiming to address the drawbacks and limitations of conventional\napproaches. Our proposed approach involves feature extraction from the data\ncollected in sport facilities. We present a problem formulation using Deep\nFeedforward Neural Networks (DFNN) and introduce threshold estimation\ntechniques to identify anomalies effectively. Furthermore, we propose methods\nto reduce false alarms, ensuring the reliability and accuracy of anomaly\ndetection. To evaluate the effectiveness of our approach, we conduct\nexperiments on aquatic center dataset at Qatar University. The results\ndemonstrate the superiority of our deep learning-based method over conventional\ntechniques, highlighting its potential in real-world applications. Typically,\n94.33% accuracy and 92.92% F1-score have been achieved using the proposed\nscheme.\n", "date": "2024-02-13", "categories": []}
{"id": "2402.08975", "title": "Research and application of Transformer based anomaly detection model: A\n  literature review", "abstract": "  Transformer, as one of the most advanced neural network models in Natural\nLanguage Processing (NLP), exhibits diverse applications in the field of\nanomaly detection. To inspire research on Transformer-based anomaly detection,\nthis review offers a fresh perspective on the concept of anomaly detection. We\nexplore the current challenges of anomaly detection and provide detailed\ninsights into the operating principles of Transformer and its variants in\nanomaly detection tasks. Additionally, we delineate various application\nscenarios for Transformer-based anomaly detection models and discuss the\ndatasets and evaluation metrics employed. Furthermore, this review highlights\nthe key challenges in Transformer-based anomaly detection research and conducts\na comprehensive analysis of future research trends in this domain. The review\nincludes an extensive compilation of over 100 core references related to\nTransformer-based anomaly detection. To the best of our knowledge, this is the\nfirst comprehensive review that focuses on the research related to Transformer\nin the context of anomaly detection. We hope that this paper can provide\ndetailed technical information to researchers interested in Transformer-based\nanomaly detection tasks.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.09082", "title": "Detection Latencies of Anomaly Detectors: An Overlooked Perspective ?", "abstract": "  The ever-evolving landscape of attacks, coupled with the growing complexity\nof ICT systems, makes crafting anomaly-based intrusion detectors (ID) and error\ndetectors (ED) a difficult task: they must accurately detect attacks, and they\nshould promptly perform detections. Although improving and comparing the\ndetection capability is the focus of most research works, the timeliness of the\ndetection is less considered and often insufficiently evaluated or discussed.\nIn this paper, we argue the relevance of measuring the temporal latency of\nattacks and errors, and we propose an evaluation approach for detectors to\nensure a pragmatic trade-off between correct and in-time detection. Briefly,\nthe approach relates the false positive rate with the temporal latency of\nattacks and errors, and this ultimately leads to guidelines for configuring a\ndetector. We apply our approach by evaluating different ED and ID solutions in\ntwo industrial cases: i) an embedded railway on-board system that optimizes\npublic mobility, and ii) an edge device for the Industrial Internet of Things.\nOur results show that considering latency in addition to traditional metrics\nlike the false positive rate, precision, and coverage gives an additional\nfundamental perspective on the actual performance of the detector and should be\nconsidered when assessing and configuring anomaly detectors.\n", "date": "2024-02-14", "categories": []}
{"id": "2312.02762", "title": "Learning Cortical Anomaly through Masked Encoding for Unsupervised\n  Heterogeneity Mapping", "abstract": "  The detection of heterogeneous mental disorders based on brain readouts\nremains challenging due to the complexity of symptoms and the absence of\nreliable biomarkers. This paper introduces CAM (Cortical Anomaly Detection\nthrough Masked Image Modeling), a novel self-supervised framework designed for\nthe unsupervised detection of complex brain disorders using cortical surface\nfeatures. We employ this framework for the detection of individuals on the\npsychotic spectrum and demonstrate its capabilities compared to\nstate-of-the-art methods, achieving an AUC of 0.696 for Schizoaffective and\n0.769 for Schizophreniform, without the need for any labels. Furthermore, the\nanalysis of atypical cortical regions, including Pars Triangularis and several\nfrontal areas often implicated in schizophrenia, provides further confidence in\nour approach. Altogether, we demonstrate a scalable approach for anomaly\ndetection of complex brain disorders based on cortical abnormalities. The code\nwill be made available at https://github.com/chadHGY/CAM.\n", "date": "2023-12-05", "categories": []}
{"id": "2302.08712", "title": "Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data", "abstract": "  Anomalies refer to the departure of systems and devices from their normal\nbehaviour in standard operating conditions. An anomaly in an industrial device\ncan indicate an upcoming failure, often in the temporal direction. In this\npaper, we make two contributions: 1) we estimate conditional quantiles and\nconsider three different ways to define anomalies based on the estimated\nquantiles. 2) we use a new learnable activation function in the popular Long\nShort Term Memory networks (LSTM) architecture to model temporal long-range\ndependency. In particular, we propose Parametric Elliot Function (PEF) as an\nactivation function (AF) inside LSTM, which saturates lately compared to\nsigmoid and tanh. The proposed algorithms are compared with other well-known\nanomaly detection algorithms, such as Isolation Forest (iForest), Elliptic\nEnvelope, Autoencoder, and modern Deep Learning models such as Deep\nAutoencoding Gaussian Mixture Model (DAGMM), Generative Adversarial Networks\n(GAN). The algorithms are evaluated in terms of various performance metrics,\nsuch as Precision and Recall. The algorithms have been tested on multiple\nindustrial time-series datasets such as Yahoo, AWS, GE, and machine sensors. We\nhave found that the LSTM-based quantile algorithms are very effective and\noutperformed the existing algorithms in identifying anomalies.\n", "date": "2023-02-17", "categories": []}
{"id": "2402.05943", "title": "A hybrid IndRNNLSTM approach for real-time anomaly detection in\n  software-defined networks", "abstract": "  Anomaly detection in SDN using data flow prediction is a difficult task. This\nproblem is included in the category of time series and regression problems.\nMachine learning approaches are challenging in this field due to the manual\nselection of features. On the other hand, deep learning approaches have\nimportant features due to the automatic selection of features. Meanwhile,\nRNN-based approaches have been used the most. The LSTM and GRU approaches learn\ndependent entities well; on the other hand, the IndRNN approach learns\nnon-dependent entities in time series. The proposed approach tried to use a\ncombination of IndRNN and LSTM approaches to learn dependent and non-dependent\nfeatures. Feature selection approaches also provide a suitable view of features\nfor the models; for this purpose, four feature selection models, Filter,\nWrapper, Embedded, and Autoencoder were used. The proposed IndRNNLSTM\nalgorithm, in combination with Embedded, was able to achieve MAE=1.22 and\nRMSE=9.92 on NSL-KDD data.\n", "date": "2024-02-02", "categories": []}
{"id": "2303.08744", "title": "Towards Phytoplankton Parasite Detection Using Autoencoders", "abstract": "  Phytoplankton parasites are largely understudied microbial components with a\npotentially significant ecological impact on phytoplankton bloom dynamics. To\nbetter understand their impact, we need improved detection methods to integrate\nphytoplankton parasite interactions in monitoring aquatic ecosystems. Automated\nimaging devices usually produce high amount of phytoplankton image data, while\nthe occurrence of anomalous phytoplankton data is rare. Thus, we propose an\nunsupervised anomaly detection system based on the similarity of the original\nand autoencoder-reconstructed samples. With this approach, we were able to\nreach an overall F1 score of 0.75 in nine phytoplankton species, which could be\nfurther improved by species-specific fine-tuning. The proposed unsupervised\napproach was further compared with the supervised Faster R-CNN based object\ndetector. With this supervised approach and the model trained on plankton\nspecies and anomalies, we were able to reach the highest F1 score of 0.86.\nHowever, the unsupervised approach is expected to be more universal as it can\ndetect also unknown anomalies and it does not require any annotated anomalous\ndata that may not be always available in sufficient quantities. Although other\nstudies have dealt with plankton anomaly detection in terms of non-plankton\nparticles, or air bubble detection, our paper is according to our best\nknowledge the first one which focuses on automated anomaly detection\nconsidering putative phytoplankton parasites or infections.\n", "date": "2023-03-15", "categories": []}
{"id": "2303.14535", "title": "EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level\n  Latencies", "abstract": "  Detecting anomalies in images is an important task, especially in real-time\ncomputer vision applications. In this work, we focus on computational\nefficiency and propose a lightweight feature extractor that processes an image\nin less than a millisecond on a modern GPU. We then use a student-teacher\napproach to detect anomalous features. We train a student network to predict\nthe extracted features of normal, i.e., anomaly-free training images. The\ndetection of anomalies at test time is enabled by the student failing to\npredict their features. We propose a training loss that hinders the student\nfrom imitating the teacher feature extractor beyond the normal images. It\nallows us to drastically reduce the computational cost of the student-teacher\nmodel, while improving the detection of anomalous features. We furthermore\naddress the detection of challenging logical anomalies that involve invalid\ncombinations of normal local features, for example, a wrong ordering of\nobjects. We detect these anomalies by efficiently incorporating an autoencoder\nthat analyzes images globally. We evaluate our method, called EfficientAD, on\n32 datasets from three industrial anomaly detection dataset collections.\nEfficientAD sets new standards for both the detection and the localization of\nanomalies. At a latency of two milliseconds and a throughput of six hundred\nimages per second, it enables a fast handling of anomalies. Together with its\nlow error rate, this makes it an economical solution for real-world\napplications and a fruitful basis for future research.\n", "date": "2023-03-25", "categories": []}
{"id": "2402.05114", "title": "A Light-weight and Unsupervised Method for Near Real-time Behavioral\n  Analysis using Operational Data Measurement", "abstract": "  Monitoring the status of large computing systems is essential to identify\nunexpected behavior and improve their performance and uptime. However, due to\nthe large-scale and distributed design of such computing systems as well as a\nlarge number of monitoring parameters, automated monitoring methods should be\napplied. Such automatic monitoring methods should also have the ability to\nadapt themselves to the continuous changes in the computing system. In\naddition, they should be able to identify behavioral anomalies in useful time,\nto perform appropriate reactions. This work proposes a general lightweight and\nunsupervised method for near real-time anomaly detection using operational data\nmeasurement on large computing systems. The proposed model requires as little\nas 4 hours of data and 50 epochs for each training process to accurately\nresemble the behavioral pattern of computing systems.\n", "date": "2024-01-10", "categories": []}
{"id": "2310.15830", "title": "Localizing Anomalies in Critical Infrastructure using Model-Based Drift\n  Explanations", "abstract": "  Facing climate change, the already limited availability of drinking water\nwill decrease in the future rendering drinking water an increasingly scarce\nresource. Considerable amounts of it are lost through leakages in water\ntransportation and distribution networks. Thus, anomaly detection and\nlocalization, in particular for leakages, are crucial but challenging tasks due\nto the complex interactions and changing demands in water distribution\nnetworks. In this work, we analyze the effects of anomalies on the dynamics of\ncritical infrastructure systems by modeling the networks employing Bayesian\nnetworks. We then discuss how the problem is connected to and can be considered\nthrough the lens of concept drift. In particular, we argue that model-based\nexplanations of concept drift are a promising tool for localizing anomalies\ngiven limited information about the network. The methodology is experimentally\nevaluated using realistic benchmark scenarios. To showcase that our methodology\napplies to critical infrastructure more generally, in addition to considering\nleakages and sensor faults in water systems, we showcase the suitability of the\nderived technique to localize sensor faults in power systems.\n", "date": "2023-10-24", "categories": []}
{"id": "2312.11549", "title": "Label-Free Multivariate Time Series Anomaly Detection", "abstract": "  Anomaly detection in multivariate time series (MTS) has been widely studied\nin one-class classification (OCC) setting. The training samples in OCC are\nassumed to be normal, which is difficult to guarantee in practical situations.\nSuch a case may degrade the performance of OCC-based anomaly detection methods\nwhich fit the training distribution as the normal distribution. In this paper,\nwe propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly\ndetection via dynamic Graph and entity-aware normalizing Flow. MTGFlow first\nestimates the density of the entire training samples and then identifies\nanomalous instances based on the density of the test samples within the fitted\ndistribution. This relies on a widely accepted assumption that anomalous\ninstances exhibit more sparse densities than normal ones, with no reliance on\nthe clean training dataset. However, it is intractable to directly estimate the\ndensity due to complex dependencies among entities and their diverse inherent\ncharacteristics. To mitigate this, we utilize the graph structure learning\nmodel to learn interdependent and evolving relations among entities, which\neffectively captures complex and accurate distribution patterns of MTS. In\naddition, our approach incorporates the unique characteristics of individual\nentities by employing an entity-aware normalizing flow. This enables us to\nrepresent each entity as a parameterized normal distribution. Furthermore,\nconsidering that some entities present similar characteristics, we propose a\ncluster strategy that capitalizes on the commonalities of entities with similar\ncharacteristics, resulting in more precise and detailed density estimation. We\nrefer to this cluster-aware extension as MTGFlow_cluster. Extensive experiments\nare conducted on six widely used benchmark datasets, in which MTGFlow and\nMTGFlow cluster demonstrate their superior detection performance.\n", "date": "2023-12-17", "categories": []}
{"id": "2402.04469", "title": "IoT Network Traffic Analysis with Deep Learning", "abstract": "  As IoT networks become more complex and generate massive amounts of dynamic\ndata, it is difficult to monitor and detect anomalies using traditional\nstatistical methods and machine learning methods. Deep learning algorithms can\nprocess and learn from large amounts of data and can also be trained using\nunsupervised learning techniques, meaning they don't require labelled data to\ndetect anomalies. This makes it possible to detect new and unknown anomalies\nthat may not have been detected before. Also, deep learning algorithms can be\nautomated and highly scalable; thereby, they can run continuously in the\nbackend and make it achievable to monitor large IoT networks instantly. In this\nwork, we conduct a literature review on the most recent works using deep\nlearning techniques and implement a model using ensemble techniques on the KDD\nCup 99 dataset. The experimental results showcase the impressive performance of\nour deep anomaly detection model, achieving an accuracy of over 98\\%.\n", "date": "2024-02-06", "categories": []}
{"id": "2402.04567", "title": "OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences", "abstract": "  Anomaly detection in decision-making sequences is a challenging problem due\nto the complexity of normality representation learning and the sequential\nnature of the task. Most existing methods based on Reinforcement Learning (RL)\nare difficult to implement in the real world due to unrealistic assumptions,\nsuch as having access to environment dynamics, reward signals, and online\ninteractions with the environment. To address these limitations, we propose an\nunsupervised method named Offline Imitation Learning based Anomaly Detection\n(OIL-AD), which detects anomalies in decision-making sequences using two\nextracted behaviour features: action optimality and sequential association. Our\noffline learning model is an adaptation of behavioural cloning with a\ntransformer policy network, where we modify the training process to learn a Q\nfunction and a state value function from normal trajectories. We propose that\nthe Q function and the state value function can provide sufficient information\nabout agents' behavioural data, from which we derive two features for anomaly\ndetection. The intuition behind our method is that the action optimality\nfeature derived from the Q function can differentiate the optimal action from\nothers at each local state, and the sequential association feature derived from\nthe state value function has the potential to maintain the temporal\ncorrelations between decisions (state-action pairs). Our experiments show that\nOIL-AD can achieve outstanding online anomaly detection performance with up to\n34.8% improvement in F1 score over comparable baselines.\n", "date": "2024-02-07", "categories": []}
{"id": "2312.14190", "title": "Machine Learning for Anomaly Detection in Particle Physics", "abstract": "  The detection of out-of-distribution data points is a common task in particle\nphysics. It is used for monitoring complex particle detectors or for\nidentifying rare and unexpected events that may be indicative of new phenomena\nor physics beyond the Standard Model. Recent advances in Machine Learning for\nanomaly detection have encouraged the utilization of such techniques on\nparticle physics problems. This review article provides an overview of the\nstate-of-the-art techniques for anomaly detection in particle physics using\nmachine learning. We discuss the challenges associated with anomaly detection\nin large and complex data sets, such as those produced by high-energy particle\ncolliders, and highlight some of the successful applications of anomaly\ndetection in particle physics experiments.\n", "date": "2023-12-20", "categories": []}
{"id": "2402.03785", "title": "Weakly Supervised Anomaly Detection via Knowledge-Data Alignment", "abstract": "  Anomaly detection (AD) plays a pivotal role in numerous web-based\napplications, including malware detection, anti-money laundering, device\nfailure detection, and network fault analysis. Most methods, which rely on\nunsupervised learning, are hard to reach satisfactory detection accuracy due to\nthe lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been\nintroduced with a limited number of labeled anomaly samples to enhance model\nperformance. Nevertheless, it is still challenging for models, trained on an\ninadequate amount of labeled data, to generalize to unseen anomalies. In this\npaper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to\nintegrate rule knowledge, typically summarized by human experts, to supplement\nthe limited labeled data. Specifically, we transpose these rules into the\nknowledge space and subsequently recast the incorporation of knowledge as the\nalignment of knowledge and data. To facilitate this alignment, we employ the\nOptimal Transport (OT) technique. We then incorporate the OT distance as an\nadditional loss term to the original objective function of WSAD methodologies.\nComprehensive experimental results on five real-world datasets demonstrate that\nour proposed KDAlign framework markedly surpasses its state-of-the-art\ncounterparts, achieving superior performance across various anomaly types.\n", "date": "2024-02-06", "categories": []}
{"id": "2402.02007", "title": "Understanding Time Series Anomaly State Detection through One-Class\n  Classification", "abstract": "  For a long time, research on time series anomaly detection has mainly focused\non finding outliers within a given time series. Admittedly, this is consistent\nwith some practical problems, but in other practical application scenarios,\npeople are concerned about: assuming a standard time series is given, how to\njudge whether another test time series deviates from the standard time series,\nwhich is more similar to the problem discussed in one-class classification\n(OCC). Therefore, in this article, we try to re-understand and define the time\nseries anomaly detection problem through OCC, which we call 'time series\nanomaly state detection problem'. We first use stochastic processes and\nhypothesis testing to strictly define the 'time series anomaly state detection\nproblem', and its corresponding anomalies. Then, we use the time series\nclassification dataset to construct an artificial dataset corresponding to the\nproblem. We compile 38 anomaly detection algorithms and correct some of the\nalgorithms to adapt to handle this problem. Finally, through a large number of\nexperiments, we fairly compare the actual performance of various time series\nanomaly detection algorithms, providing insights and directions for future\nresearch by researchers.\n", "date": "2024-02-03", "categories": []}
{"id": "2402.02820", "title": "Revisiting VAE for Unsupervised Time Series Anomaly Detection: A\n  Frequency Perspective", "abstract": "  Time series Anomaly Detection (AD) plays a crucial role for web systems.\nVarious web systems rely on time series data to monitor and identify anomalies\nin real time, as well as to initiate diagnosis and remediation procedures.\nVariational Autoencoders (VAEs) have gained popularity in recent decades due to\ntheir superior de-noising capabilities, which are useful for anomaly detection.\nHowever, our study reveals that VAE-based methods face challenges in capturing\nlong-periodic heterogeneous patterns and detailed short-periodic trends\nsimultaneously. To address these challenges, we propose Frequency-enhanced\nConditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for\nunivariate time series. To ensure an accurate AD, FCVAE exploits an innovative\napproach to concurrently integrate both the global and local frequency features\ninto the condition of Conditional Variational Autoencoder (CVAE) to\nsignificantly increase the accuracy of reconstructing the normal data. Together\nwith a carefully designed \"target attention\" mechanism, our approach allows the\nmodel to pick the most useful information from the frequency domain for better\nshort-periodic trend construction. Our FCVAE has been evaluated on public\ndatasets and a large-scale cloud system, and the results demonstrate that it\noutperforms state-of-the-art methods. This confirms the practical applicability\nof our approach in addressing the limitations of current VAE-based anomaly\ndetection models.\n", "date": "2024-02-05", "categories": []}
{"id": "2402.02963", "title": "One-class anomaly detection through color-to-thermal AI for building\n  envelope inspection", "abstract": "  We present a label-free method for detecting anomalies during thermographic\ninspection of building envelopes. It is based on the AI-driven prediction of\nthermal distributions from color images. Effectively the method performs as a\none-class classifier of the thermal image regions with high mismatch between\nthe predicted and actual thermal distributions. The algorithm can learn to\nidentify certain features as normal or anomalous by selecting the target sample\nused for training. We demonstrated this principle by training the algorithm\nwith data collected at different outdoors temperature, which lead to the\ndetection of thermal bridges. The method can be implemented to assist human\nprofessionals during routine building inspections or combined with mobile\nplatforms for automating examination of large areas.\n", "date": "2024-02-05", "categories": []}
{"id": "2311.14506", "title": "Multi-Class Anomaly Detection based on Regularized Discriminative\n  Coupled hypersphere-based Feature Adaptation", "abstract": "  In anomaly detection, identification of anomalies across diverse product\ncategories is a complex task. This paper introduces a new model by including\nclass discriminative properties obtained by a modified Regularized\nDiscriminative Variational Auto-Encoder (RD-VAE) in the feature extraction\nprocess of Coupled-hypersphere-based Feature Adaptation (CFA). By doing so, the\nproposed Regularized Discriminative Coupled-hypersphere-based Feature\nAdaptation (RD-CFA), forms a solution for multi-class anomaly detection. By\nusing the discriminative power of RD-VAE to capture intricate class\ndistributions, combined with CFA's robust anomaly detection capability, the\nproposed method excels in discerning anomalies across various classes.\nExtensive evaluations on multi-class anomaly detection and localization using\nthe MVTec AD and BeanTech AD datasets showcase the effectiveness of RD-CFA\ncompared to eight leading contemporary methods.\n", "date": "2023-11-24", "categories": []}
{"id": "2302.05087", "title": "Generalized Video Anomaly Event Detection: Systematic Taxonomy and\n  Comparison of Deep Models", "abstract": "  Video Anomaly Detection (VAD) serves as a pivotal technology in the\nintelligent surveillance systems, enabling the temporal or spatial\nidentification of anomalous events within videos. While existing reviews\npredominantly concentrate on conventional unsupervised methods, they often\noverlook the emergence of weakly-supervised and fully-unsupervised approaches.\nTo address this gap, this survey extends the conventional scope of VAD beyond\nunsupervised methods, encompassing a broader spectrum termed Generalized Video\nAnomaly Event Detection (GVAED). By skillfully incorporating recent\nadvancements rooted in diverse assumptions and learning frameworks, this survey\nintroduces an intuitive taxonomy that seamlessly navigates through\nunsupervised, weakly-supervised, supervised and fully-unsupervised VAD\nmethodologies, elucidating the distinctions and interconnections within these\nresearch trajectories. In addition, this survey facilitates prospective\nresearchers by assembling a compilation of research resources, including public\ndatasets, available codebases, programming tools, and pertinent literature.\nFurthermore, this survey quantitatively assesses model performance, delves into\nresearch challenges and directions, and outlines potential avenues for future\nexploration.\n", "date": "2023-02-10", "categories": []}
{"id": "2402.00448", "title": "Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly\n  Detection", "abstract": "  Due to the data imbalance and the diversity of defects, student-teacher\nnetworks (S-T) are favored in unsupervised anomaly detection, which explores\nthe discrepancy in feature representation derived from the knowledge\ndistillation process to recognize anomalies. However, vanilla S-T network is\nnot stable. Employing identical structures to construct the S-T network may\nweaken the representative discrepancy on anomalies. But using different\nstructures can increase the likelihood of divergent performance on normal data.\nTo address this problem, we propose a novel dual-student knowledge distillation\n(DSKD) architecture. Different from other S-T networks, we use two student\nnetworks a single pre-trained teacher network, where the students have the same\nscale but inverted structures. This framework can enhance the distillation\neffect to improve the consistency in recognition of normal data, and\nsimultaneously introduce diversity for anomaly representation. To explore\nhigh-dimensional semantic information to capture anomaly clues, we employ two\nstrategies. First, a pyramid matching mode is used to perform knowledge\ndistillation on multi-scale feature maps in the intermediate layers of\nnetworks. Second, an interaction is facilitated between the two student\nnetworks through a deep feature embedding module, which is inspired by\nreal-world group discussions. In terms of classification, we obtain pixel-wise\nanomaly segmentation maps by measuring the discrepancy between the output\nfeature maps of the teacher and student networks, from which an anomaly score\nis computed for sample-wise determination. We evaluate DSKD on three benchmark\ndatasets and probe the effects of internal modules through ablation\nexperiments. The results demonstrate that DSKD can achieve exceptional\nperformance on small models like ResNet18 and effectively improve vanilla S-T\nnetworks.\n", "date": "2024-02-01", "categories": []}
{"id": "2308.12612", "title": "Try with Simpler -- An Evaluation of Improved Principal Component\n  Analysis in Log-based Anomaly Detection", "abstract": "  The rapid growth of deep learning (DL) has spurred interest in enhancing\nlog-based anomaly detection. This approach aims to extract meaning from log\nevents (log message templates) and develop advanced DL models for anomaly\ndetection. However, these DL methods face challenges like heavy reliance on\ntraining data, labels, and computational resources due to model complexity. In\ncontrast, traditional machine learning and data mining techniques are less\ndata-dependent and more efficient but less effective than DL. To make log-based\nanomaly detection more practical, the goal is to enhance traditional techniques\nto match DL's effectiveness. Previous research in a different domain (linking\nquestions on Stack Overflow) suggests that optimized traditional techniques can\nrival state-of-the-art DL methods. Drawing inspiration from this concept, we\nconducted an empirical study. We optimized the unsupervised PCA (Principal\nComponent Analysis), a traditional technique, by incorporating lightweight\nsemantic-based log representation. This addresses the issue of unseen log\nevents in training data, enhancing log representation. Our study compared seven\nlog-based anomaly detection methods, including four DL-based, two traditional,\nand the optimized PCA technique, using public and industrial datasets. Results\nindicate that the optimized unsupervised PCA technique achieves similar\neffectiveness to advanced supervised/semi-supervised DL methods while being\nmore stable with limited training data and resource-efficient. This\ndemonstrates the adaptability and strength of traditional techniques through\nsmall yet impactful adaptations.\n", "date": "2023-08-24", "categories": []}
{"id": "2308.13352", "title": "A Generic Machine Learning Framework for Fully-Unsupervised Anomaly\n  Detection with Contaminated Data", "abstract": "  Anomaly detection (AD) tasks have been solved using machine learning\nalgorithms in various domains and applications. The great majority of these\nalgorithms use normal data to train a residual-based model and assign anomaly\nscores to unseen samples based on their dissimilarity with the learned normal\nregime. The underlying assumption of these approaches is that anomaly-free data\nis available for training. This is, however, often not the case in real-world\noperational settings, where the training data may be contaminated with an\nunknown fraction of abnormal samples. Training with contaminated data, in turn,\ninevitably leads to a deteriorated AD performance of the residual-based\nalgorithms.\n  In this paper we introduce a framework for a fully unsupervised refinement of\ncontaminated training data for AD tasks. The framework is generic and can be\napplied to any residual-based machine learning model. We demonstrate the\napplication of the framework to two public datasets of multivariate time series\nmachine data from different application fields. We show its clear superiority\nover the naive approach of training with contaminated data without refinement.\nMoreover, we compare it to the ideal, unrealistic reference in which\nanomaly-free data would be available for training. The method is based on\nevaluating the contribution of individual samples to the generalization ability\nof a given model, and contrasting the contribution of anomalies with the one of\nnormal samples. As a result, the proposed approach is comparable to, and often\noutperforms training with normal samples only.\n", "date": "2023-08-25", "categories": []}
{"id": "2401.16753", "title": "MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with\n  Mutual Scoring of the Unlabeled Images", "abstract": "  This paper studies zero-shot anomaly classification (AC) and segmentation\n(AS) in industrial vision. We reveal that the abundant normal and abnormal cues\nimplicit in unlabeled test images can be exploited for anomaly determination,\nwhich is ignored by prior methods. Our key observation is that for the\nindustrial product images, the normal image patches could find a relatively\nlarge number of similar patches in other unlabeled images, while the abnormal\nones only have a few similar patches. We leverage such a discriminative\ncharacteristic to design a novel zero-shot AC/AS method by Mutual Scoring\n(MuSc) of the unlabeled images, which does not need any training or prompts.\nSpecifically, we perform Local Neighborhood Aggregation with Multiple Degrees\n(LNAMD) to obtain the patch features that are capable of representing anomalies\nin varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to\nleverage the unlabeled test images to assign the anomaly score to each other.\nFurthermore, we present an optimization approach named Re-scoring with\nConstrained Image-level Neighborhood (RsCIN) for image-level anomaly\nclassification to suppress the false positives caused by noises in normal\nimages. The superior performance on the challenging MVTec AD and VisA datasets\ndemonstrates the effectiveness of our approach. Compared with the\nstate-of-the-art zero-shot approaches, MuSc achieves a $\\textbf{21.1%}$ PRO\nabsolute gain (from 72.7% to 93.8%) on MVTec AD, a $\\textbf{19.4%}$ pixel-AP\ngain and a $\\textbf{14.7%}$ pixel-AUROC gain on VisA. In addition, our\nzero-shot approach outperforms most of the few-shot approaches and is\ncomparable to some one-class methods. Code is available at\nhttps://github.com/xrli-U/MuSc.\n", "date": "2024-01-30", "categories": []}
{"id": "2301.13359", "title": "IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing", "abstract": "  Image anomaly detection (IAD) is an emerging and vital computer vision task\nin industrial manufacturing (IM). Recently, many advanced algorithms have been\nreported, but their performance deviates considerably with various IM settings.\nWe realize that the lack of a uniform IM benchmark is hindering the development\nand usage of IAD methods in real-world applications. In addition, it is\ndifficult for researchers to analyze IAD algorithms without a uniform\nbenchmark. To solve this problem, we propose a uniform IM benchmark, for the\nfirst time, to assess how well these algorithms perform, which includes various\nlevels of supervision (unsupervised versus fully supervised), learning\nparadigms (few-shot, continual and noisy label), and efficiency (memory usage\nand inference speed). Then, we construct a comprehensive image anomaly\ndetection benchmark (IM-IAD), which includes 19 algorithms on seven major\ndatasets with a uniform setting. Extensive experiments (17,017 total) on IM-IAD\nprovide in-depth insights into IAD algorithm redesign or selection. Moreover,\nthe proposed IM-IAD benchmark challenges existing algorithms and suggests\nfuture research directions. To foster reproducibility and accessibility, the\nsource code of IM-IAD is uploaded on the website,\nhttps://github.com/M-3LAB/IM-IAD.\n", "date": "2023-01-31", "categories": []}
{"id": "2401.12665", "title": "ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation", "abstract": "  Recently, foundational models such as CLIP and SAM have shown promising\nperformance for the task of Zero-Shot Anomaly Segmentation (ZSAS). However,\neither CLIP-based or SAM-based ZSAS methods still suffer from non-negligible\nkey drawbacks: 1) CLIP primarily focuses on global feature alignment across\ndifferent inputs, leading to imprecise segmentation of local anomalous parts;\n2) SAM tends to generate numerous redundant masks without proper prompt\nconstraints, resulting in complex post-processing requirements. In this work,\nwe innovatively propose a CLIP and SAM collaboration framework called ClipSAM\nfor ZSAS. The insight behind ClipSAM is to employ CLIP's semantic understanding\ncapability for anomaly localization and rough segmentation, which is further\nused as the prompt constraints for SAM to refine the anomaly segmentation\nresults. In details, we introduce a crucial Unified Multi-scale Cross-modal\nInteraction (UMCI) module for interacting language with visual features at\nmultiple scales of CLIP to reason anomaly positions. Then, we design a novel\nMulti-level Mask Refinement (MMR) module, which utilizes the positional\ninformation as multi-level prompts for SAM to acquire hierarchical levels of\nmasks and merges them. Extensive experiments validate the effectiveness of our\napproach, achieving the optimal segmentation performance on the MVTec-AD and\nVisA datasets.\n", "date": "2024-01-23", "categories": []}
{"id": "2401.15123", "title": "Large Language Model Guided Knowledge Distillation for Time Series\n  Anomaly Detection", "abstract": "  Self-supervised methods have gained prominence in time series anomaly\ndetection due to the scarcity of available annotations. Nevertheless, they\ntypically demand extensive training data to acquire a generalizable\nrepresentation map, which conflicts with scenarios of a few available samples,\nthereby limiting their performance. To overcome the limitation, we propose\n\\textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly\ndetection approach where the student network is trained to mimic the features\nof the large language model (LLM)-based teacher network that is pretrained on\nlarge-scale datasets. During the testing phase, anomalies are detected when the\ndiscrepancy between the features of the teacher and student networks is large.\nTo circumvent the student network from learning the teacher network's feature\nof anomalous samples, we devise two key strategies. 1) Prototypical signals are\nincorporated into the student network to consolidate the normal feature\nextraction. 2) We use synthetic anomalies to enlarge the representation gap\nbetween the two networks. AnomalyLLM demonstrates state-of-the-art performance\non 15 datasets, improving accuracy by at least 14.5\\% in the UCR dataset.\n", "date": "2024-01-26", "categories": []}
{"id": "2401.15543", "title": "Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep\n  Learning Technology", "abstract": "  A stable, reliable, and controllable orbit lock system is crucial to an\nelectron (or ion) accelerator because the beam orbit and beam energy\ninstability strongly affect the quality of the beam delivered to experimental\nhalls. Currently, when the orbit lock system fails operators must manually\nintervene. This paper develops a Machine Learning based fault detection\nmethodology to identify orbit lock anomalies and notify accelerator operations\nstaff of the off-normal behavior. Our method is unsupervised, so it does not\nrequire labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to\ncapture normal patterns and predict future values of monitoring sensors in the\norbit lock system. Anomalies are detected when the prediction error exceeds a\nthreshold. We conducted experiments using monitoring data from Jefferson Lab's\nContinuous Electron Beam Accelerator Facility (CEBAF). The results are\npromising: the percentage of real anomalies identified by our solution is\n68.6%-89.3% using monitoring data of a single component in the orbit lock\ncontrol system. The accuracy can be as high as 82%.\n", "date": "2024-01-28", "categories": []}
{"id": "2401.16363", "title": "Evaluation of pseudo-healthy image reconstruction for anomaly detection\n  with deep generative models: Application to brain FDG PET", "abstract": "  Over the past years, pseudo-healthy reconstruction for unsupervised anomaly\ndetection has gained in popularity. This approach has the great advantage of\nnot requiring tedious pixel-wise data annotation and offers possibility to\ngeneralize to any kind of anomalies, including that corresponding to rare\ndiseases. By training a deep generative model with only images from healthy\nsubjects, the model will learn to reconstruct pseudo-healthy images. This\npseudo-healthy reconstruction is then compared to the input to detect and\nlocalize anomalies. The evaluation of such methods often relies on a ground\ntruth lesion mask that is available for test data, which may not exist\ndepending on the application.\n  We propose an evaluation procedure based on the simulation of realistic\nabnormal images to validate pseudo-healthy reconstruction methods when no\nground truth is available. This allows us to extensively test generative models\non different kinds of anomalies and measuring their performance using the pair\nof normal and abnormal images corresponding to the same subject. It can be used\nas a preliminary automatic step to validate the capacity of a generative model\nto reconstruct pseudo-healthy images, before a more advanced validation step\nthat would require clinician's expertise. We apply this framework to the\nreconstruction of 3D brain FDG PET using a convolutional variational\nautoencoder with the aim to detect as early as possible the neurodegeneration\nmarkers that are specific to dementia such as Alzheimer's disease.\n", "date": "2024-01-29", "categories": []}
{"id": "2401.16402", "title": "A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect", "abstract": "  Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the\nconcept of normality in visual data, widely applied across diverse domains,\ne.g., industrial defect inspection, and medical lesion detection. This survey\ncomprehensively examines recent advancements in VAD by identifying three\nprimary challenges: 1) scarcity of training data, 2) diversity of visual\nmodalities, and 3) complexity of hierarchical anomalies. Starting with a brief\noverview of the VAD background and its generic concept definitions, we\nprogressively categorize, emphasize, and discuss the latest VAD progress from\nthe perspective of sample number, data modality, and anomaly hierarchy. Through\nan in-depth analysis of the VAD field, we finally summarize future developments\nfor VAD and conclude the key findings and contributions of this survey.\n", "date": "2024-01-29", "categories": []}
{"id": "2401.14155", "title": "Alleviating Structural Distribution Shift in Graph Anomaly Detection", "abstract": "  Graph anomaly detection (GAD) is a challenging binary classification problem\ndue to its different structural distribution between anomalies and normal nodes\n-- abnormal nodes are a minority, therefore holding high heterophily and low\nhomophily compared to normal nodes. Furthermore, due to various time factors\nand the annotation preferences of human experts, the heterophily and homophily\ncan change across training and testing data, which is called structural\ndistribution shift (SDS) in this paper. The mainstream methods are built on\ngraph neural networks (GNNs), benefiting the classification of normals from\naggregating homophilous neighbors, yet ignoring the SDS issue for anomalies and\nsuffering from poor generalization.\n  This work solves the problem from a feature view. We observe that the degree\nof SDS varies between anomalies and normal nodes. Hence to address the issue,\nthe key lies in resisting high heterophily for anomalies meanwhile benefiting\nthe learning of normals from homophily. We tease out the anomaly features on\nwhich we constrain to mitigate the effect of heterophilous neighbors and make\nthem invariant. We term our proposed framework as Graph Decomposition Network\n(GDN). Extensive experiments are conducted on two benchmark datasets, and the\nproposed framework achieves a remarkable performance boost in GAD, especially\nin an SDS environment where anomalies have largely different structural\ndistribution across training and testing environments. Codes are open-sourced\nin https://github.com/blacksingular/wsdm_GDN.\n", "date": "2024-01-25", "categories": []}
{"id": "2307.00527", "title": "Graph Neural Networks based Log Anomaly Detection and Explanation", "abstract": "  Event logs are widely used to record the status of high-tech systems, making\nlog anomaly detection important for monitoring those systems. Most existing log\nanomaly detection methods take a log event count matrix or log event sequences\nas input, exploiting quantitative and/or sequential relationships between log\nevents to detect anomalies. Unfortunately, only considering quantitative or\nsequential relationships may result in low detection accuracy. To alleviate\nthis problem, we propose a graph-based method for unsupervised log anomaly\ndetection, dubbed Logs2Graphs, which first converts event logs into attributed,\ndirected, and weighted graphs, and then leverages graph neural networks to\nperform graph-level anomaly detection. Specifically, we introduce One-Class\nDigraph Inception Convolutional Networks, abbreviated as OCDiGCN, a novel graph\nneural network model for detecting graph-level anomalies in a collection of\nattributed, directed, and weighted graphs. By coupling the graph representation\nand anomaly detection steps, OCDiGCN can learn a representation that is\nespecially suited for anomaly detection, resulting in a high detection\naccuracy. Importantly, for each identified anomaly, we additionally provide a\nsmall subset of nodes that play a crucial role in OCDiGCN's prediction as\nexplanations, which can offer valuable cues for subsequent root cause\ndiagnosis. Experiments on five benchmark datasets show that Logs2Graphs\nperforms at least on par with state-of-the-art log anomaly detection methods on\nsimple datasets while largely outperforming state-of-the-art log anomaly\ndetection methods on complicated datasets.\n", "date": "2023-07-02", "categories": []}
{"id": "2401.13210", "title": "Multitask Active Learning for Graph Anomaly Detection", "abstract": "  In the web era, graph machine learning has been widely used on ubiquitous\ngraph-structured data. As a pivotal component for bolstering web security and\nenhancing the robustness of graph-based applications, the significance of graph\nanomaly detection is continually increasing. While Graph Neural Networks (GNNs)\nhave demonstrated efficacy in supervised and semi-supervised graph anomaly\ndetection, their performance is contingent upon the availability of sufficient\nground truth labels. The labor-intensive nature of identifying anomalies from\ncomplex graph structures poses a significant challenge in real-world\napplications. Despite that, the indirect supervision signals from other tasks\n(e.g., node classification) are relatively abundant. In this paper, we propose\na novel MultItask acTIve Graph Anomaly deTEction framework, namely MITIGATE.\nFirstly, by coupling node classification tasks, MITIGATE obtains the capability\nto detect out-of-distribution nodes without known anomalies. Secondly, MITIGATE\nquantifies the informativeness of nodes by the confidence difference across\ntasks, allowing samples with conflicting predictions to provide informative yet\nnot excessively challenging information for subsequent training. Finally, to\nenhance the likelihood of selecting representative nodes that are distant from\nknown patterns, MITIGATE adopts a masked aggregation mechanism for distance\nmeasurement, considering both inherent features of nodes and current labeled\nstatus. Empirical studies on four datasets demonstrate that MITIGATE\nsignificantly outperforms the state-of-the-art methods for anomaly detection.\nOur code is publicly available at: https://github.com/AhaChang/MITIGATE.\n", "date": "2024-01-24", "categories": []}
{"id": "2302.05154", "title": "Industrial and Medical Anomaly Detection Through Cycle-Consistent\n  Adversarial Networks", "abstract": "  In this study, a new Anomaly Detection (AD) approach for industrial and\nmedical images is proposed. This method leverages the theoretical strengths of\nunsupervised learning and the data availability of both normal and abnormal\nclasses. Indeed, the AD is often formulated as an unsupervised task, implying\nonly normal images during training. These normal images are devoted to be\nreconstructed, through an autoencoder architecture for instance. However, the\ninformation contained in abnormal data, when available, is also valuable for\nthis reconstruction. The model would be able to identify its weaknesses by\nbetter learning how to transform an abnormal (respectively normal) image into a\nnormal (respectively abnormal) one, helping the entire model to learn better\nthan a single normal to normal reconstruction. To address this challenge, the\nproposed method uses Cycle-Generative Adversarial Networks (Cycle-GAN) for\n(ab)normal-to-normal translation. After an input image has been reconstructed\nby the normal generator, an anomaly score quantifies the differences between\nthe input and its reconstruction. Based on a threshold set to satisfy a\nbusiness quality constraint, the input image is then flagged as normal or not.\nThe proposed method is evaluated on industrial and medical datasets. The\nresults demonstrate accurate performance with a zero false negative constraint\ncompared to state-of-the-art methods. The code is available at\nhttps://github.com/ValDelch/CycleGANS-AnomalyDetection.\n", "date": "2023-02-10", "categories": []}
{"id": "2306.14451", "title": "Learning Prompt-Enhanced Context Features for Weakly-Supervised Video\n  Anomaly Detection", "abstract": "  Video anomaly detection under weak supervision presents significant\nchallenges, particularly due to the lack of frame-level annotations during\ntraining. While prior research has utilized graph convolution networks and\nself-attention mechanisms alongside multiple instance learning (MIL)-based\nclassification loss to model temporal relations and learn discriminative\nfeatures, these methods often employ multi-branch architectures to capture\nlocal and global dependencies separately, resulting in increased parameters and\ncomputational costs. Moreover, the coarse-grained interclass separability\nprovided by the binary constraint of MIL-based loss neglects the fine-grained\ndiscriminability within anomalous classes. In response, this paper introduces a\nweakly supervised anomaly detection framework that focuses on efficient context\nmodeling and enhanced semantic discriminability. We present a Temporal Context\nAggregation (TCA) module that captures comprehensive contextual information by\nreusing the similarity matrix and implementing adaptive fusion. Additionally,\nwe propose a Prompt-Enhanced Learning (PEL) module that integrates semantic\npriors using knowledge-based prompts to boost the discriminative capacity of\ncontext features while ensuring separability between anomaly sub-classes.\nExtensive experiments validate the effectiveness of our method's components,\ndemonstrating competitive performance with reduced parameters and computational\neffort on three challenging benchmarks: UCF-Crime, XD-Violence, and\nShanghaiTech datasets. Notably, our approach significantly improves the\ndetection accuracy of certain anomaly sub-classes, underscoring its practical\nvalue and efficacy. Our code is available at:\nhttps://github.com/yujiangpu20/PEL4VAD.\n", "date": "2023-06-26", "categories": []}
{"id": "2312.11976", "title": "When Model Meets New Normals: Test-time Adaptation for Unsupervised\n  Time-series Anomaly Detection", "abstract": "  Time-series anomaly detection deals with the problem of detecting anomalous\ntimesteps by learning normality from the sequence of observations. However, the\nconcept of normality evolves over time, leading to a \"new normal problem\",\nwhere the distribution of normality can be changed due to the distribution\nshifts between training and test data. This paper highlights the prevalence of\nthe new normal problem in unsupervised time-series anomaly detection studies.\nTo tackle this issue, we propose a simple yet effective test-time adaptation\nstrategy based on trend estimation and a self-supervised approach to learning\nnew normalities during inference. Extensive experiments on real-world\nbenchmarks demonstrate that incorporating the proposed strategy into the\nanomaly detector consistently improves the model's performance compared to the\nbaselines, leading to robustness to the distribution shifts.\n", "date": "2023-12-19", "categories": []}
{"id": "2401.11235", "title": "TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly\n  Detection with Inexact Supervision", "abstract": "  Time series anomaly detection (TSAD) plays a vital role in various domains\nsuch as healthcare, networks, and industry. Considering labels are crucial for\ndetection but difficult to obtain, we turn to TSAD with inexact supervision:\nonly series-level labels are provided during the training phase, while\npoint-level anomalies are predicted during the testing phase. Previous works\nfollow a traditional multi-instance learning (MIL) approach, which focuses on\nencouraging high anomaly scores at individual time steps. However, time series\nanomalies are not only limited to individual point anomalies, they can also be\ncollective anomalies, typically exhibiting abnormal patterns over subsequences.\nTo address the challenge of collective anomalies, in this paper, we propose a\ntree-based MIL framework (TreeMIL). We first adopt an N-ary tree structure to\ndivide the entire series into multiple nodes, where nodes at different levels\nrepresent subsequences with different lengths. Then, the subsequence features\nare extracted to determine the presence of collective anomalies. Finally, we\ncalculate point-level anomaly scores by aggregating features from nodes at\ndifferent levels. Experiments conducted on seven public datasets and eight\nbaselines demonstrate that TreeMIL achieves an average 32.3% improvement in F1-\nscore compared to previous state-of-the-art methods. The code is available at\nhttps://github.com/fly-orange/TreeMIL.\n", "date": "2024-01-20", "categories": []}
{"id": "2401.11271", "title": "DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series\n  Anomaly Detection", "abstract": "  Anomaly detection in time-series data is crucial for identifying faults,\nfailures, threats, and outliers across a range of applications. Recently, deep\nlearning techniques have been applied to this topic, but they often struggle in\nreal-world scenarios that are complex and highly dynamic, e.g., the normal data\nmay consist of multiple distributions, and various types of anomalies may\ndiffer from the normal data to different degrees. In this work, to tackle these\nchallenges, we propose Distribution-Augmented Contrastive Reconstruction\n(DACR). DACR generates extra data disjoint from the normal data distribution to\ncompress the normal data's representation space, and enhances the feature\nextractor through contrastive learning to better capture the intrinsic\nsemantics from time-series data. Furthermore, DACR employs an attention\nmechanism to model the semantic dependencies among multivariate time-series\nfeatures, thereby achieving more robust reconstruction for anomaly detection.\nExtensive experiments conducted on nine benchmark datasets in various anomaly\ndetection scenarios demonstrate the effectiveness of DACR in achieving new\nstate-of-the-art time-series anomaly detection.\n", "date": "2024-01-20", "categories": []}
{"id": "2401.12176", "title": "Broiler-Net: A Deep Convolutional Framework for Broiler Behavior\n  Analysis in Poultry Houses", "abstract": "  Detecting anomalies in poultry houses is crucial for maintaining optimal\nchicken health conditions, minimizing economic losses and bolstering\nprofitability. This paper presents a novel real-time framework for analyzing\nchicken behavior in cage-free poultry houses to detect abnormal behaviors.\nSpecifically, two significant abnormalities, namely inactive broiler and\nhuddling behavior, are investigated in this study. The proposed framework\ncomprises three key steps: (1) chicken detection utilizing a state-of-the-art\ndeep learning model, (2) tracking individual chickens across consecutive frames\nwith a fast tracker module, and (3) detecting abnormal behaviors within the\nvideo stream. Experimental studies are conducted to evaluate the efficacy of\nthe proposed algorithm in accurately assessing chicken behavior. The results\nillustrate that our framework provides a precise and efficient solution for\nreal-time anomaly detection, facilitating timely interventions to maintain\nchicken health and enhance overall productivity on poultry farms. Github:\nhttps://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis\n", "date": "2024-01-22", "categories": []}
{"id": "2209.06828", "title": "A Temporal Anomaly Detection System for Vehicles utilizing Functional\n  Working Groups and Sensor Channels", "abstract": "  A modern vehicle fitted with sensors, actuators, and Electronic Control Units\n(ECUs) can be divided into several operational subsystems called Functional\nWorking Groups (FWGs). Examples of these FWGs include the engine system,\ntransmission, fuel system, brakes, etc. Each FWG has associated sensor-channels\nthat gauge vehicular operating conditions. This data rich environment is\nconducive to the development of Predictive Maintenance (PdM) technologies.\nUndercutting various PdM technologies is the need for robust anomaly detection\nmodels that can identify events or observations which deviate significantly\nfrom the majority of the data and do not conform to a well defined notion of\nnormal vehicular operational behavior. In this paper, we introduce the Vehicle\nPerformance, Reliability, and Operations (VePRO) dataset and use it to create a\nmulti-phased approach to anomaly detection. Utilizing Temporal Convolution\nNetworks (TCN), our anomaly detection system can achieve 96% detection accuracy\nand accurately predicts 91% of true anomalies. The performance of our anomaly\ndetection system improves when sensor channels from multiple FWGs are utilized.\n", "date": "2022-09-14", "categories": []}
{"id": "2401.10547", "title": "PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology\n  Optimization", "abstract": "  A multitude of toxic online behaviors, ranging from network attacks to\nanonymous traffic and spam, have severely disrupted the smooth operation of\nnetworks. Due to the inherent sender-receiver nature of network behaviors,\ngraph-based frameworks are commonly used for detecting anomalous behaviors.\nHowever, in real-world scenarios, the boundary between normal and anomalous\nbehaviors tends to be ambiguous. The local heterophily of graphs interferes\nwith the detection, and existing methods based on nodes or edges introduce\nunwanted noise into representation results, thereby impacting the effectiveness\nof detection. To address these issues, we propose PhoGAD, a graph-based anomaly\ndetection framework. PhoGAD leverages persistent homology optimization to\nclarify behavioral boundaries. Building upon this, the weights of adjacent\nedges are designed to mitigate the effects of local heterophily. Subsequently,\nto tackle the noise problem, we conduct a formal analysis and propose a\ndisentangled representation-based explicit embedding method, ultimately\nachieving anomaly behavior detection. Experiments on intrusion, traffic, and\nspam datasets verify that PhoGAD has surpassed the performance of\nstate-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD\ndemonstrates robust detection even with diminished anomaly proportions,\nhighlighting its applicability to real-world scenarios. The analysis of\npersistent homology demonstrates its effectiveness in capturing the topological\nstructure formed by normal edge features. Additionally, ablation experiments\nvalidate the effectiveness of the innovative mechanisms integrated within\nPhoGAD.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.10561", "title": "MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised\n  Anomaly Detection in Brain Images", "abstract": "  Unsupervised anomaly detection has gained significant attention in the field\nof medical imaging due to its capability of relieving the costly pixel-level\nannotation. To achieve this, modern approaches usually utilize generative\nmodels to produce healthy references of the diseased images and then identify\nthe abnormalities by comparing the healthy references and the original diseased\nimages. Recently, diffusion models have exhibited promising potential for\nunsupervised anomaly detection in medical images for their good mode coverage\nand high sample quality. However, the intrinsic characteristics of the medical\nimages, e.g. the low contrast, and the intricate anatomical structure of the\nhuman body make the reconstruction challenging. Besides, the global information\nof medical images often remain underutilized. To address these two issues, we\npropose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for\nunsupervised anomaly detection in brain images. The MAEDiff involves a\nhierarchical patch partition. It generates healthy images by overlapping\nupper-level patches and implements a mechanism based on the masked autoencoders\noperating on the sub-level patches to enhance the condition on the unnoised\nregions. Extensive experiments on data of tumors and multiple sclerosis lesions\ndemonstrate the effectiveness of our method.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.10637", "title": "Towards Universal Unsupervised Anomaly Detection in Medical Imaging", "abstract": "  The increasing complexity of medical imaging data underscores the need for\nadvanced anomaly detection methods to automatically identify diverse\npathologies. Current methods face challenges in capturing the broad spectrum of\nanomalies, often limiting their use to specific lesion types in brain scans. To\naddress this challenge, we introduce a novel unsupervised approach, termed\n\\textit{Reversed Auto-Encoders (RA)}, designed to create realistic\npseudo-healthy reconstructions that enable the detection of a wider range of\npathologies. We evaluate the proposed method across various imaging modalities,\nincluding magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray,\nand chest X-ray, and demonstrate superior performance in detecting anomalies\ncompared to existing state-of-the-art methods. Our unsupervised anomaly\ndetection approach may enhance diagnostic accuracy in medical imaging by\nidentifying a broader range of unknown pathologies. Our code is publicly\navailable at: \\url{https://github.com/ci-ber/RA}.\n", "date": "2024-01-19", "categories": []}
{"id": "2301.00114", "title": "Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges\n  and Future Directions", "abstract": "  The existing methods for video anomaly detection mostly utilize videos\ncontaining identifiable facial and appearance-based features. The use of videos\nwith identifiable faces raises privacy concerns, especially when used in a\nhospital or community-based setting. Appearance-based features can also be\nsensitive to pixel-based noise, straining the anomaly detection methods to\nmodel the changes in the background and making it difficult to focus on the\nactions of humans in the foreground. Structural information in the form of\nskeletons describing the human motion in the videos is privacy-protecting and\ncan overcome some of the problems posed by appearance-based features. In this\npaper, we present a survey of privacy-protecting deep learning anomaly\ndetection methods using skeletons extracted from videos. We present a novel\ntaxonomy of algorithms based on the various learning approaches. We conclude\nthat skeleton-based approaches for anomaly detection can be a plausible\nprivacy-protecting alternative for video anomaly detection. Lastly, we identify\nmajor open research questions and provide guidelines to address them.\n", "date": "2022-12-31", "categories": []}
{"id": "2401.09489", "title": "PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies", "abstract": "  In recent years there has been significant progress in time series anomaly\ndetection. However, after detecting an (perhaps tentative) anomaly, can we\nexplain it? Such explanations would be useful to triage anomalies. For example,\nin an oil refinery, should we respond to an anomaly by dispatching a hydraulic\nengineer, or an intern to replace the battery on a sensor? There have been some\nparallel efforts to explain anomalies, however many proposed techniques produce\nexplanations that are indirect, and often seem more complex than the anomaly\nthey seek to explain. Our review of the literature/checklists/user-manuals used\nby frontline practitioners in various domains reveals an interesting\nnear-universal commonality. Most practitioners discuss, explain and report\nanomalies in the following format: The anomaly would be like normal data A, if\nnot for the corruption B. The reader will appreciate that is a type of\ncounterfactual explanation. In this work we introduce a domain agnostic\ncounterfactual explanation technique to produce explanations for time series\nanomalies. As we will show, our method can produce both visual and text-based\nexplanations that are objectively correct, intuitive and in many circumstances,\ndirectly actionable.\n", "date": "2024-01-16", "categories": []}
{"id": "2312.04864", "title": "Critical Analysis of 5G Networks Traffic Intrusion using PCA, t-SNE and\n  UMAP Visualization and Classifying Attacks", "abstract": "  Networks, threat models, and malicious actors are advancing quickly. With the\nincreased deployment of the 5G networks, the security issues of the attached 5G\nphysical devices have also increased. Therefore, artificial intelligence based\nautonomous end-to-end security design is needed that can deal with incoming\nthreats by detecting network traffic anomalies. To address this requirement, in\nthis research, we used a recently published 5G traffic dataset, 5G-NIDD, to\ndetect network traffic anomalies using machine and deep learning approaches.\nFirst, we analyzed the dataset using three visualization techniques:\nt-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold\nApproximation and Projection (UMAP), and Principal Component Analysis (PCA).\nSecond, we reduced the data dimensionality using mutual information and PCA\ntechniques. Third, we solve the class imbalance issue by inserting synthetic\nrecords of minority classes. Last, we performed classification using six\ndifferent classifiers and presented the evaluation metrics. We received the\nbest results when K-Nearest Neighbors classifier was used: accuracy (97.2%),\ndetection rate (96.7%), and false positive rate (2.2%).\n", "date": "2023-12-08", "categories": []}
{"id": "2401.03145", "title": "Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection", "abstract": "  Industrial anomaly detection is generally addressed as an unsupervised task\nthat aims at locating defects with only normal training samples. Recently,\nnumerous 2D anomaly detection methods have been proposed and have achieved\npromising results, however, using only the 2D RGB data as input is not\nsufficient to identify imperceptible geometric surface anomalies. Hence, in\nthis work, we focus on multi-modal anomaly detection. Specifically, we\ninvestigate early multi-modal approaches that attempted to utilize models\npre-trained on large-scale visual datasets, i.e., ImageNet, to construct\nfeature databases. And we empirically find that directly using these\npre-trained models is not optimal, it can either fail to detect subtle defects\nor mistake abnormal features as normal ones. This may be attributed to the\ndomain gap between target industrial data and source data.Towards this problem,\nwe propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method\nto finetune the adaptors and learn task-oriented representation toward anomaly\ndetection.Both intra-modal adaptation and cross-modal alignment are optimized\nfrom a local-to-global perspective in LSFA to ensure the representation quality\nand consistency in the inference stage.Extensive experiments demonstrate that\nour method not only brings a significant performance boost to feature embedding\nbased approaches, but also outperforms previous State-of-The-Art (SoTA) methods\nprominently on both MVTec-3D AD and Eyecandies datasets, e.g., LSFA achieves\n97.1% I-AUROC on MVTec-3D, surpass previous SoTA by +3.4%.\n", "date": "2024-01-06", "categories": []}
{"id": "2401.08686", "title": "Attention Modules Improve Modern Image-Level Anomaly Detection: A\n  DifferNet Case Study", "abstract": "  Within (semi-)automated visual inspection, learning-based approaches for\nassessing visual defects, including deep neural networks, enable the processing\nof otherwise small defect patterns in pixel size on high-resolution imagery.\nThe emergence of these often rarely occurring defect patterns explains the\ngeneral need for labeled data corpora. To not only alleviate this issue but to\nfurthermore advance the current state of the art in unsupervised visual\ninspection, this contribution proposes a DifferNet-based solution enhanced with\nattention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to\nimprove the detection and classification capabilities on three different visual\ninspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and\nSemiconductor Wafer. In comparison to the current state of the art, it is shown\nthat AttentDifferNet achieves improved results, which are, in turn, highlighted\nthroughout our quantitative as well as qualitative evaluation, indicated by a\ngeneral improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs.\n88.74%. As our variants to AttentDifferNet show great prospects in the context\nof currently investigated approaches, a baseline is formulated, emphasizing the\nimportance of attention for anomaly detection.\n", "date": "2024-01-13", "categories": []}
{"id": "2401.08777", "title": "Robust Anomaly Detection for Particle Physics Using Multi-Background\n  Representation Learning", "abstract": "  Anomaly, or out-of-distribution, detection is a promising tool for aiding\ndiscoveries of new particles or processes in particle physics. In this work, we\nidentify and address two overlooked opportunities to improve anomaly detection\nfor high-energy physics. First, rather than train a generative model on the\nsingle most dominant background process, we build detection algorithms using\nrepresentation learning from multiple background types, thus taking advantage\nof more information to improve estimation of what is relevant for detection.\nSecond, we generalize decorrelation to the multi-background setting, thus\ndirectly enforcing a more complete definition of robustness for anomaly\ndetection. We demonstrate the benefit of the proposed robust multi-background\nanomaly detection algorithms on a high-dimensional dataset of particle decays\nat the Large Hadron Collider.\n", "date": "2024-01-16", "categories": []}
{"id": "2401.07655", "title": "MLAD: A Unified Model for Multi-system Log Anomaly Detection", "abstract": "  In spite of the rapid advancements in unsupervised log anomaly detection\ntechniques, the current mainstream models still necessitate specific training\nfor individual system datasets, resulting in costly procedures and limited\nscalability due to dataset size, thereby leading to performance bottlenecks.\nFurthermore, numerous models lack cognitive reasoning capabilities, posing\nchallenges in direct transferability to similar systems for effective anomaly\ndetection. Additionally, akin to reconstruction networks, these models often\nencounter the \"identical shortcut\" predicament, wherein the majority of system\nlogs are classified as normal, erroneously predicting normal classes when\nconfronted with rare anomaly logs due to reconstruction errors.\n  To address the aforementioned issues, we propose MLAD, a novel anomaly\ndetection model that incorporates semantic relational reasoning across multiple\nsystems. Specifically, we employ Sentence-bert to capture the similarities\nbetween log sequences and convert them into highly-dimensional learnable\nsemantic vectors. Subsequently, we revamp the formulas of the Attention layer\nto discern the significance of each keyword in the sequence and model the\noverall distribution of the multi-system dataset through appropriate vector\nspace diffusion. Lastly, we employ a Gaussian mixture model to highlight the\nuncertainty of rare words pertaining to the \"identical shortcut\" problem,\noptimizing the vector space of the samples using the maximum expectation model.\nExperiments on three real-world datasets demonstrate the superiority of MLAD.\n", "date": "2024-01-15", "categories": []}
{"id": "2307.04956", "title": "PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly\n  Detection and Segmentation", "abstract": "  Visual anomaly detection is essential and commonly used for many tasks in the\nfield of computer vision. Recent anomaly detection datasets mainly focus on\nindustrial automated inspection, medical image analysis and video surveillance.\nIn order to broaden the application and research of anomaly detection in\nunmanned supermarkets and smart manufacturing, we introduce the supermarket\ngoods anomaly detection (GoodsAD) dataset. It contains 6124 high-resolution\nimages of 484 different appearance goods divided into 6 categories. Each\ncategory contains several common different types of anomalies such as\ndeformation, surface damage and opened. Anomalies contain both texture changes\nand structural changes. It follows the unsupervised setting and only normal\n(defect-free) images are used for training. Pixel-precise ground truth regions\nare provided for all anomalies. Moreover, we also conduct a thorough evaluation\nof current state-of-the-art unsupervised anomaly detection methods. This\ninitial benchmark indicates that some methods which perform well on the\nindustrial anomaly detection dataset (e.g., MVTec AD), show poor performance on\nour dataset. This is a comprehensive, multi-object dataset for supermarket\ngoods anomaly detection that focuses on real-world applications.\n", "date": "2023-07-11", "categories": []}
{"id": "2401.06175", "title": "MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly\n  Detection", "abstract": "  Key Performance Indicators (KPIs) are essential time-series metrics for\nensuring the reliability and stability of many software systems. They\nfaithfully record runtime states to facilitate the understanding of anomalous\nsystem behaviors and provide informative clues for engineers to pinpoint the\nroot causes. The unprecedented scale and complexity of modern software systems,\nhowever, make the volume of KPIs explode. Consequently, many traditional\nmethods of KPI anomaly detection become impractical, which serves as a catalyst\nfor the fast development of machine learning-based solutions in both academia\nand industry. However, there is currently a lack of rigorous comparison among\nthese KPI anomaly detection methods, and re-implementation demands a\nnon-trivial effort. Moreover, we observe that different works adopt independent\nevaluation processes with different metrics. Some of them may not fully reveal\nthe capability of a model and some are creating an illusion of progress. To\nbetter understand the characteristics of different KPI anomaly detectors and\naddress the evaluation issue, in this paper, we provide a comprehensive review\nand evaluation of twelve state-of-the-art methods, and propose a novel metric\ncalled salience. Particularly, the selected methods include five traditional\nmachine learning-based methods and seven deep learning-based methods. These\nmethods are evaluated with five multivariate KPI datasets that are publicly\navailable. A unified toolkit with easy-to-use interfaces is also released. We\nreport the benchmark results in terms of accuracy, salience, efficiency, and\ndelay, which are of practical importance for industrial deployment. We believe\nour work can contribute as a basis for future academic research and industrial\napplication.\n", "date": "2024-01-10", "categories": []}
{"id": "2307.05638", "title": "A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection\n  in Industrial Time Series: Methods, Applications, and Directions", "abstract": "  Automating the monitoring of industrial processes has the potential to\nenhance efficiency and optimize quality by promptly detecting abnormal events\nand thus facilitating timely interventions. Deep learning, with its capacity to\ndiscern non-trivial patterns within large datasets, plays a pivotal role in\nthis process. Standard deep learning methods are suitable to solve a specific\ntask given a specific type of data. During training, deep learning demands\nlarge volumes of labeled data. However, due to the dynamic nature of the\nindustrial processes and environment, it is impractical to acquire large-scale\nlabeled data for standard deep learning training for every slightly different\ncase anew. Deep transfer learning offers a solution to this problem. By\nleveraging knowledge from related tasks and accounting for variations in data\ndistributions, the transfer learning framework solves new tasks with little or\neven no additional labeled data. The approach bypasses the need to retrain a\nmodel from scratch for every new setup and dramatically reduces the labeled\ndata requirement. This survey first provides an in-depth review of deep\ntransfer learning, examining the problem settings of transfer learning and\nclassifying the prevailing deep transfer learning methods. Moreover, we delve\ninto applications of deep transfer learning in the context of a broad spectrum\nof time series anomaly detection tasks prevalent in primary industrial domains,\ne.g., manufacturing process monitoring, predictive maintenance, energy\nmanagement, and infrastructure facility monitoring. We discuss the challenges\nand limitations of deep transfer learning in industrial contexts and conclude\nthe survey with practical directions and actionable suggestions to address the\nneed to leverage diverse time series data for anomaly detection in an\nincreasingly dynamic production environment.\n", "date": "2023-07-11", "categories": []}
{"id": "2401.05702", "title": "Video Anomaly Detection and Explanation via Large Language Models", "abstract": "  Video Anomaly Detection (VAD) aims to localize abnormal events on the\ntimeline of long-range surveillance videos. Anomaly-scoring-based methods have\nbeen prevailing for years but suffer from the high complexity of thresholding\nand low explanability of detection results. In this paper, we conduct pioneer\nresearch on equipping video-based large language models (VLLMs) in the\nframework of VAD, making the VAD model free from thresholds and able to explain\nthe reasons for the detected anomalies. We introduce a novel network module\nLong-Term Context (LTC) to mitigate the incapability of VLLMs in long-range\ncontext modeling. We design a three-phase training method to improve the\nefficiency of fine-tuning VLLMs by substantially minimizing the requirements\nfor VAD data and lowering the costs of annotating instruction-tuning data. Our\ntrained model achieves the top performance on the anomaly videos of the\nUCF-Crime and TAD benchmarks, with the AUC improvements of +3.86\\% and +4.96\\%,\nrespectively. More impressively, our approach can provide textual explanations\nfor detected anomalies.\n", "date": "2024-01-11", "categories": []}
{"id": "2401.05800", "title": "Graph Spatiotemporal Process for Multivariate Time Series Anomaly\n  Detection with Missing Values", "abstract": "  The detection of anomalies in multivariate time series data is crucial for\nvarious practical applications, including smart power grids, traffic flow\nforecasting, and industrial process control. However, real-world time series\ndata is usually not well-structured, posting significant challenges to existing\napproaches: (1) The existence of missing values in multivariate time series\ndata along variable and time dimensions hinders the effective modeling of\ninterwoven spatial and temporal dependencies, resulting in important patterns\nbeing overlooked during model training; (2) Anomaly scoring with\nirregularly-sampled observations is less explored, making it difficult to use\nexisting detectors for multivariate series without fully-observed values. In\nthis work, we introduce a novel framework called GST-Pro, which utilizes a\ngraph spatiotemporal process and anomaly scorer to tackle the aforementioned\nchallenges in detecting anomalies on irregularly-sampled multivariate time\nseries. Our approach comprises two main components. First, we propose a graph\nspatiotemporal process based on neural controlled differential equations. This\nprocess enables effective modeling of multivariate time series from both\nspatial and temporal perspectives, even when the data contains missing values.\nSecond, we present a novel distribution-based anomaly scoring mechanism that\nalleviates the reliance on complete uniform observations. By analyzing the\npredictions of the graph spatiotemporal process, our approach allows anomalies\nto be easily detected. Our experimental results show that the GST-Pro method\ncan effectively detect anomalies in time series data and outperforms\nstate-of-the-art methods, regardless of whether there are missing values\npresent in the data. Our code is available: https://github.com/huankoh/GST-Pro.\n", "date": "2024-01-11", "categories": []}
{"id": "2305.16713", "title": "ReConPatch : Contrastive Patch Representation Learning for Industrial\n  Anomaly Detection", "abstract": "  Anomaly detection is crucial to the advanced identification of product\ndefects such as incorrect parts, misaligned components, and damages in\nindustrial manufacturing. Due to the rare observations and unknown types of\ndefects, anomaly detection is considered to be challenging in machine learning.\nTo overcome this difficulty, recent approaches utilize the common visual\nrepresentations pre-trained from natural image datasets and distill the\nrelevant features. However, existing approaches still have the discrepancy\nbetween the pre-trained feature and the target data, or require the input\naugmentation which should be carefully designed, particularly for the\nindustrial dataset. In this paper, we introduce ReConPatch, which constructs\ndiscriminative features for anomaly detection by training a linear modulation\nof patch features extracted from the pre-trained model. ReConPatch employs\ncontrastive representation learning to collect and distribute features in a way\nthat produces a target-oriented and easily separable representation. To address\nthe absence of labeled pairs for the contrastive learning, we utilize two\nsimilarity measures between data representations, pairwise and contextual\nsimilarities, as pseudo-labels. Our method achieves the state-of-the-art\nanomaly detection performance (99.72%) for the widely used and challenging\nMVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly\ndetection performance (95.8%) for the BTAD dataset.\n", "date": "2023-05-26", "categories": []}
{"id": "2401.04749", "title": "LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection", "abstract": "  Log anomaly detection is a key component in the field of artificial\nintelligence for IT operations (AIOps). Considering log data of variant\ndomains, retraining the whole network for unknown domains is inefficient in\nreal industrial scenarios. However, previous deep models merely focused on\nextracting the semantics of log sequences in the same domain, leading to poor\ngeneralization on multi-domain logs. To alleviate this issue, we propose a\nunified Transformer-based framework for Log anomaly detection (LogFormer) to\nimprove the generalization ability across different domains, where we establish\na two-stage process including the pre-training and adapter-based tuning stage.\nSpecifically, our model is first pre-trained on the source domain to obtain\nshared semantic knowledge of log data. Then, we transfer such knowledge to the\ntarget domain via shared parameters. Besides, the Log-Attention module is\nproposed to supplement the information ignored by the log-paring. The proposed\nmethod is evaluated on three public and one real-world datasets. Experimental\nresults on multiple benchmarks demonstrate the effectiveness of our LogFormer\nwith fewer trainable parameters and lower training costs.\n", "date": "2024-01-09", "categories": []}
{"id": "2401.04942", "title": "Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic\n  Dataset and New Metrics", "abstract": "  In the past several years, road anomaly segmentation is actively explored in\nthe academia and drawing growing attention in the industry. The rationale\nbehind is straightforward: if the autonomous car can brake before hitting an\nanomalous object, safety is promoted. However, this rationale naturally calls\nfor a temporally informed setting while existing methods and benchmarks are\ndesigned in an unrealistic frame-wise manner. To bridge this gap, we contribute\nthe first video anomaly segmentation dataset for autonomous driving. Since\nplacing various anomalous objects on busy roads and annotating them in every\nframe are dangerous and expensive, we resort to synthetic data. To improve the\nrelevance of this synthetic dataset to real-world applications, we train a\ngenerative adversarial network conditioned on rendering G-buffers for\nphotorealism enhancement. Our dataset consists of 120,000 high-resolution\nframes at a 60 FPS framerate, as recorded in 7 different towns. As an initial\nbenchmarking, we provide baselines using latest supervised and unsupervised\nroad anomaly segmentation methods. Apart from conventional ones, we focus on\ntwo new metrics: temporal consistency and latencyaware streaming accuracy. We\nbelieve the latter is valuable as it measures whether an anomaly segmentation\nalgorithm can truly prevent a car from crashing in a temporally informed\nsetting.\n", "date": "2024-01-10", "categories": []}
{"id": "2210.07675", "title": "Learning image representations for anomaly detection: application to\n  discovery of histological alterations in drug development", "abstract": "  We present a system for anomaly detection in histopathological images. In\nhistology, normal samples are usually abundant, whereas anomalous\n(pathological) cases are scarce or not available. Under such settings,\none-class classifiers trained on healthy data can detect out-of-distribution\nanomalous samples. Such approaches combined with pre-trained Convolutional\nNeural Network (CNN) representations of images were previously employed for\nanomaly detection (AD). However, pre-trained off-the-shelf CNN representations\nmay not be sensitive to abnormal conditions in tissues, while natural\nvariations of healthy tissue may result in distant representations. To adapt\nrepresentations to relevant details in healthy tissue we propose training a CNN\non an auxiliary task that discriminates healthy tissue of different species,\norgans, and staining reagents. Almost no additional labeling workload is\nrequired, since healthy samples come automatically with aforementioned labels.\nDuring training we enforce compact image representations with a center-loss\nterm, which further improves representations for AD. The proposed system\noutperforms established AD methods on a published dataset of liver anomalies.\nMoreover, it provided comparable results to conventional methods specifically\ntailored for quantification of liver anomalies. We show that our approach can\nbe used for toxicity assessment of candidate drugs at early development stages\nand thereby may reduce expensive late-stage drug attrition.\n", "date": "2022-10-14", "categories": []}
{"id": "2309.09676", "title": "Conditioning Latent-Space Clusters for Real-World Anomaly Classification", "abstract": "  Anomalies in the domain of autonomous driving are a major hindrance to the\nlarge-scale deployment of autonomous vehicles. In this work, we focus on\nhigh-resolution camera data from urban scenes that include anomalies of various\ntypes and sizes. Based on a Variational Autoencoder, we condition its latent\nspace to classify samples as either normal data or anomalies. In order to\nemphasize especially small anomalies, we perform experiments where we provide\nthe VAE with a discrepancy map as an additional input, evaluating its impact on\nthe detection performance. Our method separates normal data and anomalies into\nisolated clusters while still reconstructing high-quality images, leading to\nmeaningful latent representations.\n", "date": "2023-09-18", "categories": []}
{"id": "2401.04437", "title": "Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using\n  Dimension Reduction Methods", "abstract": "  Recent studies try to use hyperspectral imaging (HSI) to detect foreign\nmatters in products because it enables to visualize the invisible wavelengths\nincluding ultraviolet and infrared. Considering the enormous image channels of\nthe HSI, several dimension reduction methods-e.g., PCA or UMAP-can be\nconsidered to reduce but those cannot ease the fundamental limitations, as\nfollows: (1) latency of HSI capturing. (2) less explanation ability of the\nimportant channels. In this paper, to circumvent the aforementioned methods,\none of the ways to channel reduction, on anomaly detection proposed HSI.\nDifferent from feature extraction methods (i.e., PCA or UMAP), feature\nselection can sort the feature by impact and show better explainability so we\nmight redesign the task-optimized and cost-effective spectroscopic camera. Via\nthe extensive experiment results with synthesized MVTec AD dataset, we confirm\nthat the feature selection method shows 6.90x faster at the inference phase\ncompared with feature extraction-based approaches while preserving anomaly\ndetection performance. Ultimately, we conclude the advantage of feature\nselection which is effective yet fast.\n", "date": "2024-01-09", "categories": []}
{"id": "2401.01625", "title": "SCALA: Sparsification-based Contrastive Learning for Anomaly Detection\n  on Attributed Networks", "abstract": "  Anomaly detection on attributed networks aims to find the nodes whose\nbehaviors are significantly different from other majority nodes. Generally,\nnetwork data contains information about relationships between entities, and the\nanomaly is usually embodied in these relationships. Therefore, how to\ncomprehensively model complex interaction patterns in networks is still a major\nfocus. It can be observed that anomalies in networks violate the homophily\nassumption. However, most existing studies only considered this phenomenon\nobliquely rather than explicitly. Besides, the node representation of normal\nentities can be perturbed easily by the noise relationships introduced by\nanomalous nodes. To address the above issues, we present a novel contrastive\nlearning framework for anomaly detection on attributed networks,\n\\textbf{SCALA}, aiming to improve the embedding quality of the network and\nprovide a new measurement of qualifying the anomaly score for each node by\nintroducing sparsification into the conventional method. Extensive experiments\nare conducted on five benchmark real-world datasets and the results show that\nSCALA consistently outperforms all baseline methods significantly.\n", "date": "2024-01-03", "categories": []}
{"id": "2401.03322", "title": "Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly\n  Detection", "abstract": "  This paper introduces a hybrid attention and autoencoder (AE) model for\nunsupervised online anomaly detection in time series. The autoencoder captures\nlocal structural patterns in short embeddings, while the attention model learns\nlong-term features, facilitating parallel computing with positional encoding.\nUnique in its approach, our proposed hybrid model combines attention and\nautoencoder for the first time in time series anomaly detection. It employs an\nattention-based mechanism, akin to the deep transformer model, with key\narchitectural modifications for predicting the next time step window in the\nautoencoder's latent space. The model utilizes a threshold from the validation\ndataset for anomaly detection and introduces an alternative method based on\nanalyzing the first statistical moment of error, improving accuracy without\ndependence on a validation dataset. Evaluation on diverse real-world benchmark\ndatasets and comparing with other well-established models, confirms the\neffectiveness of our proposed model in anomaly detection.\n", "date": "2024-01-06", "categories": []}
{"id": "2401.03341", "title": "Weakly Augmented Variational Autoencoder in Time Series Anomaly\n  Detection", "abstract": "  Due to their unsupervised training and uncertainty estimation, deep\nVariational Autoencoders (VAEs) have become powerful tools for\nreconstruction-based Time Series Anomaly Detection (TSAD). Existing VAE-based\nTSAD methods, either statistical or deep, tune meta-priors to estimate the\nlikelihood probability for effectively capturing spatiotemporal dependencies in\nthe data. However, these methods confront the challenge of inherent data\nscarcity, which is often the case in anomaly detection tasks. Such scarcity\neasily leads to latent holes, discontinuous regions in latent space, resulting\nin non-robust reconstructions on these discontinuous spaces. We propose a novel\ngenerative framework that combines VAEs with self-supervised learning (SSL) to\naddress this issue.\n", "date": "2024-01-07", "categories": []}
{"id": "2401.03530", "title": "Detecting Anomalies in Blockchain Transactions using Machine Learning\n  Classifiers and Explainability Analysis", "abstract": "  As the use of Blockchain for digital payments continues to rise in\npopularity, it also becomes susceptible to various malicious attacks.\nSuccessfully detecting anomalies within Blockchain transactions is essential\nfor bolstering trust in digital payments. However, the task of anomaly\ndetection in Blockchain transaction data is challenging due to the infrequent\noccurrence of illicit transactions. Although several studies have been\nconducted in the field, a limitation persists: the lack of explanations for the\nmodel's predictions. This study seeks to overcome this limitation by\nintegrating eXplainable Artificial Intelligence (XAI) techniques and anomaly\nrules into tree-based ensemble classifiers for detecting anomalous Bitcoin\ntransactions. The Shapley Additive exPlanation (SHAP) method is employed to\nmeasure the contribution of each feature, and it is compatible with ensemble\nmodels. Moreover, we present rules for interpreting whether a Bitcoin\ntransaction is anomalous or not. Additionally, we have introduced an\nunder-sampling algorithm named XGBCLUS, designed to balance anomalous and\nnon-anomalous transaction data. This algorithm is compared against other\ncommonly used under-sampling and over-sampling techniques. Finally, the\noutcomes of various tree-based single classifiers are compared with those of\nstacking and voting ensemble classifiers. Our experimental results demonstrate\nthat: (i) XGBCLUS enhances TPR and ROC-AUC scores compared to state-of-the-art\nunder-sampling and over-sampling techniques, and (ii) our proposed ensemble\nclassifiers outperform traditional single tree-based machine learning\nclassifiers in terms of accuracy, TPR, and FPR scores.\n", "date": "2024-01-07", "categories": []}
{"id": "2401.03806", "title": "FM-AE: Frequency-masked Multimodal Autoencoder for Zinc Electrolysis\n  Plate Contact Abnormality Detection", "abstract": "  Zinc electrolysis is one of the key processes in zinc smelting, and\nmaintaining stable operation of zinc electrolysis is an important factor in\nensuring production efficiency and product quality. However, poor contact\nbetween the zinc electrolysis cathode and the anode is a common problem that\nleads to reduced production efficiency and damage to the electrolysis cell.\nTherefore, online monitoring of the contact status of the plates is crucial for\nensuring production quality and efficiency. To address this issue, we propose\nan end-to-end network, the Frequency-masked Multimodal Autoencoder (FM-AE).\nThis method takes the cell voltage signal and infrared image information as\ninput, and through automatic encoding, fuses the two features together and\npredicts the poor contact status of the plates through a cascaded detector.\nExperimental results show that the proposed method maintains high accuracy\n(86.2%) while having good robustness and generalization ability, effectively\ndetecting poor contact status of the zinc electrolysis cell, providing strong\nsupport for production practice.\n", "date": "2024-01-08", "categories": []}
{"id": "2401.02287", "title": "Distillation-based fabric anomaly detection", "abstract": "  Unsupervised texture anomaly detection has been a concerning topic in a vast\namount of industrial processes. Patterned textures inspection, particularly in\nthe context of fabric defect detection, is indeed a widely encountered use\ncase. This task involves handling a diverse spectrum of colors and textile\ntypes, encompassing a wide range of fabrics. Given the extensive variability in\ncolors, textures, and defect types, fabric defect detection poses a complex and\nchallenging problem in the field of patterned textures inspection. In this\narticle, we propose a knowledge distillation-based approach tailored\nspecifically for addressing the challenge of unsupervised anomaly detection in\ntextures resembling fabrics. Our method aims to redefine the recently\nintroduced reverse distillation approach, which advocates for an\nencoder-decoder design to mitigate classifier bias and to prevent the student\nfrom reconstructing anomalies. In this study, we present a new reverse\ndistillation technique for the specific task of fabric defect detection. Our\napproach involves a meticulous design selection that strategically highlights\nhigh-level features. To demonstrate the capabilities of our approach both in\nterms of performance and inference speed, we conducted a series of experiments\non multiple texture datasets, including MVTEC AD, AITEX, and TILDA, alongside\nconducting experiments on a dataset acquired from a textile manufacturing\nfacility. The main contributions of this paper are the following: a robust\ntexture anomaly detector utilizing a reverse knowledge-distillation technique\nsuitable for both anomaly detection and domain generalization and a novel\ndataset encompassing a diverse range of fabrics and defects.\n", "date": "2024-01-04", "categories": []}
{"id": "2306.16914", "title": "Computationally Assisted Quality Control for Public Health Data Streams", "abstract": "  Irregularities in public health data streams (like COVID-19 Cases) hamper\ndata-driven decision-making for public health stakeholders. A real-time,\ncomputer-generated list of the most important, outlying data points from\nthousands of daily-updated public health data streams could assist an expert\nreviewer in identifying these irregularities. However, existing outlier\ndetection frameworks perform poorly on this task because they do not account\nfor the data volume or for the statistical properties of public health streams.\nAccordingly, we developed FlaSH (Flagging Streams in public Health), a\npractical outlier detection framework for public health data users that uses\nsimple, scalable models to capture these statistical properties explicitly. In\nan experiment where human experts evaluate FlaSH and existing methods\n(including deep learning approaches), FlaSH scales to the data volume of this\ntask, matches or exceeds these other methods in mean accuracy, and identifies\nthe outlier points that users empirically rate as more helpful. Based on these\nresults, FlaSH has been deployed on data streams used by public health\nstakeholders.\n", "date": "2023-06-29", "categories": []}
{"id": "2308.00583", "title": "Semisupervised Anomaly Detection using Support Vector Regression with\n  Quantum Kernel", "abstract": "  Anomaly detection (AD) involves identifying observations or events that\ndeviate in some way from the rest of the data. Machine learning techniques have\nshown success in automating this process by detecting hidden patterns and\ndeviations in large-scale data. The potential of quantum computing for machine\nlearning has been widely recognized, leading to extensive research efforts to\ndevelop suitable quantum machine learning (QML) algorithms. In particular, the\nsearch for QML algorithms for near-term NISQ devices is in full swing. However,\nNISQ devices pose additional challenges due to their limited qubit coherence\ntimes, low number of qubits, and high error rates. Kernel methods based on\nquantum kernel estimation have emerged as a promising approach to QML on NISQ\ndevices, offering theoretical guarantees, versatility, and compatibility with\nNISQ constraints. Especially support vector machines (SVM) utilizing quantum\nkernel estimation have shown success in various supervised learning tasks.\nHowever, in the context of AD, semisupervised learning is of great relevance,\nand yet there is limited research published in this area. This paper introduces\nan approach to semisupervised AD based on the reconstruction loss of a support\nvector regression (SVR) with quantum kernel. This novel model is an alternative\nto the variational quantum and quantum kernel one-class classifiers, and is\ncompared to a quantum autoencoder as quantum baseline and a SVR with\nradial-basis-function (RBF) kernel as well as a classical autoencoder as\nclassical baselines. The models are benchmarked extensively on 10 real-world AD\ndata sets and one toy data set, and it is shown that our SVR model with quantum\nkernel performs better than the SVR with RBF kernel as well as all other\nmodels, achieving highest mean AUC over all data sets. In addition, our QSVR\noutperforms the quantum autoencoder on 9 out of 11 data sets.\n", "date": "2023-08-01", "categories": []}
{"id": "2401.01459", "title": "Outlier Ranking in Large-Scale Public Health Streams", "abstract": "  Disease control experts inspect public health data streams daily for outliers\nworth investigating, like those corresponding to data quality issues or disease\noutbreaks. However, they can only examine a few of the thousands of\nmaximally-tied outliers returned by univariate outlier detection methods\napplied to large-scale public health data streams. To help experts distinguish\nthe most important outliers from these thousands of tied outliers, we propose a\nnew task for algorithms to rank the outputs of any univariate method applied to\neach of many streams. Our novel algorithm for this task, which leverages\nhierarchical networks and extreme value analysis, performed the best across\ntraditional outlier detection metrics in a human-expert evaluation using public\nhealth data streams. Most importantly, experts have used our open-source Python\nimplementation since April 2023 and report identifying outliers worth\ninvestigating 9.1x faster than their prior baseline. Other organizations can\nreadily adapt this implementation to create rankings from the outputs of their\ntailored univariate methods across large-scale streams.\n", "date": "2024-01-02", "categories": []}
{"id": "2308.04789", "title": "Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection", "abstract": "  Anomaly detection has gained considerable attention due to its broad range of\napplications, particularly in industrial defect detection. To address the\nchallenges of data collection, researchers have introduced zero-/few-shot\nanomaly detection techniques that require minimal normal images for each\ncategory. However, complex industrial scenarios often involve multiple objects,\npresenting a significant challenge. In light of this, we propose a\nstraightforward yet powerful multi-scale memory comparison framework for\nzero-/few-shot anomaly detection. Our approach employs a global memory bank to\ncapture features across the entire image, while an individual memory bank\nfocuses on simplified scenes containing a single object. The efficacy of our\nmethod is validated by its remarkable achievement of 4th place in the zero-shot\ntrack and 2nd place in the few-shot track of the Visual Anomaly and Novelty\nDetection (VAND) competition.\n", "date": "2023-08-09", "categories": []}
{"id": "2401.01010", "title": "Unsupervised Continual Anomaly Detection with Contrastively-learned\n  Prompt", "abstract": "  Unsupervised Anomaly Detection (UAD) with incremental training is crucial in\nindustrial manufacturing, as unpredictable defects make obtaining sufficient\nlabeled data infeasible. However, continual learning methods primarily rely on\nsupervised annotations, while the application in UAD is limited due to the\nabsence of supervision. Current UAD methods train separate models for different\nclasses sequentially, leading to catastrophic forgetting and a heavy\ncomputational burden. To address this issue, we introduce a novel Unsupervised\nContinual Anomaly Detection framework called UCAD, which equips the UAD with\ncontinual learning capability through contrastively-learned prompts. In the\nproposed UCAD, we design a Continual Prompting Module (CPM) by utilizing a\nconcise key-prompt-knowledge memory bank to guide task-invariant `anomaly'\nmodel predictions using task-specific `normal' knowledge. Moreover,\nStructure-based Contrastive Learning (SCL) is designed with the Segment\nAnything Model (SAM) to improve prompt learning and anomaly segmentation\nresults. Specifically, by treating SAM's masks as structure, we draw features\nwithin the same mask closer and push others apart for general feature\nrepresentations. We conduct comprehensive experiments and set the benchmark on\nunsupervised continual anomaly detection and segmentation, demonstrating that\nour method is significantly better than anomaly detection methods, even with\nrehearsal training. The code will be available at\nhttps://github.com/shirowalker/UCAD.\n", "date": "2024-01-02", "categories": []}
{"id": "2401.01093", "title": "Exploring Hyperspectral Anomaly Detection with Human Vision: A Small\n  Target Aware Detector", "abstract": "  Hyperspectral anomaly detection (HAD) aims to localize pixel points whose\nspectral features differ from the background. HAD is essential in scenarios of\nunknown or camouflaged target features, such as water quality monitoring, crop\ngrowth monitoring and camouflaged target detection, where prior information of\ntargets is difficult to obtain. Existing HAD methods aim to objectively detect\nand distinguish background and anomalous spectra, which can be achieved almost\neffortlessly by human perception. However, the underlying processes of human\nvisual perception are thought to be quite complex. In this paper, we analyze\nhyperspectral image (HSI) features under human visual perception, and transfer\nthe solution process of HAD to the more robust feature space for the first\ntime. Specifically, we propose a small target aware detector (STAD), which\nintroduces saliency maps to capture HSI features closer to human visual\nperception. STAD not only extracts more anomalous representations, but also\nreduces the impact of low-confidence regions through a proposed small target\nfilter (STF). Furthermore, considering the possibility of HAD algorithms being\napplied to edge devices, we propose a full connected network to convolutional\nnetwork knowledge distillation strategy. It can learn the spectral and spatial\nfeatures of the HSI while lightening the network. We train the network on the\nHAD100 training set and validate the proposed method on the HAD100 test set.\nOur method provides a new solution space for HAD that is closer to human visual\nperception with high confidence. Sufficient experiments on real HSI with\nmultiple method comparisons demonstrate the excellent performance and unique\npotential of the proposed method. The code is available at\nhttps://github.com/majitao-xd/STAD-HAD.\n", "date": "2024-01-02", "categories": []}
{"id": "2303.03817", "title": "Region and Spatial Aware Anomaly Detection for Fundus Images", "abstract": "  Recently anomaly detection has drawn much attention in diagnosing ocular\ndiseases. Most existing anomaly detection research in fundus images has\nrelatively large anomaly scores in the salient retinal structures, such as\nblood vessels, optical cups and discs. In this paper, we propose a Region and\nSpatial Aware Anomaly Detection (ReSAD) method for fundus images, which obtains\nlocal region and long-range spatial information to reduce the false positives\nin the normal structure. ReSAD transfers a pre-trained model to extract the\nfeatures of normal fundus images and applies the Region-and-Spatial-Aware\nfeature Combination module (ReSC) for pixel-level features to build a memory\nbank. In the testing phase, ReSAD uses the memory bank to determine\nout-of-distribution samples as abnormalities. Our method significantly\noutperforms the existing anomaly detection methods for fundus images on two\npublicly benchmark datasets.\n", "date": "2023-03-07", "categories": []}
{"id": "2308.15366", "title": "AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language\n  Models", "abstract": "  Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have\ndemonstrated the capability of understanding images and achieved remarkable\nperformance in various visual tasks. Despite their strong abilities in\nrecognizing common objects due to extensive training datasets, they lack\nspecific domain knowledge and have a weaker understanding of localized details\nwithin objects, which hinders their effectiveness in the Industrial Anomaly\nDetection (IAD) task. On the other hand, most existing IAD methods only provide\nanomaly scores and necessitate the manual setting of thresholds to distinguish\nbetween normal and abnormal samples, which restricts their practical\nimplementation. In this paper, we explore the utilization of LVLM to address\nthe IAD problem and propose AnomalyGPT, a novel IAD approach based on LVLM. We\ngenerate training data by simulating anomalous images and producing\ncorresponding textual descriptions for each image. We also employ an image\ndecoder to provide fine-grained semantic and design a prompt learner to\nfine-tune the LVLM using prompt embeddings. Our AnomalyGPT eliminates the need\nfor manual threshold adjustments, thus directly assesses the presence and\nlocations of anomalies. Additionally, AnomalyGPT supports multi-turn dialogues\nand exhibits impressive few-shot in-context learning capabilities. With only\none normal shot, AnomalyGPT achieves the state-of-the-art performance with an\naccuracy of 86.1%, an image-level AUC of 94.1%, and a pixel-level AUC of 95.3%\non the MVTec-AD dataset. Code is available at\nhttps://github.com/CASIA-IVA-Lab/AnomalyGPT.\n", "date": "2023-08-29", "categories": []}
{"id": "2312.16470", "title": "ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction\n  and Synthetic Features", "abstract": "  Detecting anomalies in fundus images through unsupervised methods is a\nchallenging task due to the similarity between normal and abnormal tissues, as\nwell as their indistinct boundaries. The current methods have limitations in\naccurately detecting subtle anomalies while avoiding false positives. To\naddress these challenges, we propose the ReSynthDetect network which utilizes a\nreconstruction network for modeling normal images, and an anomaly generator\nthat produces synthetic anomalies consistent with the appearance of fundus\nimages. By combining the features of consistent anomaly generation and image\nreconstruction, our method is suited for detecting fundus abnormalities. The\nproposed approach has been extensively tested on benchmark datasets such as\nEyeQ and IDRiD, demonstrating state-of-the-art performance in both image-level\nand pixel-level anomaly detection. Our experiments indicate a substantial 9%\nimprovement in AUROC on EyeQ and a significant 17.1% improvement in AUPR on\nIDRiD.\n", "date": "2023-12-27", "categories": []}
{"id": "2312.16799", "title": "Temporal Knowledge Distillation for Time-Sensitive Financial Services\n  Applications", "abstract": "  Detecting anomalies has become an increasingly critical function in the\nfinancial service industry. Anomaly detection is frequently used in key\ncompliance and risk functions such as financial crime detection fraud and\ncybersecurity. The dynamic nature of the underlying data patterns especially in\nadversarial environments like fraud detection poses serious challenges to the\nmachine learning models. Keeping up with the rapid changes by retraining the\nmodels with the latest data patterns introduces pressures in balancing the\nhistorical and current patterns while managing the training data size.\nFurthermore the model retraining times raise problems in time-sensitive and\nhigh-volume deployment systems where the retraining period directly impacts the\nmodels ability to respond to ongoing attacks in a timely manner. In this study\nwe propose a temporal knowledge distillation-based label augmentation approach\n(TKD) which utilizes the learning from older models to rapidly boost the latest\nmodel and effectively reduces the model retraining times to achieve improved\nagility. Experimental results show that the proposed approach provides\nadvantages in retraining times while improving the model performance.\n", "date": "2023-12-28", "categories": []}
{"id": "2306.01997", "title": "UADB: Unsupervised Anomaly Detection Booster", "abstract": "  Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to\nits wide real-world applications. Due to the complete absence of supervision\nsignals, UAD methods rely on implicit assumptions about anomalous patterns\n(e.g., scattered/sparsely/densely clustered) to detect anomalies. However,\nreal-world data are complex and vary significantly across different domains. No\nsingle assumption can describe such complexity and be valid in all scenarios.\nThis is also confirmed by recent research that shows no UAD method is\nomnipotent. Based on above observations, instead of searching for a magic\nuniversal winner assumption, we seek to design a general UAD Booster (UADB)\nthat empowers any UAD models with adaptability to different data. This is a\nchallenging task given the heterogeneous model structures and assumptions\nadopted by existing UAD methods. To achieve this, we dive deep into the UAD\nproblem and find that compared to normal data, anomalies (i) lack clear\nstructure/pattern in feature space, thus (ii) harder to learn by model without\na suitable assumption, and finally, leads to (iii) high variance between\ndifferent learners. In light of these findings, we propose to (i) distill the\nknowledge of the source UAD model to an imitation learner (booster) that holds\nno data assumption, then (ii) exploit the variance between them to perform\nautomatic correction, and thus (iii) improve the booster over the original UAD\nmodel. We use a neural network as the booster for its strong expressive power\nas a universal approximator and ability to perform flexible post-hoc tuning.\nNote that UADB is a model-agnostic framework that can enhance heterogeneous UAD\nmodels in a unified way. Extensive experiments on over 80 tabular datasets\ndemonstrate the effectiveness of UADB.\n", "date": "2023-06-03", "categories": []}
{"id": "2312.15911", "title": "Generating and Reweighting Dense Contrastive Patterns for Unsupervised\n  Anomaly Detection", "abstract": "  Recent unsupervised anomaly detection methods often rely on feature\nextractors pretrained with auxiliary datasets or on well-crafted\nanomaly-simulated samples. However, this might limit their adaptability to an\nincreasing set of anomaly detection tasks due to the priors in the selection of\nauxiliary datasets or the strategy of anomaly simulation. To tackle this\nchallenge, we first introduce a prior-less anomaly generation paradigm and\nsubsequently develop an innovative unsupervised anomaly detection framework\nnamed GRAD, grounded in this paradigm. GRAD comprises three essential\ncomponents: (1) a diffusion model (PatchDiff) to generate contrastive patterns\nby preserving the local structures while disregarding the global structures\npresent in normal images, (2) a self-supervised reweighting mechanism to handle\nthe challenge of long-tailed and unlabeled contrastive patterns generated by\nPatchDiff, and (3) a lightweight patch-level detector to efficiently\ndistinguish the normal patterns and reweighted contrastive patterns. The\ngeneration results of PatchDiff effectively expose various types of anomaly\npatterns, e.g. structural and logical anomaly patterns. In addition, extensive\nexperiments on both MVTec AD and MVTec LOCO datasets also support the\naforementioned observation and demonstrate that GRAD achieves competitive\nanomaly detection accuracy and superior inference speed.\n", "date": "2023-12-26", "categories": []}
{"id": "2312.14329", "title": "Invariant Anomaly Detection under Distribution Shifts: A Causal\n  Perspective", "abstract": "  Anomaly detection (AD) is the machine learning task of identifying highly\ndiscrepant abnormal samples by solely relying on the consistency of the normal\ntraining samples. Under the constraints of a distribution shift, the assumption\nthat training samples and test samples are drawn from the same distribution\nbreaks down. In this work, by leveraging tools from causal inference we attempt\nto increase the resilience of anomaly detection models to different kinds of\ndistribution shifts. We begin by elucidating a simple yet necessary statistical\nproperty that ensures invariant representations, which is critical for robust\nAD under both domain and covariate shifts. From this property, we derive a\nregularization term which, when minimized, leads to partial distribution\ninvariance across environments. Through extensive experimental evaluation on\nboth synthetic and real-world tasks, covering a range of six different AD\nmethods, we demonstrated significant improvements in out-of-distribution\nperformance. Under both covariate and domain shift, models regularized with our\nproposed term showed marked increased robustness. Code is available at:\nhttps://github.com/JoaoCarv/invariant-anomaly-detection.\n", "date": "2023-12-21", "categories": []}
{"id": "2312.14535", "title": "ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection", "abstract": "  Graph anomaly detection is crucial for identifying nodes that deviate from\nregular behavior within graphs, benefiting various domains such as fraud\ndetection and social network. Although existing reconstruction-based methods\nhave achieved considerable success, they may face the \\textit{Anomaly\nOverfitting} and \\textit{Homophily Trap} problems caused by the abnormal\npatterns in the graph, breaking the assumption that normal nodes are often\nbetter reconstructed than abnormal ones. Our observations indicate that models\ntrained on graphs with fewer anomalies exhibit higher detection performance.\nBased on this insight, we introduce a novel two-stage framework called\nAnomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the\nfirst stage, we design a learning-free anomaly-denoised augmentation method to\ngenerate graphs with reduced anomaly levels. We pretrain graph autoencoders on\nthese augmented graphs at multiple levels, which enables the graph autoencoders\nto capture normal patterns. In the next stage, the decoders are retrained for\ndetection on the original graph, benefiting from the multi-level\nrepresentations learned in the previous stage. Meanwhile, we propose the node\nanomaly distribution regularization to further alleviate \\textit{Anomaly\nOverfitting}. We validate the effectiveness of our approach through extensive\nexperiments on both synthetic and real-world datasets.\n", "date": "2023-12-22", "categories": []}
{"id": "2312.14748", "title": "Progressing from Anomaly Detection to Automated Log Labeling and\n  Pioneering Root Cause Analysis", "abstract": "  The realm of AIOps is transforming IT landscapes with the power of AI and ML.\nDespite the challenge of limited labeled data, supervised models show promise,\nemphasizing the importance of leveraging labels for training, especially in\ndeep learning contexts. This study enhances the field by introducing a taxonomy\nfor log anomalies and exploring automated data labeling to mitigate labeling\nchallenges. It goes further by investigating the potential of diverse anomaly\ndetection techniques and their alignment with specific anomaly types. However,\nthe exploration doesn't stop at anomaly detection. The study envisions a future\nwhere root cause analysis follows anomaly detection, unraveling the underlying\ntriggers of anomalies. This uncharted territory holds immense potential for\nrevolutionizing IT systems management. In essence, this paper enriches our\nunderstanding of anomaly detection, and automated labeling, and sets the stage\nfor transformative root cause analysis. Together, these advances promise more\nresilient IT systems, elevating operational efficiency and user satisfaction in\nan ever-evolving technological landscape.\n", "date": "2023-12-22", "categories": []}
{"id": "2312.13896", "title": "Comparative Evaluation of Anomaly Detection Methods for Fraud Detection\n  in Online Credit Card Payments", "abstract": "  This study explores the application of anomaly detection (AD) methods in\nimbalanced learning tasks, focusing on fraud detection using real online credit\ncard payment data. We assess the performance of several recent AD methods and\ncompare their effectiveness against standard supervised learning methods.\nOffering evidence of distribution shift within our dataset, we analyze its\nimpact on the tested models' performances. Our findings reveal that LightGBM\nexhibits significantly superior performance across all evaluated metrics but\nsuffers more from distribution shifts than AD methods. Furthermore, our\ninvestigation reveals that LightGBM also captures the majority of frauds\ndetected by AD methods. This observation challenges the potential benefits of\nensemble methods to combine supervised, and AD approaches to enhance\nperformance. In summary, this research provides practical insights into the\nutility of these techniques in real-world scenarios, showing LightGBM's\nsuperiority in fraud detection while highlighting challenges related to\ndistribution shifts.\n", "date": "2023-12-21", "categories": []}
{"id": "2312.12010", "title": "Outlier detection using flexible categorisation and interrogative\n  agendas", "abstract": "  Categorization is one of the basic tasks in machine learning and data\nanalysis. Building on formal concept analysis (FCA), the starting point of the\npresent work is that different ways to categorize a given set of objects exist,\nwhich depend on the choice of the sets of features used to classify them, and\ndifferent such sets of features may yield better or worse categorizations,\nrelative to the task at hand. In their turn, the (a priori) choice of a\nparticular set of features over another might be subjective and express a\ncertain epistemic stance (e.g. interests, relevance, preferences) of an agent\nor a group of agents, namely, their interrogative agenda. In the present paper,\nwe represent interrogative agendas as sets of features, and explore and compare\ndifferent ways to categorize objects w.r.t. different sets of features\n(agendas). We first develop a simple unsupervised FCA-based algorithm for\noutlier detection which uses categorizations arising from different agendas. We\nthen present a supervised meta-learning algorithm to learn suitable (fuzzy)\nagendas for categorization as sets of features with different weights or\nmasses. We combine this meta-learning algorithm with the unsupervised outlier\ndetection algorithm to obtain a supervised outlier detection algorithm. We show\nthat these algorithms perform at par with commonly used algorithms for outlier\ndetection on commonly used datasets in outlier detection. These algorithms\nprovide both local and global explanations of their results.\n", "date": "2023-12-19", "categories": []}
{"id": "2312.12913", "title": "Produce Once, Utilize Twice for Anomaly Detection", "abstract": "  Visual anomaly detection aims at classifying and locating the regions that\ndeviate from the normal appearance. Embedding-based methods and\nreconstruction-based methods are two main approaches for this task. However,\nthey are either not efficient or not precise enough for the industrial\ndetection. To deal with this problem, we derive POUTA (Produce Once Utilize\nTwice for Anomaly detection), which improves both the accuracy and efficiency\nby reusing the discriminant information potential in the reconstructive\nnetwork. We observe that the encoder and decoder representations of the\nreconstructive network are able to stand for the features of the original and\nreconstructed image respectively. And the discrepancies between the symmetric\nreconstructive representations provides roughly accurate anomaly information.\nTo refine this information, a coarse-to-fine process is proposed in POUTA,\nwhich calibrates the semantics of each discriminative layer by the high-level\nrepresentations and supervision loss. Equipped with the above modules, POUTA is\nendowed with the ability to provide a more precise anomaly location than the\nprior arts. Besides, the representation reusage also enables to exclude the\nfeature extraction process in the discriminative network, which reduces the\nparameters and improves the efficiency. Extensive experiments show that, POUTA\nis superior or comparable to the prior methods with even less cost.\nFurthermore, POUTA also achieves better performance than the state-of-the-art\nfew-shot anomaly detection methods without any special design, showing that\nPOUTA has strong ability to learn representations inherent in the training\ndata.\n", "date": "2023-12-20", "categories": []}
{"id": "2210.13917", "title": "Connective Reconstruction-based Novelty Detection", "abstract": "  Detection of out-of-distribution samples is one of the critical tasks for\nreal-world applications of computer vision. The advancement of deep learning\nhas enabled us to analyze real-world data which contain unexplained samples,\naccentuating the need to detect out-of-distribution instances more than before.\nGAN-based approaches have been widely used to address this problem due to their\nability to perform distribution fitting; however, they are accompanied by\ntraining instability and mode collapse. We propose a simple yet efficient\nreconstruction-based method that avoids adding complexities to compensate for\nthe limitations of GAN models while outperforming them. Unlike previous\nreconstruction-based works that only utilize reconstruction error or generated\nsamples, our proposed method simultaneously incorporates both of them in the\ndetection task. Our model, which we call \"Connective Novelty Detection\" has two\nsubnetworks, an autoencoder, and a binary classifier. The autoencoder learns\nthe representation of the positive class by reconstructing them. Then, the\nmodel creates negative and connected positive examples using real and generated\nsamples. Negative instances are generated via manipulating the real data, so\ntheir distribution is close to the positive class to achieve a more accurate\nboundary for the classifier. To boost the robustness of the detection to\nreconstruction error, connected positive samples are created by combining the\nreal and generated samples. Finally, the binary classifier is trained using\nconnected positive and negative examples. We demonstrate a considerable\nimprovement in novelty detection over state-of-the-art methods on MNIST and\nCaltech-256 datasets.\n", "date": "2022-10-25", "categories": []}
{"id": "2312.11553", "title": "SeGA: Preference-Aware Self-Contrastive Learning with Prompts for\n  Anomalous User Detection on Twitter", "abstract": "  In the dynamic and rapidly evolving world of social media, detecting\nanomalous users has become a crucial task to address malicious activities such\nas misinformation and cyberbullying. As the increasing number of anomalous\nusers improves the ability to mimic normal users and evade detection, existing\nmethods only focusing on bot detection are ineffective in terms of capturing\nsubtle distinctions between users. To address these challenges, we proposed\nSeGA, preference-aware self-contrastive learning for anomalous user detection,\nwhich leverages heterogeneous entities and their relations in the Twittersphere\nto detect anomalous users with different malicious strategies. SeGA utilizes\nthe knowledge of large language models to summarize user preferences via posts.\nIn addition, integrating user preferences with prompts as pseudo-labels for\npreference-aware self-contrastive learning enables the model to learn\nmultifaceted aspects for describing the behaviors of users. Extensive\nexperiments on the proposed TwBNT benchmark demonstrate that SeGA significantly\noutperforms the state-of-the-art methods (+3.5\\% ~ 27.6\\%) and empirically\nvalidate the effectiveness of the model design and pre-training strategies. Our\ncode and data are publicly available at https://github.com/ying0409/SeGA.\n", "date": "2023-12-17", "categories": []}
{"id": "2312.11629", "title": "Residual ANODE", "abstract": "  We present R-ANODE, a new method for data-driven, model-agnostic resonant\nanomaly detection that raises the bar for both performance and\ninterpretability. The key to R-ANODE is to enhance the inductive bias of the\nanomaly detection task by fitting a normalizing flow directly to the small and\nunknown signal component, while holding fixed a background model (also a\nnormalizing flow) learned from sidebands. In doing so, R-ANODE is able to\noutperform all classifier-based, weakly-supervised approaches, as well as the\nprevious ANODE method which fit a density estimator to all of the data in the\nsignal region instead of just the signal. We show that the method works equally\nwell whether the unknown signal fraction is learned or fixed, and is even\nrobust to signal fraction misspecification. Finally, with the learned signal\nmodel we can sample and gain qualitative insights into the underlying anomaly,\nwhich greatly enhances the interpretability of resonant anomaly detection and\noffers the possibility of simultaneously discovering and characterizing the new\nphysics that could be hiding in the data.\n", "date": "2023-12-18", "categories": []}
{"id": "2312.11818", "title": "Root Cause Explanation of Outliers under Noisy Mechanisms", "abstract": "  Identifying root causes of anomalies in causal processes is vital across\ndisciplines. Once identified, one can isolate the root causes and implement\nnecessary measures to restore the normal operation. Causal processes are often\nmodelled as graphs with entities being nodes and their paths/interconnections\nas edge. Existing work only consider the contribution of nodes in the\ngenerative process, thus can not attribute the outlier score to the edges of\nthe mechanism if the anomaly occurs in the connections. In this paper, we\nconsider both individual edge and node of each mechanism when identifying the\nroot causes. We introduce a noisy functional causal model to account for this\npurpose. Then, we employ Bayesian learning and inference methods to infer the\nnoises of the nodes and edges. We then represent the functional form of a\ntarget outlier leaf as a function of the node and edge noises. Finally, we\npropose an efficient gradient-based attribution method to compute the anomaly\nattribution scores which scales linearly with the number of nodes and edges.\nExperiments on simulated datasets and two real-world scenario datasets show\nbetter anomaly attribution performance of the proposed method compared to the\nbaselines. Our method scales to larger graphs with more nodes and edges.\n", "date": "2023-12-19", "categories": []}
{"id": "2308.11681", "title": "VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video\n  Anomaly Detection", "abstract": "  The recent contrastive language-image pre-training (CLIP) model has shown\ngreat success in a wide range of image-level tasks, revealing remarkable\nability for learning powerful visual representations with rich semantics. An\nopen and worthwhile problem is efficiently adapting such a strong model to the\nvideo domain and designing a robust video anomaly detector. In this work, we\npropose VadCLIP, a new paradigm for weakly supervised video anomaly detection\n(WSVAD) by leveraging the frozen CLIP model directly without any pre-training\nand fine-tuning process. Unlike current works that directly feed extracted\nfeatures into the weakly supervised classifier for frame-level binary\nclassification, VadCLIP makes full use of fine-grained associations between\nvision and language on the strength of CLIP and involves dual branch. One\nbranch simply utilizes visual features for coarse-grained binary\nclassification, while the other fully leverages the fine-grained language-image\nalignment. With the benefit of dual branch, VadCLIP achieves both\ncoarse-grained and fine-grained video anomaly detection by transferring\npre-trained knowledge from CLIP to WSVAD task. We conduct extensive experiments\non two commonly-used benchmarks, demonstrating that VadCLIP achieves the best\nperformance on both coarse-grained and fine-grained WSVAD, surpassing the\nstate-of-the-art methods by a large margin. Specifically, VadCLIP achieves\n84.51% AP and 88.02% AUC on XD-Violence and UCF-Crime, respectively. Code and\nfeatures are released at https://github.com/nwpu-zxr/VadCLIP.\n", "date": "2023-08-22", "categories": []}
{"id": "2312.09480", "title": "TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks", "abstract": "  In recent years, the focus on anomaly detection and localization in\nindustrial inspection tasks has intensified. While existing studies have\ndemonstrated impressive outcomes, they often rely heavily on extensive training\ndatasets or robust features extracted from pre-trained models trained on\ndiverse datasets like ImageNet. In this work, we propose a novel framework\nleveraging the visual-linguistic CLIP model to adeptly train a backbone model\ntailored to the manufacturing domain. Our approach concurrently considers\nvisual and text-aligned embedding spaces for normal and abnormal conditions.\nThe resulting pre-trained backbone markedly enhances performance in industrial\ndownstream tasks, particularly in anomaly detection and localization. Notably,\nthis improvement is substantiated through experiments conducted on multiple\ndatasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained\nbackbone weights allows previous works to achieve superior performance in\nfew-shot scenarios with less training data. The proposed anomaly backbone\nprovides a foundation model for more precise anomaly detection and\nlocalization.\n", "date": "2023-12-15", "categories": []}
{"id": "2312.09578", "title": "Self-Supervised Learning for Anomalous Sound Detection", "abstract": "  State-of-the-art anomalous sound detection (ASD) systems are often trained by\nusing an auxiliary classification task to learn an embedding space. Doing so\nenables the system to learn embeddings that are robust to noise and are\nignoring non-target sound events but requires manually annotated meta\ninformation to be used as class labels. However, the less difficult the\nclassification task becomes, the less informative are the embeddings and the\nworse is the resulting ASD performance. A solution to this problem is to\nutilize self-supervised learning (SSL). In this work, feature exchange\n(FeatEx), a simple yet effective SSL approach for ASD, is proposed. In\naddition, FeatEx is compared to and combined with existing SSL approaches. As\nthe main result, a new state-of-the-art performance for the DCASE2023 ASD\ndataset is obtained that outperforms all other published results on this\ndataset by a large margin.\n", "date": "2023-12-15", "categories": []}
{"id": "2302.06228", "title": "Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and\n  Trajectory Analysis", "abstract": "  Real-time monitoring of human behaviours, especially in e-Health\napplications, has been an active area of research in the past decades. On top\nof IoT-based sensing environments, anomaly detection algorithms have been\nproposed for the early detection of abnormalities. Gradual change procedures,\ncommonly referred to as drift anomalies, have received much less attention in\nthe literature because they represent a much more challenging scenario than\nsudden temporary changes (point anomalies). In this paper, we propose, for the\nfirst time, a fully unsupervised real-time drift detection algorithm named\nDynAmo, which can identify drift periods as they are happening. DynAmo\ncomprises a dynamic clustering component to capture the overall trends of\nmonitored behaviours and a trajectory generation component, which extracts\nfeatures from the densest cluster centroids. Finally, we apply an ensemble of\ndivergence tests on sliding reference and detection windows to detect drift\nperiods in the behavioural sequence.\n", "date": "2023-02-13", "categories": []}
{"id": "2309.15643", "title": "Why do Angular Margin Losses work well for Semi-Supervised Anomalous\n  Sound Detection?", "abstract": "  State-of-the-art anomalous sound detection systems often utilize angular\nmargin losses to learn suitable representations of acoustic data using an\nauxiliary task, which usually is a supervised or self-supervised classification\ntask. The underlying idea is that, in order to solve this auxiliary task,\nspecific information about normal data needs to be captured in the learned\nrepresentations and that this information is also sufficient to differentiate\nbetween normal and anomalous samples. Especially in noisy conditions,\ndiscriminative models based on angular margin losses tend to significantly\noutperform systems based on generative or one-class models. The goal of this\nwork is to investigate why using angular margin losses with auxiliary tasks\nworks well for detecting anomalous sounds. To this end, it is shown, both\ntheoretically and experimentally, that minimizing angular margin losses also\nminimizes compactness loss while inherently preventing learning trivial\nsolutions. Furthermore, multiple experiments are conducted to show that using a\nrelated classification task as an auxiliary task teaches the model to learn\nrepresentations suitable for detecting anomalous sounds in noisy conditions.\nAmong these experiments are performance evaluations, visualizing the embedding\nspace with t-SNE and visualizing the input representations with respect to the\nanomaly score using randomized input sampling for explanation.\n", "date": "2023-09-27", "categories": []}
{"id": "2310.00268", "title": "Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach\n  for Time Series Anomaly Detection", "abstract": "  Traditional Time-series Anomaly Detection (TAD) methods often struggle with\nthe composite nature of complex time-series data and a diverse array of\nanomalies. We introduce TADNet, an end-to-end TAD model that leverages\nSeasonal-Trend Decomposition to link various types of anomalies to specific\ndecomposition components, thereby simplifying the analysis of complex\ntime-series and enhancing detection performance. Our training methodology,\nwhich includes pre-training on a synthetic dataset followed by fine-tuning,\nstrikes a balance between effective decomposition and precise anomaly\ndetection. Experimental validation on real-world datasets confirms TADNet's\nstate-of-the-art performance across a diverse range of anomalies.\n", "date": "2023-09-30", "categories": []}
{"id": "2307.01054", "title": "The ROAD to discovery: machine learning-driven anomaly detection in\n  radio astronomy spectrograms", "abstract": "  As radio telescopes increase in sensitivity and flexibility, so do their\ncomplexity and data-rates. For this reason automated system health management\napproaches are becoming increasingly critical to ensure nominal telescope\noperations. We propose a new machine learning anomaly detection framework for\nclassifying both commonly occurring anomalies in radio telescopes as well as\ndetecting unknown rare anomalies that the system has potentially not yet seen.\nTo evaluate our method, we present a dataset consisting of 7050\nautocorrelation-based spectrograms from the Low Frequency Array (LOFAR)\ntelescope and assign 10 different labels relating to the system-wide anomalies\nfrom the perspective of telescope operators. This includes electronic failures,\nmiscalibration, solar storms, network and compute hardware errors among many\nmore. We demonstrate how a novel Self Supervised Learning (SSL) paradigm, that\nutilises both context prediction and reconstruction losses, is effective in\nlearning normal behaviour of the LOFAR telescope. We present the Radio\nObservatory Anomaly Detector (ROAD), a framework that combines both SSL-based\nanomaly detection and a supervised classification, thereby enabling both\nclassification of both commonly occurring anomalies and detection of unseen\nanomalies. We demonstrate that our system is real-time in the context of the\nLOFAR data processing pipeline, requiring <1ms to process a single spectrogram.\nFurthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while\nmaintaining a false positive rate of ~2\\%, as well as a mean per-class\nclassification F-2 score 0.89, outperforming other related works.\n", "date": "2023-07-03", "categories": []}
{"id": "2311.02621", "title": "AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised\n  Scenarios", "abstract": "  Artificial intelligence operations (AIOps) play a pivotal role in\nidentifying, mitigating, and analyzing anomalous system behaviors and alerts.\nHowever, the research landscape in this field remains limited, leaving\nsignificant gaps unexplored. This study introduces a novel hybrid framework\nthrough an innovative algorithm that incorporates an unsupervised strategy.\nThis strategy integrates Principal Component Analysis (PCA) and Artificial\nNeural Networks (ANNs) and uses a custom loss function to substantially enhance\nthe effectiveness of log anomaly detection. The proposed approach encompasses\nthe utilization of both simulated and real-world datasets, including logs from\nSockShop and Hadoop Distributed File System (HDFS). The experimental results\nare highly promising, demonstrating significant reductions in pseudo-positives.\nMoreover, this strategy offers notable advantages, such as the ability to\nprocess logs in their raw, unprocessed form, and the potential for further\nenhancements. The successful implementation of this approach showcases a\nremarkable reduction in anomalous logs, thus unequivocally establishing the\nefficacy of the proposed methodology. Ultimately, this study makes a\nsubstantial contribution to the advancement of log anomaly detection within\nAIOps platforms, addressing the critical need for effective and efficient log\nanalysis in modern and complex systems.\n", "date": "2023-11-05", "categories": []}
{"id": "2312.07101", "title": "Meta-survey on outlier and anomaly detection", "abstract": "  The impact of outliers and anomalies on model estimation and data processing\nis of paramount importance, as evidenced by the extensive body of research\nspanning various fields over several decades: thousands of research papers have\nbeen published on the subject. As a consequence, numerous reviews, surveys, and\ntextbooks have sought to summarize the existing literature, encompassing a wide\nrange of methods from both the statistical and data mining communities. While\nthese endeavors to organize and summarize the research are invaluable, they\nface inherent challenges due to the pervasive nature of outliers and anomalies\nin all data-intensive applications, irrespective of the specific application\nfield or scientific discipline. As a result, the resulting collection of papers\nremains voluminous and somewhat heterogeneous. To address the need for\nknowledge organization in this domain, this paper implements the first\nsystematic meta-survey of general surveys and reviews on outlier and anomaly\ndetection. Employing a classical systematic survey approach, the study collects\nnearly 500 papers using two specialized scientific search engines. From this\ncomprehensive collection, a subset of 56 papers that claim to be general\nsurveys on outlier detection is selected using a snowball search technique to\nenhance field coverage. A meticulous quality assessment phase further refines\nthe selection to a subset of 25 high-quality general surveys. Using this\ncurated collection, the paper investigates the evolution of the outlier\ndetection field over a 20-year period, revealing emerging themes and methods.\nFurthermore, an analysis of the surveys sheds light on the survey writing\npractices adopted by scholars from different communities who have contributed\nto this field. Finally, the paper delves into several topics where consensus\nhas emerged from the literature. These include taxonomies of outlier types,\nchallenges posed by high-dimensional data, the importance of anomaly scores,\nthe impact of learning conditions, difficulties in benchmarking, and the\nsignificance of neural networks. Non-consensual aspects are also discussed,\nparticularly the distinction between local and global outliers and the\nchallenges in organizing detection methods into meaningful taxonomies.\n", "date": "2023-12-12", "categories": []}
{"id": "2309.14482", "title": "LogGPT: Log Anomaly Detection via GPT", "abstract": "  Detecting system anomalies based on log data is important for ensuring the\nsecurity and reliability of computer systems. Recently, deep learning models\nhave been widely used for log anomaly detection. The core idea is to model the\nlog sequences as natural language and adopt deep sequential models, such as\nLSTM or Transformer, to encode the normal patterns in log sequences via\nlanguage modeling. However, there is a gap between language modeling and\nanomaly detection as the objective of training a sequential model via a\nlanguage modeling loss is not directly related to anomaly detection. To fill up\nthe gap, we propose LogGPT, a novel framework that employs GPT for log anomaly\ndetection. LogGPT is first trained to predict the next log entry based on the\npreceding sequence. To further enhance the performance of LogGPT, we propose a\nnovel reinforcement learning strategy to finetune the model specifically for\nthe log anomaly detection task. The experimental results on three datasets show\nthat LogGPT significantly outperforms existing state-of-the-art approaches.\n", "date": "2023-09-25", "categories": []}
{"id": "2312.05526", "title": "Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly\n  Detection", "abstract": "  Unsupervised graph anomaly detection is crucial for various practical\napplications as it aims to identify anomalies in a graph that exhibit rare\npatterns deviating significantly from the majority of nodes. Recent\nadvancements have utilized Graph Neural Networks (GNNs) to learn high-quality\nnode representations for anomaly detection by aggregating information from\nneighborhoods. However, the presence of anomalies may render the observed\nneighborhood unreliable and result in misleading information aggregation for\nnode representation learning. Selecting the proper neighborhood is critical for\ngraph anomaly detection but also challenging due to the absence of\nanomaly-oriented guidance and the interdependence with representation learning.\nTo address these issues, we utilize the advantages of reinforcement learning in\nadaptively learning in complex environments and propose a novel method that\nincorporates Reinforcement neighborhood selection for unsupervised graph\nANomaly Detection (RAND). RAND begins by enriching the candidate neighbor pool\nof the given central node with multiple types of indirect neighbors. Next, RAND\ndesigns a tailored reinforcement anomaly evaluation module to assess the\nreliability and reward of considering the given neighbor. Finally, RAND selects\nthe most reliable subset of neighbors based on these rewards and introduces an\nanomaly-aware aggregator to amplify messages from reliable neighbors while\ndiminishing messages from unreliable ones. Extensive experiments on both three\nsynthetic and two real-world datasets demonstrate that RAND outperforms the\nstate-of-the-art methods.\n", "date": "2023-12-09", "categories": []}
{"id": "2312.06342", "title": "Detecting Contextual Network Anomalies with Graph Neural Networks", "abstract": "  Detecting anomalies on network traffic is a complex task due to the massive\namount of traffic flows in today's networks, as well as the highly-dynamic\nnature of traffic over time. In this paper, we propose the use of Graph Neural\nNetworks (GNN) for network traffic anomaly detection. We formulate the problem\nas contextual anomaly detection on network traffic measurements, and propose a\ncustom GNN-based solution that detects traffic anomalies on origin-destination\nflows. In our evaluation, we use real-world data from Abilene (6 months), and\nmake a comparison with other widely used methods for the same task (PCA, EWMA,\nRNN). The results show that the anomalies detected by our solution are quite\ncomplementary to those captured by the baselines (with a max. of 36.33%\noverlapping anomalies for PCA). Moreover, we manually inspect the anomalies\ndetected by our method, and find that a large portion of them can be visually\nvalidated by a network expert (64% with high confidence, 18% with mid\nconfidence, 18% normal traffic). Lastly, we analyze the characteristics of the\nanomalies through two paradigmatic cases that are quite representative of the\nbulk of anomalies.\n", "date": "2023-12-11", "categories": []}
{"id": "2312.06607", "title": "DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection", "abstract": "  Reconstruction-based approaches have achieved remarkable outcomes in anomaly\ndetection. The exceptional image reconstruction capabilities of recently\npopular diffusion models have sparked research efforts to utilize them for\nenhanced reconstruction of anomalous images. Nonetheless, these methods might\nface challenges related to the preservation of image categories and pixel-wise\nstructural integrity in the more practical multi-class setting. To solve the\nabove problems, we propose a Difusion-based Anomaly Detection (DiAD) framework\nfor multi-class anomaly detection, which consists of a pixel-space autoencoder,\na latent-space Semantic-Guided (SG) network with a connection to the stable\ndiffusion's denoising network, and a feature-space pre-trained feature\nextractor. Firstly, The SG network is proposed for reconstructing anomalous\nregions while preserving the original image's semantic information. Secondly,\nwe introduce Spatial-aware Feature Fusion (SFF) block to maximize\nreconstruction accuracy when dealing with extensively reconstructed areas.\nThirdly, the input and reconstructed images are processed by a pre-trained\nfeature extractor to generate anomaly maps based on features extracted at\ndifferent scales. Experiments on MVTec-AD and VisA datasets demonstrate the\neffectiveness of our approach which surpasses the state-of-the-art methods,\ne.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and\ndetection respectively on multi-class MVTec-AD dataset. Code will be available\nat https://lewandofskee.github.io/projects/diad.\n", "date": "2023-12-11", "categories": []}
{"id": "2312.04758", "title": "Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection\n  in Power Distribution Grids", "abstract": "  The growing trend toward the modernization of power distribution systems has\nfacilitated the installation of advanced measurement units and promotion of the\ncyber communication systems. However, these infrastructures are still prone to\nstealth cyber attacks. The existing data-driven anomaly detection methods\nsuffer from a lack of knowledge about the system's physics, lack of\ninterpretability, and scalability issues hindering their practical applications\nin real-world scenarios. To address these concerns, physics-informed neural\nnetworks (PINNs) were introduced. This paper proposes a multivariate\nphysics-informed convolutional autoencoder (PIConvAE) to detect stealthy\ncyber-attacks in power distribution grids. The proposed model integrates the\nphysical principles into the loss function of the neural network by applying\nKirchhoff's law. Simulations are performed on the modified IEEE 13-bus and\n123-bus systems using OpenDSS software to validate the efficacy of the proposed\nmodel for stealth attacks. The numerical results prove the superior performance\nof the proposed PIConvAE in three aspects: a) it provides more accurate results\ncompared to the data-driven ConvAE model, b) it requires less training time to\nconverge c) the model excels in effectively detecting a wide range of attack\nmagnitudes making it powerful in detecting stealth attacks.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.04382", "title": "Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection", "abstract": "  In this paper, we propose the Adversarial Denoising Diffusion Model (ADDM).\nThe ADDM is based on the Denoising Diffusion Probabilistic Model (DDPM) but\ncomplementarily trained by adversarial learning. The proposed adversarial\nlearning is achieved by classifying model-based denoised samples and samples to\nwhich random Gaussian noise is added to a specific sampling step. With the\naddition of explicit adversarial learning on data samples, ADDM can learn the\nsemantic characteristics of the data more robustly during training, which\nachieves a similar data sampling performance with much fewer sampling steps\nthan DDPM. We apply ADDM to anomaly detection in unsupervised MRI images.\nExperimental results show that the proposed ADDM outperformed existing\ngenerative model-based unsupervised anomaly detection methods. In particular,\ncompared to other DDPM-based anomaly detection methods, the proposed ADDM shows\nbetter performance with the same number of sampling steps and similar\nperformance with 50% fewer sampling steps.\n", "date": "2023-12-07", "categories": []}
{"id": "2303.14965", "title": "Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly\n  Detection", "abstract": "  The full understanding of plasma disruption in tokamaks is currently lacking,\nand data-driven methods are extensively used for disruption prediction.\nHowever, most existing data-driven disruption predictors employ supervised\nlearning techniques, which require labeled training data. The manual labeling\nof disruption precursors is a tedious and challenging task, as some precursors\nare difficult to accurately identify, limiting the potential of machine\nlearning models. To address this issue, commonly used labeling methods assume\nthat the precursor onset occurs at a fixed time before the disruption, which\nmay not be consistent for different types of disruptions or even the same type\nof disruption, due to the different speeds at which plasma instabilities\nescalate. This leads to mislabeled samples and suboptimal performance of the\nsupervised learning predictor. In this paper, we present a disruption\nprediction method based on anomaly detection that overcomes the drawbacks of\nunbalanced positive and negative data samples and inaccurately labeled\ndisruption precursor samples. We demonstrate the effectiveness and reliability\nof anomaly detection predictors based on different algorithms on J-TEXT and\nEAST to evaluate the reliability of the precursor onset time inferred by the\nanomaly detection predictor. The precursor onset times inferred by these\npredictors reveal that the labeling methods have room for improvement as the\nonset times of different shots are not necessarily the same. Finally, we\noptimize precursor labeling using the onset times inferred by the anomaly\ndetection predictor and test the optimized labels on supervised learning\ndisruption predictors. The results on J-TEXT and EAST show that the models\ntrained on the optimized labels outperform those trained on fixed onset time\nlabels.\n", "date": "2023-03-27", "categories": []}
{"id": "2309.07992", "title": "An Automated Machine Learning Approach for Detecting Anomalous Peak\n  Patterns in Time Series Data from a Research Watershed in the Northeastern\n  United States Critical Zone", "abstract": "  This paper presents an automated machine learning framework designed to\nassist hydrologists in detecting anomalies in time series data generated by\nsensors in a research watershed in the northeastern United States critical\nzone. The framework specifically focuses on identifying peak-pattern anomalies,\nwhich may arise from sensor malfunctions or natural phenomena. However, the use\nof classification methods for anomaly detection poses challenges, such as the\nrequirement for labeled data as ground truth and the selection of the most\nsuitable deep learning model for the given task and dataset. To address these\nchallenges, our framework generates labeled datasets by injecting synthetic\npeak patterns into synthetically generated time series data and incorporates an\nautomated hyperparameter optimization mechanism. This mechanism generates an\noptimized model instance with the best architectural and training parameters\nfrom a pool of five selected models, namely Temporal Convolutional Network\n(TCN), InceptionTime, MiniRocket, Residual Networks (ResNet), and Long\nShort-Term Memory (LSTM). The selection is based on the user's preferences\nregarding anomaly detection accuracy and computational cost. The framework\nemploys Time-series Generative Adversarial Networks (TimeGAN) as the synthetic\ndataset generator. The generated model instances are evaluated using a\ncombination of accuracy and computational cost metrics, including training time\nand memory, during the anomaly detection process. Performance evaluation of the\nframework was conducted using a dataset from a watershed, demonstrating\nconsistent selection of the most fitting model instance that satisfies the\nuser's preferences.\n", "date": "2023-09-14", "categories": []}
{"id": "2312.03005", "title": "Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature\n  Representations", "abstract": "  Anomaly detection is a critical and challenging task that aims to identify\ndata points deviating from normal patterns and distributions within a dataset.\nVarious methods have been proposed using a one-class-one-model approach, but\nthese techniques often face practical problems such as memory inefficiency and\nthe requirement of sufficient data for training. In particular, few-shot\nanomaly detection presents significant challenges in industrial applications,\nwhere limited samples are available before mass production. In this paper, we\npropose a few-shot anomaly detection method that integrates adversarial\ntraining loss to obtain more robust and generalized feature representations. We\nutilize the adversarial loss previously employed in domain adaptation to align\nfeature distributions between source and target domains, to enhance feature\nrobustness and generalization in few-shot anomaly detection tasks. We\nhypothesize that adversarial loss is effective when applied to features that\nshould have similar characteristics, such as those from the same layer in a\nSiamese network's parallel branches or input-output pairs of\nreconstruction-based methods. Experimental results demonstrate that the\nproposed method generally achieves better performance when utilizing the\nadversarial loss.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.00224", "title": "Unsupervised textile defect detection using convolutional neural\n  networks", "abstract": "  In this study, we propose a novel motif-based approach for unsupervised\ntextile anomaly detection that combines the benefits of traditional\nconvolutional neural networks with those of an unsupervised learning paradigm.\nIt consists of five main steps: preprocessing, automatic pattern period\nextraction, patch extraction, features selection and anomaly detection. This\nproposed approach uses a new dynamic and heuristic method for feature selection\nwhich avoids the drawbacks of initialization of the number of filters (neurons)\nand their weights, and those of the backpropagation mechanism such as the\nvanishing gradients, which are common practice in the state-of-the-art methods.\nThe design and training of the network are performed in a dynamic and input\ndomain-based manner and, thus, no ad-hoc configurations are required. Before\nbuilding the model, only the number of layers and the stride are defined. We do\nnot initialize the weights randomly nor do we define the filter size or number\nof filters as conventionally done in CNN-based approaches. This reduces effort\nand time spent on hyperparameter initialization and fine-tuning. Only one\ndefect-free sample is required for training and no further labeled data is\nneeded. The trained network is then used to detect anomalies on defective\nfabric samples. We demonstrate the effectiveness of our approach on the\nPatterned Fabrics benchmark dataset. Our algorithm yields reliable and\ncompetitive results (on recall, precision, accuracy and f1- measure) compared\nto state-of-the-art unsupervised approaches, in less time, with efficient\ntraining in a single epoch and a lower computational cost.\n", "date": "2023-11-30", "categories": []}
{"id": "2312.02530", "title": "MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly\n  Detection", "abstract": "  Detecting anomalies in real-world multivariate time series data is\nchallenging due to complex temporal dependencies and inter-variable\ncorrelations. Recently, reconstruction-based deep models have been widely used\nto solve the problem. However, these methods still suffer from an\nover-generalization issue and fail to deliver consistently high performance. To\naddress this issue, we propose the MEMTO, a memory-guided Transformer using a\nreconstruction-based approach. It is designed to incorporate a novel memory\nmodule that can learn the degree to which each memory item should be updated in\nresponse to the input data. To stabilize the training procedure, we use a\ntwo-phase training paradigm which involves using K-means clustering for\ninitializing memory items. Additionally, we introduce a bi-dimensional\ndeviation-based detection criterion that calculates anomaly scores considering\nboth input space and latent space. We evaluate our proposed method on five\nreal-world datasets from diverse domains, and it achieves an average anomaly\ndetection F1-score of 95.74%, significantly outperforming the previous\nstate-of-the-art methods. We also conduct extensive experiments to empirically\nvalidate the effectiveness of our proposed model's key components.\n", "date": "2023-12-05", "categories": []}
{"id": "2312.02615", "title": "Projection Regret: Reducing Background Bias for Novelty Detection via\n  Diffusion Models", "abstract": "  Novelty detection is a fundamental task of machine learning which aims to\ndetect abnormal ($\\textit{i.e.}$ out-of-distribution (OOD)) samples. Since\ndiffusion models have recently emerged as the de facto standard generative\nframework with surprising generation results, novelty detection via diffusion\nmodels has also gained much attention. Recent methods have mainly utilized the\nreconstruction property of in-distribution samples. However, they often suffer\nfrom detecting OOD samples that share similar background information to the\nin-distribution data. Based on our observation that diffusion models can\n\\emph{project} any sample to an in-distribution sample with similar background\ninformation, we propose \\emph{Projection Regret (PR)}, an efficient novelty\ndetection method that mitigates the bias of non-semantic information. To be\nspecific, PR computes the perceptual distance between the test image and its\ndiffusion-based projection to detect abnormality. Since the perceptual distance\noften fails to capture semantic changes when the background information is\ndominant, we cancel out the background bias by comparing it against recursive\nprojections. Extensive experiments demonstrate that PR outperforms the prior\nart of generative-model-based novelty detection methods by a significant\nmargin.\n", "date": "2023-12-05", "categories": []}
{"id": "2206.02598", "title": "[Reproducibility Report] Explainable Deep One-Class Classification", "abstract": "  Fully Convolutional Data Description (FCDD), an explainable version of the\nHypersphere Classifier (HSC), directly addresses image anomaly detection (AD)\nand pixel-wise AD without any post-hoc explainer methods. The authors claim\nthat FCDD achieves results comparable with the state-of-the-art in sample-wise\nAD on Fashion-MNIST and CIFAR-10 and exceeds the state-of-the-art on the\npixel-wise task on MVTec-AD. We reproduced the main results of the paper using\nthe author's code with minor changes and provide runtime requirements to\nachieve if (CPU memory, GPU memory, and training time). We propose another\nanalysis methodology using a critical difference diagram, and further\ninvestigate the test performance of the model during the training phase.\n", "date": "2022-06-06", "categories": []}
{"id": "2301.09602", "title": "Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly\n  Segmentation", "abstract": "  We propose an incremental improvement to Fully Convolutional Data Description\n(FCDD), an adaptation of the one-class classification approach from anomaly\ndetection to image anomaly segmentation (a.k.a. anomaly localization). We\nanalyze its original loss function and propose a substitute that better\nresembles its predecessor, the Hypersphere Classifier (HSC). Both are compared\non the MVTec Anomaly Detection Dataset (MVTec-AD) -- training images are\nflawless objects/textures and the goal is to segment unseen defects -- showing\nthat consistent improvement is achieved by better designing the pixel-wise\nsupervision.\n", "date": "2023-01-23", "categories": []}
{"id": "2304.06433", "title": "High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature\n  Correspondence Analysis", "abstract": "  We propose a novel method for Zero-Shot Anomaly Localization on textures. The\ntask refers to identifying abnormal regions in an otherwise homogeneous image.\nTo obtain a high-fidelity localization, we leverage a bijective mapping derived\nfrom the 1-dimensional Wasserstein Distance. As opposed to using holistic\ndistances between distributions, the proposed approach allows pinpointing the\nnon-conformity of a pixel in a local context with increased precision. By\naggregating the contribution of the pixel to the errors of all nearby patches\nwe obtain a reliable anomaly score estimate. We validate our solution on\nseveral datasets and obtain more than a 40% reduction in error over the\nprevious state of the art on the MVTec AD dataset in a zero-shot setting. Also\nsee https://reality.tf.fau.de/pub/ardelean2024highfidelity.html.\n", "date": "2023-04-13", "categories": []}
{"id": "2307.06052", "title": "Visualization for Multivariate Gaussian Anomaly Detection in Images", "abstract": "  This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly\nDetection through Instance Modeling) method for anomaly detection in images,\nfitting a single multivariate Gaussian (MVG) distribution to the feature\nvectors extracted from a backbone convolutional neural network (CNN) and using\ntheir Mahalanobis distance as the anomaly score. We introduce an intermediate\nstep in this framework by applying a whitening transformation to the feature\nvectors, which enables the generation of heatmaps capable of visually\nexplaining the features learned by the MVG. The proposed technique is evaluated\non the MVTec-AD dataset, and the results show the importance of visual model\nvalidation, providing insights into issues in this framework that were\notherwise invisible. The visualizations generated for this paper are publicly\navailable at https://doi.org/10.5281/zenodo.7937978.\n", "date": "2023-07-12", "categories": []}
{"id": "2307.11197", "title": "Heuristic Hyperparameter Choice for Image Anomaly Detection", "abstract": "  Anomaly detection (AD) in images is a fundamental computer vision problem by\ndeep learning neural network to identify images deviating significantly from\nnormality. The deep features extracted from pretrained models have been proved\nto be essential for AD based on multivariate Gaussian distribution analysis.\nHowever, since models are usually pretrained on a large dataset for\nclassification tasks such as ImageNet, they might produce lots of redundant\nfeatures for AD, which increases computational cost and degrades the\nperformance. We aim to do the dimension reduction of Negated Principal\nComponent Analysis (NPCA) for these features. So we proposed some heuristic to\nchoose hyperparameter of NPCA algorithm for getting as fewer components of\nfeatures as possible while ensuring a good performance.\n", "date": "2023-07-20", "categories": []}
{"id": "2308.04944", "title": "Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection", "abstract": "  Anomaly detection (AD) in images, identifying significant deviations from\nnormality, is a critical issue in computer vision. This paper introduces a\nnovel approach to dimensionality reduction for AD using pre-trained\nconvolutional neural network (CNN) that incorporate EfficientNet models. We\ninvestigate the importance of component selection and propose two types of tree\nsearch approaches, both employing a greedy strategy, for optimal eigencomponent\nselection. Our study conducts three main experiments to evaluate the\neffectiveness of our approach. The first experiment explores the influence of\ntest set performance on component choice, the second experiment examines the\nperformance when we train on one anomaly type and evaluate on all other types,\nand the third experiment investigates the impact of using a minimum number of\nimages for training and selecting them based on anomaly types. Our approach\naims to find the optimal subset of components that deliver the highest\nperformance score, instead of focusing solely on the proportion of variance\nexplained by each component and also understand the components behaviour in\ndifferent settings. Our results indicate that the proposed method surpasses\nboth Principal Component Analysis (PCA) and Negated Principal Component\nAnalysis (NPCA) in terms of detection accuracy, even when using fewer\ncomponents. Thus, our approach provides a promising alternative to conventional\ndimensionality reduction techniques in AD, and holds potential to enhance the\nefficiency and effectiveness of AD systems.\n", "date": "2023-08-09", "categories": []}
{"id": "2312.01296", "title": "Anomaly Detection Under Uncertainty Using Distributionally Robust\n  Optimization Approach", "abstract": "  Anomaly detection is defined as the problem of finding data points that do\nnot follow the patterns of the majority. Among the various proposed methods for\nsolving this problem, classification-based methods, including one-class Support\nVector Machines (SVM) are considered effective and state-of-the-art. The\none-class SVM method aims to find a decision boundary to distinguish between\nnormal data points and anomalies using only the normal data. On the other hand,\nmost real-world problems involve some degree of uncertainty, where the true\nprobability distribution of each data point is unknown, and estimating it is\noften difficult and costly. Assuming partial distribution information such as\nthe first and second-order moments is known, a distributionally robust\nchance-constrained model is proposed in which the probability of\nmisclassification is low. By utilizing a mapping function to a higher\ndimensional space, the proposed model will be capable of classifying\norigin-inseparable datasets. Also, by adopting the kernel idea, the need for\nexplicitly knowing the mapping is eliminated, computations can be performed in\nthe input space, and computational complexity is reduced. Computational results\nvalidate the robustness of the proposed model under different probability\ndistributions and also the superiority of the proposed model compared to the\nstandard one-class SVM in terms of various evaluation metrics.\n", "date": "2023-12-03", "categories": []}
{"id": "2312.01488", "title": "ADT: Agent-based Dynamic Thresholding for Anomaly Detection", "abstract": "  The complexity and scale of IT systems are increasing dramatically, posing\nmany challenges to real-world anomaly detection. Deep learning anomaly\ndetection has emerged, aiming at feature learning and anomaly scoring, which\nhas gained tremendous success. However, little work has been done on the\nthresholding problem despite it being a critical factor for the effectiveness\nof anomaly detection. In this paper, we model thresholding in anomaly detection\nas a Markov Decision Process and propose an agent-based dynamic thresholding\n(ADT) framework based on a deep Q-network. The proposed method can be\nintegrated into many systems that require dynamic thresholding. An auto-encoder\nis utilized in this study to obtain feature representations and produce anomaly\nscores for complex input data. ADT can adjust thresholds adaptively by\nutilizing the anomaly scores from the auto-encoder and significantly improve\nanomaly detection performance. The properties of ADT are studied through\nexperiments on three real-world datasets and compared with benchmarks, hence\ndemonstrating its thresholding capability, data-efficient learning, stability,\nand robustness. Our study validates the effectiveness of reinforcement learning\nin optimal thresholding control in anomaly detection.\n", "date": "2023-12-03", "categories": []}
{"id": "2312.01729", "title": "EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly\n  Detection in Multivariate Time Series", "abstract": "  Transformer-based models for anomaly detection in multivariate time series\ncan benefit from the self-attention mechanism due to its advantage in modeling\nlong-term dependencies. However, Transformer-based anomaly detection models\nhave problems such as a large amount of data being required for training,\nstandard positional encoding is not suitable for multivariate time series data,\nand the interdependence between time series is not considered. To address these\nlimitations, we propose a novel anomaly detection method, named EdgeConvFormer,\nwhich integrates Time2vec embedding, stacked dynamic graph CNN, and Transformer\nto extract global and local spatial-time information. This design of\nEdgeConvFormer empowers it with decomposition capacities for complex time\nseries, progressive spatiotemporal correlation discovery between time series,\nand representation aggregation of multi-scale features. Experiments demonstrate\nthat EdgeConvFormer can learn the spatial-temporal correlations from\nmultivariate time series data and achieve better anomaly detection performance\nthan the state-of-the-art approaches on many real-world datasets of different\nscales.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.01764", "title": "Dynamic Erasing Network Based on Multi-Scale Temporal Features for\n  Weakly Supervised Video Anomaly Detection", "abstract": "  The goal of weakly supervised video anomaly detection is to learn a detection\nmodel using only video-level labeled data. However, prior studies typically\ndivide videos into fixed-length segments without considering the complexity or\nduration of anomalies. Moreover, these studies usually just detect the most\nabnormal segments, potentially overlooking the completeness of anomalies. To\naddress these limitations, we propose a Dynamic Erasing Network (DE-Net) for\nweakly supervised video anomaly detection, which learns multi-scale temporal\nfeatures. Specifically, to handle duration variations of abnormal events, we\nfirst propose a multi-scale temporal modeling module, capable of extracting\nfeatures from segments of varying lengths and capturing both local and global\nvisual information across different temporal scales. Then, we design a dynamic\nerasing strategy, which dynamically assesses the completeness of the detected\nanomalies and erases prominent abnormal segments in order to encourage the\nmodel to discover gentle abnormal segments in a video. The proposed method\nobtains favorable performance compared to several state-of-the-art approaches\non three datasets: XD-Violence, TAD, and UCF-Crime. Code will be made available\nat https://github.com/ArielZc/DE-Net.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.01904", "title": "Unsupervised Anomaly Detection using Aggregated Normative Diffusion", "abstract": "  Early detection of anomalies in medical images such as brain MRI is highly\nrelevant for diagnosis and treatment of many conditions. Supervised machine\nlearning methods are limited to a small number of pathologies where there is\ngood availability of labeled data. In contrast, unsupervised anomaly detection\n(UAD) has the potential to identify a broader spectrum of anomalies by spotting\ndeviations from normal patterns. Our research demonstrates that existing\nstate-of-the-art UAD approaches do not generalise well to diverse types of\nanomalies in realistic multi-modal MR data. To overcome this, we introduce a\nnew UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by\naggregating differences between predicted denoising steps and ground truth\nbackwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that\nhave been trained on pyramidal Gaussian noise. We validate ANDi against three\nrecent UAD baselines, and across three diverse brain MRI datasets. We show that\nANDi, in some cases, substantially surpasses these baselines and shows\nincreased robustness to varying types of anomalies. Particularly in detecting\nmultiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in\nterms of AUPRC.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.00088", "title": "Anomaly Detection via Learning-Based Sequential Controlled Sensing", "abstract": "  In this paper, we address the problem of detecting anomalies among a given\nset of binary processes via learning-based controlled sensing. Each process is\nparameterized by a binary random variable indicating whether the process is\nanomalous. To identify the anomalies, the decision-making agent is allowed to\nobserve a subset of the processes at each time instant. Also, probing each\nprocess has an associated cost. Our objective is to design a sequential\nselection policy that dynamically determines which processes to observe at each\ntime with the goal to minimize the delay in making the decision and the total\nsensing cost. We cast this problem as a sequential hypothesis testing problem\nwithin the framework of Markov decision processes. This formulation utilizes\nboth a Bayesian log-likelihood ratio-based reward and an entropy-based reward.\nThe problem is then solved using two approaches: 1) a deep reinforcement\nlearning-based approach where we design both deep Q-learning and policy\ngradient actor-critic algorithms; and 2) a deep active inference-based\napproach. Using numerical experiments, we demonstrate the efficacy of our\nalgorithms and show that our algorithms adapt to any unknown statistical\ndependence pattern of the processes.\n", "date": "2023-11-30", "categories": []}
{"id": "2311.14897", "title": "Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via\n  3D Anomaly Synthesis and A Self-Supervised Learning Network", "abstract": "  Recently, 3D anomaly detection, a crucial problem involving fine-grained\ngeometry discrimination, is getting more attention. However, the lack of\nabundant real 3D anomaly data limits the scalability of current models. To\nenable scalable anomaly data collection, we propose a 3D anomaly synthesis\npipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.\nSpecifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon\nShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40\ncategories, which provides a rich and varied collection of data, enabling\nefficient training and enhancing adaptability to industrial scenarios.\nMeanwhile,to enable scalable representation learning for 3D anomaly\nlocalization, we propose a self-supervised method, i.e., Iterative Mask\nReconstruction Network (IMRNet). During training, we propose a geometry-aware\nsample module to preserve potentially anomalous local regions during point\ncloud down-sampling. Then, we randomly mask out point patches and sent the\nvisible patches to a transformer for reconstruction-based self-supervision.\nDuring testing, the point cloud repeatedly goes through the Mask Reconstruction\nNetwork, with each iteration's output becoming the next input. By merging and\ncontrasting the final reconstructed point cloud with the initial input, our\nmethod successfully locates anomalies. Experiments show that IMRNet outperforms\nprevious state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet\ndataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released\nat https://github.com/Chopper-233/Anomaly-ShapeNet\n", "date": "2023-11-25", "categories": []}
{"id": "2311.18332", "title": "Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly\n  Detection", "abstract": "  Anomaly detection (AD) is a fundamental task in computer vision. It aims to\nidentify incorrect image data patterns which deviate from the normal ones.\nConventional methods generally address AD by preparing augmented negative\nsamples to enforce self-supervised learning. However, these techniques\ntypically do not consider semantics during augmentation, leading to the\ngeneration of unrealistic or invalid negative samples. Consequently, the\nfeature extraction network can be hindered from embedding critical features. In\nthis study, inspired by visual attention learning approaches, we propose\nCutSwap, which leverages saliency guidance to incorporate semantic cues for\naugmentation. Specifically, we first employ LayerCAM to extract multilevel\nimage features as saliency maps and then perform clustering to obtain multiple\ncentroids. To fully exploit saliency guidance, on each map, we select a pixel\npair from the cluster with the highest centroid saliency to form a patch pair.\nSuch a patch pair includes highly similar context information with dense\nsemantic correlations. The resulting negative sample is created by swapping the\nlocations of the patch pair. Compared to prior augmentation methods, CutSwap\ngenerates more subtle yet realistic negative samples to facilitate quality\nfeature learning. Extensive experimental and ablative evaluations demonstrate\nthat our method achieves state-of-the-art AD performance on two mainstream AD\nbenchmark datasets.\n", "date": "2023-11-30", "categories": []}
{"id": "2306.10720", "title": "Exploring the Relationship between Samples and Masks for Robust Defect\n  Localization", "abstract": "  Defect detection aims to detect and localize regions out of the normal\ndistribution.Previous approaches model normality and compare it with the input\nto identify defective regions, potentially limiting their generalizability.This\npaper proposes a one-stage framework that detects defective patterns directly\nwithout the modeling process.This ability is adopted through the joint efforts\nof three parties: a generative adversarial network (GAN), a newly proposed\nscaled pattern loss, and a dynamic masked cycle-consistent auxiliary network.\nExplicit information that could indicate the position of defects is\nintentionally excluded to avoid learning any direct mapping.Experimental\nresults on the texture class of the challenging MVTec AD dataset show that the\nproposed method is 2.9% higher than the SOTA methods in F1-Score, while\nsubstantially outperforming SOTA methods in generalizability.\n", "date": "2023-06-19", "categories": []}
{"id": "2311.17162", "title": "Fast Particle-based Anomaly Detection Algorithm with Variational\n  Autoencoder", "abstract": "  Model-agnostic anomaly detection is one of the promising approaches in the\nsearch for new beyond the standard model physics. In this paper, we present\nSet-VAE, a particle-based variational autoencoder (VAE) anomaly detection\nalgorithm. We demonstrate a 2x signal efficiency gain compared with traditional\nsubjettiness-based jet selection. Furthermore, with an eye to the future\ndeployment to trigger systems, we propose the CLIP-VAE, which reduces the\ninference-time cost of anomaly detection by using the KL-divergence loss as the\nanomaly score, resulting in a 2x acceleration in latency and reducing the\ncaching requirement.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.17822", "title": "Anomalous Behavior Detection in Trajectory Data of Older Drivers", "abstract": "  Given a road network and a set of trajectory data, the anomalous behavior\ndetection (ABD) problem is to identify drivers that show significant\ndirectional deviations, hardbrakings, and accelerations in their trips. The ABD\nproblem is important in many societal applications, including Mild Cognitive\nImpairment (MCI) detection and safe route recommendations for older drivers.\nThe ABD problem is computationally challenging due to the large size of\ntemporally-detailed trajectories dataset. In this paper, we propose an\nEdge-Attributed Matrix that can represent the key properties of\ntemporally-detailed trajectory datasets and identify abnormal driving\nbehaviors. Experiments using real-world datasets demonstrated that our approach\nidentifies abnormal driving behaviors.\n", "date": "2023-11-29", "categories": []}
{"id": "2310.11676", "title": "PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly\n  Detection", "abstract": "  Node-level graph anomaly detection (GAD) plays a critical role in identifying\nanomalous nodes from graph-structured data in various domains such as medicine,\nsocial networks, and e-commerce. However, challenges have arisen due to the\ndiversity of anomalies and the dearth of labeled data. Existing methodologies -\nreconstruction-based and contrastive learning - while effective, often suffer\nfrom efficiency issues, stemming from their complex objectives and elaborate\nmodules. To improve the efficiency of GAD, we introduce a simple method termed\nPREprocessing and Matching (PREM for short). Our approach streamlines GAD,\nreducing time and memory consumption while maintaining powerful anomaly\ndetection capabilities. Comprising two modules - a pre-processing module and an\nego-neighbor matching module - PREM eliminates the necessity for\nmessage-passing propagation during training, and employs a simple contrastive\nloss, leading to considerable reductions in training time and memory usage.\nMoreover, through rigorous evaluations of five real-world datasets, our method\ndemonstrated robustness and effectiveness. Notably, when validated on the ACM\ndataset, PREM achieved a 5% improvement in AUC, a 9-fold increase in training\nspeed, and sharply reduce memory usage compared to the most efficient baseline.\n", "date": "2023-10-18", "categories": []}
{"id": "2304.10001", "title": "Weakly Supervised Detection of Baby Cry", "abstract": "  Detection of baby cries is an important part of baby monitoring and health\ncare. Almost all existing methods use supervised SVM, CNN, or their varieties.\nIn this work, we propose to use weakly supervised anomaly detection to detect a\nbaby cry. In this weak supervision, we only need weak annotation if there is a\ncry in an audio file. We design a data mining technique using the pre-trained\nVGGish feature extractor and an anomaly detection network on long untrimmed\naudio files. The obtained datasets are used to train a simple CNN feature\nnetwork for cry/non-cry classification. This CNN is then used as a feature\nextractor in an anomaly detection framework to achieve better cry detection\nperformance.\n", "date": "2023-04-19", "categories": []}
{"id": "2311.11235", "title": "Unraveling the \"Anomaly\" in Time Series Anomaly Detection: A\n  Self-supervised Tri-domain Solution", "abstract": "  The ongoing challenges in time series anomaly detection (TSAD), notably the\nscarcity of anomaly labels and the variability in anomaly lengths and shapes,\nhave led to the need for a more efficient solution. As limited anomaly labels\nhinder traditional supervised models in TSAD, various SOTA deep learning\ntechniques, such as self-supervised learning, have been introduced to tackle\nthis issue. However, they encounter difficulties handling variations in anomaly\nlengths and shapes, limiting their adaptability to diverse anomalies.\nAdditionally, many benchmark datasets suffer from the problem of having\nexplicit anomalies that even random functions can detect. This problem is\nexacerbated by ill-posed evaluation metrics, known as point adjustment (PA),\nwhich can result in inflated model performance. In this context, we propose a\nnovel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which\naddresses these challenges by modeling features across three data domains -\ntemporal, frequency, and residual domains - without relying on anomaly labels.\nUnlike traditional contrastive learning methods, TriAD employs both\ninter-domain and intra-domain contrastive loss to learn common attributes among\nnormal data and differentiate them from anomalies. Additionally, our approach\ncan detect anomalies of varying lengths by integrating with a discord discovery\nalgorithm. It is worth noting that this study is the first to reevaluate the\ndeep learning potential in TSAD, utilizing both rigorously designed datasets\n(i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation).\nThrough experimental results on the UCR dataset, TriAD achieves an impressive\nthree-fold increase in PA%K based F1 scores over SOTA deep learning models, and\n50% increase of accuracy as compared to SOTA discord discovery algorithms.\n", "date": "2023-11-19", "categories": []}
{"id": "2311.15367", "title": "BatchNorm-based Weakly Supervised Video Anomaly Detection", "abstract": "  In weakly supervised video anomaly detection (WVAD), where only video-level\nlabels indicating the presence or absence of abnormal events are available, the\nprimary challenge arises from the inherent ambiguity in temporal annotations of\nabnormal occurrences. Inspired by the statistical insight that temporal\nfeatures of abnormal events often exhibit outlier characteristics, we propose a\nnovel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed\nBN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of\nBatchNorm as a reliable abnormality criterion to discern potential abnormal\nsnippets in abnormal videos. The proposed DFM criterion is also discriminative\nfor anomaly recognition and more resilient to label noise, serving as the\nadditional anomaly score to amend the prediction of the anomaly classifier that\nis susceptible to noisy labels. Moreover, a batch-level selection strategy is\ndevised to filter more abnormal snippets in videos where more abnormal events\noccur. The proposed BN-WVAD model demonstrates state-of-the-art performance on\nUCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to\n84.93%. Our code implementation is accessible at\nhttps://github.com/cool-xuan/BN-WVAD.\n", "date": "2023-11-26", "categories": []}
{"id": "2311.15924", "title": "Diagnosis driven Anomaly Detection for CPS", "abstract": "  In Cyber-Physical Systems (CPS) research, anomaly detection (detecting\nabnormal behavior) and diagnosis (identifying the underlying root cause) are\noften treated as distinct, isolated tasks. However, diagnosis algorithms\nrequire symptoms, i.e. temporally and spatially isolated anomalies, as input.\nThus, anomaly detection and diagnosis must be developed together to provide a\nholistic solution for diagnosis in CPS. We therefore propose a method for\nutilizing deep learning-based anomaly detection to generate inputs for\nConsistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and\na real-world CPS dataset, where our model demonstrates strong performance\nrelative to other state-of-the-art models.\n", "date": "2023-11-27", "categories": []}
{"id": "2311.14095", "title": "Video Anomaly Detection using GAN", "abstract": "  Accounting for the increased concern for public safety, automatic abnormal\nevent detection and recognition in a surveillance scene is crucial. It is a\ncurrent open study subject because of its intricacy and utility. The\nidentification of aberrant events automatically, it's a difficult undertaking\nbecause everyone's idea of abnormality is different. A typical occurrence in\none circumstance could be seen as aberrant in another. Automatic anomaly\nidentification becomes particularly challenging in the surveillance footage\nwith a large crowd due to congestion and high occlusion. With the use of\nmachine learning techniques, this thesis study aims to offer the solution for\nthis use case so that human resources won't be required to keep an eye out for\nany unusual activity in the surveillance system records. We have developed a\nnovel generative adversarial network (GAN) based anomaly detection model. This\nmodel is trained such that it learns together about constructing a high\ndimensional picture space and determining the latent space from the video's\ncontext. The generator uses a residual Autoencoder architecture made up of a\nmulti-stage channel attention-based decoder and a two-stream, deep\nconvolutional encoder that can realise both spatial and temporal data. We have\nalso offered a technique for refining the GAN model that reduces training time\nwhile also generalising the model by utilising transfer learning between\ndatasets. Using a variety of assessment measures, we compare our model to the\ncurrent state-of-the-art techniques on four benchmark datasets. The empirical\nfindings indicate that, in comparison to existing techniques, our network\nperforms favourably on all datasets.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.14469", "title": "Fault Detection in Telecom Networks using Bi-level Federated Graph\n  Neural Networks", "abstract": "  5G and Beyond Networks become increasingly complex and heterogeneous, with\ndiversified and high requirements from a wide variety of emerging applications.\nThe complexity and diversity of Telecom networks place an increasing strain on\nmaintenance and operation efforts. Moreover, the strict security and privacy\nrequirements present a challenge for mobile operators to leverage network data.\nTo detect network faults, and mitigate future failures, prior work focused on\nleveraging traditional ML/DL methods to locate anomalies in networks. The\ncurrent approaches, although powerful, do not consider the intertwined nature\nof embedded and software-intensive Radio Access Network systems. In this paper,\nwe propose a Bi-level Federated Graph Neural Network anomaly detection and\ndiagnosis model that is able to detect anomalies in Telecom networks in a\nprivacy-preserving manner, while minimizing communication costs. Our method\nrevolves around conceptualizing Telecom data as a bi-level temporal Graph\nNeural Networks. The first graph captures the interactions between different\nRAN nodes that are exposed to different deployment scenarios in the network,\nwhile each individual Radio Access Network node is further elaborated into its\nsoftware (SW) execution graph. Additionally, we use Federated Learning to\naddress privacy and security limitations. Furthermore, we study the performance\nof anomaly detection model under three settings: (1) Centralized (2) Federated\nLearning and (3) Personalized Federated Learning using real-world data from an\noperational network. Our comprehensive experiments showed that Personalized\nFederated Temporal Graph Neural Networks method outperforms the most commonly\nused techniques for Anomaly Detection.\n", "date": "2023-11-24", "categories": []}
{"id": "2311.13571", "title": "Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding\n  Network", "abstract": "  Ball mills play a critical role in modern mining operations, making their\nbearing failures a significant concern due to the potential loss of production\nefficiency and economic consequences. This paper presents an anomaly detection\nmethod based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for\naddressing the issue of ball mill bearing fault detection. The proposed\napproach leverages vibration data collected during normal operation for\ntraining, overcoming challenges such as labeling issues and data imbalance\noften encountered in supervised learning methods. DCAN includes the modules of\nconvolutional feature extraction and transposed convolutional feature\nreconstruction, demonstrating exceptional capabilities in signal processing and\nfeature extraction. Additionally, the paper describes the practical deployment\nof the DCAN-based anomaly detection model for bearing fault detection,\nutilizing data from the ball mill bearings of Wuhan Iron & Steel Resources\nGroup and fault data from NASA's bearing vibration dataset. Experimental\nresults validate the DCAN model's reliability in recognizing fault vibration\npatterns. This method holds promise for enhancing bearing fault detection\nefficiency, reducing production interruptions, and lowering maintenance costs.\n", "date": "2023-11-09", "categories": []}
{"id": "2211.15513", "title": "Composite Score for Anomaly Detection in Imbalanced Real-World\n  Industrial Dataset", "abstract": "  In recent years, the industrial sector has evolved towards its fourth\nrevolution. The quality control domain is particularly interested in advanced\nmachine learning for computer vision anomaly detection. Nevertheless, several\nchallenges have to be faced, including imbalanced datasets, the image\ncomplexity, and the zero-false-negative (ZFN) constraint to guarantee the\nhigh-quality requirement. This paper illustrates a use case for an industrial\npartner, where Printed Circuit Board Assembly (PCBA) images are first\nreconstructed with a Vector Quantized Generative Adversarial Network (VQGAN)\ntrained on normal products. Then, several multi-level metrics are extracted on\na few normal and abnormal images, highlighting anomalies through reconstruction\ndifferences. Finally, a classifer is trained to build a composite anomaly score\nthanks to the metrics extracted. This three-step approach is performed on the\npublic MVTec-AD datasets and on the partner PCBA dataset, where it achieves a\nregular accuracy of 95.69% and 87.93% under the ZFN constraint.\n", "date": "2022-11-25", "categories": []}
{"id": "2301.10787", "title": "Unravelling physics beyond the standard model with classical and quantum\n  anomaly detection", "abstract": "  Much hope for finding new physics phenomena at microscopic scale relies on\nthe observations obtained from High Energy Physics experiments, like the ones\nperformed at the Large Hadron Collider (LHC). However, current experiments do\nnot indicate clear signs of new physics that could guide the development of\nadditional Beyond Standard Model (BSM) theories. Identifying signatures of new\nphysics out of the enormous amount of data produced at the LHC falls into the\nclass of anomaly detection and constitutes one of the greatest computational\nchallenges. In this article, we propose a novel strategy to perform anomaly\ndetection in a supervised learning setting, based on the artificial creation of\nanomalies through a random process. For the resulting supervised learning\nproblem, we successfully apply classical and quantum Support Vector Classifiers\n(CSVC and QSVC respectively) to identify the artificial anomalies among the SM\nevents. Even more promising, we find that employing an SVC trained to identify\nthe artificial anomalies, it is possible to identify realistic BSM events with\nhigh accuracy. In parallel, we also explore the potential of quantum algorithms\nfor improving the classification accuracy and provide plausible conditions for\nthe best exploitation of this novel computational paradigm.\n", "date": "2023-01-25", "categories": []}
{"id": "2311.06504", "title": "SCL-VI: Self-supervised Context Learning for Visual Inspection of\n  Industrial Defects", "abstract": "  The unsupervised visual inspection of defects in industrial products poses a\nsignificant challenge due to substantial variations in product surfaces.\nCurrent unsupervised models struggle to strike a balance between detecting\ntexture and object defects, lacking the capacity to discern latent\nrepresentations and intricate features. In this paper, we present a novel\nself-supervised learning algorithm designed to derive an optimal encoder by\ntackling the renowned jigsaw puzzle. Our approach involves dividing the target\nimage into nine patches, tasking the encoder with predicting the relative\nposition relationships between any two patches to extract rich semantics.\nSubsequently, we introduce an affinity-augmentation method to accentuate\ndifferences between normal and abnormal latent representations. Leveraging the\nclassic support vector data description algorithm yields final detection\nresults. Experimental outcomes demonstrate that our proposed method achieves\noutstanding detection and segmentation performance on the widely used MVTec AD\ndataset, with rates of 95.8% and 96.8%, respectively, establishing a\nstate-of-the-art benchmark for both texture and object defects. Comprehensive\nexperimentation underscores the effectiveness of our approach in diverse\nindustrial applications.\n", "date": "2023-11-11", "categories": []}
{"id": "2311.12041", "title": "Automated Detection of hidden Damages and Impurities in Aluminum Die\n  Casting Materials and Fibre-Metal Laminates using Low-quality X-ray\n  Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine\n  Learning", "abstract": "  Detection and characterization of hidden defects, impurities, and damages in\nlayered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), as\nwell as in monolithic materials, e.g., aluminum die casting materials, is still\na challenge. This work discusses methods and challenges in data-driven modeling\nof automated damage and defect detectors using X-ray single- and\nmulti-projection (CT) images. Three main issues are identified: Data and\nfeature variance, data feature labeling (for supervised machine learning), and\nthe missing ground truth. It will be shown that only simulation of data can\ndeliver a ground truth data set and accurate labeling. Noise has significant\nimpact on the feature detection and will be discussed. Data-driven feature\ndetectors are implemented with semantic pixel- or z-profile Convolutional\nNeural Networks and LSTM Auto-encoders. Data is measured with three different\ndevices: A low-quality and low-cost (Low-Q), a mid- and a high-quality\n(micro-CT, Mid-/High-Q) device. The goals of this work are the training of\nrobust and generalized feature detectors with synthetic data and the transition\nfrom High- and Mid-Q laboratory measuring technologies towards in-field usable\ntechnologies and methods.\n", "date": "2023-11-17", "categories": []}
{"id": "2311.12081", "title": "Leveraging healthy population variability in deep learning unsupervised\n  anomaly detection in brain FDG PET", "abstract": "  Unsupervised anomaly detection is a popular approach for the analysis of\nneuroimaging data as it allows to identify a wide variety of anomalies from\nunlabelled data. It relies on building a subject-specific model of healthy\nappearance to which a subject's image can be compared to detect anomalies. In\nthe literature, it is common for anomaly detection to rely on analysing the\nresidual image between the subject's image and its pseudo-healthy\nreconstruction. This approach however has limitations partly due to the\npseudo-healthy reconstructions being imperfect and to the lack of natural\nthresholding mechanism. Our proposed method, inspired by Z-scores, leverages\nthe healthy population variability to overcome these limitations. Our\nexperiments conducted on FDG PET scans from the ADNI database demonstrate the\neffectiveness of our approach in accurately identifying Alzheimer's disease\nrelated anomalies.\n", "date": "2023-11-20", "categories": []}
{"id": "2311.12781", "title": "Quantifying Impairment and Disease Severity Using AI Models Trained on\n  Healthy Subjects", "abstract": "  Automatic assessment of impairment and disease severity is a key challenge in\ndata-driven medicine. We propose a novel framework to address this challenge,\nwhich leverages AI models trained exclusively on healthy individuals. The\nCOnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the\ndecrease in confidence of these models when presented with impaired or diseased\npatients to quantify their deviation from the healthy population. We applied\nthe COBRA score to address a key limitation of current clinical evaluation of\nupper-body impairment in stroke patients. The gold-standard Fugl-Meyer\nAssessment (FMA) requires in-person administration by a trained assessor for\n30-45 minutes, which restricts monitoring frequency and precludes physicians\nfrom adapting rehabilitation protocols to the progress of each patient. The\nCOBRA score, computed automatically in under one minute, is shown to be\nstrongly correlated with the FMA on an independent test cohort for two\ndifferent data modalities: wearable sensors ($\\rho = 0.845$, 95% CI\n[0.743,0.908]) and video ($\\rho = 0.746$, 95% C.I [0.594, 0.847]). To\ndemonstrate the generalizability of the approach to other conditions, the COBRA\nscore was also applied to quantify severity of knee osteoarthritis from\nmagnetic-resonance imaging scans, again achieving significant correlation with\nan independent clinical assessment ($\\rho = 0.644$, 95% C.I [0.585,0.696]).\n", "date": "2023-11-21", "categories": []}
{"id": "2307.15244", "title": "BOURNE: Bootstrapped Self-supervised Learning Framework for Unified\n  Graph Anomaly Detection", "abstract": "  Graph anomaly detection (GAD) has gained increasing attention in recent years\ndue to its critical application in a wide range of domains, such as social\nnetworks, financial risk management, and traffic analysis. Existing GAD methods\ncan be categorized into node and edge anomaly detection models based on the\ntype of graph objects being detected. However, these methods typically treat\nnode and edge anomalies as separate tasks, overlooking their associations and\nfrequent co-occurrences in real-world graphs. As a result, they fail to\nleverage the complementary information provided by node and edge anomalies for\nmutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and\nSL-GAD, heavily rely on negative pair sampling in contrastive learning, which\nincurs high computational costs, hindering their scalability to large graphs.\nTo address these limitations, we propose a novel unified graph anomaly\ndetection framework based on bootstrapped self-supervised learning (named\nBOURNE). We extract a subgraph (graph view) centered on each target node as\nnode context and transform it into a dual hypergraph (hypergraph view) as edge\ncontext. These views are encoded using graph and hypergraph neural networks to\ncapture the representations of nodes, edges, and their associated contexts. By\nswapping the context embeddings between nodes and edges and measuring the\nagreement in the embedding space, we enable the mutual detection of node and\nedge anomalies. Furthermore, BOURNE can eliminate the need for negative\nsampling, thereby enhancing its efficiency in handling large graphs. Extensive\nexperiments conducted on six benchmark datasets demonstrate the superior\neffectiveness and efficiency of BOURNE in detecting both node and edge\nanomalies.\n", "date": "2023-07-28", "categories": []}
{"id": "2308.12925", "title": "Low-count Time Series Anomaly Detection", "abstract": "  Low-count time series describe sparse or intermittent events, which are\nprevalent in large-scale online platforms that capture and monitor diverse data\ntypes. Several distinct challenges surface when modelling low-count time\nseries, particularly low signal-to-noise ratios (when anomaly signatures are\nprovably undetectable), and non-uniform performance (when average metrics are\nnot representative of local behaviour). The time series anomaly detection\ncommunity currently lacks explicit tooling and processes to model and reliably\ndetect anomalies in these settings. We address this gap by introducing a novel\ngenerative procedure for creating benchmark datasets comprising of low-count\ntime series with anomalous segments. Via a mixture of theoretical and empirical\nanalysis, our work explains how widely-used algorithms struggle with the\ndistribution overlap between normal and anomalous segments. In order to\nmitigate this shortcoming, we then leverage our findings to demonstrate how\nanomaly score smoothing consistently improves performance. The practical\nutility of our analysis and recommendation is validated on a real-world dataset\ncontaining sales data for retail stores.\n", "date": "2023-08-24", "categories": []}
{"id": "2311.07355", "title": "ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A\n  Unified Neural Network Approach", "abstract": "  Given a complex graph database of node- and edge-attributed multi-graphs as\nwell as associated metadata for each graph, how can we spot the anomalous\ninstances? Many real-world problems can be cast as graph inference tasks where\nthe graph representation could capture complex relational phenomena (e.g.,\ntransactions among financial accounts in a journal entry), along with metadata\nreflecting tabular features (e.g. approver, effective date, etc.). While\nnumerous anomaly detectors based on Graph Neural Networks (GNNs) have been\nproposed, none are capable of directly handling directed graphs with\nmulti-edges and self-loops. Furthermore, the simultaneous handling of\nrelational and tabular features remains an unexplored area. In this work we\npropose ADAMM, a novel graph neural network model that handles directed\nmulti-graphs, providing a unified end-to-end architecture that fuses metadata\nand graph-level representation learning through an unsupervised anomaly\ndetection objective. Experiments on datasets from two different domains,\nnamely, general-ledger journal entries from different firms (accounting) as\nwell as human GPS trajectories from thousands of individuals (urban mobility)\nvalidate ADAMM's generality and detection effectiveness of expert-guided and\nground-truth anomalies. Notably, ADAMM outperforms existing baselines that\nhandle the two data modalities (graph and metadata) separately with post hoc\nsynthesis efforts.\n", "date": "2023-11-13", "categories": []}
{"id": "2311.09642", "title": "Weakly Supervised Anomaly Detection for Chest X-Ray Image", "abstract": "  Chest X-Ray (CXR) examination is a common method for assessing thoracic\ndiseases in clinical applications. While recent advances in deep learning have\nenhanced the significance of visual analysis for CXR anomaly detection, current\nmethods often miss key cues in anomaly images crucial for identifying disease\nregions, as they predominantly rely on unsupervised training with normal\nimages. This letter focuses on a more practical setup in which few-shot anomaly\nimages with only image-level labels are available during training. For this\npurpose, we propose WSCXR, a weakly supervised anomaly detection framework for\nCXR. WSCXR firstly constructs sets of normal and anomaly image features\nrespectively. It then refines the anomaly image features by eliminating normal\nregion features through anomaly feature mining, thus fully leveraging the\nscarce yet crucial features of diseased areas. Additionally, WSCXR employs a\nlinear mixing strategy to augment the anomaly features, facilitating the\ntraining of anomaly detector with few-shot anomaly images. Experiments on two\nCXR datasets demonstrate the effectiveness of our approach.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.11018", "title": "SORTAD: Self-Supervised Optimized Random Transformations for Anomaly\n  Detection in Tabular Data", "abstract": "  We consider a self-supervised approach to anomaly detection in tabular data.\nRandom transformations are applied to the data, and then each transformation is\nidentified based on its output. These predicted transformations are used to\nidentify anomalies. In tabular data this approach faces many challenges that\nare related to the uncorrelated nature of the data. These challenges affect the\ntransformations that should be used, as well as the use of their predictions.\nTo this end, we propose SORTAD, a novel algorithm that is tailor-made to solve\nthese challenges. SORTAD optimally chooses random transformations that help the\nclassification process, and have a scoring function that is more sensitive to\nthe changes in the transformations classification prediction encountered in\ntabular data. SORTAD achieved state-of-the-art results on multiple commonly\nused anomaly detection data sets, as well as in the overall results across all\ndata sets tested.\n", "date": "2023-11-18", "categories": []}
{"id": "2311.11901", "title": "Identifying the Defective: Detecting Damaged Grains for Cereal\n  Appearance Inspection", "abstract": "  Cereal grain plays a crucial role in the human diet as a major source of\nessential nutrients. Grain Appearance Inspection (GAI) serves as an essential\nprocess to determine grain quality and facilitate grain circulation and\nprocessing. However, GAI is routinely performed manually by inspectors with\ncumbersome procedures, which poses a significant bottleneck in smart\nagriculture.\n  In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp.\nBy analyzing the distinctive characteristics of grain kernels, we formulate GAI\nas a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible\nkernels are considered normal samples while damaged grains or unknown objects\nare regarded as anomalies. We further propose an AD model, called AD-GAI, which\nis trained using only normal samples yet can identify anomalies during\ninference. Moreover, we customize a prototype device for data acquisition and\ncreate a large-scale dataset including 220K high-quality images of wheat and\nmaize kernels. Through extensive experiments, AD-GAI achieves considerable\nperformance in comparison with advanced AD methods, and AI4GrainInsp has highly\nconsistent performance compared to human experts and excels at inspection\nefficiency over 20x speedup. The dataset, code and models will be released at\nhttps://github.com/hellodfan/AI4GrainInsp.\n", "date": "2023-11-20", "categories": []}
{"id": "2311.10370", "title": "Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly\n  Detection", "abstract": "  Graph anomaly detection plays a crucial role in identifying exceptional\ninstances in graph data that deviate significantly from the majority. It has\ngained substantial attention in various domains of information security,\nincluding network intrusion, financial fraud, and malicious comments, et al.\nExisting methods are primarily developed in an unsupervised manner due to the\nchallenge in obtaining labeled data. For lack of guidance from prior knowledge\nin unsupervised manner, the identified anomalies may prove to be data noise or\nindividual data instances. In real-world scenarios, a limited batch of labeled\nanomalies can be captured, making it crucial to investigate the few-shot\nproblem in graph anomaly detection. Taking advantage of this potential, we\npropose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot\nMessage-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a\nself-supervised contrastive learning strategy within and across views to\ncapture intrinsic and transferable structural representations. Furthermore, we\npropose the Deep-GNN message-enhanced reconstruction module, which extensively\nexploits the few-shot label information and enables long-range propagation to\ndisseminate supervision signals to deeper unlabeled nodes. This module in turn\nassists in the training of self-supervised contrastive learning. Comprehensive\nexperimental results on six real-world datasets demonstrate that FMGAD can\nachieve better performance than other state-of-the-art methods, regardless of\nartificially injected anomalies or domain-organic anomalies.\n", "date": "2023-11-17", "categories": []}
{"id": "2306.12251", "title": "GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection", "abstract": "  With a long history of traditional Graph Anomaly Detection (GAD) algorithms\nand recently popular Graph Neural Networks (GNNs), it is still not clear (1)\nhow they perform under a standard comprehensive setting, (2) whether GNNs can\noutperform traditional algorithms such as tree ensembles, and (3) how about\ntheir efficiency on large-scale graphs. In response, we introduce GADBench -- a\nbenchmark tool dedicated to supervised anomalous node detection in static\ngraphs. GADBench facilitates a detailed comparison across 29 distinct models on\nten real-world GAD datasets, encompassing thousands to millions ($\\sim$6M)\nnodes. Our main finding is that tree ensembles with simple neighborhood\naggregation can outperform the latest GNNs tailored for the GAD task. We shed\nlight on the current progress of GAD, setting a robust groundwork for\nsubsequent investigations in this domain. GADBench is open-sourced at\nhttps://github.com/squareRoot3/GADBench.\n", "date": "2023-06-21", "categories": []}
{"id": "2307.08390", "title": "Correlation-aware Spatial-Temporal Graph Learning for Multivariate\n  Time-series Anomaly Detection", "abstract": "  Multivariate time-series anomaly detection is critically important in many\napplications, including retail, transportation, power grid, and water treatment\nplants. Existing approaches for this problem mostly employ either statistical\nmodels which cannot capture the non-linear relations well or conventional deep\nlearning models (e.g., CNN and LSTM) that do not explicitly learn the pairwise\ncorrelations among variables. To overcome these limitations, we propose a novel\nmethod, correlation-aware spatial-temporal graph learning (termed CST-GL), for\ntime series anomaly detection. CST-GL explicitly captures the pairwise\ncorrelations via a multivariate time series correlation learning module based\non which a spatial-temporal graph neural network (STGNN) can be developed.\nThen, by employing a graph convolution network that exploits one- and multi-hop\nneighbor information, our STGNN component can encode rich spatial information\nfrom complex pairwise dependencies between variables. With a temporal module\nthat consists of dilated convolutional functions, the STGNN can further capture\nlong-range dependence over time. A novel anomaly scoring component is further\nintegrated into CST-GL to estimate the degree of an anomaly in a purely\nunsupervised manner. Experimental results demonstrate that CST-GL can detect\nanomalies effectively in general settings as well as enable early detection\nacross different time delays.\n", "date": "2023-07-17", "categories": []}
{"id": "2311.02782", "title": "Towards Generic Anomaly Detection and Understanding: Large-scale\n  Visual-linguistic Model (GPT-4V) Takes the Lead", "abstract": "  Anomaly detection is a crucial task across different domains and data types.\nHowever, existing anomaly detection models are often designed for specific\ndomains and modalities. This study explores the use of GPT-4V(ision), a\npowerful visual-linguistic model, to address anomaly detection tasks in a\ngeneric manner. We investigate the application of GPT-4V in multi-modality,\nmulti-domain anomaly detection tasks, including image, video, point cloud, and\ntime series data, across multiple application areas, such as industrial,\nmedical, logical, video, 3D anomaly detection, and localization tasks. To\nenhance GPT-4V's performance, we incorporate different kinds of additional cues\nsuch as class information, human expertise, and reference images as\nprompts.Based on our experiments, GPT-4V proves to be highly effective in\ndetecting and explaining global and fine-grained semantic patterns in\nzero/one-shot anomaly detection. This enables accurate differentiation between\nnormal and abnormal instances. Although we conducted extensive evaluations in\nthis study, there is still room for future evaluation to further exploit\nGPT-4V's generic anomaly detection capacity from different aspects. These\ninclude exploring quantitative metrics, expanding evaluation benchmarks,\nincorporating multi-round interactions, and incorporating human feedback loops.\nNevertheless, GPT-4V exhibits promising performance in generic anomaly\ndetection and understanding, thus opening up a new avenue for anomaly\ndetection.\n", "date": "2023-11-05", "categories": []}
{"id": "2311.08422", "title": "k-Parameter Approach for False In-Season Anomaly Suppression in Daily\n  Time Series Anomaly Detection", "abstract": "  Detecting anomalies in a daily time series with a weekly pattern is a common\ntask with a wide range of applications. A typical way of performing the task is\nby using decomposition method. However, the method often generates false\npositive results where a data point falls within its weekly range but is just\noff from its weekday position. We refer to this type of anomalies as \"in-season\nanomalies\", and propose a k-parameter approach to address the issue. The\napproach provides configurable extra tolerance for in-season anomalies to\nsuppress misleading alerts while preserving real positives. It yields favorable\nresult.\n", "date": "2023-11-10", "categories": []}
{"id": "2311.09165", "title": "Approaching adverse event detection utilizing transformers on clinical\n  time-series", "abstract": "  Patients being admitted to a hospital will most often be associated with a\ncertain clinical development during their stay. However, there is always a risk\nof patients being subject to the wrong diagnosis or to a certain treatment not\npertaining to the desired effect, potentially leading to adverse events. Our\nresearch aims to develop an anomaly detection system for identifying deviations\nfrom expected clinical trajectories. To address this goal we analyzed 16 months\nof vital sign recordings obtained from the Nordland Hospital Trust (NHT). We\nemployed an self-supervised framework based on the STraTS transformer\narchitecture to represent the time series data in a latent space. These\nrepresentations were then subjected to various clustering techniques to explore\npotential patient phenotypes based on their clinical progress. While our\npreliminary results from this ongoing research are promising, they underscore\nthe importance of enhancing the dataset with additional demographic information\nfrom patients. This additional data will be crucial for a more comprehensive\nevaluation of the method's performance.\n", "date": "2023-11-15", "categories": []}
{"id": "2307.00754", "title": "ImDiffusion: Imputed Diffusion Models for Multivariate Time Series\n  Anomaly Detection", "abstract": "  Anomaly detection in multivariate time series data is of paramount importance\nfor ensuring the efficient operation of large-scale systems across diverse\ndomains. However, accurately detecting anomalies in such data poses significant\nchallenges. Existing approaches, including forecasting and reconstruction-based\nmethods, struggle to address these challenges effectively. To overcome these\nlimitations, we propose a novel anomaly detection framework named ImDiffusion,\nwhich combines time series imputation and diffusion models to achieve accurate\nand robust anomaly detection. The imputation-based approach employed by\nImDiffusion leverages the information from neighboring values in the time\nseries, enabling precise modeling of temporal and inter-correlated\ndependencies, reducing uncertainty in the data, thereby enhancing the\nrobustness of the anomaly detection process. ImDiffusion further leverages\ndiffusion models as time series imputers to accurately capturing complex\ndependencies. We leverage the step-by-step denoised outputs generated during\nthe inference process to serve as valuable signals for anomaly prediction,\nresulting in improved accuracy and robustness of the detection process.\n  We evaluate the performance of ImDiffusion via extensive experiments on\nbenchmark datasets. The results demonstrate that our proposed framework\nsignificantly outperforms state-of-the-art approaches in terms of detection\naccuracy and timeliness. ImDiffusion is further integrated into the real\nproduction system in Microsoft and observe a remarkable 11.4% increase in\ndetection F1 score compared to the legacy approach. To the best of our\nknowledge, ImDiffusion represents a pioneering approach that combines\nimputation-based techniques with time series anomaly detection, while\nintroducing the novel use of diffusion models to the field.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.12540", "title": "UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly\n  Detection", "abstract": "  Visual anomaly detection aims to learn normality from normal images, but\nexisting approaches are fragmented across various tasks: defect detection,\nsemantic anomaly detection, multi-class anomaly detection, and anomaly\nclustering. This one-task-one-model approach is resource-intensive and incurs\nhigh maintenance costs as the number of tasks increases. We present UniFormaly,\na universal and powerful anomaly detection framework. We emphasize the\nnecessity of our off-the-shelf approach by pointing out a suboptimal issue in\nonline encoder-based methods. We introduce Back Patch Masking (BPM) and top\nk-ratio feature matching to achieve unified anomaly detection. BPM eliminates\nirrelevant background regions using a self-attention map from self-supervised\nViTs. This operates in a task-agnostic manner and alleviates memory storage\nconsumption, scaling to tasks with large-scale datasets. Top k-ratio feature\nmatching unifies anomaly levels and tasks by casting anomaly scoring into\nmultiple instance learning. Finally, UniFormaly achieves outstanding results on\nvarious tasks and datasets. Codes are available at\nhttps://github.com/YoojLee/Uniformaly.\n", "date": "2023-07-24", "categories": []}
{"id": "2307.15807", "title": "Anomaly Detection in Industrial Machinery using IoT Devices and Machine\n  Learning: a Systematic Mapping", "abstract": "  Anomaly detection is critical in the smart industry for preventing equipment\nfailure, reducing downtime, and improving safety. Internet of Things (IoT) has\nenabled the collection of large volumes of data from industrial machinery,\nproviding a rich source of information for Anomaly Detection. However, the\nvolume and complexity of data generated by the Internet of Things ecosystems\nmake it difficult for humans to detect anomalies manually. Machine learning\n(ML) algorithms can automate anomaly detection in industrial machinery by\nanalyzing generated data. Besides, each technique has specific strengths and\nweaknesses based on the data nature and its corresponding systems. However, the\ncurrent systematic mapping studies on Anomaly Detection primarily focus on\naddressing network and cybersecurity-related problems, with limited attention\ngiven to the industrial sector. Additionally, these studies do not cover the\nchallenges involved in using ML for Anomaly Detection in industrial machinery\nwithin the context of the IoT ecosystems. This paper presents a systematic\nmapping study on Anomaly Detection for industrial machinery using IoT devices\nand ML algorithms to address this gap. The study comprehensively evaluates 84\nrelevant studies spanning from 2016 to 2023, providing an extensive review of\nAnomaly Detection research. Our findings identify the most commonly used\nalgorithms, preprocessing techniques, and sensor types. Additionally, this\nreview identifies application areas and points to future challenges and\nresearch opportunities.\n", "date": "2023-07-28", "categories": []}
{"id": "2311.06794", "title": "CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for\n  Better Anomaly Detection", "abstract": "  In the anomaly detection field, the scarcity of anomalous samples has\ndirected the current research emphasis towards unsupervised anomaly detection.\nWhile these unsupervised anomaly detection methods offer convenience, they also\noverlook the crucial prior information embedded within anomalous samples.\nMoreover, among numerous deep learning methods, supervised methods generally\nexhibit superior performance compared to unsupervised methods. Considering the\nreasons mentioned above, we propose a self-supervised anomaly detection\napproach that combines contrastive learning with 2D-Flow to achieve more\nprecise detection outcomes and expedited inference processes. On one hand, we\nintroduce a novel approach to anomaly synthesis, yielding anomalous samples in\naccordance with authentic industrial scenarios, alongside their surrogate\nannotations. On the other hand, having obtained a substantial number of\nanomalous samples, we enhance the 2D-Flow framework by incorporating\ncontrastive learning, leveraging diverse proxy tasks to fine-tune the network.\nOur approach enables the network to learn more precise mapping relationships\nfrom self-generated labels while retaining the lightweight characteristics of\nthe 2D-Flow. Compared to mainstream unsupervised approaches, our\nself-supervised method demonstrates superior detection accuracy, fewer\nadditional model parameters, and faster inference speed. Furthermore, the\nentire training and inference process is end-to-end. Our approach showcases new\nstate-of-the-art results, achieving a performance of 99.6\\% in image-level\nAUROC on the MVTecAD dataset and 96.8\\% in image-level AUROC on the BTAD\ndataset.\n", "date": "2023-11-12", "categories": []}
{"id": "2311.07880", "title": "VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway\n  IoT-Applications", "abstract": "  Vehicle anomaly detection plays a vital role in highway safety applications\nsuch as accident prevention, rapid response, traffic flow optimization, and\nwork zone safety. With the surge of the Internet of Things (IoT) in recent\nyears, there has arisen a pressing demand for Artificial Intelligence (AI)\nbased anomaly detection methods designed to meet the requirements of IoT\ndevices. Catering to this futuristic vision, we introduce a lightweight\napproach to vehicle anomaly detection by utilizing the power of trajectory\nprediction. Our proposed design identifies vehicles deviating from expected\npaths, indicating highway risks from different camera-viewing angles from\nreal-world highway datasets. On top of that, we present VegaEdge - a\nsophisticated AI confluence designed for real-time security and surveillance\napplications in modern highway settings through edge-centric IoT-embedded\nplatforms equipped with our anomaly detection approach. Extensive testing\nacross multiple platforms and traffic scenarios showcases the versatility and\neffectiveness of VegaEdge. This work also presents the Carolinas Anomaly\nDataset (CAD), to bridge the existing gap in datasets tailored for highway\nanomalies. In real-world scenarios, our anomaly detection approach achieves an\nAUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform,\nprocesses 738 trajectories per second in a typical highway setting. The dataset\nis available at\nhttps://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set .\n", "date": "2023-11-14", "categories": []}
{"id": "2307.00562", "title": "A MIL Approach for Anomaly Detection in Surveillance Videos from\n  Multiple Camera Views", "abstract": "  Occlusion and clutter are two scene states that make it difficult to detect\nanomalies in surveillance video. Furthermore, anomaly events are rare and, as a\nconsequence, class imbalance and lack of labeled anomaly data are also key\nfeatures of this task. Therefore, weakly supervised methods are heavily\nresearched for this application. In this paper, we tackle these typical\nproblems of anomaly detection in surveillance video by combining Multiple\nInstance Learning (MIL) to deal with the lack of labels and Multiple Camera\nViews (MC) to reduce occlusion and clutter effects. In the resulting MC-MIL\nalgorithm we apply a multiple camera combined loss function to train a\nregression network with Sultani's MIL ranking function. To evaluate the MC-MIL\nalgorithm first proposed here, the multiple camera PETS-2009 benchmark dataset\nwas re-labeled for the anomaly detection task from multiple camera views. The\nresult shows a significant performance improvement in F1 score compared to the\nsingle-camera configuration.\n", "date": "2023-07-02", "categories": []}
{"id": "2308.01412", "title": "Achieving state-of-the-art performance in the Medical\n  Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies", "abstract": "  The detection and localization of anomalies is one important medical image\nanalysis task. Most commonly, Computer Vision anomaly detection approaches rely\non manual annotations that are both time consuming and expensive to obtain.\nUnsupervised anomaly detection, or Out-of-Distribution detection, aims at\nidentifying anomalous samples relying only on unannotated samples considered\nnormal. In this study we present a new unsupervised anomaly detection method.\nOur method builds upon the self-supervised strategy consisting on training a\nsegmentation network to identify local synthetic anomalies. Our contributions\nimprove the synthetic anomaly generation process, making synthetic anomalies\nmore heterogeneous and challenging by 1) using complex random shapes and 2)\nsmoothing the edges of synthetic anomalies so networks cannot rely on the high\ngradient between image and synthetic anomalies. In our implementation we\nadopted standard practices in 3D medical image segmentation, including 3D U-Net\narchitecture, patch-wise training and model ensembling. Our method was\nevaluated using a validation set with different types of synthetic anomalies.\nOur experiments show that our method improved substantially the baseline method\nperformance. Additionally, we evaluated our method by participating in the\nMedical Out-of-Distribution (MOOD) Challenge held at MICCAI in 2022 and\nachieved first position in both sample-wise and pixel-wise tasks. Our\nexperiments and results in the latest MOOD challenge show that our simple yet\neffective approach can substantially improve the performance of\nOut-of-Distribution detection techniques which rely on synthetic anomalies.\n", "date": "2023-08-02", "categories": []}
{"id": "2311.06735", "title": "DeepQC: A Deep Learning System for Automatic Quality Control of In-situ\n  Soil Moisture Sensor Time Series Data", "abstract": "  Amidst changing climate, real-time soil moisture monitoring is vital for the\ndevelopment of in-season decision support tools to help farmers manage weather\nrelated risks. Precision Sustainable Agriculture (PSA) recently established a\nreal-time soil moisture monitoring network across the central, Midwest, and\neastern U.S., but field-scale sensor observations often come with data gaps and\nanomalies. To maintain the data quality needed for development of decision\ntools, a quality control system is necessary. The International Soil Moisture\nNetwork (ISMN) introduced the Flagit module for anomaly detection in soil\nmoisture observations. However, under certain conditions, Flagit's quality\ncontrol approaches may underperform in identifying anomalies. Recently deep\nlearning methods have been successfully applied to detect anomalies in time\nseries data in various disciplines. However, their use in agriculture has not\nbeen yet investigated. This study focuses on developing a Bi-directional Long\nShort-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in\nsoil moisture data. Manual flagged PSA observations were used for training,\nvalidation, and testing the model, following an 80:10:10 split. The study then\ncompared the DeepQC and Flagit based estimates to assess their relative\nperformance. Flagit corrected flagged 95.5% of the corrected observations and\n50.3% of the anomaly observations, indicating its limitations in identifying\nanomalies. On the other hand, the DeepQC correctly flagged 99.7% of the correct\nobservations and 95.6% of the anomalies in significantly less time,\ndemonstrating its superiority over Flagit approach. Importantly, DeepQC's\nperformance remained consistent regardless of the number of anomalies. Given\nthe promising results obtained with the DeepQC, future studies will focus on\nimplementing this model on national and global soil moisture networks.\n", "date": "2023-11-12", "categories": []}
{"id": "2311.06797", "title": "Dual-Branch Reconstruction Network for Industrial Anomaly Detection with\n  RGB-D Data", "abstract": "  Unsupervised anomaly detection methods are at the forefront of industrial\nanomaly detection efforts and have made notable progress. Previous work\nprimarily used 2D information as input, but multi-modal industrial anomaly\ndetection based on 3D point clouds and RGB images is just beginning to emerge.\nThe regular approach involves utilizing large pre-trained models for feature\nrepresentation and storing them in memory banks. However, the above methods\nrequire a longer inference time and higher memory usage, which cannot meet the\nreal-time requirements of the industry. To overcome these issues, we propose a\nlightweight dual-branch reconstruction network(DBRN) based on RGB-D input,\nlearning the decision boundary between normal and abnormal examples. The\nrequirement for alignment between the two modalities is eliminated by using\ndepth maps instead of point cloud input. Furthermore, we introduce an\nimportance scoring module in the discriminative network to assist in fusing\nfeatures from these two modalities, thereby obtaining a comprehensive\ndiscriminative result. DBRN achieves 92.8% AUROC with high inference efficiency\non the MVTec 3D-AD dataset without large pre-trained models and memory banks.\n", "date": "2023-11-12", "categories": []}
{"id": "2310.04010", "title": "Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised\n  Anomaly Detection Strategy", "abstract": "  Due to scarcity of anomaly situations in the early manufacturing stage, an\nunsupervised anomaly detection (UAD) approach is widely adopted which only uses\nnormal samples for training. This approach is based on the assumption that the\ntrained UAD model will accurately reconstruct normal patterns but struggles\nwith unseen anomalous patterns. To enhance the UAD performance,\nreconstruction-by-inpainting based methods have recently been investigated,\nespecially on the masking strategy of suspected defective regions. However,\nthere are still issues to overcome: 1) time-consuming inference due to multiple\nmasking, 2) output inconsistency by random masking strategy, and 3) inaccurate\nreconstruction of normal patterns when the masked area is large. Motivated by\nthis, we propose a novel reconstruction-by-inpainting method, dubbed Excision\nAnd Recovery (EAR), that features single deterministic masking based on the\nImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing.\nExperimental results on the MVTec AD dataset show that deterministic masking by\npre-trained attention effectively cuts out suspected defective regions and\nresolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing\nproves to enhance the UAD performance than emptying those regions by binary\nmasking, thereby overcomes issue 3. Our approach achieves a high UAD\nperformance without any change of the neural network structure. Thus, we\nsuggest that EAR be adopted in various manufacturing industries as a\npractically deployable solution.\n", "date": "2023-10-06", "categories": []}
{"id": "2311.05160", "title": "RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM\n  considering Token-level information", "abstract": "  As the IT industry advances, system log data becomes increasingly crucial.\nMany computer systems rely on log texts for management due to restricted access\nto source code. The need for log anomaly detection is growing, especially in\nreal-world applications, but identifying anomalies in rapidly accumulating logs\nremains a challenging task. Traditional deep learning-based anomaly detection\nmodels require dataset-specific training, leading to corresponding delays.\nNotably, most methods only focus on sequence-level log information, which makes\nthe detection of subtle anomalies harder, and often involve inference processes\nthat are difficult to utilize in real-time. We introduce RAPID, a model that\ncapitalizes on the inherent features of log data to enable anomaly detection\nwithout training delays, ensuring real-time capability. RAPID treats logs as\nnatural language, extracting representations using pre-trained language models.\nGiven that logs can be categorized based on system context, we implement a\nretrieval-based technique to contrast test logs with the most similar normal\nlogs. This strategy not only obviates the need for log-specific training but\nalso adeptly incorporates token-level information, ensuring refined and robust\ndetection, particularly for unseen logs. We also propose the core set\ntechnique, which can reduce the computational cost needed for comparison.\nExperimental results show that even without training on log data, RAPID\ndemonstrates competitive performance compared to prior models and achieves the\nbest performance on certain datasets. Through various research questions, we\nverified its capability for real-time detection without delay.\n", "date": "2023-11-09", "categories": []}
{"id": "2311.05473", "title": "Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized\n  Controlled Trials?", "abstract": "  Modern multi-centre randomized controlled trials (MCRCTs) collect massive\namounts of tabular data, and are monitored intensively for irregularities by\nhumans. We began by empirically evaluating 6 modern machine learning-based\noutlier detection algorithms on the task of identifying irregular data in 838\ndatasets from 7 real-world MCRCTs with a total of 77,001 patients from over 44\ncountries. Our results reinforce key findings from prior work in the outlier\ndetection literature on data from other domains. Existing algorithms often\nsucceed at identifying irregularities without any supervision, with at least\none algorithm exhibiting positive performance 70.6% of the time. However,\nperformance across datasets varies substantially with no single algorithm\nperforming consistently well, motivating new techniques for unsupervised model\nselection or other means of aggregating potentially discordant predictions from\nmultiple candidate models. We propose the Meta-learned Probabilistic Ensemble\n(MePE), a simple algorithm for aggregating the predictions of multiple\nunsupervised models, and show that it performs favourably compared to recent\nmeta-learning approaches for outlier detection model selection. While\nmeta-learning shows promise, small ensembles outperform all forms of\nmeta-learning on average, a negative result that may guide the application of\ncurrent outlier detection approaches in healthcare and other real-world\ndomains.\n", "date": "2023-11-09", "categories": []}
{"id": "2302.07849", "title": "Zero-Shot Anomaly Detection via Batch Normalization", "abstract": "  Anomaly detection (AD) plays a crucial role in many safety-critical\napplication domains. The challenge of adapting an anomaly detector to drift in\nthe normal data distribution, especially when no training data is available for\nthe \"new normal,\" has led to the development of zero-shot AD techniques. In\nthis paper, we propose a simple yet effective method called Adaptive Centered\nRepresentations (ACR) for zero-shot batch-level AD. Our approach trains\noff-the-shelf deep anomaly detectors (such as deep SVDD) to adapt to a set of\ninter-related training data distributions in combination with batch\nnormalization, enabling automatic zero-shot generalization for unseen AD tasks.\nThis simple recipe, batch normalization plus meta-training, is a highly\neffective and versatile tool. Our theoretical results guarantee the zero-shot\ngeneralization for unseen AD tasks; our empirical results demonstrate the first\nzero-shot AD results for tabular data and outperform existing methods in\nzero-shot anomaly detection and segmentation on image data from specialized\ndomains. Code is at https://github.com/aodongli/zero-shot-ad-via-batch-norm\n", "date": "2023-02-15", "categories": []}
{"id": "2309.02274", "title": "A Comparison of Residual-based Methods on Fault Detection", "abstract": "  An important initial step in fault detection for complex industrial systems\nis gaining an understanding of their health condition. Subsequently, continuous\nmonitoring of this health condition becomes crucial to observe its evolution,\ntrack changes over time, and isolate faults. As faults are typically rare\noccurrences, it is essential to perform this monitoring in an unsupervised\nmanner. Various approaches have been proposed not only to detect faults in an\nunsupervised manner but also to distinguish between different potential fault\ntypes. In this study, we perform a comprehensive comparison between two\nresidual-based approaches: autoencoders, and the input-output models that\nestablish a mapping between operating conditions and sensor readings. We\nexplore the sensor-wise residuals and aggregated residuals for the entire\nsystem in both methods. The performance evaluation focuses on three tasks:\nhealth indicator construction, fault detection, and health indicator\ninterpretation. To perform the comparison, we utilize the Commercial Modular\nAero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a\nsubset of the turbofan engine dataset containing three different fault types.\nAll models are trained exclusively on healthy data. Fault detection is achieved\nby applying a threshold that is determined based on the healthy condition. The\ndetection results reveal that both models are capable of detecting faults with\nan average delay of around 20 cycles and maintain a low false positive rate.\nWhile the fault detection performance is similar for both models, the\ninput-output model provides better interpretability regarding potential fault\ntypes and the possible faulty components.\n", "date": "2023-09-05", "categories": []}
{"id": "2311.04351", "title": "A Deep Learning Approach to Video Anomaly Detection using Convolutional\n  Autoencoders", "abstract": "  In this research we propose a deep learning approach for detecting anomalies\nin videos using convolutional autoencoder and decoder neural networks on the\nUCSD dataset.Our method utilizes a convolutional autoencoder to learn the\nspatiotemporal patterns of normal videos and then compares each frame of a test\nvideo to this learned representation. We evaluated our approach on the UCSD\ndataset and achieved an overall accuracy of 99.35% on the Ped1 dataset and\n99.77% on the Ped2 dataset, demonstrating the effectiveness of our method for\ndetecting anomalies in surveillance videos. The results show that our method\noutperforms other state-of-the-art methods, and it can be used in real-world\napplications for video anomaly detection.\n", "date": "2023-11-07", "categories": []}
{"id": "2311.04765", "title": "The voraus-AD Dataset for Anomaly Detection in Robot Applications", "abstract": "  During the operation of industrial robots, unusual events may endanger the\nsafety of humans and the quality of production. When collecting data to detect\nsuch cases, it is not ensured that data from all potentially occurring errors\nis included as unforeseeable events may happen over time. Therefore, anomaly\ndetection (AD) delivers a practical solution, using only normal data to learn\nto detect unusual events. We introduce a dataset that allows training and\nbenchmarking of anomaly detection methods for robotic applications based on\nmachine data which will be made publicly available to the research community.\nAs a typical robot task the dataset includes a pick-and-place application which\ninvolves movement, actions of the end effector and interactions with the\nobjects of the environment. Since several of the contained anomalies are not\ntask-specific but general, evaluations on our dataset are transferable to other\nrobotics applications as well. Additionally, we present MVT-Flow (multivariate\ntime-series flow) as a new baseline method for anomaly detection: It relies on\ndeep-learning-based density estimation with normalizing flows, tailored to the\ndata domain by taking its structure into account for the architecture. Our\nevaluation shows that MVT-Flow outperforms baselines from previous work by a\nlarge margin of 6.2% in area under ROC.\n", "date": "2023-11-08", "categories": []}
{"id": "2311.02747", "title": "Attention Modules Improve Image-Level Anomaly Detection for Industrial\n  Inspection: A DifferNet Case Study", "abstract": "  Within (semi-)automated visual industrial inspection, learning-based\napproaches for assessing visual defects, including deep neural networks, enable\nthe processing of otherwise small defect patterns in pixel size on\nhigh-resolution imagery. The emergence of these often rarely occurring defect\npatterns explains the general need for labeled data corpora. To alleviate this\nissue and advance the current state of the art in unsupervised visual\ninspection, this work proposes a DifferNet-based solution enhanced with\nattention modules: AttentDifferNet. It improves image-level detection and\nclassification capabilities on three visual anomaly detection datasets for\nindustrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In\ncomparison to the state of the art, AttentDifferNet achieves improved results,\nwhich are, in turn, highlighted throughout our quali-quantitative study. Our\nquantitative evaluation shows an average improvement - compared to DifferNet -\nof 1.77 +/- 0.25 percentage points in overall AUROC considering all three\ndatasets, reaching SOTA results in InsPLAD-fault, an industrial inspection\nin-the-wild dataset. As our variants to AttentDifferNet show great prospects in\nthe context of currently investigated approaches, a baseline is formulated,\nemphasizing the importance of attention for industrial anomaly detection both\nin the wild and in controlled environments.\n", "date": "2023-11-05", "categories": []}
{"id": "2304.00092", "title": "DynamoPMU: A Physics Informed Anomaly Detection and Prediction\n  Methodology using non-linear dynamics from $\\mu$PMU Measurement Data", "abstract": "  The expansion in technology and attainability of a large number of sensors\nhas led to a huge amount of real-time streaming data. The real-time data in the\nelectrical distribution system is collected through distribution-level phasor\nmeasurement units referred to as $\\mu$PMU which report high-resolution phasor\nmeasurements comprising various event signatures which provide situational\nawareness and enable a level of visibility into the distribution system. These\nevents are infrequent, unschedule, and uncertain; it is a challenge to\nscrutinize, detect and predict the occurrence of such events. For electrical\ndistribution systems, it is challenging to explicitly identify evolution\nfunctions that describe the complex, non-linear, and non-stationary signature\npatterns of events. In this paper, we seek to address this problem by\ndeveloping a physics dynamics-based approach to detect anomalies in the\n$\\mu$PMU streaming data and simultaneously predict the events using governing\nequations. We propose a data-driven approach based on the Hankel alternative\nview of the Koopman (HAVOK) operator, called DynamoPMU, to analyze the\nunderlying dynamics of the distribution system by representing them in a linear\nintrinsic space. The key technical idea is that the proposed method separates\nout the linear dynamical behaviour pattern and intermittent forcing (anomalous\nevents) in sequential data which turns out to be very useful for anomaly\ndetection and simultaneous data prediction. We demonstrate the efficacy of our\nproposed framework through analysis of real $\\mu$PMU data taken from the LBNL\ndistribution grid. DynamoPMU is suitable for real-time event detection as well\nas prediction in an unsupervised way and adapts to varying statistics.\n", "date": "2023-03-31", "categories": []}
{"id": "2311.02863", "title": "Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly\n  Fall Detection", "abstract": "  Falls are a major cause of injuries and deaths among older adults worldwide.\nAccurate fall detection can help reduce potential injuries and additional\nhealth complications. Different types of video modalities can be used in a home\nsetting to detect falls, including RGB, Infrared, and Thermal cameras. Anomaly\ndetection frameworks using autoencoders and their variants can be used for fall\ndetection due to the data imbalance that arises from the rarity and diversity\nof falls. However, the use of reconstruction error in autoencoders can limit\nthe application of networks' structures that propagate information. In this\npaper, we propose a new multi-objective loss function called Temporal Shift,\nwhich aims to predict both future and reconstructed frames within a window of\nsequential frames. The proposed loss function is evaluated on a\nsemi-naturalistic fall detection dataset containing multiple camera modalities.\nThe autoencoders were trained on normal activities of daily living (ADL)\nperformed by older adults and tested on ADLs and falls performed by young\nadults. Temporal shift shows significant improvement to a baseline 3D\nConvolutional autoencoder, an attention U-Net CAE, and a multi-modal neural\nnetwork. The greatest improvement was observed in an attention U-Net model\nimproving by 0.20 AUC ROC for a single camera when compared to reconstruction\nalone. With significant improvement across different models, this approach has\nthe potential to be widely adopted and improve anomaly detection capabilities\nin other settings besides fall detection.\n", "date": "2023-11-06", "categories": []}
{"id": "2308.00180", "title": "General Anomaly Detection of Underwater Gliders Validated by Large-scale\n  Deployment Datasets", "abstract": "  Underwater gliders have been widely used in oceanography for a range of\napplications. However, unpredictable events like shark strikes or remora\nattachments can lead to abnormal glider behavior or even loss of the\ninstrument. This paper employs an anomaly detection algorithm to assess\noperational conditions of underwater gliders in the real-world ocean\nenvironment. Prompt alerts are provided to glider pilots upon detecting any\nanomaly, so that they can take control of the glider to prevent further harm.\nThe detection algorithm is applied to multiple datasets collected in real\nglider deployments led by the University of Georgia's Skidaway Institute of\nOceanography (SkIO) and the University of South Florida (USF). In order to\ndemonstrate the algorithm generality, the experimental evaluation is applied to\nfour glider deployment datasets, each highlighting various anomalies happening\nin different scenes. Specifically, we utilize high resolution datasets only\navailable post-recovery to perform detailed analysis of the anomaly and compare\nit with pilot logs. Additionally, we simulate the online detection based on the\nreal-time subsets of data transmitted from the glider at the surfacing events.\nWhile the real-time data may not contain as much rich information as the\npost-recovery one, the online detection is of great importance as it allows\nglider pilots to monitor potential abnormal conditions in real time.\n", "date": "2023-07-31", "categories": []}
{"id": "2308.16150", "title": "Modality Cycles with Masked Conditional Diffusion for Unsupervised\n  Anomaly Segmentation in MRI", "abstract": "  Unsupervised anomaly segmentation aims to detect patterns that are distinct\nfrom any patterns processed during training, commonly called abnormal or\nout-of-distribution patterns, without providing any associated manual\nsegmentations. Since anomalies during deployment can lead to model failure,\ndetecting the anomaly can enhance the reliability of models, which is valuable\nin high-risk domains like medical imaging. This paper introduces Masked\nModality Cycles with Conditional Diffusion (MMCCD), a method that enables\nsegmentation of anomalies across diverse patterns in multimodal MRI. The method\nis based on two fundamental ideas. First, we propose the use of cyclic modality\ntranslation as a mechanism for enabling abnormality detection.\nImage-translation models learn tissue-specific modality mappings, which are\ncharacteristic of tissue physiology. Thus, these learned mappings fail to\ntranslate tissues or image patterns that have never been encountered during\ntraining, and the error enables their segmentation. Furthermore, we combine\nimage translation with a masked conditional diffusion model, which attempts to\n`imagine' what tissue exists under a masked area, further exposing unknown\npatterns as the generative model fails to recreate them. We evaluate our method\non a proxy task by training on healthy-looking slices of BraTS2021\nmulti-modality MRIs and testing on slices with tumors. We show that our method\ncompares favorably to previous unsupervised approaches based on image\nreconstruction and denoising with autoencoders and diffusion models.\n", "date": "2023-08-30", "categories": []}
{"id": "2309.06884", "title": "Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality\n  Control", "abstract": "  Manufacturing industries require efficient and voluminous production of\nhigh-quality finished goods. In the context of Industry 4.0, visual anomaly\ndetection poses an optimistic solution for automatically controlled product\nquality with high precision. In general, automation based on computer vision is\na promising solution to prevent bottlenecks at the product quality checkpoint.\nWe considered recent advancements in machine learning to improve visual defect\nlocalization, but challenges persist in obtaining a balanced feature set and\ndatabase of the wide variety of defects occurring in the production line.\nHence, this paper proposes a defect localizing autoencoder with unsupervised\nclass selection by clustering with k-means the features extracted from a\npre-trained VGG16 network. Moreover, the selected classes of defects are\naugmented with natural wild textures to simulate artificial defects. The study\ndemonstrates the effectiveness of the defect localizing autoencoder with\nunsupervised class selection for improving defect detection in manufacturing\nindustries. The proposed methodology shows promising results with precise and\naccurate localization of quality defects on melamine-faced boards for the\nfurniture industry. Incorporating artificial defects into the training data\nshows significant potential for practical implementation in real-world quality\ncontrol scenarios.\n", "date": "2023-09-13", "categories": []}
{"id": "2311.01851", "title": "Holistic Representation Learning for Multitask Trajectory Anomaly\n  Detection", "abstract": "  Video anomaly detection deals with the recognition of abnormal events in\nvideos. Apart from the visual signal, video anomaly detection has also been\naddressed with the use of skeleton sequences. We propose a holistic\nrepresentation of skeleton trajectories to learn expected motions across\nsegments at different times. Our approach uses multitask learning to\nreconstruct any continuous unobserved temporal segment of the trajectory\nallowing the extrapolation of past or future segments and the interpolation of\nin-between segments. We use an end-to-end attention-based encoder-decoder. We\nencode temporally occluded trajectories, jointly learn latent representations\nof the occluded segments, and reconstruct trajectories based on expected\nmotions across different temporal segments. Extensive experiments on three\ntrajectory-based video anomaly detection datasets show the advantages and\neffectiveness of our approach with state-of-the-art results on anomaly\ndetection in skeleton trajectories.\n", "date": "2023-11-03", "categories": []}
{"id": "2208.08879", "title": "SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault\n  Diagnosis in Chemical Processes", "abstract": "  Modern industrial facilities generate large volumes of raw sensor data during\nthe production process. This data is used to monitor and control the processes\nand can be analyzed to detect and predict process abnormalities. Typically, the\ndata has to be annotated by experts in order to be used in predictive modeling.\nHowever, manual annotation of large amounts of data can be difficult in\nindustrial settings.\n  In this paper, we propose SensorSCAN, a novel method for unsupervised fault\ndetection and diagnosis, designed for industrial chemical process monitoring.\nWe demonstrate our model's performance on two publicly available datasets of\nthe Tennessee Eastman Process with various faults. The results show that our\nmethod significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed\nFPR) and effectively detects most of the process faults without expert\nannotation. Moreover, we show that the model fine-tuned on a small fraction of\nlabeled data nearly reaches the performance of a SOTA model trained on the full\ndataset. We also demonstrate that our method is suitable for real-world\napplications where the number of faults is not known in advance. The code is\navailable at https://github.com/AIRI-Institute/sensorscan.\n", "date": "2022-08-17", "categories": []}
{"id": "2305.07828", "title": "Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot\n  Unsupervised Anomalous Sound Detection for Machine Condition Monitoring", "abstract": "  We present the task description of the Detection and Classification of\nAcoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot\nunsupervised anomalous sound detection (ASD) for machine condition\nmonitoring''. The main goal is to enable rapid deployment of ASD systems for\nnew kinds of machines without the need for hyperparameter tuning. In the past\nASD tasks, developed methods tuned hyperparameters for each machine type, as\nthe development and evaluation datasets had the same machine types. However,\ncollecting normal and anomalous data as the development dataset can be\ninfeasible in practice. In 2023 Task 2, we focus on solving the first-shot\nproblem, which is the challenge of training a model on a completely novel\nmachine type. Specifically, (i) each machine type has only one section (a\nsubset of machine type) and (ii) machine types in the development and\nevaluation datasets are completely different. Analysis of 86 submissions from\n23 teams revealed that the keys to outperform baselines were: 1) sampling\ntechniques for dealing with class imbalances across different domains and\nattributes, 2) generation of synthetic samples for robust detection, and 3) use\nof multiple large pre-trained models to extract meaningful embeddings for the\nanomaly detector.\n", "date": "2023-05-13", "categories": []}
{"id": "2310.10702", "title": "Transparent Anomaly Detection via Concept-based Explanations", "abstract": "  Advancements in deep learning techniques have given a boost to the\nperformance of anomaly detection. However, real-world and safety-critical\napplications demand a level of transparency and reasoning beyond accuracy. The\ntask of anomaly detection (AD) focuses on finding whether a given sample\nfollows the learned distribution. Existing methods lack the ability to reason\nwith clear explanations for their outcomes. Hence to overcome this challenge,\nwe propose Transparent {A}nomaly Detection {C}oncept {E}xplanations (ACE). ACE\nis able to provide human interpretable explanations in the form of concepts\nalong with anomaly prediction. To the best of our knowledge, this is the first\npaper that proposes interpretable by-design anomaly detection. In addition to\npromoting transparency in AD, it allows for effective human-model interaction.\nOur proposed model shows either higher or comparable results to black-box\nuninterpretable models. We validate the performance of ACE across three\nrealistic datasets - bird classification on CUB-200-2011, challenging\nhistopathology slide image classification on TIL-WSI-TCGA, and gender\nclassification on CelebA. We further demonstrate that our concept learning\nparadigm can be seamlessly integrated with other classification-based AD\nmethods.\n", "date": "2023-10-16", "categories": []}
{"id": "2311.01117", "title": "Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth\n  Simulation", "abstract": "  RGB-based surface anomaly detection methods have advanced significantly.\nHowever, certain surface anomalies remain practically invisible in RGB alone,\nnecessitating the incorporation of 3D information. Existing approaches that\nemploy point-cloud backbones suffer from suboptimal representations and reduced\napplicability due to slow processing. Re-training RGB backbones, designed for\nfaster dense input processing, on industrial depth datasets is hindered by the\nlimited availability of sufficiently large datasets. We make several\ncontributions to address these challenges. (i) We propose a novel Depth-Aware\nDiscrete Autoencoder (DADA) architecture, that enables learning a general\ndiscrete latent space that jointly models RGB and 3D data for 3D surface\nanomaly detection. (ii) We tackle the lack of diverse industrial depth datasets\nby introducing a simulation process for learning informative depth features in\nthe depth encoder. (iii) We propose a new surface anomaly detection method\n3DSR, which outperforms all existing state-of-the-art on the challenging\nMVTec3D anomaly detection benchmark, both in terms of accuracy and processing\nspeed. The experimental results validate the effectiveness and efficiency of\nour approach, highlighting the potential of utilizing depth information for\nimproved surface anomaly detection.\n", "date": "2023-11-02", "categories": []}
{"id": "2311.01452", "title": "Time Series Anomaly Detection using Diffusion-based Models", "abstract": "  Diffusion models have been recently used for anomaly detection (AD) in\nimages. In this paper we investigate whether they can also be leveraged for AD\non multivariate time series (MTS). We test two diffusion-based models and\ncompare them to several strong neural baselines. We also extend the PA%K\nprotocol, by computing a ROCK-AUC metric, which is agnostic to both the\ndetection threshold and the ratio K of correctly detected points. Our models\noutperform the baselines on synthetic datasets and are competitive on\nreal-world datasets, illustrating the potential of diffusion-based methods for\nAD in multivariate time series.\n", "date": "2023-11-02", "categories": []}
{"id": "2306.08366", "title": "SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection", "abstract": "  Anomaly detection under open-set scenario is a challenging task that requires\nlearning discriminative fine-grained features to detect anomalies that were\neven unseen during training. As a cheap yet effective approach, data\naugmentation has been widely used to create pseudo anomalies for better\ntraining of such models. Recent wisdom of augmentation methods focuses on\ngenerating random pseudo instances that may lead to a mixture of augmented\ninstances with seen anomalies, or out of the typical range of anomalies. To\naddress this issue, we propose a novel saliency-guided data augmentation\nmethod, SaliencyCut, to produce pseudo but more common anomalies which tend to\nstay in the plausible range of anomalies. Furthermore, we deploy a two-head\nlearning strategy consisting of normal and anomaly learning heads, to learn the\nanomaly score of each sample. Theoretical analyses show that this mechanism\noffers a more tractable and tighter lower bound of the data log-likelihood. We\nthen design a novel patch-wise residual module in the anomaly learning head to\nextract and assess the fine-grained anomaly features from each sample,\nfacilitating the learning of discriminative representations of anomaly\ninstances. Extensive experiments conducted on six real-world anomaly detection\ndatasets demonstrate the superiority of our method to competing methods under\nvarious settings.\n", "date": "2023-06-14", "categories": []}
{"id": "2308.13068", "title": "Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed\n  Evaluation Methodology", "abstract": "  Multivariate Time Series (MVTS) anomaly detection is a long-standing and\nchallenging research topic that has attracted tremendous research effort from\nboth industry and academia recently. However, a careful study of the literature\nmakes us realize that 1) the community is active but not as organized as other\nsibling machine learning communities such as Computer Vision (CV) and Natural\nLanguage Processing (NLP), and 2) most proposed solutions are evaluated using\neither inappropriate or highly flawed protocols, with an apparent lack of\nscientific foundation. So flawed is one very popular protocol, the so-called\npoint-adjust protocol, that a random guess can be shown to systematically\noutperform all algorithms developed so far. In this paper, we review and\nevaluate many recent algorithms using more robust protocols and discuss how a\nnormally good protocol may have weaknesses in the context of MVTS anomaly\ndetection and how to mitigate them. We also share our concerns about benchmark\ndatasets, experiment design and evaluation methodology we observe in many\nworks. Furthermore, we propose a simple, yet challenging, baseline based on\nPrincipal Components Analysis (PCA) that surprisingly outperforms many recent\nDeep Learning (DL) based approaches on popular benchmark datasets. The main\nobjective of this work is to stimulate more effort towards important aspects of\nthe research such as data, experiment design, evaluation methodology and result\ninterpretability, instead of putting the highest weight on the design of\nincreasingly more complex and \"fancier\" algorithms.\n", "date": "2023-08-24", "categories": []}
{"id": "2310.18728", "title": "Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with\n  Disentangled Product-of-Experts Modeling", "abstract": "  Multi-view or even multi-modal data is appealing yet challenging for\nreal-world applications. Detecting anomalies in multi-view data is a prominent\nrecent research topic. However, most of the existing methods 1) are only\nsuitable for two views or type-specific anomalies, 2) suffer from the issue of\nfusion disentanglement, and 3) do not support online detection after model\ndeployment. To address these challenges, our main ideas in this paper are\nthree-fold: multi-view learning, disentangled representation learning, and\ngenerative model. To this end, we propose dPoE, a novel multi-view variational\nautoencoder model that involves (1) a Product-of-Experts (PoE) layer in\ntackling multi-view data, (2) a Total Correction (TC) discriminator in\ndisentangling view-common and view-specific representations, and (3) a joint\nloss function in wrapping up all components. In addition, we devise theoretical\ninformation bounds to control both view-common and view-specific\nrepresentations. Extensive experiments on six real-world datasets markedly\ndemonstrate that the proposed dPoE outperforms baselines.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.20492", "title": "Log-based Anomaly Detection of Enterprise Software: An Empirical Study", "abstract": "  Most enterprise applications use logging as a mechanism to diagnose\nanomalies, which could help with reducing system downtime. Anomaly detection\nusing software execution logs has been explored in several prior studies, using\nboth classical and deep neural network-based machine learning models. In recent\nyears, the research has largely focused in using variations of sequence-based\ndeep neural networks (e.g., Long-Short Term Memory and Transformer-based\nmodels) for log-based anomaly detection on open-source data. However, they have\nnot been applied in industrial datasets, as often. In addition, the studied\nopen-source datasets are typically very large in size with logging statements\nthat do not change much over time, which may not be the case with a dataset\nfrom an industrial service that is relatively new. In this paper, we evaluate\nseveral state-of-the-art anomaly detection models on an industrial dataset from\nour research partner, which is much smaller and loosely structured than most\nlarge scale open-source benchmark datasets. Results show that while all models\nare capable of detecting anomalies, certain models are better suited for\nless-structured datasets. We also see that model effectiveness changes when a\ncommon data leak associated with a random train-test split in some prior work\nis removed. A qualitative study of the defects' characteristics identified by\nthe developers on the industrial dataset further shows strengths and weaknesses\nof the models in detecting different types of anomalies. Finally, we explore\nthe effect of limited training data by gradually increasing the training set\nsize, to evaluate if the model effectiveness does depend on the training set\nsize.\n", "date": "2023-10-31", "categories": []}
{"id": "2309.15376", "title": "ADGym: Design Choices for Deep Anomaly Detection", "abstract": "  Deep learning (DL) techniques have recently found success in anomaly\ndetection (AD) across various fields such as finance, medical services, and\ncloud computing. However, most of the current research tends to view deep AD\nalgorithms as a whole, without dissecting the contributions of individual\ndesign choices like loss functions and network architectures. This view tends\nto diminish the value of preliminary steps like data preprocessing, as more\nattention is given to newly designed loss functions, network architectures, and\nlearning paradigms. In this paper, we aim to bridge this gap by asking two key\nquestions: (i) Which design choices in deep AD methods are crucial for\ndetecting anomalies? (ii) How can we automatically select the optimal design\nchoices for a given AD dataset, instead of relying on generic, pre-existing\nsolutions? To address these questions, we introduce ADGym, a platform\nspecifically crafted for comprehensive evaluation and automatic selection of AD\ndesign elements in deep methods. Our extensive experiments reveal that relying\nsolely on existing leading methods is not sufficient. In contrast, models\ndeveloped using ADGym significantly surpass current state-of-the-art\ntechniques.\n", "date": "2023-09-27", "categories": []}
{"id": "2310.08800", "title": "DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time\n  Series Anomaly Detection", "abstract": "  Anomaly detection in multivariate time series has emerged as a crucial\nchallenge in time series research, with significant research implications in\nvarious fields such as fraud detection, fault diagnosis, and system state\nestimation. Reconstruction-based models have shown promising potential in\nrecent years for detecting anomalies in time series data. However, due to the\nrapid increase in data scale and dimensionality, the issues of noise and Weak\nIdentity Mapping (WIM) during time series reconstruction have become\nincreasingly pronounced. To address this, we introduce a novel Adaptive Dynamic\nNeighbor Mask (ADNM) mechanism and integrate it with the Transformer and\nDenoising Diffusion Model, creating a new framework for multivariate time\nseries anomaly detection, named Denoising Diffusion Mask Transformer (DDMT).\nThe ADNM module is introduced to mitigate information leakage between input and\noutput features during data reconstruction, thereby alleviating the problem of\nWIM during reconstruction. The Denoising Diffusion Transformer (DDT) employs\nthe Transformer as an internal neural network structure for Denoising Diffusion\nModel. It learns the stepwise generation process of time series data to model\nthe probability distribution of the data, capturing normal data patterns and\nprogressively restoring time series data by removing noise, resulting in a\nclear recovery of anomalies. To the best of our knowledge, this is the first\nmodel that combines Denoising Diffusion Model and the Transformer for\nmultivariate time series anomaly detection. Experimental evaluations were\nconducted on five publicly available multivariate time series anomaly detection\ndatasets. The results demonstrate that the model effectively identifies\nanomalies in time series data, achieving state-of-the-art performance in\nanomaly detection.\n", "date": "2023-10-13", "categories": []}
{"id": "2310.18548", "title": "MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial\n  and temporal structures in vehicle traffic", "abstract": "  Currently, there are computer vision systems that help us with tasks that\nwould be dull for humans, such as surveillance and vehicle tracking. An\nimportant part of this analysis is to identify traffic anomalies. An anomaly\ntells us that something unusual has happened, in this case on the highway. This\npaper aims to model vehicle tracking using computer vision to detect traffic\nanomalies on a highway. We develop the steps of detection, tracking, and\nanalysis of traffic: the detection of vehicles from video of urban traffic, the\ntracking of vehicles using a bipartite graph and the Convex Hull algorithm to\ndelimit moving areas. Finally for anomaly detection we use two data structures\nto detect the beginning and end of the anomaly. The first is the QuadTree that\ngroups vehicles that are stopped for a long time on the road and the second\nthat approaches vehicles that are occluded. Experimental results show that our\nmethod is acceptable on the Track4 test set, with an F1 score of 85.7% and a\nmean squared error of 25.432.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.18677", "title": "Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery\n  Approach", "abstract": "  We present a new method of training energy-based models (EBMs) for anomaly\ndetection that leverages low-dimensional structures within data. The proposed\nalgorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a data\npoint along a low-dimensional manifold that approximates the training dataset.\nThen, EBM is trained to maximize the probability of recovering the original\ndata. The training involves the generation of negative samples via MCMC, as in\nconventional EBM training, but from a different distribution concentrated near\nthe manifold. The resulting near-manifold negative samples are highly\ninformative, reflecting relevant modes of variation in data. An energy function\nof MPDR effectively learns accurate boundaries of the training data\ndistribution and excels at detecting out-of-distribution samples. Experimental\nresults show that MPDR exhibits strong performance across various anomaly\ndetection tasks involving diverse data types, such as images, vectors, and\nacoustic signals.\n", "date": "2023-10-28", "categories": []}
{"id": "2310.18091", "title": "Adversarial Anomaly Detection using Gaussian Priors and Nonlinear\n  Anomaly Scores", "abstract": "  Anomaly detection in imbalanced datasets is a frequent and crucial problem,\nespecially in the medical domain where retrieving and labeling irregularities\nis often expensive. By combining the generative stability of a\n$\\beta$-variational autoencoder (VAE) with the discriminative strengths of\ngenerative adversarial networks (GANs), we propose a novel model,\n$\\beta$-VAEGAN. We investigate methods for composing anomaly scores based on\nthe discriminative and reconstructive capabilities of our model. Existing work\nfocuses on linear combinations of these components to determine if data is\nanomalous. We advance existing work by training a kernelized support vector\nmachine (SVM) on the respective error components to also consider nonlinear\nrelationships. This improves anomaly detection performance, while allowing\nfaster optimization. Lastly, we use the deviations from the Gaussian prior of\n$\\beta$-VAEGAN to form a novel anomaly score component. In comparison to\nstate-of-the-art work, we improve the $F_1$ score during anomaly detection from\n0.85 to 0.92 on the widely used MITBIH Arrhythmia Database.\n", "date": "2023-10-27", "categories": []}
{"id": "2310.18257", "title": "MIM-GAN-based Anomaly Detection for Multivariate Time Series Data", "abstract": "  The loss function of Generative adversarial network(GAN) is an important\nfactor that affects the quality and diversity of the generated samples for\nanomaly detection. In this paper, we propose an unsupervised multiple time\nseries anomaly detection algorithm based on the GAN with message importance\nmeasure(MIM-GAN). In particular, the time series data is divided into\nsubsequences using a sliding window. Then a generator and a discriminator\ndesigned based on the Long Short-Term Memory (LSTM) are employed to capture the\ntemporal correlations of the time series data. To avoid the local optimal\nsolution of loss function and the model collapse, we introduce an exponential\ninformation measure into the loss function of GAN. Additionally, a discriminant\nreconstruction score consisting on discrimination and reconstruction loss is\ntaken into account. The global optimal solution for the loss function is\nderived and the model collapse is proved to be avoided in our proposed\nMIM-GAN-based anomaly detection algorithm. Experimental results show that the\nproposed MIM-GAN-based anomaly detection algorithm has superior performance in\nterms of precision, recall, and F1 score.\n", "date": "2023-10-26", "categories": []}
{"id": "2310.17650", "title": "A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised\n  Video Anomaly Detection", "abstract": "  Detection of anomalous events in videos is an important problem in\napplications such as surveillance. Video anomaly detection (VAD) is\nwell-studied in the one-class classification (OCC) and weakly supervised (WS)\nsettings. However, fully unsupervised (US) video anomaly detection methods,\nwhich learn a complete system without any annotation or human supervision, have\nnot been explored in depth. This is because the lack of any ground truth\nannotations significantly increases the magnitude of the VAD challenge. To\naddress this challenge, we propose a simple-but-effective two-stage\npseudo-label generation framework that produces segment-level (normal/anomaly)\npseudo-labels, which can be further used to train a segment-level anomaly\ndetector in a supervised manner. The proposed coarse-to-fine pseudo-label\n(C2FPL) generator employs carefully-designed hierarchical divisive clustering\nand statistical hypothesis testing to identify anomalous video segments from a\nset of completely unlabeled videos. The trained anomaly detector can be\ndirectly applied on segments of an unseen test video to obtain segment-level,\nand subsequently, frame-level anomaly predictions. Extensive studies on two\nlarge-scale public-domain datasets, UCF-Crime and XD-Violence, demonstrate that\nthe proposed unsupervised approach achieves superior performance compared to\nall existing OCC and US methods , while yielding comparable performance to the\nstate-of-the-art WS methods.\n", "date": "2023-10-26", "categories": []}
{"id": "2310.16376", "title": "GADY: Unsupervised Anomaly Detection on Dynamic Graphs", "abstract": "  Anomaly detection on dynamic graphs refers to detecting entities whose\nbehaviors obviously deviate from the norms observed within graphs and their\ntemporal information. This field has drawn increasing attention due to its\napplication in finance, network security, social networks, and more. However,\nexisting methods face two challenges: dynamic structure constructing challenge\n- difficulties in capturing graph structure with complex time information and\nnegative sampling challenge - unable to construct excellent negative samples\nfor unsupervised learning. To address these challenges, we propose Unsupervised\nGenerative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first\nchallenge, we propose a continuous dynamic graph model to capture the\nfine-grained information, which breaks the limit of existing discrete methods.\nSpecifically, we employ a message-passing framework combined with positional\nfeatures to get edge embeddings, which are decoded to identify anomalies. For\nthe second challenge, we pioneer the use of Generative Adversarial Networks to\ngenerate negative interactions. Moreover, we design a loss function to alter\nthe training goal of the generator while ensuring the diversity and quality of\ngenerated samples. Extensive experiments demonstrate that our proposed GADY\nsignificantly outperforms the previous state-of-the-art method on three\nreal-world datasets. Supplementary experiments further validate the\neffectiveness of our model design and the necessity of each module.\n", "date": "2023-10-25", "categories": []}
{"id": "2310.16435", "title": "On Pixel-level Performance Assessment in Anomaly Detection", "abstract": "  Anomaly detection methods have demonstrated remarkable success across various\napplications. However, assessing their performance, particularly at the\npixel-level, presents a complex challenge due to the severe imbalance that is\nmost commonly present between normal and abnormal samples. Commonly adopted\nevaluation metrics designed for pixel-level detection may not effectively\ncapture the nuanced performance variations arising from this class imbalance.\nIn this paper, we dissect the intricacies of this challenge, underscored by\nvisual evidence and statistical analysis, leading to delve into the need for\nevaluation metrics that account for the imbalance. We offer insights into more\naccurate metrics, using eleven leading contemporary anomaly detection methods\non twenty-one anomaly detection problems. Overall, from this extensive\nexperimental evaluation, we can conclude that Precision-Recall-based metrics\ncan better capture relative method performance, making them more suitable for\nthe task.\n", "date": "2023-10-25", "categories": []}
{"id": "2310.16520", "title": "Towards Self-Interpretable Graph-Level Anomaly Detection", "abstract": "  Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit\nnotable dissimilarity compared to the majority in a collection. However,\ncurrent works primarily focus on evaluating graph-level abnormality while\nfailing to provide meaningful explanations for the predictions, which largely\nlimits their reliability and application scope. In this paper, we investigate a\nnew challenging problem, explainable GLAD, where the learning objective is to\npredict the abnormality of each graph sample with corresponding explanations,\ni.e., the vital subgraph that leads to the predictions. To address this\nchallenging problem, we propose a Self-Interpretable Graph aNomaly dETection\nmodel (SIGNET for short) that detects anomalous graphs as well as generates\ninformative explanations simultaneously. Specifically, we first introduce the\nmulti-view subgraph information bottleneck (MSIB) framework, serving as the\ndesign basis of our self-interpretable GLAD approach. This way SIGNET is able\nto not only measure the abnormality of each graph based on cross-view mutual\ninformation but also provide informative graph rationales by extracting\nbottleneck subgraphs from the input graph and its dual hypergraph in a\nself-supervised way. Extensive experiments on 16 datasets demonstrate the\nanomaly detection capability and self-interpretability of SIGNET.\n", "date": "2023-10-25", "categories": []}
{"id": "2206.15316", "title": "Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory\n  Models", "abstract": "  We propose a novel anomaly detection method for echocardiogram videos. The\nintroduced method takes advantage of the periodic nature of the heart cycle to\nlearn three variants of a variational latent trajectory model (TVAE). While the\nfirst two variants (TVAE-C and TVAE-R) model strict periodic movements of the\nheart, the third (TVAE-S) is more general and allows shifts in the spatial\nrepresentation throughout the video. All models are trained on the healthy\nsamples of a novel in-house dataset of infant echocardiogram videos consisting\nof multiple chamber views to learn a normative prior of the healthy population.\nDuring inference, maximum a posteriori (MAP) based anomaly detection is\nperformed to detect out-of-distribution samples in our dataset. The proposed\nmethod reliably identifies severe congenital heart defects, such as Ebstein's\nAnomaly or Shone-complex. Moreover, it achieves superior performance over\nMAP-based anomaly detection with standard variational autoencoders when\ndetecting pulmonary hypertension and right ventricular dilation. Finally, we\ndemonstrate that the proposed method enables interpretable explanations of its\noutput through heatmaps highlighting the regions corresponding to anomalous\nheart structures.\n", "date": "2022-06-30", "categories": []}
{"id": "2306.02602", "title": "ReContrast: Domain-Specific Anomaly Detection via Contrastive\n  Reconstruction", "abstract": "  Most advanced unsupervised anomaly detection (UAD) methods rely on modeling\nfeature representations of frozen encoder networks pre-trained on large-scale\ndatasets, e.g. ImageNet. However, the features extracted from the encoders that\nare borrowed from natural image domains coincide little with the features\nrequired in the target UAD domain, such as industrial inspection and medical\nimaging. In this paper, we propose a novel epistemic UAD method, namely\nReContrast, which optimizes the entire network to reduce biases towards the\npre-trained image domain and orients the network in the target domain. We start\nwith a feature reconstruction approach that detects anomalies from errors.\nEssentially, the elements of contrastive learning are elegantly embedded in\nfeature reconstruction to prevent the network from training instability,\npattern collapse, and identical shortcut, while simultaneously optimizing both\nthe encoder and decoder on the target domain. To demonstrate our transfer\nability on various image domains, we conduct extensive experiments across two\npopular industrial defect detection benchmarks and three medical image UAD\ntasks, which shows our superiority over current state-of-the-art methods.\n", "date": "2023-06-05", "categories": []}
{"id": "2307.14387", "title": "Coupled-Space Attacks against Random-Walk-based Anomaly Detection", "abstract": "  Random Walks-based Anomaly Detection (RWAD) is commonly used to identify\nanomalous patterns in various applications. An intriguing characteristic of\nRWAD is that the input graph can either be pre-existing or constructed from raw\nfeatures. Consequently, there are two potential attack surfaces against RWAD:\ngraph-space attacks and feature-space attacks. In this paper, we explore this\nvulnerability by designing practical coupled-space attacks, investigating the\ninterplay between graph-space and feature-space attacks. To this end, we\nconduct a thorough complexity analysis, proving that attacking RWAD is NP-hard.\nThen, we proceed to formulate the graph-space attack as a bi-level optimization\nproblem and propose two strategies to solve it: alternative iteration\n(alterI-attack) or utilizing the closed-form solution of the random walk model\n(cf-attack). Finally, we utilize the results from the graph-space attacks as\nguidance to design more powerful feature-space attacks (i.e., graph-guided\nattacks). Comprehensive experiments demonstrate that our proposed attacks are\neffective in enabling the target nodes from RWAD with a limited attack budget.\nIn addition, we conduct transfer attack experiments in a black-box setting,\nwhich show that our feature attack significantly decreases the anomaly scores\nof target nodes. Our study opens the door to studying the coupled-space attack\nagainst graph anomaly detection in which the graph space relies on the feature\nspace.\n", "date": "2023-07-26", "categories": []}
{"id": "2310.15416", "title": "Nominality Score Conditioned Time Series Anomaly Detection by\n  Point/Sequential Reconstruction", "abstract": "  Time series anomaly detection is challenging due to the complexity and\nvariety of patterns that can occur. One major difficulty arises from modeling\ntime-dependent relationships to find contextual anomalies while maintaining\ndetection accuracy for point anomalies. In this paper, we propose a framework\nfor unsupervised time series anomaly detection that utilizes point-based and\nsequence-based reconstruction models. The point-based model attempts to\nquantify point anomalies, and the sequence-based model attempts to quantify\nboth point and contextual anomalies. Under the formulation that the observed\ntime point is a two-stage deviated value from a nominal time point, we\nintroduce a nominality score calculated from the ratio of a combined value of\nthe reconstruction errors. We derive an induced anomaly score by further\nintegrating the nominality score and anomaly score, then theoretically prove\nthe superiority of the induced anomaly score over the original anomaly score\nunder certain conditions. Extensive studies conducted on several public\ndatasets show that the proposed framework outperforms most state-of-the-art\nbaselines for time series anomaly detection.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.15586", "title": "Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance\n  Using Self-Supervised Deep Learning", "abstract": "  In maritime traffic surveillance, detecting illegal activities, such as\nillegal fishing or transshipment of illicit products is a crucial task of the\ncoastal administration. In the open sea, one has to rely on Automatic\nIdentification System (AIS) message transmitted by on-board transponders, which\nare captured by surveillance satellites. However, insincere vessels often\nintentionally shut down their AIS transponders to hide illegal activities. In\nthe open sea, it is very challenging to differentiate intentional AIS shutdowns\nfrom missing reception due to protocol limitations, bad weather conditions or\nrestricting satellite positions. This paper presents a novel approach for the\ndetection of abnormal AIS missing reception based on self-supervised deep\nlearning techniques and transformer models. Using historical data, the trained\nmodel predicts if a message should be received in the upcoming minute or not.\nAfterwards, the model reports on detected anomalies by comparing the prediction\nwith what actually happens. Our method can process AIS messages in real-time,\nin particular, more than 500 Millions AIS messages per month, corresponding to\nthe trajectories of more than 60 000 ships. The method is evaluated on 1-year\nof real-world data coming from four Norwegian surveillance satellites. Using\nrelated research results, we validated our method by rediscovering already\ndetected intentional AIS shutdowns.\n", "date": "2023-10-24", "categories": []}
{"id": "2309.13226", "title": "Real3D-AD: A Dataset of Point Cloud Anomaly Detection", "abstract": "  High-precision point cloud anomaly detection is the gold standard for\nidentifying the defects of advancing machining and precision manufacturing.\nDespite some methodological advances in this area, the scarcity of datasets and\nthe lack of a systematic benchmark hinder its development. We introduce\nReal3D-AD, a challenging high-precision point cloud anomaly detection dataset,\naddressing the limitations in the field. With 1,254 high-resolution 3D items\nfrom forty thousand to millions of points for each item, Real3D-AD is the\nlargest dataset for high-precision 3D industrial anomaly detection to date.\nReal3D-AD surpasses existing 3D anomaly detection datasets available regarding\npoint cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect\nprototype. Additionally, we present a comprehensive benchmark for Real3D-AD,\nrevealing the absence of baseline methods for high-precision point cloud\nanomaly detection. To address this, we propose Reg3D-AD, a registration-based\n3D anomaly detection method incorporating a novel feature memory bank that\npreserves local and global representations. Extensive experiments on the\nReal3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility\nand accessibility, we provide the Real3D-AD dataset, benchmark source code, and\nReg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD.\n", "date": "2023-09-23", "categories": []}
{"id": "2310.14063", "title": "Concept-based Anomaly Detection in Retail Stores for Automatic\n  Correction using Mobile Robots", "abstract": "  Tracking of inventory and rearrangement of misplaced items are some of the\nmost labor-intensive tasks in a retail environment. While there have been\nattempts at using vision-based techniques for these tasks, they mostly use\nplanogram compliance for detection of any anomalies, a technique that has been\nfound lacking in robustness and scalability. Moreover, existing systems rely on\nhuman intervention to perform corrective actions after detection. In this\npaper, we present Co-AD, a Concept-based Anomaly Detection approach using a\nVision Transformer (ViT) that is able to flag misplaced objects without using a\nprior knowledge base such as a planogram. It uses an auto-encoder architecture\nfollowed by outlier detection in the latent space. Co-AD has a peak success\nrate of 89.90% on anomaly detection image sets of retail objects drawn from the\nRP2K dataset, compared to 80.81% on the best-performing baseline of a standard\nViT auto-encoder. To demonstrate its utility, we describe a robotic mobile\nmanipulation pipeline to autonomously correct the anomalies flagged by Co-AD.\nThis work is ultimately aimed towards developing autonomous mobile robot\nsolutions that reduce the need for human intervention in retail store\nmanagement.\n", "date": "2023-10-21", "categories": []}
{"id": "2310.14228", "title": "Hierarchical Vector Quantized Transformer for Multi-class Unsupervised\n  Anomaly Detection", "abstract": "  Unsupervised image Anomaly Detection (UAD) aims to learn robust and\ndiscriminative representations of normal samples. While separate solutions per\nclass endow expensive computation and limited generalizability, this paper\nfocuses on building a unified framework for multiple classes. Under such a\nchallenging setting, popular reconstruction-based networks with continuous\nlatent representation assumption always suffer from the \"identical shortcut\"\nissue, where both normal and abnormal samples can be well recovered and\ndifficult to distinguish. To address this pivotal issue, we propose a\nhierarchical vector quantized prototype-oriented Transformer under a\nprobabilistic framework. First, instead of learning the continuous\nrepresentations, we preserve the typical normal patterns as discrete iconic\nprototypes, and confirm the importance of Vector Quantization in preventing the\nmodel from falling into the shortcut. The vector quantized iconic prototype is\nintegrated into the Transformer for reconstruction, such that the abnormal data\npoint is flipped to a normal data point.Second, we investigate an exquisite\nhierarchical framework to relieve the codebook collapse issue and replenish\nfrail normal patterns. Third, a prototype-oriented optimal transport method is\nproposed to better regulate the prototypes and hierarchically evaluate the\nabnormal score. By evaluating on MVTec-AD and VisA datasets, our model\nsurpasses the state-of-the-art alternatives and possesses good\ninterpretability. The code is available at\nhttps://github.com/RuiyingLu/HVQ-Trans.\n", "date": "2023-10-22", "categories": []}
{"id": "2310.14608", "title": "CAD-DA: Controllable Anomaly Detection after Domain Adaptation by\n  Statistical Inference", "abstract": "  We propose a novel statistical method for testing the results of anomaly\ndetection (AD) under domain adaptation (DA), which we call CAD-DA --\ncontrollable AD under DA. The distinct advantage of the CAD-DA lies in its\nability to control the probability of misidentifying anomalies under a\npre-specified level $\\alpha$ (e.g., 0.05). The challenge within this DA setting\nis the necessity to account for the influence of DA to ensure the validity of\nthe inference results. Our solution to this challenge leverages the concept of\nconditional Selective Inference to handle the impact of DA. To our knowledge,\nthis is the first work capable of conducting a valid statistical inference\nwithin the context of DA. We evaluate the performance of the CAD-DA method on\nboth synthetic and real-world datasets.\n", "date": "2023-10-23", "categories": []}
{"id": "2310.14893", "title": "Data Drift Monitoring for Log Anomaly Detection Pipelines", "abstract": "  Logs enable the monitoring of infrastructure status and the performance of\nassociated applications. Logs are also invaluable for diagnosing the root\ncauses of any problems that may arise. Log Anomaly Detection (LAD) pipelines\nautomate the detection of anomalies in logs, providing assistance to site\nreliability engineers (SREs) in system diagnosis. Log patterns change over\ntime, necessitating updates to the LAD model defining the `normal' log activity\nprofile. In this paper, we introduce a Bayes Factor-based drift detection\nmethod that identifies when intervention, retraining, and updating of the LAD\nmodel are required with human involvement. We illustrate our method using\nsequences of log activity, both from unaltered data, and simulated activity\nwith controlled levels of anomaly contamination, based on real collected log\ndata.\n", "date": "2023-10-17", "categories": []}
{"id": "2310.14949", "title": "Predictive Maintenance Model Based on Anomaly Detection in Induction\n  Motors: A Machine Learning Approach Using Real-Time IoT Data", "abstract": "  With the support of Internet of Things (IoT) devices, it is possible to\nacquire data from degradation phenomena and design data-driven models to\nperform anomaly detection in industrial equipment. This approach not only\nidentifies potential anomalies but can also serve as a first step toward\nbuilding predictive maintenance policies. In this work, we demonstrate a novel\nanomaly detection system on induction motors used in pumps, compressors, fans,\nand other industrial machines. This work evaluates a combination of\npre-processing techniques and machine learning (ML) models with a low\ncomputational cost. We use a combination of pre-processing techniques such as\nFast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are\nwell-known approaches for extracting features from raw data. We also aim to\nguarantee an optimal balance between multiple conflicting parameters, such as\nanomaly detection rate, false positive rate, and inference speed of the\nsolution. To this end, multiobjective optimization and analysis are performed\non the evaluated models. Pareto-optimal solutions are presented to select which\nmodels have the best results regarding classification metrics and computational\neffort. Differently from most works in this field that use publicly available\ndatasets to validate their models, we propose an end-to-end solution combining\nlow-cost and readily available IoT sensors. The approach is validated by\nacquiring a custom dataset from induction motors. Also, we fuse vibration,\ntemperature, and noise data from these sensors as the input to the proposed ML\nmodel. Therefore, we aim to propose a methodology general enough to be applied\nin different industrial contexts in the future.\n", "date": "2023-10-15", "categories": []}
{"id": "2210.10487", "title": "Estimating the Contamination Factor's Distribution in Unsupervised\n  Anomaly Detection", "abstract": "  Anomaly detection methods identify examples that do not follow the expected\nbehaviour, typically in an unsupervised fashion, by assigning real-valued\nanomaly scores to the examples based on various heuristics. These scores need\nto be transformed into actual predictions by thresholding, so that the\nproportion of examples marked as anomalies equals the expected proportion of\nanomalies, called contamination factor. Unfortunately, there are no good\nmethods for estimating the contamination factor itself. We address this need\nfrom a Bayesian perspective, introducing a method for estimating the posterior\ndistribution of the contamination factor of a given unlabeled dataset. We\nleverage on outputs of several anomaly detectors as a representation that\nalready captures the basic notion of anomalousness and estimate the\ncontamination using a specific mixture formulation. Empirically on 22 datasets,\nwe show that the estimated distribution is well-calibrated and that setting the\nthreshold using the posterior mean improves the anomaly detectors' performance\nover several alternative methods. All code is publicly available for full\nreproducibility.\n", "date": "2022-10-19", "categories": []}
{"id": "2305.13189", "title": "Unsupervised Anomaly Detection with Rejection", "abstract": "  Anomaly detection aims at detecting unexpected behaviours in the data.\nBecause anomaly detection is usually an unsupervised task, traditional anomaly\ndetectors learn a decision boundary by employing heuristics based on\nintuitions, which are hard to verify in practice. This introduces some\nuncertainty, especially close to the decision boundary, that may reduce the\nuser trust in the detector's predictions. A way to combat this is by allowing\nthe detector to reject examples with high uncertainty (Learning to Reject).\nThis requires employing a confidence metric that captures the distance to the\ndecision boundary and setting a rejection threshold to reject low-confidence\npredictions. However, selecting a proper metric and setting the rejection\nthreshold without labels are challenging tasks. In this paper, we solve these\nchallenges by setting a constant rejection threshold on the stability metric\ncomputed by ExCeeD. Our insight relies on a theoretical analysis of such a\nmetric. Moreover, setting a constant threshold results in strong guarantees: we\nestimate the test rejection rate, and derive a theoretical upper bound for both\nthe rejection rate and the expected prediction cost. Experimentally, we show\nthat our method outperforms some metric-based methods.\n", "date": "2023-05-22", "categories": []}
{"id": "2310.11169", "title": "MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time\n  Series Anomaly Detection", "abstract": "  Multimodal time series (MTS) anomaly detection is crucial for maintaining the\nsafety and stability of working devices (e.g., water treatment system and\nspacecraft), whose data are characterized by multivariate time series with\ndiverse modalities. Although recent deep learning methods show great potential\nin anomaly detection, they do not explicitly capture spatial-temporal\nrelationships between univariate time series of different modalities, resulting\nin more false negatives and false positives. In this paper, we propose a\nmultimodal spatial-temporal graph attention network (MST-GAT) to tackle this\nproblem. MST-GAT first employs a multimodal graph attention network (M-GAT) and\na temporal convolution network to capture the spatial-temporal correlation in\nmultimodal time series. Specifically, M-GAT uses a multi-head attention module\nand two relational attention modules (i.e., intra- and inter-modal attention)\nto model modal correlations explicitly. Furthermore, MST-GAT optimizes the\nreconstruction and prediction modules simultaneously. Experimental results on\nfour multimodal benchmarks demonstrate that MST-GAT outperforms the\nstate-of-the-art baselines. Further analysis indicates that MST-GAT strengthens\nthe interpretability of detected anomalies by locating the most anomalous\nunivariate time series.\n", "date": "2023-10-17", "categories": []}
{"id": "2303.04038", "title": "Root Cause Identification for Collective Anomalies in Time Series given\n  an Acyclic Summary Causal Graph with Loops", "abstract": "  This paper presents an approach for identifying the root causes of collective\nanomalies given observational time series and an acyclic summary causal graph\nwhich depicts an abstraction of causal relations present in a dynamic system at\nits normal regime. The paper first shows how the problem of root cause\nidentification can be divided into many independent subproblems by grouping\nrelated anomalies using d-separation. Further, it shows how, under this\nsetting, some root causes can be found directly from the graph and from the\ntime of appearance of anomalies. Finally, it shows, how the rest of the root\ncauses can be found by comparing direct effects in the normal and in the\nanomalous regime. To this end, an adjustment set for identifying direct effects\nis introduced. Extensive experiments conducted on both simulated and real-world\ndatasets demonstrate the effectiveness of the proposed method.\n", "date": "2023-03-07", "categories": []}
{"id": "2308.07774", "title": "A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection", "abstract": "  A key component of many graph neural networks (GNNs) is the pooling\noperation, which seeks to reduce the size of a graph while preserving important\nstructural information. However, most existing graph pooling strategies rely on\nan assignment matrix obtained by employing a GNN layer, which is characterized\nby trainable parameters, often leading to significant computational complexity\nand a lack of interpretability in the pooling process. In this paper, we\npropose an unsupervised graph encoder-decoder model to detect abnormal nodes\nfrom graphs by learning an anomaly scoring function to rank nodes based on\ntheir degree of abnormality. In the encoding stage, we design a novel pooling\nmechanism, named LCPool, which leverages locality-constrained linear coding for\nfeature encoding to find a cluster assignment matrix by solving a least-squares\noptimization problem with a locality regularization term. By enforcing locality\nconstraints during the coding process, LCPool is designed to be free from\nlearnable parameters, capable of efficiently handling large graphs, and can\neffectively generate a coarser graph representation while retaining the most\nsignificant structural characteristics of the graph. In the decoding stage, we\npropose an unpooling operation, called LCUnpool, to reconstruct both the\nstructure and nodal features of the original graph. We conduct empirical\nevaluations of our method on six benchmark datasets using several evaluation\nmetrics, and the results demonstrate its superiority over state-of-the-art\nanomaly detection approaches.\n", "date": "2023-08-15", "categories": []}
{"id": "2308.08696", "title": "Improving Anomaly Segmentation with Multi-Granularity Cross-Domain\n  Alignment", "abstract": "  Anomaly segmentation plays a pivotal role in identifying atypical objects in\nimages, crucial for hazard detection in autonomous driving systems. While\nexisting methods demonstrate noteworthy results on synthetic data, they often\nfail to consider the disparity between synthetic and real-world data domains.\nAddressing this gap, we introduce the Multi-Granularity Cross-Domain Alignment\n(MGCDA) framework, tailored to harmonize features across domains at both the\nscene and individual sample levels. Our contributions are twofold: i) We\npresent the Multi-source Domain Adversarial Training module. This integrates a\nmulti-source adversarial loss coupled with dynamic label smoothing,\nfacilitating the learning of domain-agnostic representations across multiple\nprocessing stages. ii) We propose an innovative Cross-domain Anomaly-aware\nContrastive Learning methodology.} This method adeptly selects challenging\nanchor points and images using an anomaly-centric strategy, ensuring precise\nalignment at the sample level. Extensive evaluations of the Fishyscapes and\nRoadAnomaly datasets demonstrate MGCDA's superior performance and adaptability.\nAdditionally, its ability to perform parameter-free inference and function with\nvarious network architectures highlights its distinctiveness in advancing the\nfrontier of anomaly segmentation.\n", "date": "2023-08-16", "categories": []}
{"id": "2306.15489", "title": "Precursor-of-Anomaly Detection for Irregular Time Series", "abstract": "  Anomaly detection is an important field that aims to identify unexpected\npatterns or data points, and it is closely related to many real-world problems,\nparticularly to applications in finance, manufacturing, cyber security, and so\non. While anomaly detection has been studied extensively in various fields,\ndetecting future anomalies before they occur remains an unexplored territory.\nIn this paper, we present a novel type of anomaly detection, called\nPrecursor-of-Anomaly (PoA) detection. Unlike conventional anomaly detection,\nwhich focuses on determining whether a given time series observation is an\nanomaly or not, PoA detection aims to detect future anomalies before they\nhappen. To solve both problems at the same time, we present a neural controlled\ndifferential equation-based neural network and its multi-task learning\nalgorithm. We conduct experiments using 17 baselines and 3 datasets, including\nregular and irregular time series, and demonstrate that our presented method\noutperforms the baselines in almost all cases. Our ablation studies also\nindicate that the multitasking training method significantly enhances the\noverall performance for both anomaly and PoA detection.\n", "date": "2023-06-27", "categories": []}
{"id": "2310.08654", "title": "Histogram- and Diffusion-Based Medical Out-of-Distribution Detection", "abstract": "  Out-of-distribution (OOD) detection is crucial for the safety and reliability\nof artificial intelligence algorithms, especially in the medical domain. In the\ncontext of the Medical OOD (MOOD) detection challenge 2023, we propose a\npipeline that combines a histogram-based method and a diffusion-based method.\nThe histogram-based method is designed to accurately detect homogeneous\nanomalies in the toy examples of the challenge, such as blobs with constant\nintensity values. The diffusion-based method is based on one of the latest\nmethods for unsupervised anomaly detection, called DDPM-OOD. We explore this\nmethod and propose extensive post-processing steps for pixel-level and\nsample-level anomaly detection on brain MRI and abdominal CT data provided by\nthe challenge. Our results show that the proposed DDPM method is sensitive to\nblur and bias field samples, but faces challenges with anatomical deformation,\nblack slice, and swapped patches. These findings suggest that further research\nis needed to improve the performance of DDPM for OOD detection in medical\nimages.\n", "date": "2023-10-12", "categories": []}
{"id": "2310.08737", "title": "Real-Time Event Detection with Random Forests and Temporal Convolutional\n  Networks for More Sustainable Petroleum Industry", "abstract": "  The petroleum industry is crucial for modern society, but the production\nprocess is complex and risky. During the production, accidents or failures,\nresulting from undesired production events, can cause severe environmental and\neconomic damage. Previous studies have investigated machine learning (ML)\nmethods for undesired event detection. However, the prediction of event\nprobability in real-time was insufficiently addressed, which is essential since\nit is important to undertake early intervention when an event is expected to\nhappen. This paper proposes two ML approaches, random forests and temporal\nconvolutional networks, to detect undesired events in real-time. Results show\nthat our approaches can effectively classify event types and predict the\nprobability of their appearance, addressing the challenges uncovered in\nprevious studies and providing a more effective solution for failure event\nmanagement during the production.\n", "date": "2023-10-12", "categories": []}
{"id": "2301.12082", "title": "Pushing the Limits of Fewshot Anomaly Detection in Industry Vision:\n  Graphcore", "abstract": "  In the area of fewshot anomaly detection (FSAD), efficient visual feature\nplays an essential role in memory bank M-based methods. However, these methods\ndo not account for the relationship between the visual feature and its rotated\nvisual feature, drastically limiting the anomaly detection performance. To push\nthe limits, we reveal that rotation-invariant feature property has a\nsignificant impact in industrial-based FSAD. Specifically, we utilize graph\nrepresentation in FSAD and provide a novel visual isometric invariant feature\n(VIIF) as anomaly measurement feature. As a result, VIIF can robustly improve\nthe anomaly discriminating ability and can further reduce the size of redundant\nfeatures stored in M by a large amount. Besides, we provide a novel model\nGraphCore via VIIFs that can fast implement unsupervised FSAD training and can\nimprove the performance of anomaly detection. A comprehensive evaluation is\nprovided for comparing GraphCore and other SOTA anomaly detection models under\nour proposed fewshot anomaly detection setting, which shows GraphCore can\nincrease average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%,\n22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.\n", "date": "2023-01-28", "categories": []}
{"id": "2305.17382", "title": "APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation\n  Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on\n  Zero-shot AD and 4th Place on Few-shot AD", "abstract": "  In this technical report, we briefly introduce our solution for the\nZero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023\nChallenge. For industrial visual inspection, building a single model that can\nbe rapidly adapted to numerous categories without or with only a few normal\nreference images is a promising research direction. This is primarily because\nof the vast variety of the product types. For the zero-shot track, we propose a\nsolution based on the CLIP model by adding extra linear layers. These layers\nare used to map the image features to the joint embedding space, so that they\ncan compare with the text features to generate the anomaly maps. Besides, when\nthe reference images are available, we utilize multiple memory banks to store\ntheir features and compare them with the features of the test images during the\ntesting phase. In this challenge, our method achieved first place in the\nzero-shot track, especially excelling in segmentation with an impressive F1\nscore improvement of 0.0489 over the second-ranked participant. Furthermore, in\nthe few-shot track, we secured the fourth position overall, with our\nclassification F1 score of 0.8687 ranking first among all participating teams.\n", "date": "2023-05-27", "categories": []}
{"id": "2306.10347", "title": "DCdetector: Dual Attention Contrastive Representation Learning for Time\n  Series Anomaly Detection", "abstract": "  Time series anomaly detection is critical for a wide range of applications.\nIt aims to identify deviant samples from the normal sample distribution in time\nseries. The most fundamental challenge for this task is to learn a\nrepresentation map that enables effective discrimination of anomalies.\nReconstruction-based methods still dominate, but the representation learning\nwith anomalies might hurt the performance with its large abnormal loss. On the\nother hand, contrastive learning aims to find a representation that can clearly\ndistinguish any instance from the others, which can bring a more natural and\npromising representation for time series anomaly detection. In this paper, we\npropose DCdetector, a multi-scale dual attention contrastive representation\nlearning model. DCdetector utilizes a novel dual attention asymmetric design to\ncreate the permutated environment and pure contrastive loss to guide the\nlearning process, thus learning a permutation invariant representation with\nsuperior discrimination abilities. Extensive experiments show that DCdetector\nachieves state-of-the-art results on multiple time series anomaly detection\nbenchmark datasets. Code is publicly available at\nhttps://github.com/DAMO-DI-ML/KDD2023-DCdetector.\n", "date": "2023-06-17", "categories": []}
{"id": "2310.07585", "title": "A Discrepancy Aware Framework for Robust Anomaly Detection", "abstract": "  Defect detection is a critical research area in artificial intelligence.\nRecently, synthetic data-based self-supervised learning has shown great\npotential on this task. Although many sophisticated synthesizing strategies\nexist, little research has been done to investigate the robustness of models\nwhen faced with different strategies. In this paper, we focus on this issue and\nfind that existing methods are highly sensitive to them. To alleviate this\nissue, we present a Discrepancy Aware Framework (DAF), which demonstrates\nrobust performance consistently with simple and cheap strategies across\ndifferent anomaly detection benchmarks. We hypothesize that the high\nsensitivity to synthetic data of existing self-supervised methods arises from\ntheir heavy reliance on the visual appearance of synthetic data during\ndecoding. In contrast, our method leverages an appearance-agnostic cue to guide\nthe decoder in identifying defects, thereby alleviating its reliance on\nsynthetic appearance. To this end, inspired by existing knowledge distillation\nmethods, we employ a teacher-student network, which is trained based on\nsynthesized outliers, to compute the discrepancy map as the cue. Extensive\nexperiments on two challenging datasets prove the robustness of our method.\nUnder the simple synthesis strategies, it outperforms existing methods by a\nlarge margin. Furthermore, it also achieves the state-of-the-art localization\nperformance. Code is available at: https://github.com/caiyuxuan1120/DAF.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.07716", "title": "PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection", "abstract": "  Object anomaly detection is an important problem in the field of machine\nvision and has seen remarkable progress recently. However, two significant\nchallenges hinder its research and application. First, existing datasets lack\ncomprehensive visual information from various pose angles. They usually have an\nunrealistic assumption that the anomaly-free training dataset is pose-aligned,\nand the testing samples have the same pose as the training data. However, in\npractice, anomaly may exist in any regions on a object, the training and query\nsamples may have different poses, calling for the study on pose-agnostic\nanomaly detection. Second, the absence of a consensus on experimental protocols\nfor pose-agnostic anomaly detection leads to unfair comparisons of different\nmethods, hindering the research on pose-agnostic anomaly detection. To address\nthese issues, we develop Multi-pose Anomaly Detection (MAD) dataset and\nPose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to\naddress the pose-agnostic anomaly detection problem. Specifically, we build MAD\nusing 20 complex-shaped LEGO toys including 4K views with various poses, and\nhigh-quality and diverse 3D anomalies in both simulated and real environments.\nAdditionally, we propose a novel method OmniposeAD, trained using MAD,\nspecifically designed for pose-agnostic anomaly detection. Through\ncomprehensive evaluations, we demonstrate the relevance of our dataset and\nmethod. Furthermore, we provide an open-source benchmark library, including\ndataset and baseline methods that cover 8 anomaly detection paradigms, to\nfacilitate future research and application in this domain. Code, data, and\nmodels are publicly available at https://github.com/EricLee0224/PAD.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.06047", "title": "Knowledge Distillation for Anomaly Detection", "abstract": "  Unsupervised deep learning techniques are widely used to identify anomalous\nbehaviour. The performance of such methods is a product of the amount of\ntraining data and the model size. However, the size is often a limiting factor\nfor the deployment on resource-constrained devices. We present a novel\nprocedure based on knowledge distillation for compressing an unsupervised\nanomaly detection model into a supervised deployable one and we suggest a set\nof techniques to improve the detection sensitivity. Compressed models perform\ncomparably to their larger counterparts while significantly reducing the size\nand memory footprint.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.06261", "title": "Self-Discriminative Modeling for Anomalous Graph Detection", "abstract": "  This paper studies the problem of detecting anomalous graphs using a machine\nlearning model trained on only normal graphs, which has many applications in\nmolecule, biology, and social network data analysis. We present a\nself-discriminative modeling framework for anomalous graph detection. The key\nidea, mathematically and numerically illustrated, is to learn a discriminator\n(classifier) from the given normal graphs together with pseudo-anomalous graphs\ngenerated by a model jointly trained, where we never use any true anomalous\ngraphs and we hope that the generated pseudo-anomalous graphs interpolate\nbetween normal ones and (real) anomalous ones. Under the framework, we provide\nthree algorithms with different computational efficiencies and stabilities for\nanomalous graph detection. The three algorithms are compared with several\nstate-of-the-art graph-level anomaly detection baselines on nine popular graph\ndatasets (four with small size and five with moderate size) and show\nsignificant improvement in terms of AUC. The success of our algorithms stems\nfrom the integration of the discriminative classifier and the well-posed\npseudo-anomalous graphs, which provide new insights for anomaly detection.\nMoreover, we investigate our algorithms for large-scale imbalanced graph\ndatasets. Surprisingly, our algorithms, though fully unsupervised, are able to\nsignificantly outperform supervised learning algorithms of anomalous graph\ndetection. The corresponding reason is also analyzed.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.06364", "title": "Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup\n  Anomalous Sound Detection", "abstract": "  Unsupervised anomalous sound detection (ASD) aims to identify anomalous\nsounds by learning the features of normal operational sounds and sensing their\ndeviations. Recent approaches have focused on the self-supervised task\nutilizing the classification of normal data, and advanced models have shown\nthat securing representation space for anomalous data is important through\nrepresentation learning yielding compact intra-class and well-separated\nintra-class distributions. However, we show that conventional approaches often\nfail to ensure sufficient intra-class compactness and exhibit angular disparity\nbetween samples and their corresponding centers. In this paper, we propose a\ntraining technique aimed at ensuring intra-class compactness and increasing the\nangle gap between normal and abnormal samples. Furthermore, we present an\narchitecture that extracts features for important temporal regions, enabling\nthe model to learn which time frames should be emphasized or suppressed.\nExperimental results demonstrate that the proposed method achieves the best\nperformance giving 0.90%, 0.83%, and 2.16% improvement in terms of AUC, pAUC,\nand mAUC, respectively, compared to the state-of-the-art method on DCASE 2020\nChallenge Task2 dataset.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.06420", "title": "AnoDODE: Anomaly Detection with Diffusion ODE", "abstract": "  Anomaly detection is the process of identifying atypical data samples that\nsignificantly deviate from the majority of the dataset. In the realm of\nclinical screening and diagnosis, detecting abnormalities in medical images\nholds great importance. Typically, clinical practice provides access to a vast\ncollection of normal images, while abnormal images are relatively scarce. We\nhypothesize that abnormal images and their associated features tend to manifest\nin low-density regions of the data distribution. Following this assumption, we\nturn to diffusion ODEs for unsupervised anomaly detection, given their\ntractability and superior performance in density estimation tasks. More\nprecisely, we propose a new anomaly detection method based on diffusion ODEs by\nestimating the density of features extracted from multi-scale medical images.\nOur anomaly scoring mechanism depends on computing the negative log-likelihood\nof features extracted from medical images at different scales, quantified in\nbits per dimension. Furthermore, we propose a reconstruction-based anomaly\nlocalization suitable for our method. Our proposed method not only identifie\nanomalies but also provides interpretability at both the image and pixel\nlevels. Through experiments on the BraTS2021 medical dataset, our proposed\nmethod outperforms existing methods. These results confirm the effectiveness\nand robustness of our method.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.06656", "title": "Assessing the Impact of a Supervised Classification Filter on Flow-based\n  Hybrid Network Anomaly Detection", "abstract": "  Constant evolution and the emergence of new cyberattacks require the\ndevelopment of advanced techniques for defense. This paper aims to measure the\nimpact of a supervised filter (classifier) in network anomaly detection. We\nperform our experiments by employing a hybrid anomaly detection approach in\nnetwork flow data. For this purpose, we extended a state-of-the-art\nautoencoder-based anomaly detection method by prepending a binary classifier\nacting as a prefilter for the anomaly detector. The method was evaluated on the\npublicly available real-world dataset UGR'16. Our empirical results indicate\nthat the hybrid approach does offer a higher detection rate of known attacks\nthan a standalone anomaly detector while still retaining the ability to detect\nzero-day attacks. Employing a supervised binary prefilter has increased the AUC\nmetric by over 11%, detecting 30% more attacks while keeping the number of\nfalse positives approximately the same.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.06779", "title": "A Supervised Embedding and Clustering Anomaly Detection method for\n  classification of Mobile Network Faults", "abstract": "  The paper introduces Supervised Embedding and Clustering Anomaly Detection\n(SEMC-AD), a method designed to efficiently identify faulty alarm logs in a\nmobile network and alleviate the challenges of manual monitoring caused by the\ngrowing volume of alarm logs. SEMC-AD employs a supervised embedding approach\nbased on deep neural networks, utilizing historical alarm logs and their labels\nto extract numerical representations for each log, effectively addressing the\nissue of imbalanced classification due to a small proportion of anomalies in\nthe dataset without employing one-hot encoding. The robustness of the embedding\nis evaluated by plotting the two most significant principle components of the\nembedded alarm logs, revealing that anomalies form distinct clusters with\nsimilar embeddings. Multivariate normal Gaussian clustering is then applied to\nthese components, identifying clusters with a high ratio of anomalies to normal\nalarms (above 90%) and labeling them as the anomaly group. To classify new\nalarm logs, we check if their embedded vectors' two most significant principle\ncomponents fall within the anomaly-labeled clusters. If so, the log is\nclassified as an anomaly. Performance evaluation demonstrates that SEMC-AD\noutperforms conventional random forest and gradient boosting methods without\nembedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and\nXGBoost only detect 86% and 81% of anomalies, respectively. While supervised\nclassification methods may excel in labeled datasets, the results demonstrate\nthat SEMC-AD is more efficient in classifying anomalies in datasets with\nnumerous categorical features, significantly enhancing anomaly detection,\nreducing operator burden, and improving network maintenance.\n", "date": "2023-10-10", "categories": []}
{"id": "2208.04173", "title": "SIAD: Self-supervised Image Anomaly Detection System", "abstract": "  Recent trends in AIGC effectively boosted the application of visual\ninspection. However, most of the available systems work in a human-in-the-loop\nmanner and can not provide long-term support to the online application. To make\na step forward, this paper outlines an automatic annotation system called SsaA,\nworking in a self-supervised learning manner, for continuously making the\nonline visual inspection in the manufacturing automation scenarios. Benefit\nfrom the self-supervised learning, SsaA is effective to establish a visual\ninspection application for the whole life-cycle of manufacturing. In the early\nstage, with only the anomaly-free data, the unsupervised algorithms are adopted\nto process the pretext task and generate coarse labels for the following data.\nThen supervised algorithms are trained for the downstream task. With\nuser-friendly web-based interfaces, SsaA is very convenient to integrate and\ndeploy both of the unsupervised and supervised algorithms. So far, the SsaA\nsystem has been adopted for some real-life industrial applications.\n", "date": "2022-08-08", "categories": []}
{"id": "2303.03324", "title": "Time series anomaly detection with reconstruction-based state-space\n  models", "abstract": "  Recent advances in digitization have led to the availability of multivariate\ntime series data in various domains, enabling real-time monitoring of\noperations. Identifying abnormal data patterns and detecting potential failures\nin these scenarios are important yet rather challenging. In this work, we\npropose a novel unsupervised anomaly detection method for time series data. The\nproposed framework jointly learns the observation model and the dynamic model,\nand model uncertainty is estimated from normal samples. Specifically, a long\nshort-term memory (LSTM)-based encoder-decoder is adopted to represent the\nmapping between the observation space and the latent space. Bidirectional\ntransitions of states are simultaneously modeled by leveraging backward and\nforward temporal information. Regularization of the latent space places\nconstraints on the states of normal samples, and Mahalanobis distance is used\nto evaluate the abnormality level. Empirical studies on synthetic and\nreal-world datasets demonstrate the superior performance of the proposed method\nin anomaly detection tasks.\n", "date": "2023-03-06", "categories": []}
{"id": "2310.04367", "title": "A Marketplace Price Anomaly Detection System at Scale", "abstract": "  Online marketplaces execute large volume of price updates that are initiated\nby individual marketplace sellers each day on the platform. This price\ndemocratization comes with increasing challenges with data quality. Lack of\ncentralized guardrails that are available for a traditional online retailer\ncauses a higher likelihood for inaccurate prices to get published on the\nwebsite, leading to poor customer experience and potential for revenue loss. We\npresent MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling\nand Unsupervised Statistical-features), a scalable price anomaly detection\nframework for a growing marketplace platform. The goal is to leverage proximity\nand historical price trends from unsupervised statistical features to generate\nan upper price bound. We build an ensemble of models to detect irregularities\nin price-based features, exclude irregular features and use optimized weighting\nscheme to build a reliable price bound in real-time pricing pipeline. We\nobserved that our approach improves precise anchor coverage by up to 46.6% in\nhigh-vulnerability item subsets\n", "date": "2023-10-06", "categories": []}
{"id": "2310.04686", "title": "Tight Rates in Supervised Outlier Transfer Learning", "abstract": "  A critical barrier to learning an accurate decision rule for outlier\ndetection is the scarcity of outlier data. As such, practitioners often turn to\nthe use of similar but imperfect outlier data from which they might transfer\ninformation to the target outlier detection task. Despite the recent empirical\nsuccess of transfer learning approaches in outlier detection, a fundamental\nunderstanding of when and how knowledge can be transferred from a source to a\ntarget outlier detection task remains elusive. In this work, we adopt the\ntraditional framework of Neyman-Pearson classification -- which formalizes\nsupervised outlier detection -- with the added assumption that one has access\nto some related but imperfect outlier data. Our main results are as follows:\n  We first determine the information-theoretic limits of the problem under a\nmeasure of discrepancy that extends some existing notions from traditional\nbalanced classification; interestingly, unlike in balanced classification,\nseemingly very dissimilar sources can provide much information about a target,\nthus resulting in fast transfer.\n  We then show that, in principle, these information-theoretic limits are\nachievable by adaptive procedures, i.e., procedures with no a priori\ninformation on the discrepancy between source and target outlier distributions.\n", "date": "2023-10-07", "categories": []}
{"id": "2310.04688", "title": "PatchProto Networks for Few-shot Visual Anomaly Classification", "abstract": "  The visual anomaly diagnosis can automatically analyze the defective\nproducts, which has been widely applied in industrial quality inspection. The\nanomaly classification can classify the defective products into different\ncategories. However, the anomaly samples are hard to access in practice, which\nimpedes the training of canonical machine learning models. This paper studies a\npractical issue that anomaly samples for training are extremely scarce, i.e.,\nfew-shot learning (FSL). Utilizing the sufficient normal samples, we propose\nPatchProto networks for few-shot anomaly classification. Different from\nclassical FSL methods, PatchProto networks only extract CNN features of\ndefective regions of interest, which serves as the prototypes for few-shot\nlearning. Compared with basic few-shot classifier, the experiment results on\nMVTec-AD dataset show PatchProto networks significantly improve the few-shot\nanomaly classification accuracy.\n", "date": "2023-10-07", "categories": []}
{"id": "2310.04701", "title": "Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning\n  for Microservice System", "abstract": "  Microservice architecture has sprung up over recent years for managing\nenterprise applications, due to its ability to independently deploy and scale\nservices. Despite its benefits, ensuring the reliability and safety of a\nmicroservice system remains highly challenging. Existing anomaly detection\nalgorithms based on a single data modality (i.e., metrics, logs, or traces)\nfail to fully account for the complex correlations and interactions between\ndifferent modalities, leading to false negatives and false alarms, whereas\nincorporating more data modalities can offer opportunities for further\nperformance gain. As a fresh attempt, we propose in this paper a\nsemi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly\nintegrates all available data modalities via attentive multi-modal learning.\nFirst, we extract and normalize features from the three modalities, and further\nintegrate them using a graph, namely MST (microservice system twin) graph,\nwhere each node represents a service instance and the edge indicates the\nscheduling relationship between different service instances. The MST graph\nprovides a virtual representation of the status and scheduling relationships\namong service instances of a real-world microservice system. Second, we\nconstruct a transformer-based neural network with both spatial and temporal\nattention mechanisms to model the inter-correlations between different\nmodalities and temporal dependencies between the data points. This enables us\nto detect anomalies automatically and accurately in real-time. The source code\nof MSTGAD is publicly available at\nhttps://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection.\n", "date": "2023-10-07", "categories": []}
{"id": "2310.05307", "title": "Successive Data Injection in Conditional Quantum GAN Applied to Time\n  Series Anomaly Detection", "abstract": "  Classical GAN architectures have shown interesting results for solving\nanomaly detection problems in general and for time series anomalies in\nparticular, such as those arising in communication networks. In recent years,\nseveral quantum GAN architectures have been proposed in the literature. When\ndetecting anomalies in time series using QGANs, huge challenges arise due to\nthe limited number of qubits compared to the size of the data. To address these\nchallenges, we propose a new high-dimensional encoding approach, named\nSuccessive Data Injection (SuDaI). In this approach, we explore a larger\nportion of the quantum state than that in the conventional angle encoding, the\nmethod used predominantly in the literature, through repeated data injections\ninto the quantum state. SuDaI encoding allows us to adapt the QGAN for anomaly\ndetection with network data of a much higher dimensionality than with the\nexisting known QGANs implementations. In addition, SuDaI encoding applies to\nother types of high-dimensional time series and can be used in contexts beyond\nanomaly detection and QGANs, opening up therefore multiple fields of\napplication.\n", "date": "2023-10-08", "categories": []}
{"id": "2310.05848", "title": "FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior\n  knowledge", "abstract": "  Detecting anomalies in electrocardiogram data is crucial to identifying\ndeviations from normal heartbeat patterns and providing timely intervention to\nat-risk patients. Various AutoEncoder models (AE) have been proposed to tackle\nthe anomaly detection task with ML. However, these models do not consider the\nspecific patterns of ECG leads and are unexplainable black boxes. In contrast,\nwe replace the decoding part of the AE with a reconstruction head (namely,\nFMM-Head) based on prior knowledge of the ECG shape. Our model consistently\nachieves higher anomaly detection capabilities than state-of-the-art models, up\nto 0.31 increase in area under the ROC curve (AUROC), with as little as half\nthe original model size and explainable extracted features. The processing time\nof our model is four orders of magnitude lower than solving an optimization\nproblem to obtain the same parameters, thus making it suitable for real-time\nECG parameters extraction and anomaly detection.\n", "date": "2023-10-06", "categories": []}
{"id": "2209.12148", "title": "Self-Supervised Masked Convolutional Transformer Block for Anomaly\n  Detection", "abstract": "  Anomaly detection has recently gained increasing attention in the field of\ncomputer vision, likely due to its broad set of applications ranging from\nproduct fault detection on industrial production lines and impending event\ndetection in video surveillance to finding lesions in medical scans. Regardless\nof the domain, anomaly detection is typically framed as a one-class\nclassification task, where the learning is conducted on normal examples only.\nAn entire family of successful anomaly detection methods is based on learning\nto reconstruct masked normal inputs (e.g. patches, future frames, etc.) and\nexerting the magnitude of the reconstruction error as an indicator for the\nabnormality level. Unlike other reconstruction-based methods, we present a\nnovel self-supervised masked convolutional transformer block (SSMCTB) that\ncomprises the reconstruction-based functionality at a core architectural level.\nThe proposed self-supervised block is extremely flexible, enabling information\nmasking at any layer of a neural network and being compatible with a wide range\nof neural architectures. In this work, we extend our previous self-supervised\npredictive convolutional attentive block (SSPCAB) with a 3D masked\nconvolutional layer, a transformer for channel-wise attention, as well as a\nnovel self-supervised objective based on Huber loss. Furthermore, we show that\nour block is applicable to a wider variety of tasks, adding anomaly detection\nin medical images and thermal videos to the previously considered tasks based\non RGB images and surveillance videos. We exhibit the generality and\nflexibility of SSMCTB by integrating it into multiple state-of-the-art neural\nmodels for anomaly detection, bringing forth empirical results that confirm\nconsiderable performance improvements on five benchmarks. We release our code\nand data as open source at: https://github.com/ristea/ssmctb.\n", "date": "2022-09-25", "categories": []}
{"id": "2310.02280", "title": "Expert enhanced dynamic time warping based anomaly detection", "abstract": "  Dynamic time warping (DTW) is a well-known algorithm for time series elastic\ndissimilarity measure. Its ability to deal with non-linear time distortions\nmakes it helpful in variety of data mining tasks. Such a task is also anomaly\ndetection which attempts to reveal unexpected behaviour without false detection\nalarms. In this paper, we propose a novel anomaly detection method named Expert\nenhanced dynamic time warping anomaly detection (E-DTWA). It is based on DTW\nwith additional enhancements involving human-in-the-loop concept. The main\nbenefits of our approach comprise efficient detection, flexible retraining\nbased on strong consideration of the expert's detection feedback while\nretaining low computational and space complexity.\n", "date": "2023-10-02", "categories": []}
{"id": "2310.02821", "title": "Improving Vision Anomaly Detection with the Guidance of Language\n  Modality", "abstract": "  Recent years have seen a surge of interest in anomaly detection for tackling\nindustrial defect detection, event detection, etc. However, existing\nunsupervised anomaly detectors, particularly those for the vision modality,\nface significant challenges due to redundant information and sparse latent\nspace. Conversely, the language modality performs well due to its relatively\nsingle data. This paper tackles the aforementioned challenges for vision\nmodality from a multimodal point of view. Specifically, we propose Cross-modal\nGuidance (CMG), which consists of Cross-modal Entropy Reduction (CMER) and\nCross-modal Linear Embedding (CMLE), to tackle the redundant information issue\nand sparse space issue, respectively. CMER masks parts of the raw image and\ncomputes the matching score with the text. Then, CMER discards irrelevant\npixels to make the detector focus on critical contents. To learn a more compact\nlatent space for the vision anomaly detector, CMLE learns a correlation\nstructure matrix from the language modality, and then the latent space of\nvision modality will be learned with the guidance of the matrix. Thereafter,\nthe vision latent space will get semantically similar images closer. Extensive\nexperiments demonstrate the effectiveness of the proposed methods.\nParticularly, CMG outperforms the baseline that only uses images by 16.81%.\nAblation experiments further confirm the synergy among the proposed methods, as\neach component depends on the other to achieve optimal performance.\n", "date": "2023-10-04", "categories": []}
{"id": "2310.02835", "title": "Delving into CLIP latent space for Video Anomaly Recognition", "abstract": "  We tackle the complex problem of detecting and recognising anomalies in\nsurveillance videos at the frame level, utilising only video-level supervision.\nWe introduce the novel method AnomalyCLIP, the first to combine Large Language\nand Vision (LLV) models, such as CLIP, with multiple instance learning for\njoint video anomaly detection and classification. Our approach specifically\ninvolves manipulating the latent CLIP feature space to identify the normal\nevent subspace, which in turn allows us to effectively learn text-driven\ndirections for abnormal events. When anomalous frames are projected onto these\ndirections, they exhibit a large feature magnitude if they belong to a\nparticular class. We also introduce a computationally efficient Transformer\narchitecture to model short- and long-term temporal dependencies between\nframes, ultimately producing the final anomaly score and class prediction\nprobabilities. We compare AnomalyCLIP against state-of-the-art methods\nconsidering three major anomaly detection benchmarks, i.e. ShanghaiTech,\nUCF-Crime, and XD-Violence, and empirically show that it outperforms baselines\nin recognising video anomalies.\n", "date": "2023-10-04", "categories": []}
{"id": "2309.14622", "title": "Divide and Conquer in Video Anomaly Detection: A Comprehensive Review\n  and New Approach", "abstract": "  Video anomaly detection is a complex task, and the principle of \"divide and\nconquer\" is often regarded as an effective approach to tackling intricate\nissues. It's noteworthy that recent methods in video anomaly detection have\nrevealed the application of the divide and conquer philosophy (albeit with\ndistinct perspectives from traditional usage), yielding impressive outcomes.\nThis paper systematically reviews these literatures from six dimensions, aiming\nto enhance the use of the divide and conquer strategy in video anomaly\ndetection. Furthermore, based on the insights gained from this review, a novel\napproach is presented, which integrates human skeletal frameworks with video\ndata analysis techniques. This method achieves state-of-the-art performance on\nthe ShanghaiTech dataset, surpassing all existing advanced methods.\n", "date": "2023-09-26", "categories": []}
{"id": "2310.01904", "title": "Beyond the Benchmark: Detecting Diverse Anomalies in Videos", "abstract": "  Video Anomaly Detection (VAD) plays a crucial role in modern surveillance\nsystems, aiming to identify various anomalies in real-world situations.\nHowever, current benchmark datasets predominantly emphasize simple,\nsingle-frame anomalies such as novel object detection. This narrow focus\nrestricts the advancement of VAD models. In this research, we advocate for an\nexpansion of VAD investigations to encompass intricate anomalies that extend\nbeyond conventional benchmark boundaries. To facilitate this, we introduce two\ndatasets, HMDB-AD and HMDB-Violence, to challenge models with diverse\naction-based anomalies. These datasets are derived from the HMDB51 action\nrecognition dataset. We further present Multi-Frame Anomaly Detection (MFAD), a\nnovel method built upon the AI-VAD framework. AI-VAD utilizes single-frame\nfeatures such as pose estimation and deep image encoding, and two-frame\nfeatures such as object velocity. They then apply a density estimation\nalgorithm to compute anomaly scores. To address complex multi-frame anomalies,\nwe add a deep video encoding features capturing long-range temporal\ndependencies, and logistic regression to enhance final score calculation.\nExperimental results confirm our assumptions, highlighting existing models\nlimitations with new anomaly types. MFAD excels in both simple and complex\nanomaly detection scenarios.\n", "date": "2023-10-03", "categories": []}
{"id": "2211.15255", "title": "ARISE: Graph Anomaly Detection on Attributed Networks via Substructure\n  Awareness", "abstract": "  Recently, graph anomaly detection on attributed networks has attracted\ngrowing attention in data mining and machine learning communities. Apart from\nattribute anomalies, graph anomaly detection also aims at suspicious\ntopological-abnormal nodes that exhibit collective anomalous behavior. Closely\nconnected uncorrelated node groups form uncommonly dense substructures in the\nnetwork. However, existing methods overlook that the topology anomaly detection\nperformance can be improved by recognizing such a collective pattern. To this\nend, we propose a new graph anomaly detection framework on attributed networks\nvia substructure awareness (ARISE for abbreviation). Unlike previous\nalgorithms, we focus on the substructures in the graph to discern\nabnormalities. Specifically, we establish a region proposal module to discover\nhigh-density substructures in the network as suspicious regions. The average\nnode-pair similarity can be regarded as the topology anomaly degree of nodes\nwithin substructures. Generally, the lower the similarity, the higher the\nprobability that internal nodes are topology anomalies. To distill better\nembeddings of node attributes, we further introduce a graph contrastive\nlearning scheme, which observes attribute anomalies in the meantime. In this\nway, ARISE can detect both topology and attribute anomalies. Ultimately,\nextensive experiments on benchmark datasets show that ARISE greatly improves\ndetection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to\nstate-of-the-art attributed networks anomaly detection (ANAD) algorithms.\n", "date": "2022-11-28", "categories": []}
{"id": "2303.13227", "title": "Confidence-Aware and Self-Supervised Image Anomaly Localisation", "abstract": "  Universal anomaly detection still remains a challenging problem in machine\nlearning and medical image analysis. It is possible to learn an expected\ndistribution from a single class of normative samples, e.g., through epistemic\nuncertainty estimates, auto-encoding models, or from synthetic anomalies in a\nself-supervised way. The performance of self-supervised anomaly detection\napproaches is still inferior compared to methods that use examples from known\nunknown classes to shape the decision boundary. However, outlier exposure\nmethods often do not identify unknown unknowns. Here we discuss an improved\nself-supervised single-class training strategy that supports the approximation\nof probabilistic inference with loosen feature locality constraints. We show\nthat up-scaling of gradients with histogram-equalised images is beneficial for\nrecently proposed self-supervision tasks. Our method is integrated into several\nout-of-distribution (OOD) detection models and we show evidence that our method\noutperforms the state-of-the-art on various benchmark datasets.\n", "date": "2023-03-23", "categories": []}
{"id": "2306.05032", "title": "Log-based Anomaly Detection based on EVT Theory with feedback", "abstract": "  System logs play a critical role in maintaining the reliability of software\nsystems. Fruitful studies have explored automatic log-based anomaly detection\nand achieved notable accuracy on benchmark datasets. However, when applied to\nlarge-scale cloud systems, these solutions face limitations due to high\nresource consumption and lack of adaptability to evolving logs. In this paper,\nwe present an accurate, lightweight, and adaptive log-based anomaly detection\nframework, referred to as SeaLog. Our method introduces a Trie-based Detection\nAgent (TDA) that employs a lightweight, dynamically-growing trie structure for\nreal-time anomaly detection. To enhance TDA's accuracy in response to evolving\nlog data, we enable it to receive feedback from experts. Interestingly, our\nfindings suggest that contemporary large language models, such as ChatGPT, can\nprovide feedback with a level of consistency comparable to human experts, which\ncan potentially reduce manual verification efforts. We extensively evaluate\nSeaLog on two public datasets and an industrial dataset. The results show that\nSeaLog outperforms all baseline methods in terms of effectiveness, runs 2X to\n10X faster and only consumes 5% to 41% of the memory resource.\n", "date": "2023-06-08", "categories": []}
{"id": "2309.06034", "title": "Normality Learning-based Graph Anomaly Detection via Multi-Scale\n  Contrastive Learning", "abstract": "  Graph anomaly detection (GAD) has attracted increasing attention in machine\nlearning and data mining. Recent works have mainly focused on how to capture\nricher information to improve the quality of node embeddings for GAD. Despite\ntheir significant advances in detection performance, there is still a relative\ndearth of research on the properties of the task. GAD aims to discern the\nanomalies that deviate from most nodes. However, the model is prone to learn\nthe pattern of normal samples which make up the majority of samples. Meanwhile,\nanomalies can be easily detected when their behaviors differ from normality.\nTherefore, the performance can be further improved by enhancing the ability to\nlearn the normal pattern. To this end, we propose a normality learning-based\nGAD framework via multi-scale contrastive learning networks (NLGAD for\nabbreviation). Specifically, we first initialize the model with the contrastive\nnetworks on different scales. To provide sufficient and reliable normal nodes\nfor normality learning, we design an effective hybrid strategy for normality\nselection. Finally, the model is refined with the only input of reliable normal\nnodes and learns a more accurate estimate of normality so that anomalous nodes\ncan be more easily distinguished. Eventually, extensive experiments on six\nbenchmark graph datasets demonstrate the effectiveness of our normality\nlearning-based scheme on GAD. Notably, the proposed algorithm improves the\ndetection performance (up to 5.89% AUC gain) compared with the state-of-the-art\nmethods. The source code is released at https://github.com/FelixDJC/NLGAD.\n", "date": "2023-09-12", "categories": []}
{"id": "2310.00144", "title": "Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable\n  Framework for Transaction Anomaly Detection in Ethereum Networks", "abstract": "  The rapid evolution of the Ethereum network necessitates sophisticated\ntechniques to ensure its robustness against potential threats and to maintain\ntransparency. While Graph Neural Networks (GNNs) have pioneered anomaly\ndetection in such platforms, capturing the intricacies of both spatial and\ntemporal transactional patterns has remained a challenge. This study presents a\nfusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW)\nenhanced by probabilistic sampling to bridge this gap. Our approach, unlike\ntraditional GCNs, leverages the strengths of TRW to discern complex temporal\nsequences in Ethereum transactions, thereby providing a more nuanced\ntransaction anomaly detection mechanism. Preliminary evaluations demonstrate\nthat our TRW-GCN framework substantially advances the performance metrics over\nconventional GCNs in detecting anomalies and transaction bursts. This research\nnot only underscores the potential of temporal cues in Ethereum transactional\ndata but also offers a scalable and effective methodology for ensuring the\nsecurity and transparency of decentralized platforms. By harnessing both\nspatial relationships and time-based transactional sequences as node features,\nour model introduces an additional layer of granularity, making the detection\nprocess more robust and less prone to false positives. This work lays the\nfoundation for future research aimed at optimizing and enhancing the\ntransparency of blockchain technologies, and serves as a testament to the\nsignificance of considering both time and space dimensions in the ever-evolving\nlandscape of the decentralized platforms.\n", "date": "2023-09-29", "categories": []}
{"id": "2310.00332", "title": "MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection", "abstract": "  Recently, the application of computer vision for anomaly detection has been\nunder attention in several industrial fields. An important example is oil\npipeline defect detection. Failure of one oil pipeline can interrupt the\noperation of the entire transportation system or cause a far-reaching failure.\nThe automated defect detection could significantly decrease the inspection time\nand the related costs. However, there is a gap in the related literature when\nit comes to dealing with this task. The existing studies do not sufficiently\ncover the research of the Magnetic Flux Leakage data and the preprocessing\ntechniques that allow overcoming the limitations set by the available data.\nThis work focuses on alleviating these issues. Moreover, in doing so, we\nexploited the recent convolutional neural network structures and proposed\nrobust approaches, aiming to acquire high performance considering the related\nmetrics. The proposed approaches and their applicability were verified using\nreal-world data.\n", "date": "2023-09-30", "categories": []}
{"id": "2310.00335", "title": "Anomaly Detection in Power Generation Plants with Generative Adversarial\n  Networks", "abstract": "  Anomaly detection is a critical task that involves the identification of data\npoints that deviate from a predefined pattern, useful for fraud detection and\nrelated activities. Various techniques are employed for anomaly detection, but\nrecent research indicates that deep learning methods, with their ability to\ndiscern intricate data patterns, are well-suited for this task. This study\nexplores the use of Generative Adversarial Networks (GANs) for anomaly\ndetection in power generation plants. The dataset used in this investigation\ncomprises fuel consumption records obtained from power generation plants\noperated by a telecommunications company. The data was initially collected in\nresponse to observed irregularities in the fuel consumption patterns of the\ngenerating sets situated at the company's base stations. The dataset was\ndivided into anomalous and normal data points based on specific variables, with\n64.88% classified as normal and 35.12% as anomalous. An analysis of feature\nimportance, employing the random forest classifier, revealed that Running Time\nPer Day exhibited the highest relative importance. A GANs model was trained and\nfine-tuned both with and without data augmentation, with the goal of increasing\nthe dataset size to enhance performance. The generator model consisted of five\ndense layers using the tanh activation function, while the discriminator\ncomprised six dense layers, each integrated with a dropout layer to prevent\noverfitting. Following data augmentation, the model achieved an accuracy rate\nof 98.99%, compared to 66.45% before augmentation. This demonstrates that the\nmodel nearly perfectly classified data points into normal and anomalous\ncategories, with the augmented data significantly enhancing the GANs'\nperformance in anomaly detection. Consequently, this study recommends the use\nof GANs, particularly when using large datasets, for effective anomaly\ndetection.\n", "date": "2023-09-30", "categories": []}
{"id": "2310.00952", "title": "LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space\n  Virtual Outlier Synthesis", "abstract": "  LiDAR-based 3D object detectors have achieved unprecedented speed and\naccuracy in autonomous driving applications. However, similar to other neural\nnetworks, they are often biased toward high-confidence predictions or return\ndetections where no real object is present. These types of detections can lead\nto a less reliable environment perception, severely affecting the functionality\nand safety of autonomous vehicles. We address this problem by proposing LS-VOS,\na framework for identifying outliers in 3D object detections. Our approach\nbuilds on the idea of Virtual Outlier Synthesis (VOS), which incorporates\noutlier knowledge during training, enabling the model to learn more compact\ndecision boundaries. In particular, we propose a new synthesis approach that\nrelies on the latent space of an auto-encoder network to generate outlier\nfeatures with a parametrizable degree of similarity to in-distribution\nfeatures. In extensive experiments, we show that our approach improves the\noutlier detection capabilities of a state-of-the-art object detector while\nmaintaining high 3D object detection performance.\n", "date": "2023-10-02", "categories": []}
{"id": "2310.01247", "title": "Self-supervised Learning for Anomaly Detection in Computational\n  Workflows", "abstract": "  Anomaly detection is the task of identifying abnormal behavior of a system.\nAnomaly detection in computational workflows is of special interest because of\nits wide implications in various domains such as cybersecurity, finance, and\nsocial networks. However, anomaly detection in computational workflows~(often\nmodeled as graphs) is a relatively unexplored problem and poses distinct\nchallenges. For instance, when anomaly detection is performed on graph data,\nthe complex interdependency of nodes and edges, the heterogeneity of node\nattributes, and edge types must be accounted for. Although the use of graph\nneural networks can help capture complex inter-dependencies, the scarcity of\nlabeled anomalous examples from workflow executions is still a significant\nchallenge. To address this problem, we introduce an autoencoder-driven\nself-supervised learning~(SSL) approach that learns a summary statistic from\nunlabeled workflow data and estimates the normal behavior of the computational\nworkflow in the latent space. In this approach, we combine generative and\ncontrastive learning objectives to detect outliers in the summary statistics.\nWe demonstrate that by estimating the distribution of normal behavior in the\nlatent space, we can outperform state-of-the-art anomaly detection methods on\nour benchmark datasets.\n", "date": "2023-10-02", "categories": []}
{"id": "2309.16053", "title": "Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of\n  Anomalous Staining Patterns in Immunohistochemistry Images", "abstract": "  This work addresses the detection of Helicobacter pylori a bacterium\nclassified since 1994 as class 1 carcinogen to humans. By its highest\nspecificity and sensitivity, the preferred diagnosis technique is the analysis\nof histological images with immunohistochemical staining, a process in which\ncertain stained antibodies bind to antigens of the biological element of\ninterest. This analysis is a time demanding task, which is currently done by an\nexpert pathologist that visually inspects the digitized samples.\n  We propose to use autoencoders to learn latent patterns of healthy tissue and\ndetect H. pylori as an anomaly in image staining. Unlike existing\nclassification approaches, an autoencoder is able to learn patterns in an\nunsupervised manner (without the need of image annotations) with high\nperformance. In particular, our model has an overall 91% of accuracy with 86\\%\nsensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.16309", "title": "Weakly-Supervised Video Anomaly Detection with Snippet Anomalous\n  Attention", "abstract": "  With a focus on abnormal events contained within untrimmed videos, there is\nincreasing interest among researchers in video anomaly detection. Among\ndifferent video anomaly detection scenarios, weakly-supervised video anomaly\ndetection poses a significant challenge as it lacks frame-wise labels during\nthe training stage, only relying on video-level labels as coarse supervision.\nPrevious methods have made attempts to either learn discriminative features in\nan end-to-end manner or employ a twostage self-training strategy to generate\nsnippet-level pseudo labels. However, both approaches have certain limitations.\nThe former tends to overlook informative features at the snippet level, while\nthe latter can be susceptible to noises. In this paper, we propose an Anomalous\nAttention mechanism for weakly-supervised anomaly detection to tackle the\naforementioned problems. Our approach takes into account snippet-level encoded\nfeatures without the supervision of pseudo labels. Specifically, our approach\nfirst generates snippet-level anomalous attention and then feeds it together\nwith original anomaly scores into a Multi-branch Supervision Module. The module\nlearns different areas of the video, including areas that are challenging to\ndetect, and also assists the attention optimization. Experiments on benchmark\ndatasets XDViolence and UCF-Crime verify the effectiveness of our method.\nBesides, thanks to the proposed snippet-level attention, we obtain a more\nprecise anomaly localization.\n", "date": "2023-09-28", "categories": []}
{"id": "2309.16401", "title": "VAE-based latent-space classification of RNO-G data", "abstract": "  The Radio Neutrino Observatory in Greenland (RNO-G) is a radio-based\nultra-high energy neutrino detector located at Summit Station, Greenland. It is\nstill being constructed, with 7 stations currently operational. Neutrino\ndetection works by measuring Askaryan radiation produced by neutrino-nucleon\ninteractions. A neutrino candidate must be found amidst other backgrounds which\nare recorded at much higher rates -- including cosmic-rays and anthropogenic\nnoise -- the origins of which are sometimes unknown. Here we describe a method\nto classify different noise classes using the latent space of a variational\nautoencoder. The latent space forms a compact representation that makes\nclassification tractable. We analyze data from a noisy and a silent station.\nThe method automatically detects and allows us to qualitatively separate\nmultiple event classes, including physical wind-induced signals, for both the\nnoisy and the quiet station.\n", "date": "2023-09-28", "categories": []}
{"id": "2309.15245", "title": "SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial\n  Datasets", "abstract": "  We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to\ndetect geometric anomalies in Multimodal geospatial datasets. Geospatial data\ncomprises of acquired and derived heterogeneous data modalities that we\ntransform to semantically meaningful, image-like tensors to address the\nchallenges of representation, alignment, and fusion of multimodal data. SeMAnD\nis comprised of (i) a simple data augmentation strategy, called\nRandPolyAugment, capable of generating diverse augmentations of vector\ngeometries, and (ii) a self-supervised training objective with three components\nthat incentivize learning representations of multimodal data that are\ndiscriminative to local changes in one modality which are not corroborated by\nthe other modalities. Detecting local defects is crucial for geospatial anomaly\ndetection where even small anomalies (e.g., shifted, incorrectly connected,\nmalformed, or missing polygonal vector geometries like roads, buildings,\nlandcover, etc.) are detrimental to the experience and safety of users of\ngeospatial applications like mapping, routing, search, and recommendation\nsystems. Our empirical study on test sets of different types of real-world\ngeometric geospatial anomalies across 3 diverse geographical regions\ndemonstrates that SeMAnD is able to detect real-world defects and outperforms\ndomain-agnostic anomaly detection strategies by 4.8-19.7% as measured using\nanomaly classification AUC. We also show that model performance increases (i)\nup to 20.4% as the number of input modalities increase and (ii) up to 22.9% as\nthe diversity and strength of training data augmentations increase.\n", "date": "2023-09-26", "categories": []}
{"id": "2309.15662", "title": "Human Kinematics-inspired Skeleton-based Video Anomaly Detection", "abstract": "  Previous approaches to detecting human anomalies in videos have typically\nrelied on implicit modeling by directly applying the model to video or skeleton\ndata, potentially resulting in inaccurate modeling of motion information. In\nthis paper, we conduct an exploratory study and introduce a new idea called\nHKVAD (Human Kinematic-inspired Video Anomaly Detection) for video anomaly\ndetection, which involves the explicit use of human kinematic features to\ndetect anomalies. To validate the effectiveness and potential of this\nperspective, we propose a pilot method that leverages the kinematic features of\nthe skeleton pose, with a specific focus on the walking stride, skeleton\ndisplacement at feet level, and neck level. Following this, the method employs\na normalizing flow model to estimate density and detect anomalies based on the\nestimated density. Based on the number of kinematic features used, we have\ndevised three straightforward variant methods and conducted experiments on two\nhighly challenging public datasets, ShanghaiTech and UBnormal. Our method\nachieves good results with minimal computational resources, validating its\neffectiveness and potential.\n", "date": "2023-09-27", "categories": []}
{"id": "2210.03103", "title": "Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!", "abstract": "  We introduce a formalization and benchmark for the unsupervised anomaly\ndetection task in the distribution-shift scenario. Our work builds upon the\niWildCam dataset, and, to the best of our knowledge, we are the first to\npropose such an approach for visual data. We empirically validate that\nenvironment-aware methods perform better in such cases when compared with the\nbasic Empirical Risk Minimization (ERM). We next propose an extension for\ngenerating positive samples for contrastive methods that considers the\nenvironment labels when training, improving the ERM baseline score by 8.7%.\n", "date": "2022-10-06", "categories": []}
{"id": "2208.10992", "title": "Unsupervised Anomaly Localization with Structural Feature-Autoencoders", "abstract": "  Unsupervised Anomaly Detection has become a popular method to detect\npathologies in medical images as it does not require supervision or labels for\ntraining. Most commonly, the anomaly detection model generates a \"normal\"\nversion of an input image, and the pixel-wise $l^p$-difference of the two is\nused to localize anomalies. However, large residuals often occur due to\nimperfect reconstruction of the complex anatomical structures present in most\nmedical images. This method also fails to detect anomalies that are not\ncharacterized by large intensity differences to the surrounding tissue. We\npropose to tackle this problem using a feature-mapping function that transforms\nthe input intensity images into a space with multiple channels where anomalies\ncan be detected along different discriminative feature maps extracted from the\noriginal image. We then train an Autoencoder model in this space using\nstructural similarity loss that does not only consider differences in intensity\nbut also in contrast and structure. Our method significantly increases\nperformance on two medical data sets for brain MRI. Code and experiments are\navailable at https://github.com/FeliMe/feature-autoencoder\n", "date": "2022-08-23", "categories": []}
{"id": "2308.11119", "title": "Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection", "abstract": "  This paper presents a novel method that leverages a visual-language model,\nCLIP, as a data source for zero-shot anomaly detection. Tremendous efforts have\nbeen put towards developing anomaly detectors due to their potential industrial\napplications. Considering the difficulty in acquiring various anomalous samples\nfor training, most existing methods train models with only normal samples and\nmeasure discrepancies from the distribution of normal samples during inference,\nwhich requires training a model for each object category. The problem of this\ninefficient training requirement has been tackled by designing a CLIP-based\nanomaly detector that applies prompt-guided classification to each part of an\nimage in a sliding window manner. However, the method still suffers from the\nlabor of careful prompt ensembling with known object categories. To overcome\nthe issues above, we propose leveraging CLIP as a data source for training. Our\nmethod generates text embeddings with the text encoder in CLIP with typical\nprompts that include words of normal and anomaly. In addition to these words,\nwe insert several randomly generated words into prompts, which enables the\nencoder to generate a diverse set of normal and anomalous samples. Using the\ngenerated embeddings as training data, a feed-forward neural network learns to\nextract features of normal and anomaly from CLIP's embeddings, and as a result,\na category-agnostic anomaly detector can be obtained without any training\nimages. Experimental results demonstrate that our method achieves\nstate-of-the-art performance without laborious prompt ensembling in zero-shot\nsetups.\n", "date": "2023-08-22", "categories": []}
{"id": "2309.13415", "title": "Dream the Impossible: Outlier Imagination with Diffusion Models", "abstract": "  Utilizing auxiliary outlier datasets to regularize the machine learning model\nhas demonstrated promise for out-of-distribution (OOD) detection and safe\nprediction. Due to the labor intensity in data collection and cleaning,\nautomating outlier data generation has been a long-desired alternative. Despite\nthe appeal, generating photo-realistic outliers in the high dimensional pixel\nspace has been an open challenge for the field. To tackle the problem, this\npaper proposes a new framework DREAM-OOD, which enables imagining\nphoto-realistic outliers by way of diffusion models, provided with only the\nin-distribution (ID) data and classes. Specifically, DREAM-OOD learns a\ntext-conditioned latent space based on ID data, and then samples outliers in\nthe low-likelihood region via the latent, which can be decoded into images by\nthe diffusion model. Different from prior works, DREAM-OOD enables visualizing\nand understanding the imagined outliers, directly in the pixel space. We\nconduct comprehensive quantitative and qualitative studies to understand the\nefficacy of DREAM-OOD, and show that training with the samples generated by\nDREAM-OOD can benefit OOD detection performance. Code is publicly available at\nhttps://github.com/deeplearning-wisc/dream-ood.\n", "date": "2023-09-23", "categories": []}
{"id": "2309.14134", "title": "One-Class Classification for Intrusion Detection on Vehicular Networks", "abstract": "  Controller Area Network bus systems within vehicular networks are not\nequipped with the tools necessary to ward off and protect themselves from\nmodern cyber-security threats. Work has been done on using machine learning\nmethods to detect and report these attacks, but common methods are not robust\ntowards unknown attacks. These methods usually rely on there being a sufficient\nrepresentation of attack data, which may not be available due to there either\nnot being enough data present to adequately represent its distribution or the\ndistribution itself is too diverse in nature for there to be a sufficient\nrepresentation of it. With the use of one-class classification methods, this\nissue can be mitigated as only normal data is required to train a model for the\ndetection of anomalous instances. Research has been done on the efficacy of\nthese methods, most notably One-Class Support Vector Machine and Support Vector\nData Description, but many new extensions of these works have been proposed and\nhave yet to be tested for injection attacks in vehicular networks. In this\npaper, we investigate the performance of various state-of-the-art one-class\nclassification methods for detecting injection attacks on Controller Area\nNetwork bus traffic. We investigate the effectiveness of these techniques on\nattacks launched on Controller Area Network buses from two different vehicles\nduring normal operation and while being attacked. We observe that the Subspace\nSupport Vector Data Description method outperformed all other tested methods\nwith a Gmean of about 85%.\n", "date": "2023-09-25", "categories": []}
{"id": "2309.12700", "title": "mixed attention auto encoder for multi-class industrial anomaly\n  detection", "abstract": "  Most existing methods for unsupervised industrial anomaly detection train a\nseparate model for each object category. This kind of approach can easily\ncapture the category-specific feature distributions, but results in high\nstorage cost and low training efficiency. In this paper, we propose a unified\nmixed-attention auto encoder (MAAE) to implement multi-class anomaly detection\nwith a single model. To alleviate the performance degradation due to the\ndiverse distribution patterns of different categories, we employ spatial\nattentions and channel attentions to effectively capture the global category\ninformation and model the feature distributions of multiple classes.\nFurthermore, to simulate the realistic noises on features and preserve the\nsurface semantics of objects from different categories which are essential for\ndetecting the subtle anomalies, we propose an adaptive noise generator and a\nmulti-scale fusion module for the pre-trained features. MAAE delivers\nremarkable performances on the benchmark dataset compared with the\nstate-of-the-art methods.\n", "date": "2023-09-22", "categories": []}
{"id": "2309.11450", "title": "Distribution and volume based scoring for Isolation Forests", "abstract": "  We make two contributions to the Isolation Forest method for anomaly and\noutlier detection. The first contribution is an information-theoretically\nmotivated generalisation of the score function that is used to aggregate the\nscores across random tree estimators. This generalisation allows one to take\ninto account not just the ensemble average across trees but instead the whole\ndistribution. The second contribution is an alternative scoring function at the\nlevel of the individual tree estimator, in which we replace the depth-based\nscoring of the Isolation Forest with one based on hyper-volumes associated to\nan isolation tree's leaf nodes.\n  We motivate the use of both of these methods on generated data and also\nevaluate them on 34 datasets from the recent and exhaustive ``ADBench''\nbenchmark, finding significant improvement over the standard isolation forest\nfor both variants on some datasets and improvement on average across all\ndatasets for one of the two variants. The code to reproduce our results is made\navailable as part of the submission.\n", "date": "2023-09-20", "categories": []}
{"id": "2301.12739", "title": "FractalAD: A simple industrial anomaly detection method using fractal\n  anomaly generation and backbone knowledge distillation", "abstract": "  Although industrial anomaly detection (AD) technology has made significant\nprogress in recent years, generating realistic anomalies and learning priors of\nnormal remain challenging tasks. In this study, we propose an end-to-end\nindustrial anomaly detection method called FractalAD. Training samples are\nobtained by synthesizing fractal images and patches from normal samples. This\nfractal anomaly generation method is designed to sample the full morphology of\nanomalies. Moreover, we designed a backbone knowledge distillation structure to\nextract prior knowledge contained in normal samples. The differences between a\nteacher and a student model are converted into anomaly attention using a cosine\nsimilarity attention module. The proposed method enables an end-to-end semantic\nsegmentation network to be used for anomaly detection without adding any\ntrainable parameters to the backbone and segmentation head, and has obvious\nadvantages over other methods in training and inference speed.. The results of\nablation studies confirmed the effectiveness of fractal anomaly generation and\nbackbone knowledge distillation. The results of performance experiments showed\nthat FractalAD achieved competitive results on the MVTec AD dataset and MVTec\n3D-AD dataset compared with other state-of-the-art anomaly detection methods.\n", "date": "2023-01-30", "categories": []}
{"id": "2309.09436", "title": "An Iterative Method for Unsupervised Robust Anomaly Detection Under Data\n  Contamination", "abstract": "  Most deep anomaly detection models are based on learning normality from\ndatasets due to the difficulty of defining abnormality by its diverse and\ninconsistent nature. Therefore, it has been a common practice to learn\nnormality under the assumption that anomalous data are absent in a training\ndataset, which we call normality assumption. However, in practice, the\nnormality assumption is often violated due to the nature of real data\ndistributions that includes anomalous tails, i.e., a contaminated dataset.\nThereby, the gap between the assumption and actual training data affects\ndetrimentally in learning of an anomaly detection model. In this work, we\npropose a learning framework to reduce this gap and achieve better normality\nrepresentation. Our key idea is to identify sample-wise normality and utilize\nit as an importance weight, which is updated iteratively during the training.\nOur framework is designed to be model-agnostic and hyperparameter insensitive\nso that it applies to a wide range of existing methods without careful\nparameter tuning. We apply our framework to three different representative\napproaches of deep anomaly detection that are classified into one-class\nclassification-, probabilistic model-, and reconstruction-based approaches. In\naddition, we address the importance of a termination condition for iterative\nmethods and propose a termination criterion inspired by the anomaly detection\nobjective. We validate that our framework improves the robustness of the\nanomaly detection models under different levels of contamination ratios on five\nanomaly detection benchmark datasets and two image datasets. On various\ncontaminated datasets, our framework improves the performance of three\nrepresentative anomaly detection methods, measured by area under the ROC curve.\n", "date": "2023-09-18", "categories": []}
{"id": "2309.09465", "title": "Active anomaly detection based on deep one-class classification", "abstract": "  Active learning has been utilized as an efficient tool in building anomaly\ndetection models by leveraging expert feedback. In an active learning\nframework, a model queries samples to be labeled by experts and re-trains the\nmodel with the labeled data samples. It unburdens in obtaining annotated\ndatasets while improving anomaly detection performance. However, most of the\nexisting studies focus on helping experts identify as many abnormal data\nsamples as possible, which is a sub-optimal approach for one-class\nclassification-based deep anomaly detection. In this paper, we tackle two\nessential problems of active learning for Deep SVDD: query strategy and\nsemi-supervised learning method. First, rather than solely identifying\nanomalies, our query strategy selects uncertain samples according to an\nadaptive boundary. Second, we apply noise contrastive estimation in training a\none-class classification model to incorporate both labeled normal and abnormal\ndata effectively. We analyze that the proposed query strategy and\nsemi-supervised loss individually improve an active learning process of anomaly\ndetection and further improve when combined together on seven anomaly detection\ndatasets.\n", "date": "2023-09-18", "categories": []}
{"id": "2211.06660", "title": "Far Away in the Deep Space: Dense Nearest-Neighbor-Based\n  Out-of-Distribution Detection", "abstract": "  The key to out-of-distribution detection is density estimation of the\nin-distribution data or of its feature representations. This is particularly\nchallenging for dense anomaly detection in domains where the in-distribution\ndata has a complex underlying structure. Nearest-Neighbors approaches have been\nshown to work well in object-centric data domains, such as industrial\ninspection and image classification. In this paper, we show that\nnearest-neighbor approaches also yield state-of-the-art results on dense\nnovelty detection in complex driving scenes when working with an appropriate\nfeature representation. In particular, we find that transformer-based\narchitectures produce representations that yield much better similarity metrics\nfor the task. We identify the multi-head structure of these models as one of\nthe reasons, and demonstrate a way to transfer some of the improvements to\nCNNs. Ultimately, the approach is simple and non-invasive, i.e., it does not\naffect the primary segmentation performance, refrains from training on examples\nof anomalies, and achieves state-of-the-art results on RoadAnomaly,\nStreetHazards, and SegmentMeIfYouCan-Anomaly.\n", "date": "2022-11-12", "categories": []}
{"id": "2309.04573", "title": "Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation", "abstract": "  Segmenting unknown or anomalous object instances is a critical task in\nautonomous driving applications, and it is approached traditionally as a\nper-pixel classification problem. However, reasoning individually about each\npixel without considering their contextual semantics results in high\nuncertainty around the objects' boundaries and numerous false positives. We\npropose a paradigm change by shifting from a per-pixel classification to a mask\nclassification. Our mask-based method, Mask2Anomaly, demonstrates the\nfeasibility of integrating a mask-classification architecture to jointly\naddress anomaly segmentation, open-set semantic segmentation, and open-set\npanoptic segmentation. Mask2Anomaly includes several technical novelties that\nare designed to improve the detection of anomalies/unknown objects: i) a global\nmasked attention module to focus individually on the foreground and background\nregions; ii) a mask contrastive learning that maximizes the margin between an\nanomaly and known classes; iii) a mask refinement solution to reduce false\npositives; and iv) a novel approach to mine unknown instances based on the\nmask-architecture properties. By comprehensive qualitative and qualitative\nevaluation, we show Mask2Anomaly achieves new state-of-the-art results across\nthe benchmarks of anomaly segmentation, open-set semantic segmentation, and\nopen-set panoptic segmentation.\n", "date": "2023-09-08", "categories": []}
{"id": "2309.04616", "title": "Knowledge Distillation-Empowered Digital Twin for Anomaly Detection", "abstract": "  Cyber-physical systems (CPSs), like train control and management systems\n(TCMS), are becoming ubiquitous in critical infrastructures. As safety-critical\nsystems, ensuring their dependability during operation is crucial. Digital\ntwins (DTs) have been increasingly studied for this purpose owing to their\ncapability of runtime monitoring and warning, prediction and detection of\nanomalies, etc. However, constructing a DT for anomaly detection in TCMS\nnecessitates sufficient training data and extracting both chronological and\ncontext features with high quality. Hence, in this paper, we propose a novel\nmethod named KDDT for TCMS anomaly detection. KDDT harnesses a language model\n(LM) and a long short-term memory (LSTM) network to extract contexts and\nchronological features, respectively. To enrich data volume, KDDT benefits from\nout-of-domain data with knowledge distillation (KD). We evaluated KDDT with two\ndatasets from our industry partner Alstom and obtained the F1 scores of 0.931\nand 0.915, respectively, demonstrating the effectiveness of KDDT. We also\nexplored individual contributions of the DT model, LM, and KD to the overall\nperformance of KDDT, via a comprehensive empirical study, and observed average\nF1 score improvements of 12.4%, 3%, and 6.05%, respectively.\n", "date": "2023-09-08", "categories": []}
{"id": "2309.05845", "title": "Effective Abnormal Activity Detection on Multivariate Time Series\n  Healthcare Data", "abstract": "  Multivariate time series (MTS) data collected from multiple sensors provide\nthe potential for accurate abnormal activity detection in smart healthcare\nscenarios. However, anomalies exhibit diverse patterns and become unnoticeable\nin MTS data. Consequently, achieving accurate anomaly detection is challenging\nsince we have to capture both temporal dependencies of time series and\ninter-relationships among variables. To address this problem, we propose a\nResidual-based Anomaly Detection approach, Rs-AD, for effective representation\nlearning and abnormal activity detection. We evaluate our scheme on a\nreal-world gait dataset and the experimental results demonstrate an F1 score of\n0.839.\n", "date": "2023-09-11", "categories": []}
{"id": "2309.05953", "title": "GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection", "abstract": "  Logs play a crucial role in system monitoring and debugging by recording\nvaluable system information, including events and states. Although various\nmethods have been proposed to detect anomalies in log sequences, they often\noverlook the significance of considering relations among system components,\nsuch as services and users, which can be identified from log contents.\nUnderstanding these relations is vital for detecting anomalies and their\nunderlying causes. To address this issue, we introduce GLAD, a Graph-based Log\nAnomaly Detection framework designed to detect relational anomalies in system\nlogs. GLAD incorporates log semantics, relational patterns, and sequential\npatterns into a unified framework for anomaly detection. Specifically, GLAD\nfirst introduces a field extraction module that utilizes prompt-based few-shot\nlearning to identify essential fields from log contents. Then GLAD constructs\ndynamic log graphs for sliding windows by interconnecting extracted fields and\nlog events parsed from the log parser. These graphs represent events and fields\nas nodes and their relations as edges. Subsequently, GLAD utilizes a\ntemporal-attentive graph edge anomaly detection model for identifying anomalous\nrelations in these dynamic log graphs. This model employs a Graph Neural\nNetwork (GNN)-based encoder enhanced with transformers to capture content,\nstructural and temporal features. We evaluate our proposed method on three\ndatasets, and the results demonstrate the effectiveness of GLAD in detecting\nanomalies indicated by varying relational patterns.\n", "date": "2023-09-12", "categories": []}
{"id": "2212.04031", "title": "On Root Cause Localization and Anomaly Mitigation through Causal\n  Inference", "abstract": "  Due to a wide spectrum of applications in the real world, such as security,\nfinancial surveillance, and health risk, various deep anomaly detection models\nhave been proposed and achieved state-of-the-art performance. However, besides\nbeing effective, in practice, the practitioners would further like to know what\ncauses the abnormal outcome and how to further fix it. In this work, we propose\nRootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation\nfrom a causal perspective. Especially, we formulate anomalies caused by\nexternal interventions on the normal causal mechanism and aim to locate the\nabnormal features with external interventions as root causes. After that, we\nfurther propose an anomaly mitigation approach that aims to recommend\nmitigation actions on abnormal features to revert the abnormal outcomes such\nthat the counterfactuals guided by the causal mechanism are normal. Experiments\non three datasets show that our approach can locate the root causes and further\nflip the abnormal labels.\n", "date": "2022-12-08", "categories": []}
{"id": "2303.00601", "title": "Multimodal Industrial Anomaly Detection via Hybrid Fusion", "abstract": "  2D-based Industrial Anomaly Detection has been widely discussed, however,\nmultimodal industrial anomaly detection based on 3D point clouds and RGB images\nstill has many untouched fields. Existing multimodal industrial anomaly\ndetection methods directly concatenate the multimodal features, which leads to\na strong disturbance between features and harms the detection performance. In\nthis paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly\ndetection method with hybrid fusion scheme: firstly, we design an unsupervised\nfeature fusion with patch-wise contrastive learning to encourage the\ninteraction of different modal features; secondly, we use a decision layer\nfusion with multiple memory banks to avoid loss of information and additional\nnovelty classifiers to make the final decision. We further propose a point\nfeature alignment operation to better align the point cloud and RGB features.\nExtensive experiments show that our multimodal industrial anomaly detection\nmodel outperforms the state-of-the-art (SOTA) methods on both detection and\nsegmentation precision on MVTec-3D AD dataset. Code is available at\nhttps://github.com/nomewang/M3DM.\n", "date": "2023-03-01", "categories": []}
{"id": "2306.10741", "title": "Machine Learning for Real-Time Anomaly Detection in Optical Networks", "abstract": "  This work proposes a real-time anomaly detection scheme that leverages the\nmulti-step ahead prediction capabilities of encoder-decoder (ED) deep learning\nmodels with recurrent units. Specifically, an encoder-decoder is used to model\nsoft-failure evolution over a long future horizon (i.e., for several days\nahead) by analyzing past quality-of-transmission (QoT) observations. This\ninformation is subsequently used for real-time anomaly detection (e.g., of\nattack incidents), as the knowledge of how the QoT is expected to evolve allows\ncapturing unexpected network behavior. Specifically, for anomaly detection, a\nstatistical hypothesis testing scheme is used, alleviating the limitations of\nsupervised (SL) and unsupervised learning (UL) schemes, usually applied for\nthis purpose. Indicatively, the proposed scheme eliminates the need for labeled\nanomalies, required when SL is applied, and the need for on-line analyzing\nentire datasets to identify abnormal instances (i.e., UL). Overall, it is shown\nthat by utilizing QoT evolution information, the proposed approach can\neffectively detect abnormal deviations in real-time. Importantly, it is shown\nthat the information concerning soft-failure evolution (i.e., QoT predictions)\nis essential to accurately detect anomalies.\n", "date": "2023-06-19", "categories": []}
{"id": "2309.03401", "title": "Reasonable Anomaly Detection in Long Sequences", "abstract": "  Video anomaly detection is a challenging task due to the lack in approaches\nfor representing samples. The visual representations of most existing\napproaches are limited by short-term sequences of observations which cannot\nprovide enough clues for achieving reasonable detections. In this paper, we\npropose to completely represent the motion patterns of objects by learning from\nlong-term sequences. Firstly, a Stacked State Machine (SSM) model is proposed\nto represent the temporal dependencies which are consistent across long-range\nobservations. Then SSM model functions in predicting future states based on\npast ones, the divergence between the predictions with inherent normal patterns\nand observed ones determines anomalies which violate normal motion patterns.\nExtensive experiments are carried out to evaluate the proposed approach on the\ndataset and existing ones. Improvements over state-of-the-art methods can be\nobserved. Our code is available at\nhttps://github.com/AllenYLJiang/Anomaly-Detection-in-Sequences.\n", "date": "2023-09-06", "categories": []}
{"id": "2305.08977", "title": "Autoencoder-based Anomaly Detection in Streaming Data with Incremental\n  Learning and Concept Drift Adaptation", "abstract": "  In our digital universe nowadays, enormous amount of data are produced in a\nstreaming manner in a variety of application areas. These data are often\nunlabelled. In this case, identifying infrequent events, such as anomalies,\nposes a great challenge. This problem becomes even more difficult in\nnon-stationary environments, which can cause deterioration of the predictive\nperformance of a model. To address the above challenges, the paper proposes an\nautoencoder-based incremental learning method with drift detection\n(strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of\nboth incremental learning and drift detection. We conduct an experimental study\nusing real-world and synthetic datasets with severe or extreme class imbalance,\nand provide an empirical analysis of strAEm++DD. We further conduct a\ncomparative study, showing that the proposed method significantly outperforms\nexisting baseline and advanced methods.\n", "date": "2023-05-15", "categories": []}
{"id": "2305.18798", "title": "AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on\n  Anomalies", "abstract": "  Semi-supervised anomaly detection (SSAD) methods have demonstrated their\neffectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging\nfew-shot but instructive abnormal instances. However, the dominance of\nhomogeneous normal data over anomalies biases the SSAD models against\neffectively perceiving anomalies. To address this issue and achieve balanced\nsupervision between heavily imbalanced normal and abnormal data, we develop a\nnovel framework called AnoOnly (Anomaly Only). Unlike existing SSAD methods\nthat resort to strict loss supervision, AnoOnly suspends it and introduces a\nform of weak supervision for normal data. This weak supervision is instantiated\nthrough the utilization of batch normalization, which implicitly performs\ncluster learning on normal data. When integrated into existing SSAD methods,\nthe proposed AnoOnly demonstrates remarkable performance enhancements across\nvarious models and datasets, achieving new state-of-the-art performance.\nAdditionally, our AnoOnly is natively robust to label noise when suffering from\ndata contamination. Our code is publicly available at\nhttps://github.com/cool-xuan/AnoOnly.\n", "date": "2023-05-30", "categories": []}
{"id": "2306.07056", "title": "Kernel Random Projection Depth for Outlier Detection", "abstract": "  This paper proposes an extension of Random Projection Depth (RPD) to cope\nwith multiple modalities and non-convexity on data clouds. In the framework of\nthe proposed method, the RPD is computed in a reproducing kernel Hilbert space.\nWith the help of kernel principal component analysis, we expect that the\nproposed method can cope with the above multiple modalities and non-convexity.\nThe experimental results demonstrate that the proposed method outperforms RPD\nand is comparable to other existing detection models on benchmark datasets\nregarding Area Under the Curves (AUCs) of Receiver Operating Characteristic\n(ROC).\n", "date": "2023-06-12", "categories": []}
{"id": "2309.02458", "title": "Towards frugal unsupervised detection of subtle abnormalities in medical\n  imaging", "abstract": "  Anomaly detection in medical imaging is a challenging task in contexts where\nabnormalities are not annotated. This problem can be addressed through\nunsupervised anomaly detection (UAD) methods, which identify features that do\nnot match with a reference model of normal profiles. Artificial neural networks\nhave been extensively used for UAD but they do not generally achieve an optimal\ntrade-o$\\hookleftarrow$ between accuracy and computational demand. As an\nalternative, we investigate mixtures of probability distributions whose\nversatility has been widely recognized for a variety of data and tasks, while\nnot requiring excessive design e$\\hookleftarrow$ort or tuning. Their\nexpressivity makes them good candidates to account for complex multivariate\nreference models. Their much smaller number of parameters makes them more\namenable to interpretation and e cient learning. However, standard estimation\nprocedures, such as the Expectation-Maximization algorithm, do not scale well\nto large data volumes as they require high memory usage. To address this issue,\nwe propose to incrementally compute inferential quantities. This online\napproach is illustrated on the challenging detection of subtle abnormalities in\nMR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The\nidentified structural abnormalities are consistent with the disease\nprogression, as accounted by the Hoehn and Yahr scale.\n", "date": "2023-09-04", "categories": []}
{"id": "2301.11368", "title": "Coincident Learning for Unsupervised Anomaly Detection", "abstract": "  Anomaly detection is an important task for complex systems (e.g., industrial\nfacilities, manufacturing, large-scale science experiments), where failures in\na sub-system can lead to low yield, faulty products, or even damage to\ncomponents. While complex systems often have a wealth of data, labeled\nanomalies are typically rare (or even nonexistent) and expensive to acquire.\nUnsupervised approaches are therefore common and typically search for anomalies\neither by distance or density of examples in the input feature space (or some\nassociated low-dimensional representation). This paper presents a novel\napproach called CoAD, which is specifically designed for multi-modal tasks and\nidentifies anomalies based on \\textit{coincident} behavior across two different\nslices of the feature space. We define an \\textit{unsupervised} metric,\n$\\hat{F}_\\beta$, out of analogy to the supervised classification $F_\\beta$\nstatistic. CoAD uses $\\hat{F}_\\beta$ to train an anomaly detection algorithm on\n\\textit{unlabeled data}, based on the expectation that anomalous behavior in\none feature slice is coincident with anomalous behavior in the other. The\nmethod is illustrated using a synthetic outlier data set and a MNIST-based\nimage data set, and is compared to prior state-of-the-art on two real-world\ntasks: a metal milling data set and a data set from a particle accelerator.\n", "date": "2023-01-26", "categories": []}
{"id": "2303.13845", "title": "Anomaly Detection under Distribution Shift", "abstract": "  Anomaly detection (AD) is a crucial machine learning task that aims to learn\npatterns from a set of normal training samples to identify abnormal samples in\ntest data. Most existing AD studies assume that the training and test data are\ndrawn from the same data distribution, but the test data can have large\ndistribution shifts arising in many real-world applications due to different\nnatural variations such as new lighting conditions, object poses, or background\nappearances, rendering existing AD methods ineffective in such cases. In this\npaper, we consider the problem of anomaly detection under distribution shift\nand establish performance benchmarks on four widely-used AD and\nout-of-distribution (OOD) generalization datasets. We demonstrate that simple\nadaptation of state-of-the-art OOD generalization methods to AD settings fails\nto work effectively due to the lack of labeled anomaly data. We further\nintroduce a novel robust AD approach to diverse distribution shifts by\nminimizing the distribution gap between in-distribution and OOD normal samples\nin both the training and inference stages in an unsupervised way. Our extensive\nempirical results on the four datasets show that our approach substantially\noutperforms state-of-the-art AD methods and OOD generalization methods on data\nwith various distribution shifts, while maintaining the detection accuracy on\nin-distribution data. Code and data are available at\nhttps://github.com/mala-lab/ADShift.\n", "date": "2023-03-24", "categories": []}
{"id": "2306.09067", "title": "2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty\n  Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection", "abstract": "  This technical report introduces the winning solution of the team Segment Any\nAnomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.\nGoing beyond uni-modal prompt, e.g., language prompt, we present a novel\nframework, i.e., Segment Any Anomaly + (SAA$+$), for zero-shot anomaly\nsegmentation with multi-modal prompts for the regularization of cascaded modern\nfoundation models. Inspired by the great zero-shot generalization ability of\nfoundation models like Segment Anything, we first explore their assembly (SAA)\nto leverage diverse multi-modal prior knowledge for anomaly localization.\nSubsequently, we further introduce multimodal prompts (SAA$+$) derived from\ndomain expert knowledge and target image context to enable the non-parameter\nadaptation of foundation models to anomaly segmentation. The proposed SAA$+$\nmodel achieves state-of-the-art performance on several anomaly segmentation\nbenchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will\nrelease the code of our winning solution for the CVPR2023 VAN.\n", "date": "2023-06-15", "categories": []}
{"id": "2309.01189", "title": "LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection", "abstract": "  The increasing volume of log data produced by software-intensive systems\nmakes it impractical to analyze them manually. Many deep learning-based methods\nhave been proposed for log-based anomaly detection. These methods face several\nchallenges such as high-dimensional and noisy log data, class imbalance,\ngeneralization, and model interpretability. Recently, ChatGPT has shown\npromising results in various domains. However, there is still a lack of study\non the application of ChatGPT for log-based anomaly detection. In this work, we\nproposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By\nleveraging the ChatGPT's language interpretation capabilities, LogGPT aims to\nexplore the transferability of knowledge from large-scale corpora to log-based\nanomaly detection. We conduct experiments to evaluate the performance of LogGPT\nand compare it with three deep learning-based methods on BGL and Spirit\ndatasets. LogGPT shows promising results and has good interpretability. This\nstudy provides preliminary insights into prompt-based models, such as ChatGPT,\nfor the log-based anomaly detection task.\n", "date": "2023-09-03", "categories": []}
{"id": "2309.01682", "title": "Prior Knowledge Guided Network for Video Anomaly Detection", "abstract": "  Video Anomaly Detection (VAD) involves detecting anomalous events in videos,\npresenting a significant and intricate task within intelligent video\nsurveillance. Existing studies often concentrate solely on features acquired\nfrom limited normal data, disregarding the latent prior knowledge present in\nextensive natural image datasets. To address this constraint, we propose a\nPrior Knowledge Guided Network(PKG-Net) for the VAD task. First, an\nauto-encoder network is incorporated into a teacher-student architecture to\nlearn two designated proxy tasks: future frame prediction and teacher network\nimitation, which can provide better generalization ability on unknown samples.\nSecond, knowledge distillation on proper feature blocks is also proposed to\nincrease the multi-scale detection ability of the model. In addition,\nprediction error and teacher-student feature inconsistency are combined to\nevaluate anomaly scores of inference samples more comprehensively. Experimental\nresults on three public benchmarks validate the effectiveness and accuracy of\nour method, which surpasses recent state-of-the-arts.\n", "date": "2023-09-04", "categories": []}
{"id": "2309.02206", "title": "Language Models for Novelty Detection in System Call Traces", "abstract": "  Due to the complexity of modern computer systems, novel and unexpected\nbehaviors frequently occur. Such deviations are either normal occurrences, such\nas software updates and new user activities, or abnormalities, such as\nmisconfigurations, latency issues, intrusions, and software bugs. Regardless,\nnovel behaviors are of great interest to developers, and there is a genuine\nneed for efficient and effective methods to detect them. Nowadays, researchers\nconsider system calls to be the most fine-grained and accurate source of\ninformation to investigate the behavior of computer systems. Accordingly, this\npaper introduces a novelty detection methodology that relies on a probability\ndistribution over sequences of system calls, which can be seen as a language\nmodel. Language models estimate the likelihood of sequences, and since\nnovelties deviate from previously observed behaviors by definition, they would\nbe unlikely under the model. Following the success of neural networks for\nlanguage models, three architectures are evaluated in this work: the widespread\nLSTM, the state-of-the-art Transformer, and the lower-complexity Longformer.\nHowever, large neural networks typically require an enormous amount of data to\nbe trained effectively, and to the best of our knowledge, no massive modern\ndatasets of kernel traces are publicly available. This paper addresses this\nlimitation by introducing a new open-source dataset of kernel traces comprising\nover 2 million web requests with seven distinct behaviors. The proposed\nmethodology requires minimal expert hand-crafting and achieves an F-score and\nAuROC greater than 95% on most novelties while being data- and task-agnostic.\nThe source code and trained models are publicly available on GitHub while the\ndatasets are available on Zenodo.\n", "date": "2023-09-05", "categories": []}
{"id": "2309.02333", "title": "Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent\n  Light Source", "abstract": "  Significant advances in utilizing deep learning for anomaly detection have\nbeen made in recent years. However, these methods largely assume the existence\nof a normal training set (i.e., uncontaminated by anomalies) or even a\ncompletely labeled training set. In many complex engineering systems, such as\nparticle accelerators, labels are sparse and expensive; in order to perform\nanomaly detection in these cases, we must drop these assumptions and utilize a\ncompletely unsupervised method. This paper introduces the Resilient Variational\nAutoencoder (ResVAE), a deep generative model specifically designed for anomaly\ndetection. ResVAE exhibits resilience to anomalies present in the training data\nand provides feature-level anomaly attribution. During the training process,\nResVAE learns the anomaly probability for each sample as well as each\nindividual feature, utilizing these probabilities to effectively disregard\nanomalous examples in the training data. We apply our proposed method to detect\nanomalies in the accelerator status at the SLAC Linac Coherent Light Source\n(LCLS). By utilizing shot-to-shot data from the beam position monitoring\nsystem, we demonstrate the exceptional capability of ResVAE in identifying\nvarious types of anomalies that are visible in the accelerator.\n", "date": "2023-09-05", "categories": []}
{"id": "2304.02216", "title": "Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and\n  Masked Multi-scale Reconstruction", "abstract": "  Industrial anomaly detection (IAD) is crucial for automating industrial\nquality inspection. The diversity of the datasets is the foundation for\ndeveloping comprehensive IAD algorithms. Existing IAD datasets focus on the\ndiversity of data categories, overlooking the diversity of domains within the\nsame data category. In this paper, to bridge this gap, we propose the\nAero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two\nsub-datasets: the single-blade dataset and the video anomaly detection dataset\nof blades. Compared to existing datasets, AeBAD has the following two\ncharacteristics: 1.) The target samples are not aligned and at different\nscales. 2.) There is a domain shift between the distribution of normal samples\nin the test set and the training set, where the domain shifts are mainly caused\nby the changes in illumination and view. Based on this dataset, we observe that\ncurrent state-of-the-art (SOTA) IAD methods exhibit limitations when the domain\nof normal samples in the test set undergoes a shift. To address this issue, we\npropose a novel method called masked multi-scale reconstruction (MMR), which\nenhances the model's capacity to deduce causality among patches in normal\nsamples by a masked reconstruction task. MMR achieves superior performance\ncompared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves\ncompetitive performance with SOTA methods to detect the anomalies of different\ntypes on the MVTec AD dataset. Code and dataset are available at\nhttps://github.com/zhangzilongc/MMR.\n", "date": "2023-04-05", "categories": []}
{"id": "2307.13925", "title": "EasyNet: An Easy Network for 3D Industrial Anomaly Detection", "abstract": "  3D anomaly detection is an emerging and vital computer vision task in\nindustrial manufacturing (IM). Recently many advanced algorithms have been\npublished, but most of them cannot meet the needs of IM. There are several\ndisadvantages: i) difficult to deploy on production lines since their\nalgorithms heavily rely on large pre-trained models; ii) hugely increase\nstorage overhead due to overuse of memory banks; iii) the inference speed\ncannot be achieved in real-time. To overcome these issues, we propose an easy\nand deployment-friendly network (called EasyNet) without using pre-trained\nmodels and memory banks: firstly, we design a multi-scale multi-modality\nfeature encoder-decoder to accurately reconstruct the segmentation maps of\nanomalous regions and encourage the interaction between RGB images and depth\nimages; secondly, we adopt a multi-modality anomaly segmentation network to\nachieve a precise anomaly map; thirdly, we propose an attention-based\ninformation entropy fusion module for feature fusion during inference, making\nit suitable for real-time deployment. Extensive experiments show that EasyNet\nachieves an anomaly detection AUROC of 92.6% without using pre-trained models\nand memory banks. In addition, EasyNet is faster than existing methods, with a\nhigh frame rate of 94.55 FPS on a Tesla V100 GPU.\n", "date": "2023-07-26", "categories": []}
{"id": "2309.00088", "title": "Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures\n  Market", "abstract": "  Modern financial electronic exchanges are an exciting and fast-paced\nmarketplace where billions of dollars change hands every day. They are also\nrife with manipulation and fraud. Detecting such activity is a major\nundertaking, which has historically been a job reserved exclusively for humans.\nRecently, more research and resources have been focused on automating these\nprocesses via machine learning and artificial intelligence. Fraud detection is\noverwhelmingly associated with the greater field of anomaly detection, which is\nusually performed via unsupervised learning techniques because of the lack of\nlabeled data needed for supervised learning. However, a small quantity of\nlabeled data does often exist. This research article aims to evaluate the\nefficacy of a deep semi-supervised anomaly detection technique, called Deep\nSAD, for detecting fraud in high-frequency financial data. We use exclusive\nproprietary limit order book data from the TMX exchange in Montr\\'eal, with a\nsmall set of true labeled instances of fraud, to evaluate Deep SAD against its\nunsupervised predecessor. We show that incorporating a small amount of labeled\ndata into an unsupervised anomaly detection framework can greatly improve its\naccuracy.\n", "date": "2023-08-31", "categories": []}
{"id": "2309.00379", "title": "Anomaly detection with semi-supervised classification based on risk\n  estimators", "abstract": "  A significant limitation of one-class classification anomaly detection\nmethods is their reliance on the assumption that unlabeled training data only\ncontains normal instances. To overcome this impractical assumption, we propose\ntwo novel classification-based anomaly detection methods. Firstly, we introduce\na semi-supervised shallow anomaly detection method based on an unbiased risk\nestimator. Secondly, we present a semi-supervised deep anomaly detection method\nutilizing a nonnegative (biased) risk estimator. We establish estimation error\nbounds and excess risk bounds for both risk minimizers. Additionally, we\npropose techniques to select appropriate regularization parameters that ensure\nthe nonnegativity of the empirical risk in the shallow model under specific\nloss functions. Our extensive experiments provide strong evidence of the\neffectiveness of the risk-based anomaly detection methods.\n", "date": "2023-09-01", "categories": []}
{"id": "2308.16279", "title": "Classification of Anomalies in Telecommunication Network KPI Time Series", "abstract": "  The increasing complexity and scale of telecommunication networks have led to\na growing interest in automated anomaly detection systems. However, the\nclassification of anomalies detected on network Key Performance Indicators\n(KPI) has received less attention, resulting in a lack of information about\nanomaly characteristics and classification processes. To address this gap, this\npaper proposes a modular anomaly classification framework. The framework\nassumes separate entities for the anomaly classifier and the detector, allowing\nfor a distinct treatment of anomaly detection and classification tasks on time\nseries. The objectives of this study are (1) to develop a time series simulator\nthat generates synthetic time series resembling real-world network KPI\nbehavior, (2) to build a detection model to identify anomalies in the time\nseries, (3) to build classification models that accurately categorize detected\nanomalies into predefined classes (4) to evaluate the classification framework\nperformance on simulated and real-world network KPI time series. This study has\ndemonstrated the good performance of the anomaly classification models trained\non simulated anomalies when applied to real-world network time series data.\n", "date": "2023-08-30", "categories": []}
{"id": "2308.16659", "title": "Autoencoder-based Online Data Quality Monitoring for the CMS\n  Electromagnetic Calorimeter", "abstract": "  The online Data Quality Monitoring system (DQM) of the CMS electromagnetic\ncalorimeter (ECAL) is a crucial operational tool that allows ECAL experts to\nquickly identify, localize, and diagnose a broad range of detector issues that\nwould otherwise hinder physics-quality data taking. Although the existing ECAL\nDQM system has been continuously updated to respond to new problems, it remains\none step behind newer and unforeseen issues. Using unsupervised deep learning,\na real-time autoencoder-based anomaly detection system is developed that is\nable to detect ECAL anomalies unseen in past data. After accounting for spatial\nvariations in the response of the ECAL and the temporal evolution of anomalies,\nthe new system is able to efficiently detect anomalies while maintaining an\nestimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing\nbenchmarks by about two orders of magnitude. The real-world performance of the\nsystem is validated using anomalies found in 2018 and 2022 LHC collision data.\nAdditionally, first results from deploying the autoencoder-based system in the\nCMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are\npresented, showing its promising performance in detecting obscure issues that\ncould have been missed in the existing DQM system.\n", "date": "2023-08-31", "categories": []}
{"id": "2308.15973", "title": "Demo: A Digital Twin of the 5G Radio Access Network for Anomaly\n  Detection Functionality", "abstract": "  Recently, the concept of digital twins (DTs) has received significant\nattention within the realm of 5G/6G. This demonstration shows an innovative DT\ndesign and implementation framework tailored toward integration within the 5G\ninfrastructure. The proposed DT enables near real-time anomaly detection\ncapability pertaining to user connectivity. It empowers the 5G system to\nproactively execute decisions for resource control and connection restoration.\n", "date": "2023-08-30", "categories": []}
{"id": "2305.19867", "title": "Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion\n  Model", "abstract": "  It can be challenging to identify brain MRI anomalies using supervised\ndeep-learning techniques due to anatomical heterogeneity and the requirement\nfor pixel-level labeling. Unsupervised anomaly detection approaches provide an\nalternative solution by relying only on sample-level labels of healthy brains\nto generate a desired representation to identify abnormalities at the pixel\nlevel. Although, generative models are crucial for generating such anatomically\nconsistent representations of healthy brains, accurately generating the\nintricate anatomy of the human brain remains a challenge. In this study, we\npresent a method called masked-DDPM (mDPPM), which introduces masking-based\nregularization to reframe the generation task of diffusion models.\nSpecifically, we introduce Masked Image Modeling (MIM) and Masked Frequency\nModeling (MFM) in our self-supervised approach that enables models to learn\nvisual representations from unlabeled data. To the best of our knowledge, this\nis the first attempt to apply MFM in DPPM models for medical applications. We\nevaluate our approach on datasets containing tumors and numerous sclerosis\nlesions and exhibit the superior performance of our unsupervised method as\ncompared to the existing fully/weakly supervised baselines. Code is available\nat https://github.com/hasan1292/mDDPM.\n", "date": "2023-05-31", "categories": []}
{"id": "2307.12676", "title": "Few-shot $\\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining\n  Opportunity and Embedding Feature Imbalance", "abstract": "  Over the past decade, previous balanced datasets have been used to advance\ndeep learning algorithms for industrial applications. In urban infrastructures\nand living environments, damage data mining cannot avoid imbalanced data issues\nbecause of rare unseen events and the high-quality status of improved\noperations. For visual inspection, the deteriorated class acquired from the\nsurface of concrete and steel components are occasionally imbalanced. From\nnumerous related surveys, we conclude that imbalanced data problems can be\ncategorised into four types: 1) missing range of target and label valuables, 2)\nmajority-minority class imbalance, 3) foreground background of spatial\nimbalance, and 4) long-tailed class of pixel-wise imbalance. Since 2015, many\nimbalanced studies have been conducted using deep-learning approaches,\nincluding regression, image classification, object detection, and semantic\nsegmentation. However, anomaly detection for imbalanced data is not well known.\nIn this study, we highlight a one-class anomaly detection application, whether\nanomalous class or not, and demonstrate clear examples of imbalanced vision\ndatasets: medical disease, hazardous behaviour, material deterioration, plant\ndisease, river sludge, and disaster damage. We provide key results on the\nadvantage of damage-vision mining, hypothesising that the more effective the\nrange of the positive ratio, the higher the accuracy gain of the anomalies\nfeedback. In our imbalanced studies, compared with the balanced case with a\npositive ratio of $1/1$, we find that there is an applicable positive ratio\n$1/a$ where the accuracy is consistently high. However, the extremely\nimbalanced range is from one shot to $1/2a$, the accuracy of which is inferior\nto that of the applicable ratio. In contrast, with a positive ratio ranging\nover $2/a$, it shifts in the over-mining phase without an effective gain in\naccuracy.\n", "date": "2023-07-24", "categories": []}
{"id": "2308.15069", "title": "MadSGM: Multivariate Anomaly Detection with Score-based Generative\n  Models", "abstract": "  The time-series anomaly detection is one of the most fundamental tasks for\ntime-series. Unlike the time-series forecasting and classification, the\ntime-series anomaly detection typically requires unsupervised (or\nself-supervised) training since collecting and labeling anomalous observations\nare difficult. In addition, most existing methods resort to limited forms of\nanomaly measurements and therefore, it is not clear whether they are optimal in\nall circumstances. To this end, we present a multivariate time-series anomaly\ndetector based on score-based generative models, called MadSGM, which considers\nthe broadest ever set of anomaly measurement factors: i) reconstruction-based,\nii) density-based, and iii) gradient-based anomaly measurements. We also design\na conditional score network and its denoising score matching loss for the\ntime-series anomaly detection. Experiments on five real-world benchmark\ndatasets illustrate that MadSGM achieves the most robust and accurate\npredictions.\n", "date": "2023-08-29", "categories": []}
{"id": "2308.15280", "title": "ADFA: Attention-augmented Differentiable top-k Feature Adaptation for\n  Unsupervised Medical Anomaly Detection", "abstract": "  The scarcity of annotated data, particularly for rare diseases, limits the\nvariability of training data and the range of detectable lesions, presenting a\nsignificant challenge for supervised anomaly detection in medical imaging. To\nsolve this problem, we propose a novel unsupervised method for medical image\nanomaly detection: Attention-Augmented Differentiable top-k Feature Adaptation\n(ADFA). The method utilizes Wide-ResNet50-2 (WR50) network pre-trained on\nImageNet to extract initial feature representations. To reduce the channel\ndimensionality while preserving relevant channel information, we employ an\nattention-augmented patch descriptor on the extracted features. We then apply\ndifferentiable top-k feature adaptation to train the patch descriptor, mapping\nthe extracted feature representations to a new vector space, enabling effective\ndetection of anomalies. Experiments show that ADFA outperforms state-of-the-art\n(SOTA) methods on multiple challenging medical image datasets, confirming its\neffectiveness in medical anomaly detection.\n", "date": "2023-08-29", "categories": []}
{"id": "2308.15300", "title": "MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly\n  Detection", "abstract": "  Unsupervised anomaly detection (UAD) attracts a lot of research interest and\ndrives widespread applications, where only anomaly-free samples are available\nfor training. Some UAD applications intend to further locate the anomalous\nregions without any anomaly information.\n  Although the absence of anomalous samples and annotations deteriorates the\nUAD performance, an inconspicuous yet powerful statistics model, the\nnormalizing flows, is appropriate for anomaly detection and localization in an\nunsupervised fashion. The flow-based probabilistic models, only trained on\nanomaly-free data, can efficiently distinguish unpredictable anomalies by\nassigning them much lower likelihoods than normal data.\n  Nevertheless, the size variation of unpredictable anomalies introduces\nanother inconvenience to the flow-based methods for high-precision anomaly\ndetection and localization. To generalize the anomaly size variation, we\npropose a novel Multi-Scale Flow-based framework dubbed MSFlow composed of\nasymmetrical parallel flows followed by a fusion flow to exchange multi-scale\nperceptions. Moreover, different multi-scale aggregation strategies are adopted\nfor image-wise anomaly detection and pixel-wise anomaly localization according\nto the discrepancy between them. The proposed MSFlow is evaluated on three\nanomaly detection datasets, significantly outperforming existing methods.\nNotably, on the challenging MVTec AD benchmark, our MSFlow achieves a new\nstate-of-the-art with a detection AUORC score of up to 99.7%, localization\nAUCROC score of 98.8%, and PRO score of 97.1%. The reproducible code is\navailable at https://github.com/cool-xuan/msflow.\n", "date": "2023-08-29", "categories": []}
{"id": "2305.16972", "title": "Maskomaly:Zero-Shot Mask Anomaly Segmentation", "abstract": "  We present a simple and practical framework for anomaly segmentation called\nMaskomaly. It builds upon mask-based standard semantic segmentation networks by\nadding a simple inference-time post-processing step which leverages the raw\nmask outputs of such networks. Maskomaly does not require additional training\nand only adds a small computational overhead to inference. Most importantly, it\ndoes not require anomalous data at training. We show top results for our method\non SMIYC, RoadAnomaly, and StreetHazards. On the most central benchmark, SMIYC,\nMaskomaly outperforms all directly comparable approaches. Further, we introduce\na novel metric that benefits the development of robust anomaly segmentation\nmethods and demonstrate its informativeness on RoadAnomaly.\n", "date": "2023-05-26", "categories": []}
{"id": "2307.07205", "title": "Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video\n  Anomaly Detection", "abstract": "  Anomalies are rare and anomaly detection is often therefore framed as\nOne-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC\ntechniques constrain the latent representations of normal motions to limited\nvolumes and detect as abnormal anything outside, which accounts satisfactorily\nfor the openset'ness of anomalies. But normalcy shares the same openset'ness\nproperty since humans can perform the same action in several ways, which the\nleading techniques neglect. We propose a novel generative model for video\nanomaly detection (VAD), which assumes that both normality and abnormality are\nmultimodal. We consider skeletal representations and leverage state-of-the-art\ndiffusion probabilistic models to generate multimodal future human poses. We\ncontribute a novel conditioning on the past motion of people and exploit the\nimproved mode coverage capabilities of diffusion processes to generate\ndifferent-but-plausible future motions. Upon the statistical aggregation of\nfuture modes, an anomaly is detected when the generated set of motions is not\npertinent to the actual future. We validate our model on 4 established\nbenchmarks: UBnormal, HR-UBnormal, HR-STC, and HR-Avenue, with extensive\nexperiments surpassing state-of-the-art results.\n", "date": "2023-07-14", "categories": []}
{"id": "2308.13861", "title": "Bias in Unsupervised Anomaly Detection in Brain MRI", "abstract": "  Unsupervised anomaly detection methods offer a promising and flexible\nalternative to supervised approaches, holding the potential to revolutionize\nmedical scan analysis and enhance diagnostic performance.\n  In the current landscape, it is commonly assumed that differences between a\ntest case and the training distribution are attributed solely to pathological\nconditions, implying that any disparity indicates an anomaly. However, the\npresence of other potential sources of distributional shift, including scanner,\nage, sex, or race, is frequently overlooked. These shifts can significantly\nimpact the accuracy of the anomaly detection task. Prominent instances of such\nfailures have sparked concerns regarding the bias, credibility, and fairness of\nanomaly detection.\n  This work presents a novel analysis of biases in unsupervised anomaly\ndetection. By examining potential non-pathological distributional shifts\nbetween the training and testing distributions, we shed light on the extent of\nthese biases and their influence on anomaly detection results. Moreover, this\nstudy examines the algorithmic limitations that arise due to biases, providing\nvaluable insights into the challenges encountered by anomaly detection\nalgorithms in accurately learning and capturing the entire range of variability\npresent in the normative distribution. Through this analysis, we aim to enhance\nthe understanding of these biases and pave the way for future improvements in\nthe field. Here, we specifically investigate Alzheimer's disease detection from\nbrain MR imaging as a case study, revealing significant biases related to sex,\nrace, and scanner variations that substantially impact the results. These\nfindings align with the broader goal of improving the reliability, fairness,\nand effectiveness of anomaly detection in medical imaging.\n", "date": "2023-08-26", "categories": []}
{"id": "2308.14340", "title": "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical\n  Relation-augmented Graph Neural Networks", "abstract": "  This work considers the problem of heterogeneous graph-level anomaly\ndetection. Heterogeneous graphs are commonly used to represent behaviours\nbetween different types of entities in complex industrial systems for capturing\nas much information about the system operations as possible. Detecting\nanomalous heterogeneous graphs from a large set of system behaviour graphs is\ncrucial for many real-world applications like online web/mobile service and\ncloud access control. To address the problem, we propose HRGCN, an unsupervised\ndeep heterogeneous graph neural network, to model complex heterogeneous\nrelations between different entities in the system for effectively identifying\nthese anomalous behaviour graphs. HRGCN trains a hierarchical\nrelation-augmented Heterogeneous Graph Neural Network (HetGNN), which learns\nbetter graph representations by modelling the interactions among all the system\nentities and considering both source-to-destination entity (node) types and\ntheir relation (edge) types. Extensive evaluation on two real-world application\ndatasets shows that HRGCN outperforms state-of-the-art competing anomaly\ndetection approaches. We further present a real-world industrial case study to\njustify the effectiveness of HRGCN in detecting anomalous (e.g., congested)\nnetwork devices in a mobile communication service. HRGCN is available at\nhttps://github.com/jiaxililearn/HRGCN.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.14380", "title": "Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls\n  and Opportunities", "abstract": "  Self-supervised learning (SSL) is a growing torrent that has recently\ntransformed machine learning and its many real world applications, by learning\non massive amounts of unlabeled data via self-generated supervisory signals.\nUnsupervised anomaly detection (AD) has also capitalized on SSL, by\nself-generating pseudo-anomalies through various data augmentation functions or\nexternal data exposure. In this vision paper, we first underline the importance\nof the choice of SSL strategies on AD performance, by presenting evidences and\nstudies from the AD literature. Equipped with the understanding that SSL incurs\nvarious hyperparameters (HPs) to carefully tune, we present recent developments\non unsupervised model selection and augmentation tuning for SSL-based AD. We\nthen highlight emerging challenges and future opportunities; on designing new\npretext tasks and augmentation functions for different data modalities,\ncreating novel model selection solutions for systematically tuning the SSL HPs,\nas well as on capitalizing on the potential of pretrained foundation models on\nAD through effective density estimation.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.14595", "title": "Neural Network Training Strategy to Enhance Anomaly Detection\n  Performance: A Perspective on Reconstruction Loss Amplification", "abstract": "  Unsupervised anomaly detection (UAD) is a widely adopted approach in industry\ndue to rare anomaly occurrences and data imbalance. A desirable characteristic\nof an UAD model is contained generalization ability which excels in the\nreconstruction of seen normal patterns but struggles with unseen anomalies.\nRecent studies have pursued to contain the generalization capability of their\nUAD models in reconstruction from different perspectives, such as design of\nneural network (NN) structure and training strategy. In contrast, we note that\ncontaining of generalization ability in reconstruction can also be obtained\nsimply from steep-shaped loss landscape. Motivated by this, we propose a loss\nlandscape sharpening method by amplifying the reconstruction loss, dubbed Loss\nAMPlification (LAMP). LAMP deforms the loss landscape into a steep shape so the\nreconstruction error on unseen anomalies becomes greater. Accordingly, the\nanomaly detection performance is improved without any change of the NN\narchitecture. Our findings suggest that LAMP can be easily applied to any\nreconstruction error metrics in UAD settings where the reconstruction model is\ntrained with anomaly-free samples only.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.08915", "title": "Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly\n  Detection", "abstract": "  Massive key performance indicators (KPIs) are monitored as multivariate time\nseries data (MTS) to ensure the reliability of the software applications and\nservice system. Accurately detecting the abnormality of MTS is very critical\nfor subsequent fault elimination. The scarcity of anomalies and manual labeling\nhas led to the development of various self-supervised MTS anomaly detection\n(AD) methods, which optimize an overall objective/loss encompassing all\nmetrics' regression objectives/losses. However, our empirical study uncovers\nthe prevalence of conflicts among metrics' regression objectives, causing MTS\nmodels to grapple with different losses. This critical aspect significantly\nimpacts detection performance but has been overlooked in existing approaches.\nTo address this problem, by mimicking the design of multi-gate\nmixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI\nAnomaly Detection algorithm. CAD offers an exclusive structure for each metric\nto mitigate potential conflicts while fostering inter-metric promotions. Upon\nthorough investigation, we find that the poor performance of vanilla MMoE\nmainly comes from the input-output misalignment settings of MTS formulation and\nconvergence issues arising from expansive tasks. To address these challenges,\nwe propose a straightforward yet effective task-oriented metric selection and\np&s (personalized and shared) gating mechanism, which establishes CAD as the\nfirst practicable multi-task learning (MTL) based MTS AD model. Evaluations on\nmultiple public datasets reveal that CAD obtains an average F1-score of 0.943\nacross three public datasets, notably outperforming state-of-the-art methods.\nOur code is accessible at https://github.com/dawnvince/MTS_CAD.\n", "date": "2023-08-17", "categories": []}
{"id": "2308.11898", "title": "Exploring the Optimization Objective of One-Class Classification for\n  Anomaly Detection", "abstract": "  One-class classification (OCC) is a longstanding method for anomaly\ndetection. With the powerful representation capability of the pre-trained\nbackbone, OCC methods have witnessed significant performance improvements.\nTypically, most of these OCC methods employ transfer learning to enhance the\ndiscriminative nature of the pre-trained backbone's features, thus achieving\nremarkable efficacy. While most current approaches emphasize feature transfer\nstrategies, we argue that the optimization objective space within OCC methods\ncould also be an underlying critical factor influencing performance. In this\nwork, we conducted a thorough investigation into the optimization objective of\nOCC. Through rigorous theoretical analysis and derivation, we unveil a key\ninsights: any space with the suitable norm can serve as an equivalent\nsubstitute for the hypersphere center, without relying on the distribution\nassumption of training samples. Further, we provide guidelines for determining\nthe feasible domain of norms for the OCC optimization objective. This novel\ninsight sparks a simple and data-agnostic deep one-class classification method.\nOur method is straightforward, with a single 1x1 convolutional layer as a\ntrainable projector and any space with suitable norm as the optimization\nobjective. Extensive experiments validate the reliability and efficacy of our\nfindings and the corresponding methodology, resulting in state-of-the-art\nperformance in both one-class classification and industrial vision anomaly\ndetection and segmentation tasks.\n", "date": "2023-08-23", "categories": []}
{"id": "2308.11669", "title": "Class Label-aware Graph Anomaly Detection", "abstract": "  Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a\nnode is anomalous or not. One common observation we made from previous\nunsupervised methods is that they not only assume the absence of such anomaly\nlabels, but also the absence of class labels (the class a node belongs to used\nin a general node classification task). In this work, we study the utility of\nclass labels for unsupervised GAD; in particular, how they enhance the\ndetection of structural anomalies. To this end, we propose a Class Label-aware\nGraph Anomaly Detection framework (CLAD) that utilizes a limited amount of\nlabeled nodes to enhance the performance of unsupervised GAD. Extensive\nexperiments on ten datasets demonstrate the superior performance of CLAD in\ncomparison to existing unsupervised GAD methods, even in the absence of\nground-truth class label information. The source code for CLAD is available at\n\\url{https://github.com/jhkim611/CLAD}.\n", "date": "2023-08-22", "categories": []}
{"id": "2308.11780", "title": "Few-shot Anomaly Detection in Text with Deviation Learning", "abstract": "  Most current methods for detecting anomalies in text concentrate on\nconstructing models solely relying on unlabeled data. These models operate on\nthe presumption that no labeled anomalous examples are available, which\nprevents them from utilizing prior knowledge of anomalies that are typically\npresent in small numbers in many real-world applications. Furthermore, these\nmodels prioritize learning feature embeddings rather than optimizing anomaly\nscores directly, which could lead to suboptimal anomaly scoring and inefficient\nuse of data during the learning process. In this paper, we introduce FATE, a\ndeep few-shot learning-based framework that leverages limited anomaly examples\nand learns anomaly scores explicitly in an end-to-end method using deviation\nlearning. In this approach, the anomaly scores of normal examples are adjusted\nto closely resemble reference scores obtained from a prior distribution.\nConversely, anomaly samples are forced to have anomalous scores that\nconsiderably deviate from the reference score in the upper tail of the prior.\nAdditionally, our model is optimized to learn the distinct behavior of\nanomalies by utilizing a multi-head self-attention layer and multiple instance\nlearning approaches. Comprehensive experiments on several benchmark datasets\ndemonstrate that our proposed approach attains a new level of state-of-the-art\nperformance.\n", "date": "2023-08-22", "categories": []}
{"id": "2308.11834", "title": "Performance Comparison and Implementation of Bayesian Variants for\n  Network Intrusion Detection", "abstract": "  Bayesian classifiers perform well when each of the features is completely\nindependent of the other which is not always valid in real world application.\nThe aim of this study is to implement and compare the performances of each\nvariant of Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on\nanomaly detection in network intrusion, and to investigate whether there is any\nassociation between each variant assumption and their performance. Our\ninvestigation showed that each variant of Bayesian algorithm blindly follows\nits assumption regardless of feature property, and that the assumption is the\nsingle most important factor that influences their accuracy. Experimental\nresults show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial\nhas accuracy of 31.2% test (31.2% train), while Gaussian has accuracy of 81.69%\ntest (82.84% train). Going deeper, we investigated and found that each Naive\nBayes variants performances and accuracy is largely due to each classifier\nassumption, Gaussian classifier performed best on anomaly detection due to its\nassumption that features follow normal distributions which are continuous,\nwhile multinomial classifier have a dismal performance as it simply assumes\ndiscreet and multinomial distribution.\n", "date": "2023-08-22", "categories": []}
{"id": "2308.11072", "title": "TeD-SPAD: Temporal Distinctiveness for Self-supervised\n  Privacy-preservation for video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) without human monitoring is a complex computer\nvision task that can have a positive impact on society if implemented\nsuccessfully. While recent advances have made significant progress in solving\nthis task, most existing approaches overlook a critical real-world concern:\nprivacy. With the increasing popularity of artificial intelligence\ntechnologies, it becomes crucial to implement proper AI ethics into their\ndevelopment. Privacy leakage in VAD allows models to pick up and amplify\nunnecessary biases related to people's personal information, which may lead to\nundesirable decision making. In this paper, we propose TeD-SPAD, a\nprivacy-aware video anomaly detection framework that destroys visual private\ninformation in a self-supervised manner. In particular, we propose the use of a\ntemporally-distinct triplet loss to promote temporally discriminative features,\nwhich complements current weakly-supervised VAD methods. Using TeD-SPAD, we\nachieve a positive trade-off between privacy protection and utility anomaly\ndetection performance on three popular weakly supervised VAD datasets:\nUCF-Crime, XD-Violence, and ShanghaiTech. Our proposed anonymization model\nreduces private attribute prediction by 32.25% while only reducing frame-level\nROC AUC on the UCF-Crime anomaly detection dataset by 3.69%. Project Page:\nhttps://joefioresi718.github.io/TeD-SPAD_webpage/\n", "date": "2023-08-21", "categories": []}
{"id": "2302.09200", "title": "Brainomaly: Unsupervised Neurologic Disease Detection Utilizing\n  Unannotated T1-weighted Brain MR Images", "abstract": "  Harnessing the power of deep neural networks in the medical imaging domain is\nchallenging due to the difficulties in acquiring large annotated datasets,\nespecially for rare diseases, which involve high costs, time, and effort for\nannotation. Unsupervised disease detection methods, such as anomaly detection,\ncan significantly reduce human effort in these scenarios. While anomaly\ndetection typically focuses on learning from images of healthy subjects only,\nreal-world situations often present unannotated datasets with a mixture of\nhealthy and diseased subjects. Recent studies have demonstrated that utilizing\nsuch unannotated images can improve unsupervised disease and anomaly detection.\nHowever, these methods do not utilize knowledge specific to registered\nneuroimages, resulting in a subpar performance in neurologic disease detection.\nTo address this limitation, we propose Brainomaly, a GAN-based image-to-image\ntranslation method specifically designed for neurologic disease detection.\nBrainomaly not only offers tailored image-to-image translation suitable for\nneuroimages but also leverages unannotated mixed images to achieve superior\nneurologic disease detection. Additionally, we address the issue of model\nselection for inference without annotated samples by proposing a pseudo-AUC\nmetric, further enhancing Brainomaly's detection performance. Extensive\nexperiments and ablation studies demonstrate that Brainomaly outperforms\nexisting state-of-the-art unsupervised disease and anomaly detection methods by\nsignificant margins in Alzheimer's disease detection using a publicly available\ndataset and headache detection using an institutional dataset. The code is\navailable from https://github.com/mahfuzmohammad/Brainomaly.\n", "date": "2023-02-18", "categories": []}
{"id": "2307.00750", "title": "Feasibility of Universal Anomaly Detection without Knowing the\n  Abnormality in Medical Images", "abstract": "  Many anomaly detection approaches, especially deep learning methods, have\nbeen recently developed to identify abnormal image morphology by only employing\nnormal images during training. Unfortunately, many prior anomaly detection\nmethods were optimized for a specific \"known\" abnormality (e.g., brain tumor,\nbone fraction, cell types). Moreover, even though only the normal images were\nused in the training process, the abnormal images were often employed during\nthe validation process (e.g., epoch selection, hyper-parameter tuning), which\nmight leak the supposed ``unknown\" abnormality unintentionally. In this study,\nwe investigated these two essential aspects regarding universal anomaly\ndetection in medical images by (1) comparing various anomaly detection methods\nacross four medical datasets, (2) investigating the inevitable but often\nneglected issues on how to unbiasedly select the optimal anomaly detection\nmodel during the validation phase using only normal images, and (3) proposing a\nsimple decision-level ensemble method to leverage the advantage of different\nkinds of anomaly detection without knowing the abnormality. The results of our\nexperiments indicate that none of the evaluated methods consistently achieved\nthe best performance across all datasets. Our proposed method enhanced the\nrobustness of performance in general (average AUC 0.956).\n", "date": "2023-07-03", "categories": []}
{"id": "2308.09937", "title": "Practical Anomaly Detection over Multivariate Monitoring Metrics for\n  Online Services", "abstract": "  As modern software systems continue to grow in terms of complexity and\nvolume, anomaly detection on multivariate monitoring metrics, which profile\nsystems' health status, becomes more and more critical and challenging. In\nparticular, the dependency between different metrics and their historical\npatterns plays a critical role in pursuing prompt and accurate anomaly\ndetection. Existing approaches fall short of industrial needs for being unable\nto capture such information efficiently. To fill this significant gap, in this\npaper, we propose CMAnomaly, an anomaly detection framework on multivariate\nmonitoring metrics based on collaborative machine. The proposed collaborative\nmachine is a mechanism to capture the pairwise interactions along with feature\nand temporal dimensions with linear time complexity. Cost-effective models can\nthen be employed to leverage both the dependency between monitoring metrics and\ntheir historical patterns for anomaly detection. The proposed framework is\nextensively evaluated with both public data and industrial data collected from\na large-scale online service system of Huawei Cloud. The experimental results\ndemonstrate that compared with state-of-the-art baseline models, CMAnomaly\nachieves an average F1 score of 0.9494, outperforming baselines by 6.77% to\n10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience\nof deploying CMAnomaly in Huawei Cloud.\n", "date": "2023-08-19", "categories": []}
{"id": "2308.09965", "title": "Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation", "abstract": "  Within the context of autonomous driving, encountering unknown objects\nbecomes inevitable during deployment in the open world. Therefore, it is\ncrucial to equip standard semantic segmentation models with anomaly awareness.\nMany previous approaches have utilized synthetic out-of-distribution (OoD) data\naugmentation to tackle this problem. In this work, we advance the OoD synthesis\nprocess by reducing the domain gap between the OoD data and driving scenes,\neffectively mitigating the style difference that might otherwise act as an\nobvious shortcut during training. Additionally, we propose a simple fine-tuning\nloss that effectively induces a pre-trained semantic segmentation model to\ngenerate a ``none of the given classes\" prediction, leveraging per-pixel OoD\nscores for anomaly segmentation. With minimal fine-tuning effort, our pipeline\nenables the use of pre-trained models for anomaly segmentation while\nmaintaining the performance on the original task.\n", "date": "2023-08-19", "categories": []}
{"id": "2308.10036", "title": "Semi-Supervised Anomaly Detection for the Determination of Vehicle\n  Hijacking Tweets", "abstract": "  In South Africa, there is an ever-growing issue of vehicle hijackings. This\nleads to travellers constantly being in fear of becoming a victim to such an\nincident. This work presents a new semi-supervised approach to using tweets to\nidentify hijacking incidents by using unsupervised anomaly detection\nalgorithms. Tweets consisting of the keyword \"hijacking\" are obtained, stored,\nand processed using the term frequency-inverse document frequency (TF-IDF) and\nfurther analyzed by using two anomaly detection algorithms: 1) K-Nearest\nNeighbour (KNN); 2) Cluster Based Outlier Factor (CBLOF). The comparative\nevaluation showed that the KNN method produced an accuracy of 89%, whereas the\nCBLOF produced an accuracy of 90%. The CBLOF method was also able to obtain a\nF1-Score of 0.8, whereas the KNN produced a 0.78. Therefore, there is a slight\ndifference between the two approaches, in favour of CBLOF, which has been\nselected as a preferred unsupervised method for the determination of relevant\nhijacking tweets. In future, a comparison will be done between supervised\nlearning methods and the unsupervised methods presented in this work on larger\ndataset. Optimisation mechanisms will also be employed in order to increase the\noverall performance.\n", "date": "2023-08-19", "categories": []}
{"id": "2308.10155", "title": "Unilaterally Aggregated Contrastive Learning with Hierarchical\n  Augmentation for Anomaly Detection", "abstract": "  Anomaly detection (AD), aiming to find samples that deviate from the training\ndistribution, is essential in safety-critical applications. Though recent\nself-supervised learning based attempts achieve promising results by creating\nvirtual outliers, their training objectives are less faithful to AD which\nrequires a concentrated inlier distribution as well as a dispersive outlier\ndistribution. In this paper, we propose Unilaterally Aggregated Contrastive\nLearning with Hierarchical Augmentation (UniCon-HA), taking into account both\nthe requirements above. Specifically, we explicitly encourage the concentration\nof inliers and the dispersion of virtual outliers via supervised and\nunsupervised contrastive losses, respectively. Considering that standard\ncontrastive data augmentation for generating positive views may induce\noutliers, we additionally introduce a soft mechanism to re-weight each\naugmented inlier according to its deviation from the inlier distribution, to\nensure a purified concentration. Moreover, to prompt a higher concentration,\ninspired by curriculum learning, we adopt an easy-to-hard hierarchical\naugmentation strategy and perform contrastive aggregation at different depths\nof the network based on the strengths of data augmentation. Our method is\nevaluated under three AD settings including unlabeled one-class, unlabeled\nmulti-class, and labeled multi-class, demonstrating its consistent superiority\nover other competitors.\n", "date": "2023-08-20", "categories": []}
{"id": "2308.10504", "title": "Adaptive Thresholding Heuristic for KPI Anomaly Detection", "abstract": "  A plethora of outlier detectors have been explored in the time series domain,\nhowever, in a business sense, not all outliers are anomalies of interest.\nExisting anomaly detection solutions are confined to certain outlier detectors\nlimiting their applicability to broader anomaly detection use cases. Network\nKPIs (Key Performance Indicators) tend to exhibit stochastic behaviour\nproducing statistical outliers, most of which do not adversely affect business\noperations. Thus, a heuristic is required to capture the business definition of\nan anomaly for time series KPI. This article proposes an Adaptive Thresholding\nHeuristic (ATH) to dynamically adjust the detection threshold based on the\nlocal properties of the data distribution and adapt to changes in time series\npatterns. The heuristic derives the threshold based on the expected periodicity\nand the observed proportion of anomalies minimizing false positives and\naddressing concept drift. ATH can be used in conjunction with any underlying\nseasonality decomposition method and an outlier detector that yields an outlier\nscore. This method has been tested on EON1-Cell-U, a labeled KPI anomaly\ndataset produced by Ericsson, to validate our hypothesis. Experimental results\nshow that ATH is computationally efficient making it scalable for near real\ntime anomaly detection and flexible with multiple forecasters and outlier\ndetectors.\n", "date": "2023-08-21", "categories": []}
{"id": "2308.10893", "title": "Online Transition-Based Feature Generation for Anomaly Detection in\n  Concurrent Data Streams", "abstract": "  In this paper, we introduce the transition-based feature generator (TFGen)\ntechnique, which reads general activity data with attributes and generates\nstep-by-step generated data. The activity data may consist of network activity\nfrom packets, system calls from processes or classified activity from\nsurveillance cameras. TFGen processes data online and will generate data with\nencoded historical data for each incoming activity with high computational\nefficiency. The input activities may concurrently originate from distinct\ntraces or channels. The technique aims to address issues such as\ndomain-independent applicability, the ability to discover global process\nstructures, the encoding of time-series data, and online processing capability.\n", "date": "2023-08-17", "categories": []}
{"id": "2308.09171", "title": "Forensic Data Analytics for Anomaly Detection in Evolving Networks", "abstract": "  In the prevailing convergence of traditional infrastructure-based deployment\n(i.e., Telco and industry operational networks) towards evolving deployments\nenabled by 5G and virtualization, there is a keen interest in elaborating\neffective security controls to protect these deployments in-depth. By\nconsidering key enabling technologies like 5G and virtualization, evolving\nnetworks are democratized, facilitating the establishment of point presences\nintegrating different business models ranging from media, dynamic web content,\ngaming, and a plethora of IoT use cases. Despite the increasing services\nprovided by evolving networks, many cybercrimes and attacks have been launched\nin evolving networks to perform malicious activities. Due to the limitations of\ntraditional security artifacts (e.g., firewalls and intrusion detection\nsystems), the research on digital forensic data analytics has attracted more\nattention. Digital forensic analytics enables people to derive detailed\ninformation and comprehensive conclusions from different perspectives of\ncybercrimes to assist in convicting criminals and preventing future crimes.\nThis chapter presents a digital analytics framework for network anomaly\ndetection, including multi-perspective feature engineering, unsupervised\nanomaly detection, and comprehensive result correction procedures. Experiments\non real-world evolving network data show the effectiveness of the proposed\nforensic data analytics solution.\n", "date": "2023-08-17", "categories": []}
{"id": "2211.10946", "title": "Normalizing Flows for Human Pose Anomaly Detection", "abstract": "  Video anomaly detection is an ill-posed problem because it relies on many\nparameters such as appearance, pose, camera angle, background, and more. We\ndistill the problem to anomaly detection of human pose, thus decreasing the\nrisk of nuisance parameters such as appearance affecting the result. Focusing\non pose alone also has the side benefit of reducing bias against distinct\nminority groups. Our model works directly on human pose graph sequences and is\nexceptionally lightweight (~1K parameters), capable of running on any machine\nable to run the pose estimation with negligible additional resources. We\nleverage the highly compact pose representation in a normalizing flows\nframework, which we extend to tackle the unique characteristics of\nspatio-temporal pose data and show its advantages in this use case. The\nalgorithm is quite general and can handle training data of only normal examples\nas well as a supervised setting that consists of labeled normal and abnormal\nexamples. We report state-of-the-art results on two anomaly detection\nbenchmarks - the unsupervised ShanghaiTech dataset and the recent supervised\nUBnormal dataset.\n", "date": "2022-11-20", "categories": []}
{"id": "2308.07604", "title": "Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine\n  Learning for Anomaly Detection", "abstract": "  The next generation of telescopes will yield a substantial increase in the\navailability of high-resolution spectroscopic data for thousands of exoplanets.\nThe sheer volume of data and number of planets to be analyzed greatly motivate\nthe development of new, fast and efficient methods for flagging interesting\nplanets for reobservation and detailed analysis. We advocate the application of\nmachine learning (ML) techniques for anomaly (novelty) detection to exoplanet\ntransit spectra, with the goal of identifying planets with unusual chemical\ncomposition and even searching for unknown biosignatures. We successfully\ndemonstrate the feasibility of two popular anomaly detection methods (Local\nOutlier Factor and One Class Support Vector Machine) on a large public database\nof synthetic spectra. We consider several test cases, each with different\nlevels of instrumental noise. In each case, we use ROC curves to quantify and\ncompare the performance of the two ML techniques.\n", "date": "2023-08-15", "categories": []}
{"id": "2308.07783", "title": "Future Video Prediction from a Single Frame for Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) is an important but challenging task in\ncomputer vision. The main challenge rises due to the rarity of training samples\nto model all anomaly cases. Hence, semi-supervised anomaly detection methods\nhave gotten more attention, since they focus on modeling normals and they\ndetect anomalies by measuring the deviations from normal patterns. Despite\nimpressive advances of these methods in modeling normal motion and appearance,\nlong-term motion modeling has not been effectively explored so far. Inspired by\nthe abilities of the future frame prediction proxy-task, we introduce the task\nof future video prediction from a single frame, as a novel proxy-task for video\nanomaly detection. This proxy-task alleviates the challenges of previous\nmethods in learning longer motion patterns. Moreover, we replace the initial\nand future raw frames with their corresponding semantic segmentation map, which\nnot only makes the method aware of object class but also makes the prediction\ntask less complex for the model. Extensive experiments on the benchmark\ndatasets (ShanghaiTech, UCSD-Ped1, and UCSD-Ped2) show the effectiveness of the\nmethod and the superiority of its performance compared to SOTA prediction-based\nVAD methods.\n", "date": "2023-08-15", "categories": []}
{"id": "2308.06481", "title": "Out-of-distribution multi-view auto-encoders for prostate cancer lesion\n  detection", "abstract": "  Traditional deep learning (DL) approaches based on supervised learning\nparadigms require large amounts of annotated data that are rarely available in\nthe medical domain. Unsupervised Out-of-distribution (OOD) detection is an\nalternative that requires less annotated data. Further, OOD applications\nexploit the class skewness commonly present in medical data. Magnetic resonance\nimaging (MRI) has proven to be useful for prostate cancer (PCa) diagnosis and\nmanagement, but current DL approaches rely on T2w axial MRI, which suffers from\nlow out-of-plane resolution. We propose a multi-stream approach to accommodate\ndifferent T2w directions to improve the performance of PCa lesion detection in\nan OOD approach. We evaluate our approach on a publicly available data-set,\nobtaining better detection results in terms of AUC when compared to a single\ndirection approach (73.1 vs 82.3). Our results show the potential of OOD\napproaches for PCa lesion detection based on MRI.\n", "date": "2023-08-12", "categories": []}
{"id": "2308.06748", "title": "Target before Shooting: Accurate Anomaly Detection and Localization\n  under One Millisecond via Cascade Patch Retrieval", "abstract": "  In this work, by re-examining the \"matching\" nature of Anomaly Detection\n(AD), we propose a new AD framework that simultaneously enjoys new records of\nAD accuracy and dramatically high running speed. In this framework, the anomaly\ndetection problem is solved via a cascade patch retrieval procedure that\nretrieves the nearest neighbors for each test image patch in a coarse-to-fine\nfashion. Given a test sample, the top-K most similar training images are first\nselected based on a robust histogram matching process. Secondly, the nearest\nneighbor of each test patch is retrieved over the similar geometrical locations\non those \"global nearest neighbors\", by using a carefully trained local metric.\nFinally, the anomaly score of each test image patch is calculated based on the\ndistance to its \"local nearest neighbor\" and the \"non-background\" probability.\nThe proposed method is termed \"Cascade Patch Retrieval\" (CPR) in this work.\nDifferent from the conventional patch-matching-based AD algorithms, CPR selects\nproper \"targets\" (reference images and locations) before \"shooting\"\n(patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD\ndatasets, the proposed algorithm consistently outperforms all the comparing\nSOTA methods by remarkable margins, measured by various AD metrics.\nFurthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with\nthe standard setting while its simplified version only requires less than 1 ms\nto process an image at the cost of a trivial accuracy drop. The code of CPR is\navailable at https://github.com/flyinghu123/CPR.\n", "date": "2023-08-13", "categories": []}
{"id": "2308.07050", "title": "Survey on video anomaly detection in dynamic scenes with moving cameras", "abstract": "  The increasing popularity of compact and inexpensive cameras, e.g.~dash\ncameras, body cameras, and cameras equipped on robots, has sparked a growing\ninterest in detecting anomalies within dynamic scenes recorded by moving\ncameras. However, existing reviews primarily concentrate on Video Anomaly\nDetection (VAD) methods assuming static cameras. The VAD literature with moving\ncameras remains fragmented, lacking comprehensive reviews to date. To address\nthis gap, we endeavor to present the first comprehensive survey on Moving\nCamera Video Anomaly Detection (MC-VAD). We delve into the research papers\nrelated to MC-VAD, critically assessing their limitations and highlighting\nassociated challenges. Our exploration encompasses three application domains:\nsecurity, urban transportation, and marine environments, which in turn cover\nsix specific tasks. We compile an extensive list of 25 publicly-available\ndatasets spanning four distinct environments: underwater, water surface,\nground, and aerial. We summarize the types of anomalies these datasets\ncorrespond to or contain, and present five main categories of approaches for\ndetecting such anomalies. Lastly, we identify future research directions and\ndiscuss novel contributions that could advance the field of MC-VAD. With this\nsurvey, we aim to offer a valuable reference for researchers and practitioners\nstriving to develop and advance state-of-the-art MC-VAD methods.\n", "date": "2023-08-14", "categories": []}
{"id": "2308.05011", "title": "Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with\n  Distinct Inlier Categories", "abstract": "  With the increasing volume of astronomical data generated by modern survey\ntelescopes, automated pipelines and machine learning techniques have become\ncrucial for analyzing and extracting knowledge from these datasets. Anomaly\ndetection, i.e. the task of identifying irregular or unexpected patterns in the\ndata, is a complex challenge in astronomy. In this paper, we propose\nMulti-Class Deep Support Vector Data Description (MCDSVDD), an extension of the\nstate-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically\ndesigned to handle different inlier categories with distinct data\ndistributions. MCDSVDD uses a neural network to map the data into hyperspheres,\nwhere each hypersphere represents a specific inlier category. The distance of\neach sample from the centers of these hyperspheres determines the anomaly\nscore. We evaluate the effectiveness of MCDSVDD by comparing its performance\nwith several anomaly detection algorithms on a large dataset of astronomical\nlight-curves obtained from the Zwicky Transient Facility. Our results\ndemonstrate the efficacy of MCDSVDD in detecting anomalous sources while\nleveraging the presence of different inlier categories. The code and the data\nneeded to reproduce our results are publicly available at\nhttps://github.com/mperezcarrasco/AnomalyALeRCE.\n", "date": "2023-08-09", "categories": []}
{"id": "2308.02914", "title": "Anomaly Detection in Global Financial Markets with Graph Neural Networks\n  and Nonextensive Entropy", "abstract": "  Anomaly detection is a challenging task, particularly in systems with many\nvariables. Anomalies are outliers that statistically differ from the analyzed\ndata and can arise from rare events, malfunctions, or system misuse. This study\ninvestigated the ability to detect anomalies in global financial markets\nthrough Graph Neural Networks (GNN) considering an uncertainty scenario\nmeasured by a nonextensive entropy. The main findings show that the complex\nstructure of highly correlated assets decreases in a crisis, and the number of\nanomalies is statistically different for nonextensive entropy parameters\nconsidering before, during, and after crisis.\n", "date": "2023-08-05", "categories": []}
{"id": "2308.04708", "title": "Generative Perturbation Analysis for Probabilistic Black-Box Anomaly\n  Attribution", "abstract": "  We address the task of probabilistic anomaly attribution in the black-box\nregression setting, where the goal is to compute the probability distribution\nof the attribution score of each input variable, given an observed anomaly. The\ntraining dataset is assumed to be unavailable. This task differs from the\nstandard XAI (explainable AI) scenario, since we wish to explain the anomalous\ndeviation from a black-box prediction rather than the black-box model itself.\n  We begin by showing that mainstream model-agnostic explanation methods, such\nas the Shapley values, are not suitable for this task because of their\n``deviation-agnostic property.'' We then propose a novel framework for\nprobabilistic anomaly attribution that allows us to not only compute\nattribution scores as the predictive mean but also quantify the uncertainty of\nthose scores. This is done by considering a generative process for\nperturbations that counter-factually bring the observed anomalous observation\nback to normalcy. We introduce a variational Bayes algorithm for deriving the\ndistributions of per variable attribution scores. To the best of our knowledge,\nthis is the first probabilistic anomaly attribution framework that is free from\nbeing deviation-agnostic.\n", "date": "2023-08-09", "categories": []}
{"id": "2212.10579", "title": "Resonant Anomaly Detection with Multiple Reference Datasets", "abstract": "  An important class of techniques for resonant anomaly detection in high\nenergy physics builds models that can distinguish between reference and target\ndatasets, where only the latter has appreciable signal. Such techniques,\nincluding Classification Without Labels (CWoLa) and Simulation Assisted\nLikelihood-free Anomaly Detection (SALAD) rely on a single reference dataset.\nThey cannot take advantage of commonly-available multiple datasets and thus\ncannot fully exploit available information. In this work, we propose\ngeneralizations of CWoLa and SALAD for settings where multiple reference\ndatasets are available, building on weak supervision techniques. We demonstrate\nimproved performance in a number of settings with realistic and synthetic data.\nAs an added benefit, our generalizations enable us to provide finite-sample\nguarantees, improving on existing asymptotic analyses.\n", "date": "2022-12-20", "categories": []}
{"id": "2308.02983", "title": "Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image\n  Anomaly Detection", "abstract": "  Humans recognize anomalies through two aspects: larger patch-wise\nrepresentation discrepancies and weaker patch-to-normal-patch correlations.\nHowever, the previous AD methods didn't sufficiently combine the two\ncomplementary aspects to design AD models. To this end, we find that\nTransformer can ideally satisfy the two aspects as its great power in the\nunified modeling of patch-wise representations and patch-to-patch correlations.\nIn this paper, we propose a novel AD framework: FOcus-the-Discrepancy (FOD),\nwhich can simultaneously spot the patch-wise, intra- and inter-discrepancies of\nanomalies. The major characteristic of our method is that we renovate the\nself-attention maps in transformers to Intra-Inter-Correlation (I2Correlation).\nThe I2Correlation contains a two-branch structure to first explicitly establish\nintra- and inter-image correlations, and then fuses the features of two-branch\nto spotlight the abnormal patterns. To learn the intra- and inter-correlations\nadaptively, we propose the RBF-kernel-based target-correlations as learning\ntargets for self-supervised learning. Besides, we introduce an entropy\nconstraint strategy to solve the mode collapse issue in optimization and\nfurther amplify the normal-abnormal distinguishability. Extensive experiments\non three unsupervised real-world AD benchmarks show the superior performance of\nour approach. Code will be available at https://github.com/xcyao00/FOD.\n", "date": "2023-08-06", "categories": []}
{"id": "2308.03171", "title": "Detection of Anomalies in Multivariate Time Series Using Ensemble\n  Techniques", "abstract": "  Anomaly Detection in multivariate time series is a major problem in many\nfields. Due to their nature, anomalies sparsely occur in real data, thus making\nthe task of anomaly detection a challenging problem for classification\nalgorithms to solve. Methods that are based on Deep Neural Networks such as\nLSTM, Autoencoders, Convolutional Autoencoders etc., have shown positive\nresults in such imbalanced data. However, the major challenge that algorithms\nface when applied to multivariate time series is that the anomaly can arise\nfrom a small subset of the feature set. To boost the performance of these base\nmodels, we propose a feature-bagging technique that considers only a subset of\nfeatures at a time, and we further apply a transformation that is based on\nnested rotation computed from Principal Component Analysis (PCA) to improve the\neffectiveness and generalization of the approach. To further enhance the\nprediction performance, we propose an ensemble technique that combines multiple\nbase models toward the final decision. In addition, a semi-supervised approach\nusing a Logistic Regressor to combine the base models' outputs is proposed. The\nproposed methodology is applied to the Skoltech Anomaly Benchmark (SKAB)\ndataset, which contains time series data related to the flow of water in a\nclosed circuit, and the experimental results show that the proposed ensemble\ntechnique outperforms the basic algorithms. More specifically, the performance\nimprovement in terms of anomaly detection accuracy reaches 2% for the\nunsupervised and at least 10% for the semi-supervised models.\n", "date": "2023-08-06", "categories": []}
{"id": "2302.11239", "title": "Explainable Contextual Anomaly Detection using Quantile Regression\n  Forests", "abstract": "  Traditional anomaly detection methods aim to identify objects that deviate\nfrom most other objects by treating all features equally. In contrast,\ncontextual anomaly detection methods aim to detect objects that deviate from\nother objects within a context of similar objects by dividing the features into\ncontextual features and behavioral features. In this paper, we develop\nconnections between dependency-based traditional anomaly detection methods and\ncontextual anomaly detection methods. Based on resulting insights, we propose a\nnovel approach to inherently interpretable contextual anomaly detection that\nuses Quantile Regression Forests to model dependencies between features.\nExtensive experiments on various synthetic and real-world datasets demonstrate\nthat our method outperforms state-of-the-art anomaly detection methods in\nidentifying contextual anomalies in terms of accuracy and interpretability.\n", "date": "2023-02-22", "categories": []}
{"id": "2308.01947", "title": "Discriminative Graph-level Anomaly Detection via Dual-students-teacher\n  Model", "abstract": "  Different from the current node-level anomaly detection task, the goal of\ngraph-level anomaly detection is to find abnormal graphs that significantly\ndiffer from others in a graph set. Due to the scarcity of research on the work\nof graph-level anomaly detection, the detailed description of graph-level\nanomaly is insufficient. Furthermore, existing works focus on capturing\nanomalous graph information to learn better graph representations, but they\nignore the importance of an effective anomaly score function for evaluating\nabnormal graphs. Thus, in this work, we first define anomalous graph\ninformation including node and graph property anomalies in a graph set and\nadopt node-level and graph-level information differences to identify them,\nrespectively. Then, we introduce a discriminative graph-level anomaly detection\nframework with dual-students-teacher model, where the teacher model with a\nheuristic loss are trained to make graph representations more divergent. Then,\ntwo competing student models trained by normal and abnormal graphs respectively\nfit graph representations of the teacher model in terms of node-level and\ngraph-level representation perspectives. Finally, we combine representation\nerrors between two student models to discriminatively distinguish anomalous\ngraphs. Extensive experiment analysis demonstrates that our method is effective\nfor the graph-level anomaly detection task on graph datasets in the real world.\n", "date": "2023-08-03", "categories": []}
{"id": "2308.02046", "title": "UGainS: Uncertainty Guided Anomaly Instance Segmentation", "abstract": "  A single unexpected object on the road can cause an accident or may lead to\ninjuries. To prevent this, we need a reliable mechanism for finding anomalous\nobjects on the road. This task, called anomaly segmentation, can be a stepping\nstone to safe and reliable autonomous driving. Current approaches tackle\nanomaly segmentation by assigning an anomaly score to each pixel and by\ngrouping anomalous regions using simple heuristics. However, pixel grouping is\na limiting factor when it comes to evaluating the segmentation performance of\nindividual anomalous objects. To address the issue of grouping multiple anomaly\ninstances into one, we propose an approach that produces accurate anomaly\ninstance masks. Our approach centers on an out-of-distribution segmentation\nmodel for identifying uncertain regions and a strong generalist segmentation\nmodel for anomaly instances segmentation. We investigate ways to use uncertain\nregions to guide such a segmentation model to perform segmentation of anomalous\ninstances. By incorporating strong object priors from a generalist model we\nadditionally improve the per-pixel anomaly segmentation performance. Our\napproach outperforms current pixel-level anomaly segmentation methods,\nachieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the\nRoadAnomaly validation sets respectively. Project page:\nhttps://vision.rwth-aachen.de/ugains\n", "date": "2023-08-03", "categories": []}
{"id": "2308.02184", "title": "Synthetic outlier generation for anomaly detection in autonomous driving", "abstract": "  Anomaly detection, or outlier detection, is a crucial task in various domains\nto identify instances that significantly deviate from established patterns or\nthe majority of data. In the context of autonomous driving, the identification\nof anomalies is particularly important to prevent safety-critical incidents, as\ndeep learning models often exhibit overconfidence in anomalous or outlier\nsamples. In this study, we explore different strategies for training an image\nsemantic segmentation model with an anomaly detection module. By introducing\nmodifications to the training stage of the state-of-the-art DenseHybrid model,\nwe achieve significant performance improvements in anomaly detection. Moreover,\nwe propose a simplified detector that achieves comparable results to our\nmodified DenseHybrid approach, while also surpassing the performance of the\noriginal DenseHybrid model. These findings demonstrate the efficacy of our\nproposed strategies for enhancing anomaly detection in the context of\nautonomous driving.\n", "date": "2023-08-04", "categories": []}
{"id": "2307.14701", "title": "MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution\n  Detection in Medical Images", "abstract": "  Unsupervised Out-of-Distribution (OOD) detection consists in identifying\nanomalous regions in images leveraging only models trained on images of healthy\nanatomy. An established approach is to tokenize images and model the\ndistribution of tokens with Auto-Regressive (AR) models. AR models are used to\n1) identify anomalous tokens and 2) in-paint anomalous representations with\nin-distribution tokens. However, AR models are slow at inference time and prone\nto error accumulation issues which negatively affect OOD detection performance.\nOur novel method, MIM-OOD, overcomes both speed and error accumulation issues\nby replacing the AR model with two task-specific networks: 1) a transformer\noptimized to identify anomalous tokens and 2) a transformer optimized to\nin-paint anomalous tokens using masked image modelling (MIM). Our experiments\nwith brain MRI anomalies show that MIM-OOD substantially outperforms AR models\n(DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5s vs 244s).\n", "date": "2023-07-27", "categories": []}
{"id": "2308.01639", "title": "Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly\n  Detection", "abstract": "  Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart\nconditions. Rare cardiac diseases may be underdiagnosed using traditional ECG\nanalysis, considering that no training dataset can exhaust all possible cardiac\ndisorders. This paper proposes using anomaly detection to identify any\nunhealthy status, with normal ECGs solely for training. However, detecting\nanomalies in ECG can be challenging due to significant inter-individual\ndifferences and anomalies present in both global rhythm and local morphology.\nTo address this challenge, this paper introduces a novel multi-scale\ncross-restoration framework for ECG anomaly detection and localization that\nconsiders both local and global ECG characteristics. The proposed framework\nemploys a two-branch autoencoder to facilitate multi-scale feature learning\nthrough a masking and restoration process, with one branch focusing on global\nfeatures from the entire ECG and the other on local features from\nheartbeat-level details, mimicking the diagnostic process of cardiologists.\nAnomalies are identified by their high restoration errors. To evaluate the\nperformance on a large number of individuals, this paper introduces a new\nchallenging benchmark with signal point-level ground truths annotated by\nexperienced cardiologists. The proposed method demonstrates state-of-the-art\nperformance on this benchmark and two other well-known ECG datasets. The\nbenchmark dataset and source code are available at:\n\\url{https://github.com/MediaBrain-SJTU/ECGAD}\n", "date": "2023-08-03", "categories": []}
{"id": "2308.01063", "title": "Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced\n  Unsupervised Approach", "abstract": "  Graph anomaly detection (GAD) has achieved success and has been widely\napplied in various domains, such as fraud detection, cybersecurity, finance\nsecurity, and biochemistry. However, existing graph anomaly detection\nalgorithms focus on distinguishing individual entities (nodes or graphs) and\noverlook the possibility of anomalous groups within the graph. To address this\nlimitation, this paper introduces a novel unsupervised framework for a new task\ncalled Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework\nfirst employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that\nbelong to potential anomaly groups by capturing long-range inconsistencies.\nSubsequently, group sampling is employed to sample candidate groups, which are\nthen fed into the proposed Topology Pattern-based Graph Contrastive Learning\n(TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to\ngenerate embeddings for each candidate group and thus distinct anomaly groups.\nThe experimental results on both real-world and synthetic datasets demonstrate\nthat the proposed framework shows superior performance in identifying and\nlocalizing anomaly groups, highlighting it as a promising solution for Gr-GAD.\nDatasets and codes of the proposed framework are at the github repository\nhttps://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.\n", "date": "2023-08-02", "categories": []}
{"id": "2308.00074", "title": "Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection\n  Model", "abstract": "  Anomaly detection and its explanation is important in many research areas\nsuch as intrusion detection, fraud detection, unknown attack detection in\nnetwork traffic and logs. It is challenging to identify the cause or\nexplanation of why one instance is an anomaly? and the other is not due to its\nunbounded and lack of supervisory nature. The answer to this question is\npossible with the emerging technique of explainable artificial intelligence\n(XAI). XAI provides tools and techniques to interpret and explain the output\nand working of complex models such as Deep Learning (DL). This paper aims to\ndetect and explain network anomalies with XAI, kernelSHAP method. The same\napproach is used to improve the network anomaly detection model in terms of\naccuracy, recall, precision and f score. The experiment is conduced with the\nlatest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and\ncompared. The overall accuracy and F score of OPT_Model (when trained in\nunsupervised way) are 0.90 and 0.76, respectively.\n", "date": "2023-07-31", "categories": []}
{"id": "2308.00393", "title": "A Survey of Time Series Anomaly Detection Methods in the AIOps Domain", "abstract": "  Internet-based services have seen remarkable success, generating vast amounts\nof monitored key performance indicators (KPIs) as univariate or multivariate\ntime series. Monitoring and analyzing these time series are crucial for\nresearchers, service operators, and on-call engineers to detect outliers or\nanomalies indicating service failures or significant events. Numerous advanced\nanomaly detection methods have emerged to address availability and performance\nissues. This review offers a comprehensive overview of time series anomaly\ndetection in Artificial Intelligence for IT operations (AIOps), which uses AI\ncapabilities to automate and optimize operational workflows. Additionally, it\nexplores future directions for real-world and next-generation time-series\nanomaly detection based on recent advancements.\n", "date": "2023-08-01", "categories": []}
{"id": "2303.00609", "title": "Unsupervised Pathology Detection: A Deep Dive Into the State of the Art", "abstract": "  Deep unsupervised approaches are gathering increased attention for\napplications such as pathology detection and segmentation in medical images\nsince they promise to alleviate the need for large labeled datasets and are\nmore generalizable than their supervised counterparts in detecting any kind of\nrare pathology. As the Unsupervised Anomaly Detection (UAD) literature\ncontinuously grows and new paradigms emerge, it is vital to continuously\nevaluate and benchmark new methods in a common framework, in order to reassess\nthe state-of-the-art (SOTA) and identify promising research directions. To this\nend, we evaluate a diverse selection of cutting-edge UAD methods on multiple\nmedical datasets, comparing them against the established SOTA in UAD for brain\nMRI. Our experiments demonstrate that newly developed feature-modeling methods\nfrom the industrial and medical literature achieve increased performance\ncompared to previous work and set the new SOTA in a variety of modalities and\ndatasets. Additionally, we show that such methods are capable of benefiting\nfrom recently developed self-supervised pre-training algorithms, further\nincreasing their performance. Finally, we perform a series of experiments in\norder to gain further insights into some unique characteristics of selected\nmodels and datasets. Our code can be found under\nhttps://github.com/iolag/UPD_study/.\n", "date": "2023-03-01", "categories": []}
{"id": "2208.07734", "title": "Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision\n  for Unsupervised Anomaly Detection is Creating the Illusion of Success", "abstract": "  Self-supervised learning (SSL) has emerged as a promising alternative to\ncreate supervisory signals to real-world problems, avoiding the extensive cost\nof manual labeling. SSL is particularly attractive for unsupervised tasks such\nas anomaly detection (AD), where labeled anomalies are rare or often\nnonexistent. A large catalog of augmentation functions has been used for\nSSL-based AD (SSAD) on image data, and recent works have reported that the type\nof augmentation has a significant impact on accuracy. Motivated by those, this\nwork sets out to put image-based SSAD under a larger lens and investigate the\nrole of data augmentation in SSAD. Through extensive experiments on 3 different\ndetector models and across 420 AD tasks, we provide comprehensive numerical and\nvisual evidences that the alignment between data augmentation and\nanomaly-generating mechanism is the key to the success of SSAD, and in the lack\nthereof, SSL may even impair accuracy. To the best of our knowledge, this is\nthe first meta-analysis on the role of data augmentation in SSAD.\n", "date": "2022-08-16", "categories": []}
{"id": "2307.12721", "title": "AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution\n  Anomaly Detection in Chest X-Rays", "abstract": "  Unsupervised anomaly detection in medical images such as chest radiographs is\nstepping into the spotlight as it mitigates the scarcity of the labor-intensive\nand costly expert annotation of anomaly data. However, nearly all existing\nmethods are formulated as a one-class classification trained only on\nrepresentations from the normal class and discard a potentially significant\nportion of the unlabeled data. This paper focuses on a more practical setting,\ndual distribution anomaly detection for chest X-rays, using the entire training\ndata, including both normal and unlabeled images. Inspired by a modern\nself-supervised vision transformer model trained using partial image inputs to\nreconstruct missing image regions -- we propose AMAE, a two-stage algorithm for\nadaptation of the pre-trained masked autoencoder (MAE). Starting from MAE\ninitialization, AMAE first creates synthetic anomalies from only normal\ntraining images and trains a lightweight classifier on frozen transformer\nfeatures. Subsequently, we propose an adaptation strategy to leverage unlabeled\nimages containing anomalies. The adaptation scheme is accomplished by assigning\npseudo-labels to unlabeled images and using two separate MAE based modules to\nmodel the normative and anomalous distributions of pseudo-labeled images. The\neffectiveness of the proposed adaptation strategy is evaluated with different\nanomaly ratios in an unlabeled training set. AMAE leads to consistent\nperformance gains over competing self-supervised and dual distribution anomaly\ndetection methods, setting the new state-of-the-art on three public chest X-ray\nbenchmarks: RSNA, NIH-CXR, and VinDr-CXR.\n", "date": "2023-07-24", "categories": []}
{"id": "2307.14575", "title": "A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised\n  Traffic Accident Detection in Driving Videos", "abstract": "  Identifying traffic accidents in driving videos is crucial to ensuring the\nsafety of autonomous driving and driver assistance systems. To address the\npotential danger caused by the long-tailed distribution of driving events,\nexisting traffic accident detection (TAD) methods mainly rely on unsupervised\nlearning. However, TAD is still challenging due to the rapid movement of\ncameras and dynamic scenes in driving scenarios. Existing unsupervised TAD\nmethods mainly rely on a single pretext task, i.e., an appearance-based or\nfuture object localization task, to detect accidents. However, appearance-based\napproaches are easily disturbed by the rapid movement of the camera and changes\nin illumination, which significantly reduce the performance of traffic accident\ndetection. Methods based on future object localization may fail to capture\nappearance changes in video frames, making it difficult to detect ego-involved\naccidents (e.g., out of control of the ego-vehicle). In this paper, we propose\na novel memory-augmented multi-task collaborative framework (MAMTCF) for\nunsupervised traffic accident detection in driving videos. Different from\nprevious approaches, our method can more accurately detect both ego-involved\nand non-ego accidents by simultaneously modeling appearance changes and object\nmotions in video frames through the collaboration of optical flow\nreconstruction and future object localization tasks. Further, we introduce a\nmemory-augmented motion representation mechanism to fully explore the\ninterrelation between different types of motion representations and exploit the\nhigh-level features of normal traffic patterns stored in memory to augment\nmotion representations, thus enlarging the difference from anomalies.\nExperimental results on recently published large-scale dataset demonstrate that\nour method achieves better performance compared to previous state-of-the-art\napproaches.\n", "date": "2023-07-27", "categories": []}
{"id": "2307.12994", "title": "Multi-representations Space Separation based Graph-level Anomaly-aware\n  Detection", "abstract": "  Graph structure patterns are widely used to model different area data\nrecently. How to detect anomalous graph information on these graph data has\nbecome a popular research problem. The objective of this research is centered\non the particular issue that how to detect abnormal graphs within a graph set.\nThe previous works have observed that abnormal graphs mainly show node-level\nand graph-level anomalies, but these methods equally treat two anomaly forms\nabove in the evaluation of abnormal graphs, which is contrary to the fact that\ndifferent types of abnormal graph data have different degrees in terms of\nnode-level and graph-level anomalies. Furthermore, abnormal graphs that have\nsubtle differences from normal graphs are easily escaped detection by the\nexisting methods. Thus, we propose a multi-representations space separation\nbased graph-level anomaly-aware detection framework in this paper. To consider\nthe different importance of node-level and graph-level anomalies, we design an\nanomaly-aware module to learn the specific weight between them in the abnormal\ngraph evaluation process. In addition, we learn strictly separate normal and\nabnormal graph representation spaces by four types of weighted graph\nrepresentations against each other including anchor normal graphs, anchor\nabnormal graphs, training normal graphs, and training abnormal graphs. Based on\nthe distance error between the graph representations of the test graph and both\nnormal and abnormal graph representation spaces, we can accurately determine\nwhether the test graph is anomalous. Our approach has been extensively\nevaluated against baseline methods using ten public graph datasets, and the\nresults demonstrate its effectiveness.\n", "date": "2023-07-22", "categories": []}
{"id": "2307.13239", "title": "RoSAS: Deep Semi-Supervised Anomaly Detection with\n  Contamination-Resilient Continuous Supervision", "abstract": "  Semi-supervised anomaly detection methods leverage a few anomaly examples to\nyield drastically improved performance compared to unsupervised models.\nHowever, they still suffer from two limitations: 1) unlabeled anomalies (i.e.,\nanomaly contamination) may mislead the learning process when all the unlabeled\ndata are employed as inliers for model training; 2) only discrete supervision\ninformation (such as binary or ordinal data labels) is exploited, which leads\nto suboptimal learning of anomaly scores that essentially take on a continuous\ndistribution. Therefore, this paper proposes a novel semi-supervised anomaly\ndetection method, which devises \\textit{contamination-resilient continuous\nsupervisory signals}. Specifically, we propose a mass interpolation method to\ndiffuse the abnormality of labeled anomalies, thereby creating new data samples\nlabeled with continuous abnormal degrees. Meanwhile, the contaminated area can\nbe covered by new data samples generated via combinations of data with correct\nlabels. A feature learning-based objective is added to serve as an optimization\nconstraint to regularize the network and further enhance the robustness w.r.t.\nanomaly contamination. Extensive experiments on 11 real-world datasets show\nthat our approach significantly outperforms state-of-the-art competitors by\n20%-30% in AUC-PR and obtains more robust and superior performance in settings\nwith different anomaly contamination levels and varying numbers of labeled\nanomalies. The source code is available at https://github.com/xuhongzuo/rosas/.\n", "date": "2023-07-25", "categories": []}
{"id": "2307.13316", "title": "Unmasking Anomalies in Road-Scene Segmentation", "abstract": "  Anomaly segmentation is a critical task for driving applications, and it is\napproached traditionally as a per-pixel classification problem. However,\nreasoning individually about each pixel without considering their contextual\nsemantics results in high uncertainty around the objects' boundaries and\nnumerous false positives. We propose a paradigm change by shifting from a\nper-pixel classification to a mask classification. Our mask-based method,\nMask2Anomaly, demonstrates the feasibility of integrating an anomaly detection\nmethod in a mask-classification architecture. Mask2Anomaly includes several\ntechnical novelties that are designed to improve the detection of anomalies in\nmasks: i) a global masked attention module to focus individually on the\nforeground and background regions; ii) a mask contrastive learning that\nmaximizes the margin between an anomaly and known classes; and iii) a mask\nrefinement solution to reduce false positives. Mask2Anomaly achieves new\nstate-of-the-art results across a range of benchmarks, both in the per-pixel\nand component-level evaluations. In particular, Mask2Anomaly reduces the\naverage false positives rate by 60% wrt the previous state-of-the-art. Github\npage:\nhttps://github.com/shyam671/Mask2Anomaly-Unmasking-Anomalies-in-Road-Scene-Segmentation.\n", "date": "2023-07-25", "categories": []}
{"id": "2209.10168", "title": "Improving Generalizability of Graph Anomaly Detection Models via Data\n  Augmentation", "abstract": "  Graph anomaly detection (GAD) is a vital task since even a few anomalies can\npose huge threats to benign users. Recent semi-supervised GAD methods, which\ncan effectively leverage the available labels as prior knowledge, have achieved\nsuperior performances than unsupervised methods. In practice, people usually\nneed to identify anomalies on new (sub)graphs to secure their business, but\nthey may lack labels to train an effective detection model. One natural idea is\nto directly adopt a trained GAD model to the new (sub)graph for testing.\nHowever, we find that existing semi-supervised GAD methods suffer from poor\ngeneralization issue, i.e., well-trained models could not perform well on an\nunseen area (i.e., not accessible in training) of the same graph. It may cause\ngreat troubles. In this paper, we base on the phenomenon and propose a general\nand novel research problem of generalized graph anomaly detection that aims to\neffectively identify anomalies on both the training-domain graph and unseen\ntesting graph to eliminate potential dangers. Nevertheless, it is a challenging\ntask since only limited labels are available, and the normal background may\ndiffer between training and testing data. Accordingly, we propose a data\naugmentation method named \\textit{AugAN} (\\uline{Aug}mentation for\n\\uline{A}nomaly and \\uline{N}ormal distributions) to enrich training data and\nboost the generalizability of GAD models. Experiments verify the effectiveness\nof our method in improving model generalizability.\n", "date": "2022-09-21", "categories": []}
{"id": "2210.10495", "title": "ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly\n  Detection", "abstract": "  Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the\nteacher-student paradigm to detect and segment anomalous regions by contrasting\nthe unique features extracted by both networks. However, existing KDAD methods\nsuffer from two main limitations: 1) the student network can effortlessly\nreplicate the teacher network's representations, and 2) the features of the\nteacher network serve solely as a ``reference standard\" and are not fully\nleveraged. Toward this end, we depart from the established paradigm and instead\npropose an innovative approach called Asymmetric Distillation Post-Segmentation\n(ADPS). Our ADPS employs an asymmetric distillation paradigm that takes\ndistinct forms of the same image as the input of the teacher-student networks,\ndriving the student network to learn discriminating representations for\nanomalous regions.\n  Meanwhile, a customized Weight Mask Block (WMB) is proposed to generate a\ncoarse anomaly localization mask that transfers the distilled knowledge\nacquired from the asymmetric paradigm to the teacher network. Equipped with\nWMB, the proposed Post-Segmentation Module (PSM) is able to effectively detect\nand segment abnormal regions with fine structures and clear boundaries.\nExperimental results demonstrate that the proposed ADPS outperforms the\nstate-of-the-art methods in detecting and segmenting anomalies. Surprisingly,\nADPS significantly improves Average Precision (AP) metric by 9% and 20% on the\nMVTec AD and KolektorSDD2 datasets, respectively.\n", "date": "2022-10-19", "categories": []}
{"id": "2307.12336", "title": "TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models", "abstract": "  Tables are an abundant form of data with use cases across all scientific\nfields. Real-world datasets often contain anomalous samples that can negatively\naffect downstream analysis. In this work, we only assume access to contaminated\ndata and present a diffusion-based probabilistic model effective for\nunsupervised anomaly detection. Our model is trained to learn the density of\nnormal samples by utilizing a unique rejection scheme to attenuate the\ninfluence of anomalies on the density estimation. At inference, we identify\nanomalies as samples in low-density regions. We use real data to demonstrate\nthat our method improves detection capabilities over baselines. Furthermore,\nour method is relatively stable to the dimension of the data and does not\nrequire extensive hyperparameter tuning.\n", "date": "2023-07-23", "categories": []}
{"id": "2306.09260", "title": "IsoEx: an explainable unsupervised approach to process event logs cyber\n  investigation", "abstract": "  39 seconds. That is the timelapse between two consecutive cyber attacks as of\n2023. Meaning that by the time you are done reading this abstract, about 1 or 2\nadditional cyber attacks would have occurred somewhere in the world. In this\ncontext of highly increased frequency of cyber threats, Security Operation\nCenters (SOC) and Computer Emergency Response Teams (CERT) can be overwhelmed.\nIn order to relieve the cybersecurity teams in their investigative effort and\nhelp them focus on more added-value tasks, machine learning approaches and\nmethods started to emerge. This paper introduces a novel method, IsoEx, for\ndetecting anomalous and potentially problematic command lines during the\ninvestigation of contaminated devices. IsoEx is built around a set of features\nthat leverages the log structure of the command line, as well as its\nparent/child relationship, to achieve a greater accuracy than traditional\nmethods. To detect anomalies, IsoEx resorts to an unsupervised anomaly\ndetection technique that is both highly sensitive and lightweight. A key\ncontribution of the paper is its emphasis on interpretability, achieved through\nthe features themselves and the application of eXplainable Artificial\nIntelligence (XAI) techniques and visualizations. This is critical to ensure\nthe adoption of the method by SOC and CERT teams, as the paper argues that the\ncurrent literature on machine learning for log investigation has not adequately\naddressed the issue of explainability. This method was proven efficient in a\nreal-life environment as it was built to support a company\\'s SOC and CERT\n", "date": "2023-06-07", "categories": []}
{"id": "2307.05256", "title": "Towards exploring adversarial learning for anomaly detection in complex\n  driving scenes", "abstract": "  One of the many Autonomous Systems (ASs), such as autonomous driving cars,\nperforms various safety-critical functions. Many of these autonomous systems\ntake advantage of Artificial Intelligence (AI) techniques to perceive their\nenvironment. But these perceiving components could not be formally verified,\nsince, the accuracy of such AI-based components has a high dependency on the\nquality of training data. So Machine learning (ML) based anomaly detection, a\ntechnique to identify data that does not belong to the training data could be\nused as a safety measuring indicator during the development and operational\ntime of such AI-based components. Adversarial learning, a sub-field of machine\nlearning has proven its ability to detect anomalies in images and videos with\nimpressive results on simple data sets. Therefore, in this work, we investigate\nand provide insight into the performance of such techniques on a highly complex\ndriving scenes dataset called Berkeley DeepDrive.\n", "date": "2023-06-17", "categories": []}
{"id": "2307.11239", "title": "Edgewise outliers of network indexed signals", "abstract": "  We consider models for network indexed multivariate data involving a\ndependence between variables as well as across graph nodes.\n  In the framework of these models, we focus on outliers detection and\nintroduce the concept of edgewise outliers. For this purpose, we first derive\nthe distribution of some sums of squares, in particular squared Mahalanobis\ndistances that can be used to fix detection rules and thresholds for outlier\ndetection. We then propose a robust version of the deterministic MCD algorithm\nthat we call edgewise MCD. An application on simulated data shows the interest\nof taking the dependence structure into account. We also illustrate the utility\nof the proposed method with a real data set.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.10596", "title": "Ensemble Learning based Anomaly Detection for IoT Cybersecurity via\n  Bayesian Hyperparameters Sensitivity Analysis", "abstract": "  The Internet of Things (IoT) integrates more than billions of intelligent\ndevices over the globe with the capability of communicating with other\nconnected devices with little to no human intervention. IoT enables data\naggregation and analysis on a large scale to improve life quality in many\ndomains. In particular, data collected by IoT contain a tremendous amount of\ninformation for anomaly detection. The heterogeneous nature of IoT is both a\nchallenge and an opportunity for cybersecurity. Traditional approaches in\ncybersecurity monitoring often require different kinds of data pre-processing\nand handling for various data types, which might be problematic for datasets\nthat contain heterogeneous features. However, heterogeneous types of network\ndevices can often capture a more diverse set of signals than a single type of\ndevice readings, which is particularly useful for anomaly detection. In this\npaper, we present a comprehensive study on using ensemble machine learning\nmethods for enhancing IoT cybersecurity via anomaly detection. Rather than\nusing one single machine learning model, ensemble learning combines the\npredictive power from multiple models, enhancing their predictive accuracy in\nheterogeneous datasets rather than using one single machine learning model. We\npropose a unified framework with ensemble learning that utilises Bayesian\nhyperparameter optimisation to adapt to a network environment that contains\nmultiple IoT sensor readings. Experimentally, we illustrate their high\npredictive power when compared to traditional methods.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.10653", "title": "Refining the Optimization Target for Automatic Univariate Time Series\n  Anomaly Detection in Monitoring Services", "abstract": "  Time series anomaly detection is crucial for industrial monitoring services\nthat handle a large volume of data, aiming to ensure reliability and optimize\nsystem performance. Existing methods often require extensive labeled resources\nand manual parameter selection, highlighting the need for automation. This\npaper proposes a comprehensive framework for automatic parameter optimization\nin time series anomaly detection models. The framework introduces three\noptimization targets: prediction score, shape score, and sensitivity score,\nwhich can be easily adapted to different model backbones without prior\nknowledge or manual labeling efforts. The proposed framework has been\nsuccessfully applied online for over six months, serving more than 50,000 time\nseries every minute. It simplifies the user's experience by requiring only an\nexpected sensitive value, offering a user-friendly interface, and achieving\ndesired detection results. Extensive evaluations conducted on public datasets\nand comparison with other methods further confirm the effectiveness of the\nproposed framework.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.10792", "title": "Optimizing PatchCore for Few/many-shot Anomaly Detection", "abstract": "  Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and\ntries to distinguish between normal and anomalous data using only few selected\nsamples. While newly proposed few-shot AD methods do compare against\npre-existing algorithms developed for the full-shot domain as baselines, they\ndo not dedicatedly optimize them for the few-shot setting. It thus remains\nunclear if the performance of such pre-existing algorithms can be further\nimproved. We address said question in this work. Specifically, we present a\nstudy on the AD/anomaly segmentation (AS) performance of PatchCore, the current\nstate-of-the-art full-shot AD/AS algorithm, in both the few-shot and the\nmany-shot settings. We hypothesize that further performance improvements can be\nrealized by (I) optimizing its various hyperparameters, and by (II)\ntransferring techniques known to improve few-shot supervised learning to the AD\ndomain. Exhaustive experiments on the public VisA and MVTec AD datasets reveal\nthat (I) significant performance improvements can be realized by optimizing\nhyperparameters such as the underlying feature extractor, and that (II)\nimage-level augmentations can, but are not guaranteed, to improve performance.\nBased on these findings, we achieve a new state of the art in few-shot AD on\nVisA, further demonstrating the merit of adapting pre-existing AD/AS methods to\nthe few-shot setting. Last, we identify the investigation of feature extractors\nwith a strong inductive bias as a potential future research direction for\n(few-shot) AD/AS.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.11085", "title": "Representation Learning in Anomaly Detection: Successes, Limits and a\n  Grand Challenge", "abstract": "  In this perspective paper, we argue that the dominant paradigm in anomaly\ndetection cannot scale indefinitely and will eventually hit fundamental limits.\nThis is due to the a no free lunch principle for anomaly detection. These\nlimitations can be overcome when there are strong tasks priors, as is the case\nfor many industrial tasks. When such priors do not exists, the task is much\nharder for anomaly detection. We pose two such tasks as grand challenges for\nanomaly detection: i) scientific discovery by anomaly detection ii) a\n\"mini-grand\" challenge of detecting the most anomalous image in the ImageNet\ndataset. We believe new anomaly detection tools and ideas would need to be\ndeveloped to overcome these challenges.\n", "date": "2023-07-20", "categories": []}
{"id": "2305.09446", "title": "A Probabilistic Transformation of Distance-Based Outliers", "abstract": "  The scores of distance-based outlier detection methods are difficult to\ninterpret, making it challenging to determine a cut-off threshold between\nnormal and outlier data points without additional context. We describe a\ngeneric transformation of distance-based outlier scores into interpretable,\nprobabilistic estimates. The transformation is ranking-stable and increases the\ncontrast between normal and outlier data points. Determining distance\nrelationships between data points is necessary to identify the nearest-neighbor\nrelationships in the data, yet, most of the computed distances are typically\ndiscarded. We show that the distances to other data points can be used to model\ndistance probability distributions and, subsequently, use the distributions to\nturn distance-based outlier scores into outlier probabilities. Our experiments\nshow that the probabilistic transformation does not impact detection\nperformance over numerous tabular and image benchmark datasets but results in\ninterpretable outlier scores with increased contrast between normal and outlier\nsamples. Our work generalizes to a wide range of distance-based outlier\ndetection methods, and because existing distance computations are used, it adds\nno significant computational overhead.\n", "date": "2023-05-16", "categories": []}
{"id": "2307.01533", "title": "Unsupervised Video Anomaly Detection with Diffusion Models Conditioned\n  on Compact Motion Representations", "abstract": "  This paper aims to address the unsupervised video anomaly detection (VAD)\nproblem, which involves classifying each frame in a video as normal or\nabnormal, without any access to labels. To accomplish this, the proposed method\nemploys conditional diffusion models, where the input data is the\nspatiotemporal features extracted from a pre-trained network, and the condition\nis the features extracted from compact motion representations that summarize a\ngiven video segment in terms of its motion and appearance. Our method utilizes\na data-driven threshold and considers a high reconstruction error as an\nindicator of anomalous events. This study is the first to utilize compact\nmotion representations for VAD and the experiments conducted on two large-scale\nVAD benchmarks demonstrate that they supply relevant information to the\ndiffusion model, and consequently improve VAD performances w.r.t the prior art.\nImportantly, our method exhibits better generalization performance across\ndifferent datasets, notably outperforming both the state-of-the-art and\nbaseline methods. The code of our method is available at\nhttps://github.com/AnilOsmanTur/conditioned_video_anomaly_diffusion\n", "date": "2023-07-04", "categories": []}
{"id": "2307.06975", "title": "Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for\n  Real-time Anomaly Detection in Industry 4.0", "abstract": "  Industry 4.0 involves the integration of digital technologies, such as IoT,\nBig Data, and AI, into manufacturing and industrial processes to increase\nefficiency and productivity. As these technologies become more interconnected\nand interdependent, Industry 4.0 systems become more complex, which brings the\ndifficulty of identifying and stopping anomalies that may cause disturbances in\nthe manufacturing process. This paper aims to propose a diffusion-based model\nfor real-time anomaly prediction in Industry 4.0 processes. Using a\nneuro-symbolic approach, we integrate industrial ontologies in the model,\nthereby adding formal knowledge on smart manufacturing. Finally, we propose a\nsimple yet effective way of distilling diffusion models through Random Fourier\nFeatures for deployment on an embedded system for direct integration into the\nmanufacturing process. To the best of our knowledge, this approach has never\nbeen explored before.\n", "date": "2023-07-13", "categories": []}
{"id": "2307.08782", "title": "Unsupervised Learning of Distributional Properties can Supplement Human\n  Labeling and Increase Active Learning Efficiency in Anomaly Detection", "abstract": "  Exfiltration of data via email is a serious cybersecurity threat for many\norganizations. Detecting data exfiltration (anomaly) patterns typically\nrequires labeling, most often done by a human annotator, to reduce the high\nnumber of false alarms. Active Learning (AL) is a promising approach for\nlabeling data efficiently, but it needs to choose an efficient order in which\ncases are to be labeled, and there are uncertainties as to what scoring\nprocedure should be used to prioritize cases for labeling, especially when\ndetecting rare cases of interest is crucial. We propose an adaptive AL sampling\nstrategy that leverages the underlying prior data distribution, as well as\nmodel uncertainty, to produce batches of cases to be labeled that contain\ninstances of rare anomalies. We show that (1) the classifier benefits from a\nbatch of representative and informative instances of both normal and anomalous\nexamples, (2) unsupervised anomaly detection plays a useful role in building\nthe classifier in the early stages of training when relatively little labeling\nhas been done thus far. Our approach to AL for anomaly detection outperformed\nexisting AL approaches on three highly unbalanced UCI benchmarks and on one\nreal-world redacted email data set.\n", "date": "2023-07-13", "categories": []}
{"id": "2307.08807", "title": "Anomaly Detection with Selective Dictionary Learning", "abstract": "  In this paper we present new methods of anomaly detection based on Dictionary\nLearning (DL) and Kernel Dictionary Learning (KDL). The main contribution\nconsists in the adaption of known DL and KDL algorithms in the form of\nunsupervised methods, used for outlier detection. We propose a reduced kernel\nversion (RKDL), which is useful for problems with large data sets, due to the\nlarge kernel matrix. We also improve the DL and RKDL methods by the use of a\nrandom selection of signals, which aims to eliminate the outliers from the\ntraining procedure. All our algorithms are introduced in an anomaly detection\ntoolbox and are compared to standard benchmark results.\n", "date": "2023-07-17", "categories": []}
{"id": "2307.07534", "title": "Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images", "abstract": "  Pathological anomalies exhibit diverse appearances in medical imaging, making\nit difficult to collect and annotate a representative amount of data required\nto train deep learning models in a supervised setting. Therefore, in this work,\nwe tackle anomaly detection in medical images training our framework using only\nhealthy samples. We propose to use the Masked Autoencoder model to learn the\nstructure of the normal samples, then train an anomaly classifier on top of the\ndifference between the original image and the reconstruction provided by the\nmasked autoencoder. We train the anomaly classifier in a supervised manner\nusing as negative samples the reconstruction of the healthy scans, while as\npositive samples, we use pseudo-abnormal scans obtained via our novel\npseudo-abnormal module. The pseudo-abnormal module alters the reconstruction of\nthe normal samples by changing the intensity of several regions. We conduct\nexperiments on two medical image data sets, namely BRATS2020 and LUNA16 and\ncompare our method with four state-of-the-art anomaly detection frameworks,\nnamely AST, RD4AD, AnoVAEGAN and f-AnoGAN.\n", "date": "2023-07-14", "categories": []}
{"id": "2307.08059", "title": "LafitE: Latent Diffusion Model with Feature Editing for Unsupervised\n  Multi-class Anomaly Detection", "abstract": "  In the context of flexible manufacturing systems that are required to produce\ndifferent types and quantities of products with minimal reconfiguration, this\npaper addresses the problem of unsupervised multi-class anomaly detection:\ndevelop a unified model to detect anomalies from objects belonging to multiple\nclasses when only normal data is accessible. We first explore the\ngenerative-based approach and investigate latent diffusion models for\nreconstruction to mitigate the notorious ``identity shortcut'' issue in\nauto-encoder based methods. We then introduce a feature editing strategy that\nmodifies the input feature space of the diffusion model to further alleviate\n``identity shortcuts'' and meanwhile improve the reconstruction quality of\nnormal regions, leading to fewer false positive predictions. Moreover, we are\nthe first who pose the problem of hyperparameter selection in unsupervised\nanomaly detection, and propose a solution of synthesizing anomaly data for a\npseudo validation set to address this problem. Extensive experiments on\nbenchmark datasets MVTec-AD and MPDD show that the proposed LafitE, \\ie, Latent\nDiffusion Model with Feature Editing, outperforms state-of-art methods by a\nsignificant margin in terms of average AUROC. The hyperparamters selected via\nour pseudo validation set are well-matched to the real test set.\n", "date": "2023-07-16", "categories": []}
{"id": "2307.07254", "title": "cOOpD: Reformulating COPD classification on chest CT scans as anomaly\n  detection using contrastive representations", "abstract": "  Classification of heterogeneous diseases is challenging due to their\ncomplexity, variability of symptoms and imaging findings. Chronic Obstructive\nPulmonary Disease (COPD) is a prime example, being underdiagnosed despite being\nthe third leading cause of death. Its sparse, diffuse and heterogeneous\nappearance on computed tomography challenges supervised binary classification.\nWe reformulate COPD binary classification as an anomaly detection task,\nproposing cOOpD: heterogeneous pathological regions are detected as\nOut-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we\nlearn representations of unlabeled lung regions employing a self-supervised\ncontrastive pretext model, potentially capturing specific characteristics of\ndiseased and healthy unlabeled regions. A generative model then learns the\ndistribution of healthy representations and identifies abnormalities (stemming\nfrom COPD) as deviations. Patient-level scores are obtained by aggregating\nregion OOD scores. We show that cOOpD achieves the best performance on two\npublic datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared\nto the previous supervised state-of-the-art. Additionally, cOOpD yields\nwell-interpretable spatial anomaly maps and patient-level scores which we show\nto be of additional value in identifying individuals in the early stage of\nprogression. Experiments in artificially designed real-world prevalence\nsettings further support that anomaly detection is a powerful way of tackling\nCOPD classification.\n", "date": "2023-07-14", "categories": []}
{"id": "2307.06380", "title": "Personalized Anomaly Detection in PPG Data using Representation Learning\n  and Biometric Identification", "abstract": "  Photoplethysmography (PPG) signals, typically acquired from wearable devices,\nhold significant potential for continuous fitness-health monitoring. In\nparticular, heart conditions that manifest in rare and subtle deviating heart\npatterns may be interesting. However, robust and reliable anomaly detection\nwithin these data remains a challenge due to the scarcity of labeled data and\nhigh inter-subject variability. This paper introduces a two-stage framework\nleveraging representation learning and personalization to improve anomaly\ndetection performance in PPG data. The proposed framework first employs\nrepresentation learning to transform the original PPG signals into a more\ndiscriminative and compact representation. We then apply three different\nunsupervised anomaly detection methods for movement detection and biometric\nidentification. We validate our approach using two different datasets in both\ngeneralized and personalized scenarios. The results show that representation\nlearning significantly improves anomaly detection performance while reducing\nthe high inter-subject variability. Personalized models further enhance anomaly\ndetection performance, underscoring the role of personalization in PPG-based\nfitness-health monitoring systems. The results from biometric identification\nshow that it's easier to distinguish a new user from one intended authorized\nuser than from a group of users. Overall, this study provides evidence of the\neffectiveness of representation learning and personalization for anomaly\ndetection in PPG data.\n", "date": "2023-07-12", "categories": []}
{"id": "2307.06534", "title": "DSV: An Alignment Validation Loss for Self-supervised Outlier Model\n  Selection", "abstract": "  Self-supervised learning (SSL) has proven effective in solving various\nproblems by generating internal supervisory signals. Unsupervised anomaly\ndetection, which faces the high cost of obtaining true labels, is an area that\ncan greatly benefit from SSL. However, recent literature suggests that tuning\nthe hyperparameters (HP) of data augmentation functions is crucial to the\nsuccess of SSL-based anomaly detection (SSAD), yet a systematic method for\ndoing so remains unknown. In this work, we propose DSV (Discordance and\nSeparability Validation), an unsupervised validation loss to select\nhigh-performing detection models with effective augmentation HPs. DSV captures\nthe alignment between an augmentation function and the anomaly-generating\nmechanism with surrogate losses, which approximate the discordance and\nseparability of test data, respectively. As a result, the evaluation via DSV\nleads to selecting an effective SSAD model exhibiting better alignment, which\nresults in high detection accuracy. We theoretically derive the degree of\napproximation conducted by the surrogate losses and empirically show that DSV\noutperforms a wide range of baselines on 21 real-world tasks.\n", "date": "2023-07-13", "categories": []}
{"id": "2307.06720", "title": "Weakly supervised marine animal detection from remote sensing images\n  using vector-quantized variational autoencoder", "abstract": "  This paper studies a reconstruction-based approach for weakly-supervised\nanimal detection from aerial images in marine environments. Such an approach\nleverages an anomaly detection framework that computes metrics directly on the\ninput space, enhancing interpretability and anomaly localization compared to\nfeature embedding methods. Building upon the success of Vector-Quantized\nVariational Autoencoders in anomaly detection on computer vision datasets, we\nadapt them to the marine animal detection domain and address the challenge of\nhandling noisy data. To evaluate our approach, we compare it with existing\nmethods in the context of marine animal detection from aerial image data.\nExperiments conducted on two dedicated datasets demonstrate the superior\nperformance of the proposed method over recent studies in the literature. Our\nframework offers improved interpretability and localization of anomalies,\nproviding valuable insights for monitoring marine ecosystems and mitigating the\nimpact of human activities on marine animals.\n", "date": "2023-07-13", "categories": []}
{"id": "2307.05136", "title": "Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated\n  Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D\n  Augmented Reality", "abstract": "  Solar Photovoltaic (PV) is increasingly being used to address the global\nconcern of energy security. However, hot spot and snail trails in PV modules\ncaused mostly by crakes reduce their efficiency and power capacity. This\narticle presents a groundbreaking methodology for automatically identifying and\nanalyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV)\nmodules, leveraging unsupervised sensing algorithms and 3D Augmented Reality\n(AR) visualization. By transforming the traditional methods of diagnosis and\nrepair, our approach not only enhances efficiency but also substantially cuts\ndown the cost of PV system maintenance. Validated through computer simulations\nand real-world image datasets, the proposed framework accurately identifies\ndirty regions, emphasizing the critical role of regular maintenance in\noptimizing the power capacity of solar PV modules. Our immediate objective is\nto leverage drone technology for real-time, automatic solar panel detection,\nsignificantly boosting the efficacy of PV maintenance. The proposed methodology\ncould revolutionize solar PV maintenance, enabling swift, precise anomaly\ndetection without human intervention. This could result in significant cost\nsavings, heightened energy production, and improved overall performance of\nsolar PV systems. Moreover, the novel combination of unsupervised sensing\nalgorithms with 3D AR visualization heralds new opportunities for further\nresearch and development in solar PV maintenance.\n", "date": "2023-07-11", "categories": []}
{"id": "2210.06959", "title": "A Survey on Explainable Anomaly Detection", "abstract": "  In the past two decades, most research on anomaly detection has focused on\nimproving the accuracy of the detection, while largely ignoring the\nexplainability of the corresponding methods and thus leaving the explanation of\noutcomes to practitioners. As anomaly detection algorithms are increasingly\nused in safety-critical domains, providing explanations for the high-stakes\ndecisions made in those domains has become an ethical and regulatory\nrequirement. Therefore, this work provides a comprehensive and structured\nsurvey on state-of-the-art explainable anomaly detection techniques. We propose\na taxonomy based on the main aspects that characterize each explainable anomaly\ndetection technique, aiming to help practitioners and researchers find the\nexplainable anomaly detection method that best suits their needs.\n", "date": "2022-10-13", "categories": []}
{"id": "2307.03777", "title": "Unsupervised 3D out-of-distribution detection with latent diffusion\n  models", "abstract": "  Methods for out-of-distribution (OOD) detection that scale to 3D data are\ncrucial components of any real-world clinical deep learning system. Classic\ndenoising diffusion probabilistic models (DDPMs) have been recently proposed as\na robust way to perform reconstruction-based OOD detection on 2D datasets, but\ndo not trivially scale to 3D data. In this work, we propose to use Latent\nDiffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution\n3D medical data. We validate the proposed approach on near- and far-OOD\ndatasets and compare it to a recently proposed, 3D-enabled approach using\nLatent Transformer Models (LTMs). Not only does the proposed LDM-based approach\nachieve statistically significant better performance, it also shows less\nsensitivity to the underlying latent representation, more favourable memory\nscaling, and produces better spatial anomaly maps. Code is available at\nhttps://github.com/marksgraham/ddpm-ood\n", "date": "2023-07-07", "categories": []}
{"id": "2307.04097", "title": "Restricted Generative Projection for One-Class Classification and\n  Anomaly Detection", "abstract": "  We present a simple framework for one-class classification and anomaly\ndetection. The core idea is to learn a mapping to transform the unknown\ndistribution of training (normal) data to a known target distribution.\nCrucially, the target distribution should be sufficiently simple, compact, and\ninformative. The simplicity is to ensure that we can sample from the\ndistribution easily, the compactness is to ensure that the decision boundary\nbetween normal data and abnormal data is clear and reliable, and the\ninformativeness is to ensure that the transformed data preserve the important\ninformation of the original data. Therefore, we propose to use truncated\nGaussian, uniform in hypersphere, uniform on hypersphere, or uniform between\nhyperspheres, as the target distribution. We then minimize the distance between\nthe transformed data distribution and the target distribution while keeping the\nreconstruction error for the original data small enough. Comparative studies on\nmultiple benchmark datasets verify the effectiveness of our methods in\ncomparison to baselines.\n", "date": "2023-07-09", "categories": []}
{"id": "2307.04574", "title": "TFR: Texture Defect Detection with Fourier Transform using Normal\n  Reconstructed Template of Simple Autoencoder", "abstract": "  Texture is an essential information in image representation, capturing\npatterns and structures. As a result, texture plays a crucial role in the\nmanufacturing industry and is extensively studied in the fields of computer\nvision and pattern recognition. However, real-world textures are susceptible to\ndefects, which can degrade image quality and cause various issues. Therefore,\nthere is a need for accurate and effective methods to detect texture defects.\nIn this study, a simple autoencoder and Fourier transform are employed for\ntexture defect detection. The proposed method combines Fourier transform\nanalysis with the reconstructed template obtained from the simple autoencoder.\nFourier transform is a powerful tool for analyzing the frequency domain of\nimages and signals. Moreover, since texture defects often exhibit\ncharacteristic changes in specific frequency ranges, analyzing the frequency\ndomain enables effective defect detection. The proposed method demonstrates\neffectiveness and accuracy in detecting texture defects. Experimental results\nare presented to evaluate its performance and compare it with existing\napproaches.\n", "date": "2023-07-10", "categories": []}
{"id": "2307.03243", "title": "That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering", "abstract": "  Recent studies on visual anomaly detection (AD) of industrial\nobjects/textures have achieved quite good performance. They consider an\nunsupervised setting, specifically the one-class setting, in which we assume\nthe availability of a set of normal (\\textit{i.e.}, anomaly-free) images for\ntraining. In this paper, we consider a more challenging scenario of\nunsupervised AD, in which we detect anomalies in a given set of images that\nmight contain both normal and anomalous samples. The setting does not assume\nthe availability of known normal data and thus is completely free from human\nannotation, which differs from the standard AD considered in recent studies.\nFor clarity, we call the setting blind anomaly detection (BAD). We show that\nBAD can be converted into a local outlier detection problem and propose a novel\nmethod named PatchCluster that can accurately detect image- and pixel-level\nanomalies. Experimental results show that PatchCluster shows a promising\nperformance without the knowledge of normal data, even comparable to the SOTA\nmethods applied in the one-class setting needing it.\n", "date": "2023-07-06", "categories": []}
{"id": "2307.03359", "title": "CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection\n  Method", "abstract": "  Anomaly detection based on system logs plays an important role in intelligent\noperations, which is a challenging task due to the extremely complex log\npatterns. Existing methods detect anomalies by capturing the sequential\ndependencies in log sequences, which ignore the interactions of subsequences.\nTo this end, we propose CSCLog, a Component Subsequence Correlation-Aware Log\nanomaly detection method, which not only captures the sequential dependencies\nin subsequences, but also models the implicit correlations of subsequences.\nSpecifically, subsequences are extracted from log sequences based on components\nand the sequential dependencies in subsequences are captured by Long Short-Term\nMemory Networks (LSTMs). An implicit correlation encoder is introduced to model\nthe implicit correlations of subsequences adaptively. In addition, Graph\nConvolution Networks (GCNs) are employed to accomplish the information\ninteractions of subsequences. Finally, attention mechanisms are exploited to\nfuse the embeddings of all subsequences. Extensive experiments on four publicly\navailable log datasets demonstrate the effectiveness of CSCLog, outperforming\nthe best baseline by an average of 7.41% in Macro F1-Measure.\n", "date": "2023-07-07", "categories": []}
{"id": "2306.10239", "title": "Multi-scale Spatial-temporal Interaction Network for Video Anomaly\n  Detection", "abstract": "  Video Anomaly Detection (VAD) is an essential yet challenging task in signal\nprocessing. Since certain anomalies cannot be detected by isolated analysis of\neither temporal or spatial information, the interaction between these two types\nof data is considered crucial for VAD. However, current dual-stream\narchitectures either confine this integral interaction to the bottleneck of the\nautoencoder or introduce anomaly-irrelevant background pixels into the\ninteractive process, hindering the accuracy of VAD. To address these\ndeficiencies, we propose a Multi-scale Spatial-Temporal Interaction Network\n(MSTI-Net) for VAD. First, to prioritize the detection of moving objects in the\nscene and harmonize the substantial semantic discrepancies between the two\ntypes of data, we propose an Attention-based Spatial-Temporal Fusion Module\n(ASTFM) as a substitute for the conventional direct fusion. Furthermore, we\ninject multi-ASTFM-based connections that bridge the appearance and motion\nstreams of the dual-stream network, thus fostering multi-scale spatial-temporal\ninteraction. Finally, to bolster the delineation between normal and abnormal\nactivities, our system records the regular information in a memory module.\nExperimental results on three benchmark datasets validate the effectiveness of\nour approach, which achieves AUCs of 96.8%, 87.6%, and 73.9% on the UCSD Ped2,\nCUHK Avenue, and ShanghaiTech datasets, respectively.\n", "date": "2023-06-17", "categories": []}
{"id": "2307.02495", "title": "Anomaly detection in image or latent space of patch-based auto-encoders\n  for industrial image analysis", "abstract": "  We study several methods for detecting anomalies in color images, constructed\non patch-based auto-encoders. Wecompare the performance of three types of\nmethods based, first, on the error between the original image and its\nreconstruction,second, on the support estimation of the normal image\ndistribution in the latent space, and third, on the error between the\noriginalimage and a restored version of the reconstructed image. These methods\nare evaluated on the industrial image database MVTecADand compared to two\ncompetitive state-of-the-art methods.\n", "date": "2023-07-04", "categories": []}
{"id": "2307.02836", "title": "Noise-to-Norm Reconstruction for Industrial Anomaly Detection and\n  Localization", "abstract": "  Anomaly detection has a wide range of applications and is especially\nimportant in industrial quality inspection. Currently, many top-performing\nanomaly-detection models rely on feature-embedding methods. However, these\nmethods do not perform well on datasets with large variations in object\nlocations. Reconstruction-based methods use reconstruction errors to detect\nanomalies without considering positional differences between samples. In this\nstudy, a reconstruction-based method using the noise-to-norm paradigm is\nproposed, which avoids the invariant reconstruction of anomalous regions. Our\nreconstruction network is based on M-net and incorporates multiscale fusion and\nresidual attention modules to enable end-to-end anomaly detection and\nlocalization. Experiments demonstrate that the method is effective in\nreconstructing anomalous regions into normal patterns and achieving accurate\nanomaly detection and localization. On the MPDD and VisA datasets, our proposed\nmethod achieved more competitive results than the latest methods, and it set a\nnew state-of-the-art standard on the MPDD dataset.\n", "date": "2023-07-06", "categories": []}
{"id": "2307.03101", "title": "Contextual Affinity Distillation for Image Anomaly Detection", "abstract": "  Previous works on unsupervised industrial anomaly detection mainly focus on\nlocal structural anomalies such as cracks and color contamination. While\nachieving significantly high detection performance on this kind of anomaly,\nthey are faced with logical anomalies that violate the long-range dependencies\nsuch as a normal object placed in the wrong position. In this paper, based on\nprevious knowledge distillation works, we propose to use two students (local\nand global) to better mimic the teacher's behavior. The local student, which is\nused in previous studies mainly focuses on structural anomaly detection while\nthe global student pays attention to logical anomalies. To further encourage\nthe global student's learning to capture long-range dependencies, we design the\nglobal context condensing block (GCCB) and propose a contextual affinity loss\nfor the student training and anomaly scoring. Experimental results show the\nproposed method doesn't need cumbersome training techniques and achieves a new\nstate-of-the-art performance on the MVTec LOCO AD dataset.\n", "date": "2023-07-06", "categories": []}
{"id": "2212.05136", "title": "CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised\n  Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) -- commonly formulated as a multiple-instance\nlearning problem in a weakly-supervised manner due to its labor-intensive\nnature -- is a challenging problem in video surveillance where the frames of\nanomaly need to be localized in an untrimmed video. In this paper, we first\npropose to utilize the ViT-encoded visual features from CLIP, in contrast with\nthe conventional C3D or I3D features in the domain, to efficiently extract\ndiscriminative representations in the novel technique. We then model temporal\ndependencies and nominate the snippets of interest by leveraging our proposed\nTemporal Self-Attention (TSA). The ablation study confirms the effectiveness of\nTSA and ViT feature. The extensive experiments show that our proposed CLIP-TSA\noutperforms the existing state-of-the-art (SOTA) methods by a large margin on\nthree commonly-used benchmark datasets in the VAD problem (UCF-Crime,\nShanghaiTech Campus, and XD-Violence). Our source code is available at\nhttps://github.com/joos2010kj/CLIP-TSA.\n", "date": "2022-12-09", "categories": []}
{"id": "2302.07832", "title": "Deep Anomaly Detection under Labeling Budget Constraints", "abstract": "  Selecting informative data points for expert feedback can significantly\nimprove the performance of anomaly detection (AD) in various contexts, such as\nmedical diagnostics or fraud detection. In this paper, we determine a set of\ntheoretical conditions under which anomaly scores generalize from labeled\nqueries to unlabeled data. Motivated by these results, we propose a data\nlabeling strategy with optimal data coverage under labeling budget constraints.\nIn addition, we propose a new learning framework for semi-supervised AD.\nExtensive experiments on image, tabular, and video data sets show that our\napproach results in state-of-the-art semi-supervised AD performance under\nlabeling budget constraints.\n", "date": "2023-02-15", "categories": []}
{"id": "2307.01601", "title": "Prototypes as Explanation for Time Series Anomaly Detection", "abstract": "  Detecting abnormal patterns that deviate from a certain regular repeating\npattern in time series is essential in many big data applications. However, the\nlack of labels, the dynamic nature of time series data, and unforeseeable\nabnormal behaviors make the detection process challenging. Despite the success\nof recent deep anomaly detection approaches, the mystical mechanisms in such\nblack-box models have become a new challenge in safety-critical applications.\nThe lack of model transparency and prediction reliability hinders further\nbreakthroughs in such domains. This paper proposes ProtoAD, using prototypes as\nthe example-based explanation for the state of regular patterns during anomaly\ndetection. Without significant impact on the detection performance, prototypes\nshed light on the deep black-box models and provide intuitive understanding for\ndomain experts and stakeholders. We extend the widely used prototype learning\nin classification problems into anomaly detection. By visualizing both the\nlatent space and input space prototypes, we intuitively demonstrate how regular\ndata are modeled and why specific patterns are considered abnormal.\n", "date": "2023-07-04", "categories": []}
{"id": "2304.05841", "title": "Exploring Diffusion Models for Unsupervised Video Anomaly Detection", "abstract": "  This paper investigates the performance of diffusion models for video anomaly\ndetection (VAD) within the most challenging but also the most operational\nscenario in which the data annotations are not used. As being sparse, diverse,\ncontextual, and often ambiguous, detecting abnormal events precisely is a very\nambitious task. To this end, we rely only on the information-rich\nspatio-temporal data, and the reconstruction power of the diffusion models such\nthat a high reconstruction error is utilized to decide the abnormality.\nExperiments performed on two large-scale video anomaly detection datasets\ndemonstrate the consistent improvement of the proposed method over the\nstate-of-the-art generative models while in some cases our method achieves\nbetter scores than the more complex models. This is the first study using a\ndiffusion model and examining its parameters' influence to present guidance for\nVAD in surveillance scenarios.\n", "date": "2023-04-12", "categories": []}
{"id": "2307.00305", "title": "Applied Bayesian Structural Health Monitoring: inclinometer data anomaly\n  detection and forecasting", "abstract": "  Inclinometer probes are devices that can be used to measure deformations\nwithin earthwork slopes. This paper demonstrates a novel application of\nBayesian techniques to real-world inclinometer data, providing both anomaly\ndetection and forecasting. Specifically, this paper details an analysis of data\ncollected from inclinometer data across the entire UK rail network.\n  Practitioners have effectively two goals when processing monitoring data. The\nfirst is to identify any anomalous or dangerous movements, and the second is to\npredict potential future adverse scenarios by forecasting. In this paper we\napply Uncertainty Quantification (UQ) techniques by implementing a Bayesian\napproach to anomaly detection and forecasting for inclinometer data.\nSubsequently, both costs and risks may be minimised by quantifying and\nevaluating the appropriate uncertainties. This framework may then act as an\nenabler for enhanced decision making and risk analysis.\n  We show that inclinometer data can be described by a latent autocorrelated\nMarkov process derived from measurements. This can be used as the transition\nmodel of a non-linear Bayesian filter. This allows for the prediction of system\nstates. This learnt latent model also allows for the detection of anomalies:\nobservations that are far from their expected value may be considered to have\n`high surprisal', that is they have a high information content relative to the\nmodel encoding represented by the learnt latent model.\n  We successfully apply the forecasting and anomaly detection techniques to a\nlarge real-world data set in a computationally efficient manner. Although this\npaper studies inclinometers in particular, the techniques are broadly\napplicable to all areas of engineering UQ and Structural Health Monitoring\n(SHM).\n", "date": "2023-07-01", "categories": []}
{"id": "2307.00755", "title": "Graph-level Anomaly Detection via Hierarchical Memory Networks", "abstract": "  Graph-level anomaly detection aims to identify abnormal graphs that exhibit\ndeviant structures and node attributes compared to the majority in a graph set.\nOne primary challenge is to learn normal patterns manifested in both\nfine-grained and holistic views of graphs for identifying graphs that are\nabnormal in part or in whole. To tackle this challenge, we propose a novel\napproach called Hierarchical Memory Networks (HimNet), which learns\nhierarchical memory modules -- node and graph memory modules -- via a graph\nautoencoder network architecture. The node-level memory module is trained to\nmodel fine-grained, internal graph interactions among nodes for detecting\nlocally abnormal graphs, while the graph-level memory module is dedicated to\nthe learning of holistic normal patterns for detecting globally abnormal\ngraphs. The two modules are jointly optimized to detect both locally- and\nglobally-anomalous graphs. Extensive empirical results on 16 real-world graph\ndatasets from various domains show that i) HimNet significantly outperforms the\nstate-of-art methods and ii) it is robust to anomaly contamination. Codes are\navailable at: https://github.com/Niuchx/HimNet.\n", "date": "2023-07-03", "categories": []}
{"id": "2307.00899", "title": "Many tasks make light work: Learning to localise medical anomalies from\n  multiple synthetic tasks", "abstract": "  There is a growing interest in single-class modelling and out-of-distribution\ndetection as fully supervised machine learning models cannot reliably identify\nclasses not included in their training. The long tail of infinitely many\nout-of-distribution classes in real-world scenarios, e.g., for screening,\ntriage, and quality control, means that it is often necessary to train\nsingle-class models that represent an expected feature distribution, e.g., from\nonly strictly healthy volunteer data. Conventional supervised machine learning\nwould require the collection of datasets that contain enough samples of all\npossible diseases in every imaging modality, which is not realistic.\nSelf-supervised learning methods with synthetic anomalies are currently amongst\nthe most promising approaches, alongside generative auto-encoders that analyse\nthe residual reconstruction error. However, all methods suffer from a lack of\nstructured validation, which makes calibration for deployment difficult and\ndataset-dependant. Our method alleviates this by making use of multiple\nvisually-distinct synthetic anomaly learning tasks for both training and\nvalidation. This enables more robust training and generalisation. With our\napproach we can readily outperform state-of-the-art methods, which we\ndemonstrate on exemplars in brain MRI and chest X-rays. Code is available at\nhttps://github.com/matt-baugh/many-tasks-make-light-work .\n", "date": "2023-07-03", "categories": []}
{"id": "2306.15938", "title": "Interpretable Anomaly Detection in Cellular Networks by Learning\n  Concepts in Variational Autoencoders", "abstract": "  This paper addresses the challenges of detecting anomalies in cellular\nnetworks in an interpretable way and proposes a new approach using variational\nautoencoders (VAEs) that learn interpretable representations of the latent\nspace for each Key Performance Indicator (KPI) in the dataset. This enables the\ndetection of anomalies based on reconstruction loss and Z-scores. We ensure the\ninterpretability of the anomalies via additional information centroids (c)\nusing the K-means algorithm to enhance representation learning. We evaluate the\nperformance of the model by analyzing patterns in the latent dimension for\nspecific KPIs and thereby demonstrate the interpretability and anomalies. The\nproposed framework offers a faster and autonomous solution for detecting\nanomalies in cellular networks and showcases the potential of deep\nlearning-based algorithms in handling big data.\n", "date": "2023-06-28", "categories": []}
{"id": "2306.15324", "title": "Anomaly Detection in Networks via Score-Based Generative Models", "abstract": "  Node outlier detection in attributed graphs is a challenging problem for\nwhich there is no method that would work well across different datasets.\nMotivated by the state-of-the-art results of score-based models in graph\ngenerative modeling, we propose to incorporate them into the aforementioned\nproblem. Our method achieves competitive results on small-scale graphs. We\nprovide an empirical analysis of the Dirichlet energy, and show that generative\nmodels might struggle to accurately reconstruct it.\n", "date": "2023-06-27", "categories": []}
{"id": "2306.14403", "title": "Anomaly Detection with Score Distribution Discrimination", "abstract": "  Recent studies give more attention to the anomaly detection (AD) methods that\ncan leverage a handful of labeled anomalies along with abundant unlabeled data.\nThese existing anomaly-informed AD methods rely on manually predefined score\ntarget(s), e.g., prior constant or margin hyperparameter(s), to realize\ndiscrimination in anomaly scores between normal and abnormal data. However,\nsuch methods would be vulnerable to the existence of anomaly contamination in\nthe unlabeled data, and also lack adaptation to different data scenarios. In\nthis paper, we propose to optimize the anomaly scoring function from the view\nof score distribution, thus better retaining the diversity and more\nfine-grained information of input data, especially when the unlabeled data\ncontains anomaly noises in more practical AD scenarios. We design a novel loss\nfunction called Overlap loss that minimizes the overlap area between the score\ndistributions of normal and abnormal samples, which no longer depends on prior\nanomaly score targets and thus acquires adaptability to various datasets.\nOverlap loss consists of Score Distribution Estimator and Overlap Area\nCalculation, which are introduced to overcome challenges when estimating\narbitrary score distributions, and to ensure the boundness of training loss. As\na general loss component, Overlap loss can be effectively integrated into\nmultiple network architectures for constructing AD models. Extensive\nexperimental results indicate that Overlap loss based AD models significantly\noutperform their state-of-the-art counterparts, and achieve better performance\non different types of anomalies.\n", "date": "2023-06-26", "categories": []}
{"id": "2306.12703", "title": "OptIForest: Optimal Isolation Forest for Anomaly Detection", "abstract": "  Anomaly detection plays an increasingly important role in various fields for\ncritical tasks such as intrusion detection in cybersecurity, financial risk\ndetection, and human health monitoring. A variety of anomaly detection methods\nhave been proposed, and a category based on the isolation forest mechanism\nstands out due to its simplicity, effectiveness, and efficiency, e.g., iForest\nis often employed as a state-of-the-art detector for real deployment. While the\nmajority of isolation forests use the binary structure, a framework LSHiForest\nhas demonstrated that the multi-fork isolation tree structure can lead to\nbetter detection performance. However, there is no theoretical work answering\nthe fundamentally and practically important question on the optimal tree\nstructure for an isolation forest with respect to the branching factor. In this\npaper, we establish a theory on isolation efficiency to answer the question and\ndetermine the optimal branching factor for an isolation tree. Based on the\ntheoretical underpinning, we design a practical optimal isolation forest\nOptIForest incorporating clustering based learning to hash which enables more\ninformation to be learned from data for better isolation quality. The rationale\nof our approach relies on a better bias-variance trade-off achieved by bias\nreduction in OptIForest. Extensive experiments on a series of benchmarking\ndatasets for comparative and ablation studies demonstrate that our approach can\nefficiently and robustly achieve better detection performance in general than\nthe state-of-the-arts including the deep learning based methods.\n", "date": "2023-06-22", "categories": []}
{"id": "2208.02108", "title": "Detecting Multivariate Time Series Anomalies with Zero Known Label", "abstract": "  Multivariate time series anomaly detection has been extensively studied under\nthe semi-supervised setting, where a training dataset with all normal instances\nis required. However, preparing such a dataset is very laborious since each\nsingle data instance should be fully guaranteed to be normal. It is, therefore,\ndesired to explore multivariate time series anomaly detection methods based on\nthe dataset without any label knowledge. In this paper, we propose MTGFlow, an\nunsupervised anomaly detection approach for multivariate time series anomaly\ndetection via dynamic graph and entity-aware normalizing flow, leaning only on\na widely accepted hypothesis that abnormal instances exhibit sparse densities\nthan the normal. However, the complex interdependencies among entities and the\ndiverse inherent characteristics of each entity pose significant challenges on\nthe density estimation, let alone to detect anomalies based on the estimated\npossibility distribution. To tackle these problems, we propose to learn the\nmutual and dynamic relations among entities via a graph structure learning\nmodel, which helps to model accurate distribution of multivariate time series.\nMoreover, taking account of distinct characteristics of the individual\nentities, an entity-aware normalizing flow is developed to describe each entity\ninto a parameterized normal distribution, thereby producing fine-grained\ndensity estimation. Incorporating these two strategies, MTGFlow achieves\nsuperior anomaly detection performance. Experiments on five public datasets\nwith seven baselines are conducted, MTGFlow outperforms the SOTA methods by up\nto 5.0 AUROC\\%. Codes will be released at\nhttps://github.com/zqhang/Detecting-Multivariate-Time-Series-Anomalies-with-Zero-Known-Label.\n", "date": "2022-08-03", "categories": []}
{"id": "2306.10089", "title": "FABLE : Fabric Anomaly Detection Automation Process", "abstract": "  Unsupervised anomaly in industry has been a concerning topic and a stepping\nstone for high performance industrial automation process. The vast majority of\nindustry-oriented methods focus on learning from good samples to detect anomaly\nnotwithstanding some specific industrial scenario requiring even less specific\ntraining and therefore a generalization for anomaly detection. The obvious use\ncase is the fabric anomaly detection, where we have to deal with a really wide\nrange of colors and types of textile and a stoppage of the production line for\ntraining could not be considered. In this paper, we propose an automation\nprocess for industrial fabric texture defect detection with a\nspecificity-learning process during the domain-generalized anomaly detection.\nCombining the ability to generalize and the learning process offer a fast and\nprecise anomaly detection and segmentation. The main contributions of this\npaper are the following: A domain-generalization texture anomaly detection\nmethod achieving the state-of-the-art performances, a fast specific training on\ngood samples extracted by the proposed method, a self-evaluation method based\non custom defect creation and an automatic detection of already seen fabric to\nprevent re-training.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.10534", "title": "Improving Generalizability of Graph Anomaly Detection Models via Data\n  Augmentation", "abstract": "  Graph anomaly detection (GAD) is a vital task since even a few anomalies can\npose huge threats to benign users. Recent semi-supervised GAD methods, which\ncan effectively leverage the available labels as prior knowledge, have achieved\nsuperior performances than unsupervised methods. In practice, people usually\nneed to identify anomalies on new (sub)graphs to secure their business, but\nthey may lack labels to train an effective detection model. One natural idea is\nto directly adopt a trained GAD model to the new (sub)graph for testing.\nHowever, we find that existing semi-supervised GAD methods suffer from poor\ngeneralization issue, i.e., well-trained models could not perform well on an\nunseen area (i.e., not accessible in training) of the same graph. It may cause\ngreat troubles. In this paper, we base on the phenomenon and propose a general\nand novel research problem of generalized graph anomaly detection that aims to\neffectively identify anomalies on both the training-domain graph and unseen\ntesting graph to eliminate potential dangers. Nevertheless, it is a challenging\ntask since only limited labels are available, and the normal background may\ndiffer between training and testing data. Accordingly, we propose a data\naugmentation method named \\textit{AugAN} (\\uline{Aug}mentation for\n\\uline{A}nomaly and \\uline{N}ormal distributions) to enrich training data and\nboost the generalizability of GAD models. Experiments verify the effectiveness\nof our method in improving model generalizability.\n", "date": "2023-06-18", "categories": []}
{"id": "2306.10808", "title": "Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A\n  focus-SVDD with Complex-Valued Auto-Encoder Approach", "abstract": "  The occurrence of manufacturing defects in wind turbine blade (WTB)\nproduction can result in significant increases in operation and maintenance\ncosts and lead to severe and disastrous consequences. Therefore, inspection\nduring the manufacturing process is crucial to ensure consistent fabrication of\ncomposite materials. Non-contact sensing techniques, such as Frequency\nModulated Continuous Wave (FMCW) radar, are becoming increasingly popular as\nthey offer a full view of these complex structures during curing. In this\npaper, we enhance the quality assurance of manufacturing utilizing FMCW radar\nas a non-destructive sensing modality. Additionally, a novel anomaly detection\npipeline is developed that offers the following advantages: (1) We use the\nanalytic representation of the Intermediate Frequency signal of the FMCW radar\nas a feature to disentangle material-specific and round-trip delay information\nfrom the received wave. (2) We propose a novel anomaly detection methodology\ncalled focus Support Vector Data Description (focus-SVDD). This methodology\ninvolves defining the limit boundaries of the dataset after removing healthy\ndata features, thereby focusing on the attributes of anomalies. (3) The\nproposed method employs a complex-valued autoencoder to remove healthy features\nand we introduces a new activation function called Exponential Amplitude Decay\n(EAD). EAD takes advantage of the Rayleigh distribution, which characterizes an\ninstantaneous amplitude signal. The effectiveness of the proposed method is\ndemonstrated through its application to collected data, where it shows superior\nperformance compared to other state-of-the-art unsupervised anomaly detection\nmethods. This method is expected to make a significant contribution not only to\nstructural health monitoring but also to the field of deep complex-valued data\nprocessing and SVDD application.\n", "date": "2023-06-19", "categories": []}
{"id": "2306.09441", "title": "Unsupervised Anomaly Detection via Nonlinear Manifold Learning", "abstract": "  Anomalies are samples that significantly deviate from the rest of the data\nand their detection plays a major role in building machine learning models that\ncan be reliably used in applications such as data-driven design and novelty\ndetection. The majority of existing anomaly detection methods either are\nexclusively developed for (semi) supervised settings, or provide poor\nperformance in unsupervised applications where there is no training data with\nlabeled anomalous samples. To bridge this research gap, we introduce a robust,\nefficient, and interpretable methodology based on nonlinear manifold learning\nto detect anomalies in unsupervised settings. The essence of our approach is to\nlearn a low-dimensional and interpretable latent representation (aka manifold)\nfor all the data points such that normal samples are automatically clustered\ntogether and hence can be easily and robustly identified. We learn this\nlow-dimensional manifold by designing a learning algorithm that leverages\neither a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our\nLMGP-based approach, in particular, provides a probabilistic perspective on the\nlearning task and is ideal for high-dimensional applications with scarce data.\nWe demonstrate the superior performance of our approach over existing\ntechnologies via multiple analytic examples and real-world datasets.\n", "date": "2023-06-15", "categories": []}
{"id": "2306.09646", "title": "Vacant Holes for Unsupervised Detection of the Outliers in Compact\n  Latent Representation", "abstract": "  Detection of the outliers is pivotal for any machine learning model deployed\nand operated in real-world. It is essential for the Deep Neural Networks that\nwere shown to be overconfident with such inputs. Moreover, even deep generative\nmodels that allow estimation of the probability density of the input fail in\nachieving this task. In this work, we concentrate on the specific type of these\nmodels: Variational Autoencoders (VAEs). First, we unveil a significant\ntheoretical flaw in the assumption of the classical VAE model. Second, we\nenforce an accommodating topological property to the image of the deep neural\nmapping to the latent space: compactness to alleviate the flaw and obtain the\nmeans to provably bound the image within the determined limits by squeezing\nboth inliers and outliers together. We enforce compactness using two\napproaches: (i) Alexandroff extension and (ii) fixed Lipschitz continuity\nconstant on the mapping of the encoder of the VAEs. Finally and most\nimportantly, we discover that the anomalous inputs predominantly tend to land\non the vacant latent holes within the compact space, enabling their successful\nidentification. For that reason, we introduce a specifically devised score for\nhole detection and evaluate the solution against several baseline benchmarks\nachieving promising results.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.09859", "title": "MixedTeacher : Knowledge Distillation for fast inference textural\n  anomaly detection", "abstract": "  For a very long time, unsupervised learning for anomaly detection has been at\nthe heart of image processing research and a stepping stone for high\nperformance industrial automation process. With the emergence of CNN, several\nmethods have been proposed such as Autoencoders, GAN, deep feature extraction,\netc. In this paper, we propose a new method based on the promising concept of\nknowledge distillation which consists of training a network (the student) on\nnormal samples while considering the output of a larger pretrained network (the\nteacher). The main contributions of this paper are twofold: First, a reduced\nstudent architecture with optimal layer selection is proposed, then a new\nStudent-Teacher architecture with network bias reduction combining two teachers\nis proposed in order to jointly enhance the performance of anomaly detection\nand its localization accuracy. The proposed texture anomaly detector has an\noutstanding capability to detect defects in any texture and a fast inference\ntime compared to the SOTA methods.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.07983", "title": "A Hybrid Approach for Smart Alert Generation", "abstract": "  Anomaly detection is an important task in network management. However,\ndeploying intelligent alert systems in real-world large-scale networking\nsystems is challenging when we take into account (i) scalability, (ii) data\nheterogeneity, and (iii) generalizability and maintainability. In this paper,\nwe propose a hybrid model for an alert system that combines statistical models\nwith a whitelist mechanism to tackle these challenges and reduce false positive\nalerts. The statistical models take advantage of a large database to detect\nanomalies in time-series data, while the whitelist filters out persistently\nalerted nodes to further reduce false positives. Our model is validated using\nqualitative data from customer support cases. Future work includes more feature\nengineering and input data, as well as including human feedback in the model\ndevelopment process.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.09269", "title": "Zero-Shot Anomaly Detection with Pre-trained Segmentation Models", "abstract": "  This technical report outlines our submission to the zero-shot track of the\nVisual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the\nperformance of the WINCLIP framework, we aim to enhance the system's\nlocalization capabilities by integrating zero-shot segmentation models. In\naddition, we perform foreground instance segmentation which enables the model\nto focus on the relevant parts of the image, thus allowing the models to better\nidentify small or subtle deviations. Our pipeline requires no external data or\ninformation, allowing for it to be directly applied to new datasets. Our team\n(Variance Vigilance Vanguard) ranked third in the zero-shot track of the VAND\nchallenge, and achieve an average F1-max score of 81.5/24.2 at a sample/pixel\nlevel on the VisA dataset.\n", "date": "2023-06-15", "categories": []}
{"id": "2301.06957", "title": "FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks", "abstract": "  Recent Anomaly Detection techniques have progressed the field considerably\nbut at the cost of increasingly complex training pipelines. Such techniques\nrequire large amounts of training data, resulting in computationally expensive\nalgorithms that are unsuitable for settings where only a small amount of normal\nsamples are available for training. We propose 'Few Shot anOMaly detection'\n(FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to\naccurately detect anomalies having trained on 'few' examples of the normal\nclass and no examples of the anomalous class. We describe FewSOME to be of low\ncomplexity given its low data requirement and short training time. FewSOME is\naided by pretrained weights with an architecture based on Siamese Networks. By\nmeans of an ablation study, we demonstrate how our proposed loss, 'Stop Loss',\nimproves the robustness of FewSOME. Our experiments demonstrate that FewSOME\nperforms at state-of-the-art level on benchmark datasets MNIST, CIFAR-10,\nF-MNIST and MVTec AD while training on only 30 normal samples, a minute\nfraction of the data that existing methods are trained on. Moreover, our\nexperiments show FewSOME to be robust to contaminated datasets. We also report\nF1 score and balanced accuracy in addition to AUC as a benchmark for future\ntechniques to be compared against. Code available;\nhttps://github.com/niamhbelton/FewSOME.\n", "date": "2023-01-17", "categories": []}
{"id": "2306.02517", "title": "Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial\n  Responses", "abstract": "  Extreme natural disasters can have devastating effects on both urban and\nrural areas. In any disaster event, an initial response is the key to rescue\nwithin 72 hours and prompt recovery. During the initial stage of disaster\nresponse, it is important to quickly assess the damage over a wide area and\nidentify priority areas. Among machine learning algorithms, deep anomaly\ndetection is effective in detecting devastation features that are different\nfrom everyday features. In addition, explainable computer vision applications\nshould justify the initial responses. In this paper, we propose an anomaly\ndetection application utilizing deeper fully convolutional data descriptions\n(FCDDs), that enables the localization of devastation features and\nvisualization of damage-marked heatmaps. More specifically, we show numerous\ntraining and test results for a dataset AIDER with the four disaster\ncategories: collapsed buildings, traffic incidents, fires, and flooded areas.\nWe also implement ablation studies of anomalous class imbalance and the data\nscale competing against the normal class. Our experiments provide results of\nhigh accuracies over 95% for F1. Furthermore, we found that the deeper FCDD\nwith a VGG16 backbone consistently outperformed other baselines CNN27,\nResNet101, and Inceptionv3. This study presents a new solution that offers a\ndisaster anomaly detection application for initial responses with higher\naccuracy and devastation explainability, providing a novel contribution to the\nprompt disaster recovery problem in the research area of anomaly scene\nunderstanding. Finally, we discuss future works to improve more robust,\nexplainable applications for effective initial responses.\n", "date": "2023-06-05", "categories": []}
{"id": "2306.06139", "title": "WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern\n  Approaches for Mass Data Mining", "abstract": "  Weighted Outlier Detection is a method for identifying unusual or anomalous\ndata points in a dataset, which can be caused by various factors like human\nerror, fraud, or equipment malfunctions. Detecting outliers can reveal vital\ninformation about system faults, fraudulent activities, and patterns in the\ndata, assisting experts in addressing the root causes of these anomalies.\nHowever,creating a model of normal data patterns to identify outliers can be\nchallenging due to the nature of input data, labeled data availability, and\nspecific requirements of the problem. This article proposed the\nWePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating\nthat such techniques are domain-dependent and usually developed for specific\nproblem formulations. Nevertheless, similar domains can adapt solutions with\nmodifications. This work also investigates the significance of data modeling in\noutlier detection techniques in surveillance, fault detection, and trend\nanalysis, also referred to as novelty detection, a semisupervised task where\nthe algorithm learns to recognize abnormality while being taught the normal\nclass.\n", "date": "2023-06-09", "categories": []}
{"id": "2306.07114", "title": "Coupled Attention Networks for Multivariate Time Series Anomaly\n  Detection", "abstract": "  Multivariate time series anomaly detection (MTAD) plays a vital role in a\nwide variety of real-world application domains. Over the past few years, MTAD\nhas attracted rapidly increasing attention from both academia and industry.\nMany deep learning and graph learning models have been developed for effective\nanomaly detection in multivariate time series data, which enable advanced\napplications such as smart surveillance and risk management with unprecedented\ncapabilities. Nevertheless, MTAD is facing critical challenges deriving from\nthe dependencies among sensors and variables, which often change over time. To\naddress this issue, we propose a coupled attention-based neural network\nframework (CAN) for anomaly detection in multivariate time series data\nfeaturing dynamic variable relationships. We combine adaptive graph learning\nmethods with graph attention to generate a global-local graph that can\nrepresent both global correlations and dynamic local correlations among\nsensors. To capture inter-sensor relationships and temporal dependencies, a\nconvolutional neural network based on the global-local graph is integrated with\na temporal self-attention module to construct a coupled attention module. In\naddition, we develop a multilevel encoder-decoder architecture that\naccommodates reconstruction and prediction tasks to better characterize\nmultivariate time series data. Extensive experiments on real-world datasets\nhave been conducted to evaluate the performance of the proposed CAN approach,\nand the results show that CAN significantly outperforms state-of-the-art\nbaselines.\n", "date": "2023-06-12", "categories": []}
{"id": "2306.07284", "title": "No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly\n  Detection", "abstract": "  Anomaly detection methods, powered by deep learning, have recently been\nmaking significant progress, mostly due to improved representations. It is\ntempting to hypothesize that anomaly detection can improve indefinitely by\nincreasing the scale of our networks, making their representations more\nexpressive. In this paper, we provide theoretical and empirical evidence to the\ncontrary. In fact, we empirically show cases where very expressive\nrepresentations fail to detect even simple anomalies when evaluated beyond the\nwell-studied object-centric datasets. To investigate this phenomenon, we begin\nby introducing a novel theoretical toy model for anomaly detection performance.\nThe model uncovers a fundamental trade-off between representation sufficiency\nand over-expressivity. It provides evidence for a no-free-lunch theorem in\nanomaly detection stating that increasing representation expressivity will\neventually result in performance degradation. Instead, guidance must be\nprovided to focus the representation on the attributes relevant to the\nanomalies of interest. We conduct an extensive empirical investigation\ndemonstrating that state-of-the-art representations often suffer from\nover-expressivity, failing to detect many types of anomalies. Our investigation\ndemonstrates how this over-expressivity impairs image anomaly detection in\npractical settings. We conclude with future directions for mitigating this\nissue.\n", "date": "2023-06-12", "categories": []}
{"id": "2206.06602", "title": "Deep Isolation Forest for Anomaly Detection", "abstract": "  Isolation forest (iForest) has been emerging as arguably the most popular\nanomaly detector in recent years due to its general effectiveness across\ndifferent benchmarks and strong scalability. Nevertheless, its linear\naxis-parallel isolation method often leads to (i) failure in detecting hard\nanomalies that are difficult to isolate in\nhigh-dimensional/non-linear-separable data space, and (ii) notorious\nalgorithmic bias that assigns unexpectedly lower anomaly scores to artefact\nregions. These issues contribute to high false negative errors. Several iForest\nextensions are introduced, but they essentially still employ shallow, linear\ndata partition, restricting their power in isolating true anomalies. Therefore,\nthis paper proposes deep isolation forest. We introduce a new representation\nscheme that utilises casually initialised neural networks to map original data\ninto random representation ensembles, where random axis-parallel cuts are\nsubsequently applied to perform the data partition. This representation scheme\nfacilitates high freedom of the partition in the original data space\n(equivalent to non-linear partition on subspaces of varying sizes), encouraging\na unique synergy between random representations and random partition-based\nisolation. Extensive experiments show that our model achieves significant\nimprovement over state-of-the-art isolation-based methods and deep detectors on\ntabular, graph and time series datasets; our model also inherits desired\nscalability from iForest.\n", "date": "2022-06-14", "categories": []}
{"id": "2207.03579", "title": "DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection", "abstract": "  Graph Anomaly Detection (GAD) has recently become a hot research spot due to\nits practicability and theoretical value. Since GAD emphasizes the application\nand the rarity of anomalous samples, enriching the varieties of its datasets is\nfundamental work. Thus, this paper present DGraph, a real-world dynamic graph\nin the finance domain. DGraph overcomes many limitations of current GAD\ndatasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truth\nnodes. We provide a comprehensive observation of DGraph, revealing that\nanomalous nodes and normal nodes generally have different structures, neighbor\ndistribution, and temporal dynamics. Moreover, it suggests that unlabeled nodes\nare also essential for detecting fraudsters. Furthermore, we conduct extensive\nexperiments on DGraph. Observation and experiments demonstrate that DGraph is\npropulsive to advance GAD research and enable in-depth exploration of anomalous\nnodes.\n", "date": "2022-06-30", "categories": []}
{"id": "2306.05376", "title": "Anomaly Detection in Satellite Videos using Diffusion Models", "abstract": "  The definition of anomaly detection is the identification of an unexpected\nevent. Real-time detection of extreme events such as wildfires, cyclones, or\nfloods using satellite data has become crucial for disaster management.\nAlthough several earth-observing satellites provide information about\ndisasters, satellites in the geostationary orbit provide data at intervals as\nfrequent as every minute, effectively creating a video from space. There are\nmany techniques that have been proposed to identify anomalies in surveillance\nvideos; however, the available datasets do not have dynamic behavior, so we\ndiscuss an anomaly framework that can work on very high-frequency datasets to\nfind very fast-moving anomalies. In this work, we present a diffusion model\nwhich does not need any motion component to capture the fast-moving anomalies\nand outperforms the other baseline methods.\n", "date": "2023-05-25", "categories": []}
{"id": "2306.04466", "title": "Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal\n  Auto-Encoder", "abstract": "  Video anomaly detection has great potential in enhancing safety in the\nproduction and monitoring of crucial areas. Currently, most video anomaly\ndetection methods are based on RGB modality, but its redundant semantic\ninformation may breach the privacy of residents or patients. The 3D data\nobtained by depth camera and LiDAR can accurately locate anomalous events in 3D\nspace while preserving human posture and motion information. Identifying\nindividuals through the point cloud is difficult due to its sparsity, which\nprotects personal privacy. In this study, we propose Point Spatio-Temporal\nAuto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as\ninput to detect anomalies in point cloud videos. We introduce PSTOp and\nPSTTransOp to maintain spatial geometric and temporal motion information in\npoint cloud videos. To measure the reconstruction loss of the proposed\nautoencoder framework, we propose a reconstruction loss measurement strategy\nbased on a shallow feature extractor. Experimental results on the TIMo dataset\nshow that our method outperforms currently representative depth modality-based\nmethods in terms of AUROC and has superior performance in detecting Medical\nIssue anomalies. These results suggest the potential of point cloud modality in\nvideo anomaly detection. Our method sets a new state-of-the-art (SOTA) on the\nTIMo dataset.\n", "date": "2023-06-04", "categories": []}
{"id": "2306.03331", "title": "A Robust Likelihood Model for Novelty Detection", "abstract": "  Current approaches to novelty or anomaly detection are based on deep neural\nnetworks. Despite their effectiveness, neural networks are also vulnerable to\nimperceptible deformations of the input data. This is a serious issue in\ncritical applications, or when data alterations are generated by an adversarial\nattack. While this is a known problem that has been studied in recent years for\nthe case of supervised learning, the case of novelty detection has received\nvery limited attention. Indeed, in this latter setting the learning is\ntypically unsupervised because outlier data is not available during training,\nand new approaches for this case need to be investigated. We propose a new\nprior that aims at learning a robust likelihood for the novelty test, as a\ndefense against attacks. We also integrate the same prior with a\nstate-of-the-art novelty detection approach. Because of the geometric\nproperties of that approach, the resulting robust training is computationally\nvery efficient. An initial evaluation of the method indicates that it is\neffective at improving performance with respect to the standard models in the\nabsence and presence of attacks.\n", "date": "2023-06-06", "categories": []}
{"id": "2306.01977", "title": "AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn", "abstract": "  Data-driven companies use AI models extensively to develop products and\nintelligent business solutions, making the health of these models crucial for\nbusiness success. Model monitoring and alerting in industries pose unique\nchallenges, including a lack of clear model health metrics definition, label\nsparsity, and fast model iterations that result in short-lived models and\nfeatures. As a product, there are also requirements for scalability,\ngeneralizability, and explainability. To tackle these challenges, we propose\nAlerTiger, a deep-learning-based MLOps model monitoring system that helps AI\nteams across the company monitor their AI models' health by detecting anomalies\nin models' input features and output score over time. The system consists of\nfour major steps: model statistics generation, deep-learning-based anomaly\ndetection, anomaly post-processing, and user alerting. Our solution generates\nthree categories of statistics to indicate AI model health, offers a two-stage\ndeep anomaly detection solution to address label sparsity and attain the\ngeneralizability of monitoring new models, and provides holistic reports for\nactionable alerts. This approach has been deployed to most of LinkedIn's\nproduction AI models for over a year and has identified several model issues\nthat later led to significant business metric gains after fixing.\n", "date": "2023-06-03", "categories": []}
{"id": "2306.02025", "title": "Exploring Global and Local Information for Anomaly Detection with Normal\n  Samples", "abstract": "  Anomaly detection aims to detect data that do not conform to regular\npatterns, and such data is also called outliers. The anomalies to be detected\nare often tiny in proportion, containing crucial information, and are suitable\nfor application scenes like intrusion detection, fraud detection, fault\ndiagnosis, e-commerce platforms, et al. However, in many realistic scenarios,\nonly the samples following normal behavior are observed, while we can hardly\nobtain any anomaly information. To address such problem, we propose an anomaly\ndetection method GALDetector which is combined of global and local information\nbased on observed normal samples. The proposed method can be divided into a\nthree-stage method. Firstly, the global similar normal scores and the local\nsparsity scores of unlabeled samples are computed separately. Secondly,\npotential anomaly samples are separated from the unlabeled samples\ncorresponding to these two scores and corresponding weights are assigned to the\nselected samples. Finally, a weighted anomaly detector is trained by loads of\nsamples, then the detector is utilized to identify else anomalies. To evaluate\nthe effectiveness of the proposed method, we conducted experiments on three\ncategories of real-world datasets from diverse domains, and experimental\nresults show that our method achieves better performance when compared with\nother state-of-the-art methods.\n", "date": "2023-06-03", "categories": []}
{"id": "2306.02473", "title": "Anomaly Detection Techniques in Smart Grid Systems: A Review", "abstract": "  Smart grid data can be evaluated for anomaly detection in numerous fields,\nincluding cyber-security, fault detection, electricity theft, etc. The strange\nanomalous behaviors may have been caused by various reasons, including peculiar\nconsumption patterns of the consumers, malfunctioning grid infrastructures,\noutages, external cyber-attacks, or energy fraud. Recently, anomaly detection\nof the smart grid has attracted a large amount of interest from researchers,\nand it is widely applied in a number of high-impact fields. One of the most\nsignificant challenges within the smart grid is the implementation of efficient\nanomaly detection for multiple forms of aberrant behaviors. In this paper, we\nprovide a scoping review of research from the recent advancements in anomaly\ndetection in the context of smart grids. We categorize our study from numerous\naspects for deep understanding and inspection of the research challenges so\nfar. Finally, after analyzing the gap in the reviewed paper, the direction for\nfuture research on anomaly detection in smart-grid systems has been provided\nbriefly.\n", "date": "2023-06-04", "categories": []}
{"id": "2206.04149", "title": "A Survey of Graph-based Deep Learning for Anomaly Detection in\n  Distributed Systems", "abstract": "  Anomaly detection is a crucial task in complex distributed systems. A\nthorough understanding of the requirements and challenges of anomaly detection\nis pivotal to the security of such systems, especially for real-world\ndeployment. While there are many works and application domains that deal with\nthis problem, few have attempted to provide an in-depth look at such systems.\nIn this survey, we explore the potentials of graph-based algorithms to identify\nanomalies in distributed systems. These systems can be heterogeneous or\nhomogeneous, which can result in distinct requirements. One of our objectives\nis to provide an in-depth look at graph-based approaches to conceptually\nanalyze their capability to handle real-world challenges such as heterogeneity\nand dynamic structure. This study gives an overview of the State-of-the-Art\n(SotA) research articles in the field and compare and contrast their\ncharacteristics. To facilitate a more comprehensive understanding, we present\nthree systems with varying abstractions as use cases. We examine the specific\nchallenges involved in anomaly detection within such systems. Subsequently, we\nelucidate the efficacy of graphs in such systems and explicate their\nadvantages. We then delve into the SotA methods and highlight their strength\nand weaknesses, pointing out the areas for possible improvements and future\nworks.\n", "date": "2022-06-08", "categories": []}
{"id": "2212.09258", "title": "CHAD: Charlotte Anomaly Dataset", "abstract": "  In recent years, we have seen a significant interest in data-driven deep\nlearning approaches for video anomaly detection, where an algorithm must\ndetermine if specific frames of a video contain abnormal behaviors. However,\nvideo anomaly detection is particularly context-specific, and the availability\nof representative datasets heavily limits real-world accuracy. Additionally,\nthe metrics currently reported by most state-of-the-art methods often do not\nreflect how well the model will perform in real-world scenarios. In this\narticle, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a\nhigh-resolution, multi-camera anomaly dataset in a commercial parking lot\nsetting. In addition to frame-level anomaly labels, CHAD is the first anomaly\ndataset to include bounding box, identity, and pose annotations for each actor.\nThis is especially beneficial for skeleton-based anomaly detection, which is\nuseful for its lower computational demand in real-world settings. CHAD is also\nthe first anomaly dataset to contain multiple views of the same scene. With\nfour camera views and over 1.15 million frames, CHAD is the largest fully\nannotated anomaly detection dataset including person annotations, collected\nfrom continuous video streams from stationary cameras for smart video\nsurveillance applications. To demonstrate the efficacy of CHAD for training and\nevaluation, we benchmark two state-of-the-art skeleton-based anomaly detection\nalgorithms on CHAD and provide comprehensive analysis, including both\nquantitative results and qualitative examination. The dataset is available at\nhttps://github.com/TeCSAR-UNCC/CHAD.\n", "date": "2022-12-19", "categories": []}
{"id": "2304.09367", "title": "Graph Neural Network-Based Anomaly Detection for River Network Systems", "abstract": "  Water is the lifeblood of river networks, and its quality plays a crucial\nrole in sustaining both aquatic ecosystems and human societies. Real-time\nmonitoring of water quality is increasingly reliant on in-situ sensor\ntechnology. Anomaly detection is crucial for identifying erroneous patterns in\nsensor data, but can be a challenging task due to the complexity and\nvariability of the data, even under normal conditions. This paper presents a\nsolution to the challenging task of anomaly detection for river network sensor\ndata, which is essential for accurate and continuous monitoring. We use a graph\nneural network model, the recently proposed Graph Deviation Network (GDN),\nwhich employs graph attention-based forecasting to capture the complex\nspatio-temporal relationships between sensors. We propose an alternate anomaly\nscoring method, GDN+, based on the learned graph. To evaluate the model's\nefficacy, we introduce new benchmarking simulation experiments with\nhighly-sophisticated dependency structures and subsequence anomalies of various\ntypes. We further examine the strengths and weaknesses of this baseline\napproach, GDN, in comparison to other benchmarking methods on complex\nreal-world river network data. Findings suggest that GDN+ outperforms the\nbaseline approach in high-dimensional data, while also providing improved\ninterpretability. We also introduce software called gnnad.\n", "date": "2023-04-19", "categories": []}
{"id": "2305.19643", "title": "Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability\n  in Anomaly Detection through Automatic Diffusion Models", "abstract": "  The introduction of diffusion models in anomaly detection has paved the way\nfor more effective and accurate image reconstruction in pathologies. However,\nthe current limitations in controlling noise granularity hinder diffusion\nmodels' ability to generalize across diverse anomaly types and compromise the\nrestoration of healthy tissues. To overcome these challenges, we propose\nAutoDDPM, a novel approach that enhances the robustness of diffusion models.\nAutoDDPM utilizes diffusion models to generate initial likelihood maps of\npotential anomalies and seamlessly integrates them with the original image.\nThrough joint noised distribution re-sampling, AutoDDPM achieves harmonization\nand in-painting effects. Our study demonstrates the efficacy of AutoDDPM in\nreplacing anomalous regions while preserving healthy tissues, considerably\nsurpassing diffusion models' limitations. It also contributes valuable insights\nand analysis on the limitations of current diffusion models, promoting robust\nand interpretable anomaly detection in medical imaging - an essential aspect of\nbuilding autonomous clinical decision systems with higher interpretability.\n", "date": "2023-05-31", "categories": []}
{"id": "2305.18389", "title": "AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by\n  Random Labeling", "abstract": "  Anomaly detection or more generally outliers detection is one of the most\npopular and challenging subject in theoretical and applied machine learning.\nThe main challenge is that in general we have access to very few labeled data\nor no labels at all. In this paper, we present a new semi-supervised anomaly\ndetection method called \\textbf{AnoRand} by combining a deep learning\narchitecture with random synthetic label generation. The proposed architecture\nhas two building blocks: (1) a noise detection (ND) block composed of feed\nforward ferceptron and (2) an autoencoder (AE) block. The main idea of this new\narchitecture is to learn one class (e.g. the majority class in case of anomaly\ndetection) as well as possible by taking advantage of the ability of auto\nencoders to represent data in a latent space and the ability of Feed Forward\nPerceptron (FFP) to learn one class when the data is highly imbalanced. First,\nwe create synthetic anomalies by randomly disturbing (add noise) few samples\n(e.g. 2\\%) from the training set. Second, we use the normal and the synthetic\nsamples as input to our model. We compared the performance of the proposed\nmethod to 17 state-of-the-art unsupervised anomaly detection method on\nsynthetic datasets and 57 real-world datasets. Our results show that this new\nmethod generally outperforms most of the state-of-the-art methods and has the\nbest performance (AUC ROC and AUC PR) on the vast majority of reference\ndatasets. We also tested our method in a supervised way by using the actual\nlabels to train the model. The results show that it has very good performance\ncompared to most of state-of-the-art supervised algorithms.\n", "date": "2023-05-28", "categories": []}
{"id": "2305.17149", "title": "Diagnostic Spatio-temporal Transformer with Faithful Encoding", "abstract": "  This paper addresses the task of anomaly diagnosis when the underlying data\ngeneration process has a complex spatio-temporal (ST) dependency. The key\ntechnical challenge is to extract actionable insights from the dependency\ntensor characterizing high-order interactions among temporal and spatial\nindices. We formalize the problem as supervised dependency discovery, where the\nST dependency is learned as a side product of multivariate time-series\nclassification. We show that temporal positional encoding used in existing ST\ntransformer works has a serious limitation in capturing higher frequencies\n(short time scales). We propose a new positional encoding with a theoretical\nguarantee, based on discrete Fourier transform. We also propose a new ST\ndependency discovery framework, which can provide readily consumable diagnostic\ninformation in both spatial and temporal directions. Finally, we demonstrate\nthe utility of the proposed model, DFStrans (Diagnostic Fourier-based\nSpatio-temporal Transformer), in a real industrial application of building\nelevator control.\n", "date": "2023-05-26", "categories": []}
{"id": "2305.17695", "title": "k-NNN: Nearest Neighbors of Neighbors for Anomaly Detection", "abstract": "  Anomaly detection aims at identifying images that deviate significantly from\nthe norm. We focus on algorithms that embed the normal training examples in\nspace and when given a test image, detect anomalies based on the features\ndistance to the k-nearest training neighbors. We propose a new operator that\ntakes into account the varying structure & importance of the features in the\nembedding space. Interestingly, this is done by taking into account not only\nthe nearest neighbors, but also the neighbors of these neighbors (k-NNN). We\nshow that by simply replacing the nearest neighbor component in existing\nalgorithms by our k-NNN operator, while leaving the rest of the algorithms\nuntouched, each algorithms own results are improved. This is the case both for\ncommon homogeneous datasets, such as flowers or nuts of a specific type, as\nwell as for more diverse datasets\n", "date": "2023-05-28", "categories": []}
{"id": "2211.01716", "title": "Discussion of Features for Acoustic Anomaly Detection under Industrial\n  Disturbing Noise in an End-of-Line Test of Geared Motors", "abstract": "  In the end-of-line test of geared motors, the evaluation of product qual-ity\nis important. Due to time constraints and the high diversity of variants,\nacous-tic measurements are more economical than vibration measurements.\nHowever, the acoustic data is affected by industrial disturbing noise.\nTherefore, the aim of this study is to investigate the robustness of features\nused for anomaly detection in geared motor end-of-line testing. A real-world\ndataset with typical faults and acoustic disturbances is recorded by an\nacoustic array. This includes industrial noise from the production and\nsystematically produced disturbances, used to compare the robustness. Overall,\nit is proposed to apply features extracted from a log-envelope spectrum\ntogether with psychoacoustic features. The anomaly de-tection is done by using\nthe isolation forest or the more universal bagging random miner. Most\ndisturbances can be circumvented, while the use of a hammer or air pressure\noften causes problems. In general, these results are important for condi-tion\nmonitoring tasks that are based on acoustic or vibration measurements.\nFur-thermore, a real-world problem description is presented to improve common\nsig-nal processing and machine learning tasks.\n", "date": "2022-11-03", "categories": []}
{"id": "2305.16497", "title": "AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate\n  Anomaly Detection", "abstract": "  Anomaly detection tools and methods present a key capability in modern\ncyberphysical and failure prediction systems. Despite the fast-paced\ndevelopment in deep learning architectures for anomaly detection, model\noptimization for a given dataset is a cumbersome and time consuming process.\nNeuroevolution could be an effective and efficient solution to this problem, as\na fully automated search method for learning optimal neural networks,\nsupporting both gradient and non-gradient fine tuning. However, existing\nmethods mostly focus on optimizing model architectures without taking into\naccount feature subspaces and model weights. In this work, we propose Anomaly\nDetection Neuroevolution (AD-NEv) - a scalable multi-level optimized\nneuroevolution framework for multivariate time series anomaly detection. The\nmethod represents a novel approach to synergically: i) optimize feature\nsubspaces for an ensemble model based on the bagging technique; ii) optimize\nthe model architecture of single anomaly detection models; iii) perform\nnon-gradient fine-tuning of network weights. An extensive experimental\nevaluation on widely adopted multivariate anomaly detection benchmark datasets\nshows that the models extracted by AD-NEv outperform well-known deep learning\narchitectures for anomaly detection. Moreover, results show that AD-NEv can\nperform the whole process efficiently, presenting high scalability when\nmultiple GPUs are available.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.16509", "title": "RoLA: A Real-Time Online Lightweight Anomaly Detection System for\n  Multivariate Time Series", "abstract": "  A multivariate time series refers to observations of two or more variables\ntaken from a device or a system simultaneously over time. There is an\nincreasing need to monitor multivariate time series and detect anomalies in\nreal time to ensure proper system operation and good service quality. It is\nalso highly desirable to have a lightweight anomaly detection system that\nconsiders correlations between different variables, adapts to changes in the\npattern of the multivariate time series, offers immediate responses, and\nprovides supportive information regarding detection results based on\nunsupervised learning and online model training. In the past decade, many\nmultivariate time series anomaly detection approaches have been introduced.\nHowever, they are unable to offer all the above-mentioned features. In this\npaper, we propose RoLA, a real-time online lightweight anomaly detection system\nfor multivariate time series based on a divide-and-conquer strategy, parallel\nprocessing, and the majority rule. RoLA employs multiple lightweight anomaly\ndetectors to monitor multivariate time series in parallel, determine the\ncorrelations between variables dynamically on the fly, and then jointly detect\nanomalies based on the majority rule in real time. To demonstrate the\nperformance of RoLA, we conducted an experiment based on a public dataset\nprovided by the FerryBox of the One Ocean Expedition. The results show that\nRoLA provides satisfactory detection accuracy and lightweight performance.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.15652", "title": "Towards Total Online Unsupervised Anomaly Detection and Localization in\n  Industrial Vision", "abstract": "  Although existing image anomaly detection methods yield impressive results,\nthey are mostly an offline learning paradigm that requires excessive data\npre-collection, limiting their adaptability in industrial scenarios with online\nstreaming data. Online learning-based image anomaly detection methods are more\ncompatible with industrial online streaming data but are rarely noticed. For\nthe first time, this paper presents a fully online learning image anomaly\ndetection method, namely LeMO, learning memory for online image anomaly\ndetection. LeMO leverages learnable memory initialized with orthogonal random\nnoise, eliminating the need for excessive data in memory initialization and\ncircumventing the inefficiencies of offline data collection. Moreover, a\ncontrastive learning-based loss function for anomaly detection is designed to\nenable online joint optimization of memory and image target-oriented features.\nThe presented method is simple and highly effective. Extensive experiments\ndemonstrate the superior performance of LeMO in the online setting.\nAdditionally, in the offline setting, LeMO is also competitive with the current\nstate-of-the-art methods and achieves excellent performance in few-shot\nscenarios.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.16114", "title": "Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly\n  Detection with Scale Learning", "abstract": "  Due to the unsupervised nature of anomaly detection, the key to fueling deep\nmodels is finding supervisory signals. Different from current\nreconstruction-guided generative models and transformation-based contrastive\nmodels, we devise novel data-driven supervision for tabular data by introducing\na characteristic -- scale -- as data labels. By representing varied sub-vectors\nof data instances, we define scale as the relationship between the\ndimensionality of original sub-vectors and that of representations. Scales\nserve as labels attached to transformed representations, thus offering ample\nlabeled data for neural network training. This paper further proposes a scale\nlearning-based anomaly detection method. Supervised by the learning objective\nof scale distribution alignment, our approach learns the ranking of\nrepresentations converted from varied subspaces of each data instance. Through\nthis proxy task, our approach models inherent regularities and patterns within\ndata, which well describes data \"normality\". Abnormal degrees of testing\ninstances are obtained by measuring whether they fit these learned patterns.\nExtensive experiments show that our approach leads to significant improvement\nover state-of-the-art generative/contrastive anomaly detection methods.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.14880", "title": "Multiresolution Feature Guidance Based Transformer for Anomaly Detection", "abstract": "  Anomaly detection is represented as an unsupervised learning to identify\ndeviated images from normal images. In general, there are two main challenges\nof anomaly detection tasks, i.e., the class imbalance and the unexpectedness of\nanomalies. In this paper, we propose a multiresolution feature guidance method\nbased on Transformer named GTrans for unsupervised anomaly detection and\nlocalization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on\nImageNet is developed to provide surrogate labels for features and tokens.\nUnder the tacit knowledge guidance of the AGN, the anomaly detection network\nnamed Trans utilizes Transformer to effectively establish a relationship\nbetween features with multiresolution, enhancing the ability of the Trans in\nfitting the normal data manifold. Due to the strong generalization ability of\nAGN, GTrans locates anomalies by comparing the differences in spatial distance\nand direction of multi-scale features extracted from the AGN and the Trans. Our\nexperiments demonstrate that the proposed GTrans achieves state-of-the-art\nperformance in both detection and localization on the MVTec AD dataset. GTrans\nachieves image-level and pixel-level anomaly detection AUROC scores of 99.0%\nand 97.9% on the MVTec AD dataset, respectively.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.15084", "title": "Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos", "abstract": "  We introduce the first audio-visual dataset for traffic anomaly detection\ntaken from real-world scenes, called MAVAD, with a diverse range of weather and\nillumination conditions. In addition, we propose a novel method named AVACA\nthat combines visual and audio features extracted from video sequences by means\nof cross-attention to detect anomalies. We demonstrate that the addition of\naudio improves the performance of AVACA by up to 5.2%. We also evaluate the\nimpact of image anonymization, showing only a minor decrease in performance\naveraging at 1.7%.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.15227", "title": "Real time dense anomaly detection by learning on synthetic negative data", "abstract": "  Most approaches to dense anomaly detection rely on generative modeling or on\ndiscriminative methods that train with negative data. We consider a recent\nhybrid method that optimizes the same shared representation according to\ncross-entropy of the discriminative predictions, and negative log likelihood of\nthe predicted energy-based density. We extend that work with a jointly trained\ngenerative flow that samples synthetic negatives at the border of the inlier\ndistribution. The proposed extension provides potential to learn the hybrid\nmethod without real negative data. Our experiments analyze the impact of\ntraining with synthetic negative data and validate contribution of the\nenergy-based density during training and evaluation.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.13573", "title": "SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs", "abstract": "  Anomaly detection aims to distinguish abnormal instances that deviate\nsignificantly from the majority of benign ones. As instances that appear in the\nreal world are naturally connected and can be represented with graphs, graph\nneural networks become increasingly popular in tackling the anomaly detection\nproblem. Despite the promising results, research on anomaly detection has\nalmost exclusively focused on static graphs while the mining of anomalous\npatterns from dynamic graphs is rarely studied but has significant application\nvalue. In addition, anomaly detection is typically tackled from semi-supervised\nperspectives due to the lack of sufficient labeled data. However, most proposed\nmethods are limited to merely exploiting labeled data, leaving a large number\nof unlabeled samples unexplored. In this work, we present semi-supervised\nanomaly detection (SAD), an end-to-end framework for anomaly detection on\ndynamic graphs. By a combination of a time-equipped memory bank and a\npseudo-label contrastive learning module, SAD is able to fully exploit the\npotential of large unlabeled samples and uncover underlying anomalies on\nevolving graph streams. Extensive experiments on four real-world datasets\ndemonstrate that SAD efficiently discovers anomalies from dynamic graphs and\noutperforms existing advanced methods even when provided with only little\nlabeled data.\n", "date": "2023-05-23", "categories": []}
{"id": "2305.13611", "title": "A New Comprehensive Benchmark for Semi-supervised Video Anomaly\n  Detection and Anticipation", "abstract": "  Semi-supervised video anomaly detection (VAD) is a critical task in the\nintelligent surveillance system. However, an essential type of anomaly in VAD\nnamed scene-dependent anomaly has not received the attention of researchers.\nMoreover, there is no research investigating anomaly anticipation, a more\nsignificant task for preventing the occurrence of anomalous events. To this\nend, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes,\n28 classes of abnormal events, and 16 hours of videos. At present, it is the\nlargest semi-supervised VAD dataset with the largest number of scenes and\nclasses of anomalies, the longest duration, and the only one considering the\nscene-dependent anomaly. Meanwhile, it is also the first dataset proposed for\nvideo anomaly anticipation. We further propose a novel model capable of\ndetecting and anticipating anomalous events simultaneously. Compared with 7\noutstanding VAD algorithms in recent years, our method can cope with\nscene-dependent anomaly detection and anomaly anticipation both well, achieving\nstate-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and\nthe newly proposed NWPU Campus datasets consistently. Our dataset and code is\navailable at: https://campusvad.github.io.\n", "date": "2023-05-23", "categories": []}
{"id": "2208.13579", "title": "Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier\n  Detection with PixelCNN++", "abstract": "  Reliable outlier detection is critical for real-world deployment of deep\nlearning models. Although extensively studied, likelihoods produced by deep\ngenerative models have been largely dismissed as being impractical for outlier\ndetection. First, deep generative model likelihoods are readily biased by\nlow-level input statistics. Second, many recent solutions for correcting these\nbiases are computationally expensive, or do not generalize well to complex,\nnatural datasets. Here, we explore outlier detection with a state-of-the-art\ndeep autoregressive model: PixelCNN++. We show that biases in PixelCNN++\nlikelihoods arise primarily from predictions based on local dependencies. We\npropose two families of bijective transformations -- ``stirring'' and\n``shaking'' -- which ameliorate low-level biases and isolate the contribution\nof long-range dependencies to PixelCNN++ likelihoods. These transformations are\ninexpensive and readily computed at evaluation time. We test our approaches\nextensively with five grayscale and six natural image datasets and show that\nthey achieve or exceed state-of-the-art outlier detection, particularly on\ndatasets with complex, natural images. We also show that our solutions work\nwell with other types of generative models (generative flows and variational\nautoencoders) and that their efficacy is governed by each model's reliance on\nlocal dependencies. In sum, lightweight remedies suffice to achieve robust\noutlier detection on image data with deep generative models.\n", "date": "2022-08-29", "categories": []}
{"id": "2305.12068", "title": "Technical outlier detection via convolutional variational autoencoder\n  for the ADMANI breast mammogram dataset", "abstract": "  The ADMANI datasets (annotated digital mammograms and associated non-image\ndatasets) from the Transforming Breast Cancer Screening with AI programme\n(BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large\nscale, clinically curated, real-world databases. The datasets are expected to\naid in the development of clinically relevant Artificial Intelligence (AI)\nalgorithms for breast cancer detection, early diagnosis, and other\napplications. To ensure high data quality, technical outliers must be removed\nbefore any downstream algorithm development. As a first step, we randomly\nselect 30,000 individual mammograms and use Convolutional Variational\nAutoencoder (CVAE), a deep generative neural network, to detect outliers. CVAE\nis expected to detect all sorts of outliers, although its detection performance\ndiffers among different types of outliers. Traditional image processing\ntechniques such as erosion and pectoral muscle analysis can compensate for the\npoor performance of CVAE in certain outlier types. We identify seven types of\ntechnical outliers: implant, pacemaker, cardiac loop recorder, improper\nradiography, atypical lesion/calcification, incorrect exposure parameter and\nimproper placement. The outlier recall rate for the test set is 61% if CVAE,\nerosion and pectoral muscle analysis each select the top 1% images ranked in\nascending or descending order according to image outlier score under each\ndetection method, and 83% if each selects the top 5% images. This study offers\nan overview of technical outliers in the ADMANI dataset and suggests future\ndirections to improve outlier detection effectiveness.\n", "date": "2023-05-20", "categories": []}
{"id": "2305.12266", "title": "LightESD: Fully-Automated and Lightweight Anomaly Detection Framework\n  for Edge Computing", "abstract": "  Anomaly detection is widely used in a broad range of domains from\ncybersecurity to manufacturing, finance, and so on. Deep learning based anomaly\ndetection has recently drawn much attention because of its superior capability\nof recognizing complex data patterns and identifying outliers accurately.\nHowever, deep learning models are typically iteratively optimized in a central\nserver with input data gathered from edge devices, and such data transfer\nbetween edge devices and the central server impose substantial overhead on the\nnetwork and incur additional latency and energy consumption. To overcome this\nproblem, we propose a fully-automated, lightweight, statistical learning based\nanomaly detection framework called LightESD. It is an on-device learning method\nwithout the need for data transfer between edge and server, and is extremely\nlightweight that most low-end edge devices can easily afford with negligible\ndelay, CPU/memory utilization, and power consumption. Yet, it achieves highly\ncompetitive detection accuracy. Another salient feature is that it can\nauto-adapt to probably any dataset without manually setting or configuring\nmodel parameters or hyperparameters, which is a drawback of most existing\nmethods. We focus on time series data due to its pervasiveness in edge\napplications such as IoT. Our evaluation demonstrates that LightESD outperforms\nother SOTA methods on detection accuracy, efficiency, and resource consumption.\nAdditionally, its fully automated feature gives it another competitive\nadvantage in terms of practical usability and generalizability.\n", "date": "2023-05-20", "categories": []}
{"id": "2305.12329", "title": "Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices", "abstract": "  The article deals with anomaly detection of Juniper router logs. Abnormal\nJuniper router logs include logs that are usually different from the normal\noperation, and they often reflect the abnormal operation of router devices. To\nprevent router devices from being damaged and help administrator to grasp the\nsituation of error quickly, detecting abnormal operation soon is very\nimportant. In this work, we present a new way to get important features from\nlog data of Juniper router devices and use machine learning method (basing on\nOne-Class SVM model) for anomaly detection. One-Class SVM model requires some\nknowledge and comprehension about logs of Juniper router devices so that it can\nanalyze, interpret, and test the knowledge ac-quired. We collect log data from\na lot of real Juniper router devices and clas-sify them based on our knowledge.\nBefore these logs are used for training and testing the One-Class SVM model,\nthe feature extraction phase for these data was carried out. Finally, with the\nproposed method, the system errors of the routers were dectected quickly and\naccurately. This may help our com-pany to reduce the operation cost for the\nrouter systems.\n", "date": "2023-05-21", "categories": []}
{"id": "2305.12358", "title": "AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection", "abstract": "  Robust and accurate detection and segmentation of heterogenous tumors\nappearing in different anatomical organs with supervised methods require\nlarge-scale labeled datasets covering all possible types of diseases. Due to\nthe unavailability of such rich datasets and the high cost of annotations,\nunsupervised anomaly detection (UAD) methods have been developed aiming to\ndetect the pathologies as deviation from the normality by utilizing the\nunlabeled healthy image data. However, developed UAD models are often trained\nwith an incomplete distribution of healthy anatomies and have difficulties in\npreserving anatomical constraints. This work intends to, first, propose a\nrobust inpainting model to learn the details of healthy anatomies and\nreconstruct high-resolution images by preserving anatomical constraints.\nSecond, we propose an autoinpainting pipeline to automatically detect tumors,\nreplace their appearance with the learned healthy anatomies, and based on that\nsegment the tumoral volumes in a purely unsupervised fashion. Three imaging\ndatasets, including PET, CT, and PET-CT scans of lung tumors and head and neck\ntumors, are studied as benchmarks for evaluation. Experimental results\ndemonstrate the significant superiority of the proposed method over a wide\nrange of state-of-the-art UAD methods. Moreover, the unsupervised method we\npropose produces comparable results to a robust supervised segmentation method\nwhen applied to multimodal images.\n", "date": "2023-05-21", "categories": []}
{"id": "2305.12958", "title": "AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly\n  Detection", "abstract": "  Most anomaly detection systems try to model normal behavior and assume\nanomalies deviate from it in diverse manners. However, there may be patterns in\nthe anomalies as well. Ideally, an anomaly detection system can exploit\npatterns in both normal and anomalous behavior. In this paper, we present\nAD-MERCS, an unsupervised approach to anomaly detection that explicitly aims at\ndoing both. AD-MERCS identifies multiple subspaces of the instance space within\nwhich patterns exist, and identifies conditions (possibly in other subspaces)\nthat characterize instances that deviate from these patterns. Experiments show\nthat this modeling of both normality and abnormality makes the anomaly detector\nperformant on a wide range of types of anomalies. Moreover, by identifying\npatterns and conditions in (low-dimensional) subspaces, the anomaly detector\ncan provide simple explanations of why something is considered an anomaly.\nThese explanations can be both negative (deviation from some pattern) as\npositive (meeting some condition that is typical for anomalies).\n", "date": "2023-05-22", "categories": []}
{"id": "2304.11438", "title": "Constructing a meta-learner for unsupervised anomaly detection", "abstract": "  Unsupervised anomaly detection (AD) is critical for a wide range of practical\napplications, from network security to health and medical tools. Due to the\ndiversity of problems, no single algorithm has been found to be superior for\nall AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection\nProblem (ASP), has been extensively examined in supervised classification\nproblems, through the use of meta-learning and AutoML, however, it has received\nlittle attention in unsupervised AD tasks. This research proposes a new\nmeta-learning approach that identifies an appropriate unsupervised AD algorithm\ngiven a set of meta-features generated from the unlabelled input dataset. The\nperformance of the proposed meta-learner is superior to the current state of\nthe art solution. In addition, a mixed model statistical analysis has been\nconducted to examine the impact of the meta-learner components: the meta-model,\nmeta-features, and the base set of AD algorithms, on the overall performance of\nthe meta-learner. The analysis was conducted using more than 10,000 datasets,\nwhich is significantly larger than previous studies. Results indicate that a\nrelatively small number of meta-features can be used to identify an appropriate\nAD algorithm, but the choice of a meta-model in the meta-learner has a\nconsiderable impact.\n", "date": "2023-04-22", "categories": []}
{"id": "2305.09691", "title": "Evaluation Strategy of Time-series Anomaly Detection with Decay Function", "abstract": "  Recent algorithms of time-series anomaly detection have been evaluated by\napplying a Point Adjustment (PA) protocol. However, the PA protocol has a\nproblem of overestimating the performance of the detection algorithms because\nit only depends on the number of detected abnormal segments and their size. We\npropose a novel evaluation protocol called the Point-Adjusted protocol with\ndecay function (PAdf) to evaluate the time-series anomaly detection algorithm\nby reflecting the following ideal requirements: detect anomalies quickly and\naccurately without false alarms. This paper theoretically and experimentally\nshows that the PAdf protocol solves the over- and under-estimation problems of\nexisting protocols such as PA and PA\\%K. By conducting re-evaluations of SOTA\nmodels in benchmark datasets, we show that the PA protocol only focuses on\nfinding many anomalous segments, whereas the score of the PAdf protocol\nconsiders not only finding many segments but also detecting anomalies quickly\nwithout delay.\n", "date": "2023-05-15", "categories": []}
{"id": "2305.10082", "title": "Imbalanced Aircraft Data Anomaly Detection", "abstract": "  Anomaly detection in temporal data from sensors under aviation scenarios is a\npractical but challenging task: 1) long temporal data is difficult to extract\ncontextual information with temporal correlation; 2) the anomalous data are\nrare in time series, causing normal/abnormal imbalance in anomaly detection,\nmaking the detector classification degenerate or even fail. To remedy the\naforementioned problems, we propose a Graphical Temporal Data Analysis (GTDA)\nframework. It consists three modules, named Series-to-Image (S2I),\nCluster-based Resampling Approach using Euclidean Distance (CRD) and\nVariance-Based Loss (VBL). Specifically, for better extracts global information\nin temporal data from sensors, S2I converts the data to curve images to\ndemonstrate abnormalities in data changes. CRD and VBL balance the\nclassification to mitigate the unequal distribution of classes. CRD extracts\nminority samples with similar features to majority samples by clustering and\nover-samples them. And VBL fine-tunes the decision boundary by balancing the\nfitting degree of the network to each class. Ablation experiments on the\nFlights dataset indicate the effectiveness of CRD and VBL on precision and\nrecall, respectively. Extensive experiments demonstrate the synergistic\nadvantages of CRD and VBL on F1-score on Flights and three other temporal\ndatasets.\n", "date": "2023-05-17", "categories": []}
{"id": "2207.03820", "title": "Deep Learning for Anomaly Detection in Log Data: A Survey", "abstract": "  Automatic log file analysis enables early detection of relevant incidents\nsuch as system failures. In particular, self-learning anomaly detection\ntechniques capture patterns in log data and subsequently report unexpected log\nevent occurrences to system operators without the need to provide or manually\nmodel anomalous scenarios in advance. Recently, an increasing number of\napproaches leveraging deep learning neural networks for this purpose have been\npresented. These approaches have demonstrated superior detection performance in\ncomparison to conventional machine learning techniques and simultaneously\nresolve issues with unstable data formats. However, there exist many different\narchitectures for deep learning and it is non-trivial to encode raw and\nunstructured log data to be analyzed by neural networks. We therefore carry out\na systematic literature review that provides an overview of deployed models,\ndata pre-processing mechanisms, anomaly detection techniques, and evaluations.\nThe survey does not quantitatively compare existing approaches but instead aims\nto help readers understand relevant aspects of different model architectures\nand emphasizes open issues for future work.\n", "date": "2022-07-08", "categories": []}
{"id": "2305.08509", "title": "Component-aware anomaly detection framework for adjustable and logical\n  industrial visual inspection", "abstract": "  Industrial visual inspection aims at detecting surface defects in products\nduring the manufacturing process. Although existing anomaly detection models\nhave shown great performance on many public benchmarks, their limited\nadjustability and ability to detect logical anomalies hinder their broader use\nin real-world settings. To this end, in this paper, we propose a novel\ncomponent-aware anomaly detection framework (ComAD) which can simultaneously\nachieve adjustable and logical anomaly detection for industrial scenarios.\nSpecifically, we propose to segment images into multiple components based on a\nlightweight and nearly training-free unsupervised semantic segmentation model.\nThen, we design an interpretable logical anomaly detection model through\nmodeling the metrological features of each component and their relationships.\nDespite its simplicity, our framework achieves state-of-the-art performance on\nimage-level logical anomaly detection. Meanwhile, segmenting a product image\ninto multiple components provides a novel perspective for industrial visual\ninspection, demonstrating great potential in model customization, noise\nresistance, and anomaly classification. The code will be available at\nhttps://github.com/liutongkun/ComAD.\n", "date": "2023-05-15", "categories": []}
{"id": "2302.13748", "title": "Unsupervised Video Anomaly Detection for Stereotypical Behaviours in\n  Autism", "abstract": "  Monitoring and analyzing stereotypical behaviours is important for early\nintervention and care taking in Autism Spectrum Disorder (ASD). This paper\nfocuses on automatically detecting stereotypical behaviours with computer\nvision techniques. Off-the-shelf methods tackle this task by supervised\nclassification and activity recognition techniques. However, the unbounded\ntypes of stereotypical behaviours and the difficulty in collecting video\nrecordings of ASD patients largely limit the feasibility of the existing\nsupervised detection methods. As a result, we tackle these challenges from a\nnew perspective, i.e. unsupervised video anomaly detection for stereotypical\nbehaviours detection. The models can be trained among unlabeled videos\ncontaining only normal behaviours and unknown types of abnormal behaviours can\nbe detected during inference. Correspondingly, we propose a Dual Stream deep\nmodel for Stereotypical Behaviours Detection, DS-SBD, based on the temporal\ntrajectory of human poses and the repetition patterns of human actions.\nExtensive experiments are conducted to verify the effectiveness of our proposed\nmethod and suggest that it serves as a potential benchmark for future research.\n", "date": "2023-02-27", "categories": []}
{"id": "2210.07697", "title": "Multi-Task Learning based Video Anomaly Detection with Attention", "abstract": "  Multi-task learning based video anomaly detection methods combine multiple\nproxy tasks in different branches to detect video anomalies in different\nsituations. Most existing methods either do not combine complementary tasks to\neffectively cover all motion patterns, or the class of the objects is not\nexplicitly considered. To address the aforementioned shortcomings, we propose a\nnovel multi-task learning based method that combines complementary proxy tasks\nto better consider the motion and appearance features. We combine the semantic\nsegmentation and future frame prediction tasks in a single branch to learn the\nobject class and consistent motion patterns, and to detect respective anomalies\nsimultaneously. In the second branch, we added several attention mechanisms to\ndetect motion anomalies with attention to object parts, the direction of\nmotion, and the distance of the objects from the camera. Our qualitative\nresults show that the proposed method considers the object class effectively\nand learns motion with attention to the aforementioned important factors which\nresults in a precise motion modeling and a better motion anomaly detection.\nAdditionally, quantitative results show the superiority of our method compared\nwith state-of-the-art methods.\n", "date": "2022-10-14", "categories": []}
{"id": "2305.00595", "title": "Impact of Deep Learning Libraries on Online Adaptive Lightweight Time\n  Series Anomaly Detection", "abstract": "  Providing online adaptive lightweight time series anomaly detection without\nhuman intervention and domain knowledge is highly valuable. Several such\nanomaly detection approaches have been introduced in the past years, but all of\nthem were only implemented in one deep learning library. With the development\nof deep learning libraries, it is unclear how different deep learning libraries\nimpact these anomaly detection approaches since there is no such evaluation\navailable. Randomly choosing a deep learning library to implement an anomaly\ndetection approach might not be able to show the true performance of the\napproach. It might also mislead users in believing one approach is better than\nanother. Therefore, in this paper, we investigate the impact of deep learning\nlibraries on online adaptive lightweight time series anomaly detection by\nimplementing two state-of-the-art anomaly detection approaches in three\nwell-known deep learning libraries and evaluating how these two approaches are\nindividually affected by the three deep learning libraries. A series of\nexperiments based on four real-world open-source time series datasets were\nconducted. The results provide a good reference to select an appropriate deep\nlearning library for online adaptive lightweight anomaly detection.\n", "date": "2023-04-30", "categories": []}
{"id": "2210.17258", "title": "Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few\n  Normal Samples", "abstract": "  Anomaly detection, which is a critical and popular topic in computer vision,\naims to detect anomalous samples that are different from the normal (i.e.,\nnon-anomalous) ones. The current mainstream methods focus on anomaly detection\nfor images, whereas little attention has been paid to 3D point cloud. In this\npaper, drawing inspiration from the knowledge transfer ability of\nteacher-student architecture and the impressive feature extraction capability\nof recent neural networks, we design a teacher-student structured model for 3D\nanomaly detection. Specifically, we use feature space alignment, dimension\nzoom, and max pooling to extract the features of the point cloud and then\nminimize a multi-scale loss between the feature vectors produced by the teacher\nand the student networks. Moreover, our method only requires very few normal\nsamples to train the student network due to the teacher-student distillation\nmechanism. Once trained, the teacher-student network pair can be leveraged\njointly to fulfill 3D point cloud anomaly detection based on the calculated\nanomaly score. For evaluation, we compare our method against the\nreconstruction-based method on the ShapeNet-Part dataset. The experimental\nresults and ablation studies quantitatively and qualitatively confirm that our\nmodel can achieve higher performance compared with the state of the arts in 3D\nanomaly detection with very few training samples.\n", "date": "2022-10-31", "categories": []}
{"id": "2305.05495", "title": "Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors", "abstract": "  IoT data is a central element in the successful digital transformation of\nagriculture. However, IoT data comes with its own set of challenges. E.g., the\nrisk of data contamination due to rogue sensors. A sensor is considered rogue\nwhen it provides incorrect measurements over time. To ensure correct analytical\nresults, an essential preprocessing step when working with IoT data is the\ndetection of such rogue sensors. Existing methods assume that well-behaving\nsensors are known or that a large majority of the sensors is well-behaving.\nHowever, real-world data is often completely unlabeled and voluminous, calling\nfor self-supervised methods that can detect rogue sensors without prior\ninformation. We present a self-supervised anomalous sensor detector based on a\nneural network with a contrastive loss, followed by DBSCAN. A core contribution\nof our paper is the use of Dynamic Time Warping in the negative sampling for\nthe triplet loss. This novelty makes the use of triplet networks feasible for\nanomalous sensor detection. Our method shows promising results on a challenging\ndataset of soil moisture sensors deployed in multiple pear orchards.\n", "date": "2023-05-09", "categories": []}
{"id": "2305.05538", "title": "Efficient pattern-based anomaly detection in a network of multivariate\n  devices", "abstract": "  Many organisations manage service quality and monitor a large set devices and\nservers where each entity is associated with telemetry or physical sensor data\nseries. Recently, various methods have been proposed to detect behavioural\nanomalies, however existing approaches focus on multivariate time series and\nignore communication between entities. Moreover, we aim to support end-users in\nnot only in locating entities and sensors causing an anomaly at a certain\nperiod, but also explain this decision. We propose a scalable approach to\ndetect anomalies using a two-step approach. First, we recover relations between\nentities in the network, since relations are often dynamic in nature and caused\nby an unknown underlying process. Next, we report anomalies based on an\nembedding of sequential patterns. Pattern mining is efficient and supports\ninterpretation, i.e. patterns represent frequent occurring behaviour in time\nseries. We extend pattern mining to filter sequential patterns based on\nfrequency, temporal constraints and minimum description length. We collect and\nrelease two public datasets for international broadcasting and X from an\nInternet company. \\textit{BAD} achieves an overall F1-Score of 0.78 on 9\nbenchmark datasets, significantly outperforming the best baseline by 3\\%.\nAdditionally, \\textit{BAD} is also an order-of-magnitude faster than\nstate-of-the-art anomaly detection methods.\n", "date": "2023-05-07", "categories": []}
{"id": "2303.01732", "title": "One-class Damage Detector Using Deeper Fully-Convolutional Data\n  Descriptions for Civil Application", "abstract": "  Infrastructure managers must maintain high standards to ensure user\nsatisfaction during the lifecycle of infrastructures. Surveillance cameras and\nvisual inspections have enabled progress in automating the detection of\nanomalous features and assessing the occurrence of deterioration. However,\ncollecting damage data is typically time consuming and requires repeated\ninspections. The one-class damage detection approach has an advantage in that\nnormal images can be used to optimize model parameters. Additionally, visual\nevaluation of heatmaps enables us to understand localized anomalous features.\nThe authors highlight damage vision applications utilized in the robust\nproperty and localized damage explainability. First, we propose a civil-purpose\napplication for automating one-class damage detection reproducing a fully\nconvolutional data description (FCDD) as a baseline model. We have obtained\naccurate and explainable results demonstrating experimental studies on concrete\ndamage and steel corrosion in civil engineering. Additionally, to develop a\nmore robust application, we applied our method to another outdoor domain that\ncontains complex and noisy backgrounds using natural disaster datasets\ncollected using various devices. Furthermore, we propose a valuable solution of\ndeeper FCDDs focusing on other powerful backbones to improve the performance of\ndamage detection and implement ablation studies on disaster datasets. The key\nresults indicate that the deeper FCDDs outperformed the baseline FCDD on\ndatasets representing natural disaster damage caused by hurricanes, typhoons,\nearthquakes, and four-event disasters.\n", "date": "2023-03-03", "categories": []}
{"id": "2305.04275", "title": "RSC-VAE: Recoding Semantic Consistency Based VAE for One-Class Novelty\n  Detection", "abstract": "  In recent years, there is an increasing interests in reconstruction based\ngenerative models for image One-Class Novelty Detection, most of which only\nfocus on image-level information. While in this paper, we further exploit the\nlatent space of Variational Auto-encoder (VAE), a typical reconstruction based\nmodel, and we innovatively divide it into three regions:\nNormal/Anomalous/Unknown-semantic-region. Based on this hypothesis, we propose\na new VAE architecture, Recoding Semantic Consistency Based VAE (RSC-VAE),\ncombining VAE with recoding mechanism and constraining the semantic consistency\nof two encodings. We come up with three training modes of RSC-VAE: 1. One-Class\nTraining Mode, alleviating False Positive problem of normal samples; 2.\nDistributionally-Shifted Training Mode, alleviating False Negative problem of\nanomalous samples; 3. Extremely-Imbalanced Training Mode, introducing a small\nnumber of anomalous samples for training to enhance the second mode. The\nexperimental results on multiple datasets demonstrate that our mechanism\nachieves state-of-the-art performance in various baselines including VAE.\n", "date": "2023-05-07", "categories": []}
{"id": "2305.04468", "title": "AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly\n  Detection using Data Degradation Scheme", "abstract": "  Mechanical defects in real situations affect observation values and cause\nabnormalities in multivariate time series, such as sensor values or network\ndata. To perceive abnormalities in such data, it is crucial to understand the\ntemporal context and interrelation between variables simultaneously. The\nanomaly detection task for time series, especially for unlabeled data, has been\na challenging problem, and we address it by applying a suitable data\ndegradation scheme to self-supervised model training. We define four types of\nsynthetic outliers and propose the degradation scheme in which a portion of\ninput data is replaced with one of the synthetic outliers. Inspired by the\nself-attention mechanism, we design a Transformer-based architecture to\nrecognize the temporal context and detect unnatural sequences with high\nefficiency. Our model converts multivariate data points into temporal\nrepresentations with relative position bias and yields anomaly scores from\nthese representations. Our method, AnomalyBERT, shows a great capability of\ndetecting anomalies contained in complex time series and surpasses previous\nstate-of-the-art methods on five real-world benchmarks. Our code is available\nat https://github.com/Jhryu30/AnomalyBERT.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.04646", "title": "CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum\n  Likelihood Estimation", "abstract": "  Model independent techniques for constructing background data templates using\ngenerative models have shown great promise for use in searches for new physics\nprocesses at the LHC. We introduce a major improvement to the CURTAINs method\nby training the conditional normalizing flow between two side-band regions\nusing maximum likelihood estimation instead of an optimal transport loss. The\nnew training objective improves the robustness and fidelity of the transformed\ndata and is much faster and easier to train.\n  We compare the performance against the previous approach and the current\nstate of the art using the LHC Olympics anomaly detection dataset, where we see\na significant improvement in sensitivity over the original CURTAINs method.\nFurthermore, CURTAINsF4F requires substantially less computational resources to\ncover a large number of signal regions than other fully data driven approaches.\nWhen using an efficient configuration, an order of magnitude more models can be\ntrained in the same time required for ten signal regions, without a significant\ndrop in performance.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.04754", "title": "Is AUC the best measure for practical comparison of anomaly detectors?", "abstract": "  The area under receiver operating characteristics (AUC) is the standard\nmeasure for comparison of anomaly detectors. Its advantage is in providing a\nscalar number that allows a natural ordering and is independent on a threshold,\nwhich allows to postpone the choice. In this work, we question whether AUC is a\ngood metric for anomaly detection, or if it gives a false sense of comfort, due\nto relying on assumptions which are unlikely to hold in practice. Our\ninvestigation shows that variations of AUC emphasizing accuracy at low false\npositive rate seem to be better correlated with the needs of practitioners, but\nalso that we can compare anomaly detectors only in the case when we have\nrepresentative examples of anomalous samples. This last result is disturbing,\nas it suggests that in many cases, we should do active or few-show learning\ninstead of pure anomaly detection.\n", "date": "2023-05-08", "categories": []}
{"id": "2305.02323", "title": "Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection\n  on Multiple Energy Sources", "abstract": "  Advanced metering infrastructure (AMI) has been widely used as an intelligent\nenergy consumption measurement system. Electric power was the representative\nenergy source that can be collected by AMI; most existing studies to detect\nabnormal energy consumption have focused on a single energy source, i.e.,\npower. Recently, other energy sources such as water, gas, and heating have also\nbeen actively collected. As a result, it is necessary to develop a unified\nmethodology for anomaly detection across multiple energy sources; however,\nresearch efforts have rarely been made to tackle this issue. The inherent\ndifficulty with this issue stems from the fact that anomalies are not usually\nannotated. Moreover, existing works of anomaly definition depend on only\nindividual energy sources. In this paper, we first propose a method for\ndefining anomalies considering not only individual energy sources but also\ncorrelations between them. Then, we propose a new Correlation-driven\nMulti-Level Multimodal Learning model for anomaly detection on multiple energy\nsources. The distinguishing property of the model incorporates multiple energy\nsources in multi-levels based on the strengths of the correlations between\nthem. Furthermore, we generalize the proposed model in order to integrate\narbitrary new energy sources with further performance improvement, considering\nnot only correlated but also non-correlated sources. Through extensive\nexperiments on real-world datasets consisting of three to five energy sources,\nwe demonstrate that the proposed model clearly outperforms the existing\nmultimodal learning and recent time-series anomaly detection models, and we\nobserve that our model makes further the performance improvement as more\ncorrelated or non-correlated energy sources are integrated.\n", "date": "2023-05-01", "categories": []}
{"id": "2305.02496", "title": "Revisiting Graph Contrastive Learning for Anomaly Detection", "abstract": "  Combining Graph neural networks (GNNs) with contrastive learning for anomaly\ndetection has drawn rising attention recently. Existing graph contrastive\nanomaly detection (GCAD) methods have primarily focused on improving detection\ncapability through graph augmentation and multi-scale contrast modules.\nHowever, the underlying mechanisms of how these modules work have not been\nfully explored. We dive into the multi-scale and graph augmentation mechanism\nand observed that multi-scale contrast modules do not enhance the expression,\nwhile the multi-GNN modules are the hidden contributors. Previous studies have\ntended to attribute the benefits brought by multi-GNN to the multi-scale\nmodules. In the paper, we delve into the misconception and propose Multi-GNN\nand Augmented Graph contrastive framework MAG, which unified the existing GCAD\nmethods in the contrastive self-supervised perspective. We extracted two\nvariants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight\ninstance of the MAG, which outperform the state-of-the-art on Cora and Pubmed\nwith the low computational cost. The variant M-MAG equipped with multi-GNN\nmodules further improve the detection performance. Our study sheds light on the\ndrawback of the existing GCAD methods and demonstrates the potential of\nmulti-GNN and graph augmentation modules. Our code is available at\nhttps://github.com/liuyishoua/MAG-Framework.\n", "date": "2023-05-04", "categories": []}
{"id": "2305.02695", "title": "In-situ Anomaly Detection in Additive Manufacturing with Graph Neural\n  Networks", "abstract": "  Transforming a design into a high-quality product is a challenge in metal\nadditive manufacturing due to rare events which can cause defects to form.\nDetecting these events in-situ could, however, reduce inspection costs, enable\ncorrective action, and is the first step towards a future of tailored material\nproperties. In this study a model is trained on laser input information to\npredict nominal laser melting conditions. An anomaly score is then calculated\nby taking the difference between the predictions and new observations. The\nmodel is evaluated on a dataset with known defects achieving an F1 score of\n0.821. This study shows that anomaly detection methods are an important tool in\ndeveloping robust defect detection methods.\n", "date": "2023-05-04", "categories": []}
{"id": "2305.00982", "title": "Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control\n  System", "abstract": "  Critical infrastructures like water treatment facilities and power plants\ndepend on industrial control systems (ICS) for monitoring and control, making\nthem vulnerable to cyber attacks and system malfunctions. Traditional ICS\nanomaly detection methods lack transparency and interpretability, which make it\ndifficult for practitioners to understand and trust the results. This paper\nproposes a two-phase dual Copula-based Outlier Detection (COPOD) method that\naddresses these challenges. The first phase removes unwanted outliers using an\nempirical cumulative distribution algorithm, and the second phase develops two\nparallel COPOD models based on the output data of phase 1. The method is based\non empirical distribution functions, parameter-free, and provides\ninterpretability by quantifying each feature's contribution to an anomaly. The\nmethod is also computationally and memory-efficient, suitable for low- and\nhigh-dimensional datasets. Experimental results demonstrate superior\nperformance in terms of F1-score and recall on three open-source ICS datasets,\nenabling real-time ICS anomaly detection.\n", "date": "2023-04-30", "categories": []}
{"id": "2303.17354", "title": "ISSTAD: Incremental Self-Supervised Learning Based on Transformer for\n  Anomaly Detection and Localization", "abstract": "  In the realm of machine learning, the study of anomaly detection and\nlocalization within image data has gained substantial traction, particularly\nfor practical applications such as industrial defect detection. While the\nmajority of existing methods predominantly use Convolutional Neural Networks\n(CNN) as their primary network architecture, we introduce a novel approach\nbased on the Transformer backbone network. Our method employs a two-stage\nincremental learning strategy. During the first stage, we train a Masked\nAutoencoder (MAE) model solely on normal images. In the subsequent stage, we\napply pixel-level data augmentation techniques to generate corrupted normal\nimages and their corresponding pixel labels. This process allows the model to\nlearn how to repair corrupted regions and classify the status of each pixel.\nUltimately, the model generates a pixel reconstruction error matrix and a pixel\nanomaly probability matrix. These matrices are then combined to produce an\nanomaly scoring matrix that effectively detects abnormal regions. When\nbenchmarked against several state-of-the-art CNN-based methods, our approach\nexhibits superior performance on the MVTec AD dataset, achieving an impressive\n97.6% AUC.\n", "date": "2023-03-30", "categories": []}
{"id": "2304.12749", "title": "Blockchain Large Language Models", "abstract": "  This paper presents a dynamic, real-time approach to detecting anomalous\nblockchain transactions. The proposed tool, BlockGPT, generates tracing\nrepresentations of blockchain activity and trains from scratch a large language\nmodel to act as a real-time Intrusion Detection System. Unlike traditional\nmethods, BlockGPT is designed to offer an unrestricted search space and does\nnot rely on predefined rules or patterns, enabling it to detect a broader range\nof anomalies. We demonstrate the effectiveness of BlockGPT through its use as\nan anomaly detection tool for Ethereum transactions. In our experiments, it\neffectively identifies abnormal transactions among a dataset of 68M\ntransactions and has a batched throughput of 2284 transactions per second on\naverage. Our results show that, BlockGPT identifies abnormal transactions by\nranking 49 out of 124 attacks among the top-3 most abnormal transactions\ninteracting with their victim contracts. This work makes contributions to the\nfield of blockchain transaction analysis by introducing a custom data encoding\ncompatible with the transformer architecture, a domain-specific tokenization\ntechnique, and a tree encoding method specifically crafted for the Ethereum\nVirtual Machine (EVM) trace representation.\n", "date": "2023-04-25", "categories": []}
{"id": "2305.00398", "title": "SLSG: Industrial Image Anomaly Detection by Learning Better Feature\n  Embeddings and One-Class Classification", "abstract": "  Industrial image anomaly detection under the setting of one-class\nclassification has significant practical value. However, most existing models\nstruggle to extract separable feature representations when performing feature\nembedding and struggle to build compact descriptions of normal features when\nperforming one-class classification. One direct consequence of this is that\nmost models perform poorly in detecting logical anomalies which violate\ncontextual relationships. Focusing on more effective and comprehensive anomaly\ndetection, we propose a network based on self-supervised learning and\nself-attentive graph convolution (SLSG) for anomaly detection. SLSG uses a\ngenerative pre-training network to assist the encoder in learning the embedding\nof normal patterns and the reasoning of position relationships. Subsequently,\nSLSG introduces the pseudo-prior knowledge of anomaly through simulated\nabnormal samples. By comparing the simulated anomalies, SLSG can better\nsummarize the normal features and narrow down the hypersphere used for\none-class classification. In addition, with the construction of a more general\ngraph structure, SLSG comprehensively models the dense and sparse relationships\namong elements in the image, which further strengthens the detection of logical\nanomalies. Extensive experiments on benchmark datasets show that SLSG achieves\nsuperior anomaly detection performance, demonstrating the effectiveness of our\nmethod.\n", "date": "2023-04-30", "categories": []}
{"id": "2301.13422", "title": "Anomaly Segmentation for High-Resolution Remote Sensing Images Based on\n  Pixel Descriptors", "abstract": "  Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery\nis aimed at segmenting anomaly patterns of the earth deviating from normal\npatterns, which plays an important role in various Earth vision applications.\nHowever, it is a challenging task due to the complex distribution and the\nirregular shapes of objects, and the lack of abnormal samples. To tackle these\nproblems, an anomaly segmentation model based on pixel descriptors (ASD) is\nproposed for anomaly segmentation in HSR imagery. Specifically, deep one-class\nclassification is introduced for anomaly segmentation in the feature space with\ndiscriminative pixel descriptors. The ASD model incorporates the data argument\nfor generating virtual ab-normal samples, which can force the pixel descriptors\nto be compact for normal data and meanwhile to be diverse to avoid the model\ncollapse problems when only positive samples participated in the training. In\naddition, the ASD introduced a multi-level and multi-scale feature extraction\nstrategy for learning the low-level and semantic information to make the pixel\ndescriptors feature-rich. The proposed ASD model was validated using four HSR\ndatasets and compared with the recent state-of-the-art models, showing its\npotential value in Earth vision applications.\n", "date": "2023-01-31", "categories": []}
{"id": "2208.14024", "title": "Positive Difference Distribution for Image Outlier Detection using\n  Normalizing Flows and Contrastive Data", "abstract": "  Detecting test data deviating from training data is a central problem for\nsafe and robust machine learning. Likelihoods learned by a generative model,\ne.g., a normalizing flow via standard log-likelihood training, perform poorly\nas an outlier score. We propose to use an unlabelled auxiliary dataset and a\nprobabilistic outlier score for outlier detection. We use a self-supervised\nfeature extractor trained on the auxiliary dataset and train a normalizing flow\non the extracted features by maximizing the likelihood on in-distribution data\nand minimizing the likelihood on the contrastive dataset. We show that this is\nequivalent to learning the normalized positive difference between the\nin-distribution and the contrastive feature density. We conduct experiments on\nbenchmark datasets and compare to the likelihood, the likelihood ratio and\nstate-of-the-art anomaly detection methods.\n", "date": "2022-08-30", "categories": []}
{"id": "2304.13590", "title": "Synthetic Aperture Anomaly Imaging", "abstract": "  Previous research has shown that in the presence of foliage occlusion,\nanomaly detection performs significantly better in integral images resulting\nfrom synthetic aperture imaging compared to applying it to conventional aerial\nimages. In this article, we hypothesize and demonstrate that integrating\ndetected anomalies is even more effective than detecting anomalies in\nintegrals. This results in enhanced occlusion removal, outlier suppression, and\nhigher chances of visually as well as computationally detecting targets that\nare otherwise occluded. Our hypothesis was validated through both: simulations\nand field experiments. We also present a real-time application that makes our\nfindings practically available for blue-light organizations and others using\ncommercial drone platforms. It is designed to address use-cases that suffer\nfrom strong occlusion caused by vegetation, such as search and rescue, wildlife\nobservation, early wildfire detection, and sur-veillance.\n", "date": "2023-04-26", "categories": []}
{"id": "2206.10259", "title": "R2-AD2: Detecting Anomalies by Analysing the Raw Gradient", "abstract": "  Neural networks follow a gradient-based learning scheme, adapting their\nmapping parameters by back-propagating the output loss. Samples unlike the ones\nseen during training cause a different gradient distribution. Based on this\nintuition, we design a novel semi-supervised anomaly detection method called\nR2-AD2. By analysing the temporal distribution of the gradient over multiple\ntraining steps, we reliably detect point anomalies in strict semi-supervised\nsettings. Instead of domain dependent features, we input the raw gradient\ncaused by the sample under test to an end-to-end recurrent neural network\narchitecture. R2-AD2 works in a purely data-driven way, thus is readily\napplicable in a variety of important use cases of anomaly detection.\n", "date": "2022-06-21", "categories": []}
{"id": "2210.15741", "title": "Spatio-temporal predictive tasks for abnormal event detection in videos", "abstract": "  Abnormal event detection in videos is a challenging problem, partly due to\nthe multiplicity of abnormal patterns and the lack of their corresponding\nannotations. In this paper, we propose new constrained pretext tasks to learn\nobject level normality patterns. Our approach consists in learning a mapping\nbetween down-scaled visual queries and their corresponding normal appearance\nand motion characteristics at the original resolution. The proposed tasks are\nmore challenging than reconstruction and future frame prediction tasks which\nare widely used in the literature, since our model learns to jointly predict\nspatial and temporal features rather than reconstructing them. We believe that\nmore constrained pretext tasks induce a better learning of normality patterns.\nExperiments on several benchmark datasets demonstrate the effectiveness of our\napproach to localize and track anomalies as it outperforms or reaches the\ncurrent state-of-the-art on spatio-temporal evaluation metrics.\n", "date": "2022-10-27", "categories": []}
{"id": "2304.03294", "title": "What makes a good data augmentation for few-shot unsupervised image\n  anomaly detection?", "abstract": "  Data augmentation is a promising technique for unsupervised anomaly detection\nin industrial applications, where the availability of positive samples is often\nlimited due to factors such as commercial competition and sample collection\ndifficulties. In this paper, how to effectively select and apply data\naugmentation methods for unsupervised anomaly detection is studied. The impact\nof various data augmentation methods on different anomaly detection algorithms\nis systematically investigated through experiments. The experimental results\nshow that the performance of different industrial image anomaly detection\n(termed as IAD) algorithms is not significantly affected by the specific data\naugmentation method employed and that combining multiple data augmentation\nmethods does not necessarily yield further improvements in the accuracy of\nanomaly detection, although it can achieve excellent results on specific\nmethods. These findings provide useful guidance on selecting appropriate data\naugmentation methods for different requirements in IAD.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.11513", "title": "Detecting Socially Abnormal Highway Driving Behaviors via Recurrent\n  Graph Attention Networks", "abstract": "  With the rapid development of Internet of Things technologies, the next\ngeneration traffic monitoring infrastructures are connected via the web, to aid\ntraffic data collection and intelligent traffic management. One of the most\nimportant tasks in traffic is anomaly detection, since abnormal drivers can\nreduce traffic efficiency and cause safety issues. This work focuses on\ndetecting abnormal driving behaviors from trajectories produced by highway\nvideo surveillance systems. Most of the current abnormal driving behavior\ndetection methods focus on a limited category of abnormal behaviors that deal\nwith a single vehicle without considering vehicular interactions. In this work,\nwe consider the problem of detecting a variety of socially abnormal driving\nbehaviors, i.e., behaviors that do not conform to the behavior of other nearby\ndrivers. This task is complicated by the variety of vehicular interactions and\nthe spatial-temporal varying nature of highway traffic. To solve this problem,\nwe propose an autoencoder with a Recurrent Graph Attention Network that can\ncapture the highway driving behaviors contextualized on the surrounding cars,\nand detect anomalies that deviate from learned patterns. Our model is scalable\nto large freeways with thousands of cars. Experiments on data generated from\ntraffic simulation software show that our model is the only one that can spot\nthe exact vehicle conducting socially abnormal behaviors, among the\nstate-of-the-art anomaly detection models. We further show the performance on\nreal world HighD traffic dataset, where our model detects vehicles that violate\nthe local driving norms.\n", "date": "2023-04-23", "categories": []}
{"id": "2304.11940", "title": "MoniLog: An Automated Log-Based Anomaly Detection System for Cloud\n  Computing Infrastructures", "abstract": "  Within today's large-scale systems, one anomaly can impact millions of users.\nDetecting such events in real-time is essential to maintain the quality of\nservices. It allows the monitoring team to prevent or diminish the impact of a\nfailure. Logs are a core part of software development and maintenance, by\nrecording detailed information at runtime. Such log data are universally\navailable in nearly all computer systems. They enable developers as well as\nsystem maintainers to monitor and dissect anomalous events. For Cloud computing\ncompanies and large online platforms in general, growth is linked to the\nscaling potential. Automatizing the anomaly detection process is a promising\nway to ensure the scalability of monitoring capacities regarding the increasing\nvolume of logs generated by modern systems. In this paper, we will introduce\nMoniLog, a distributed approach to detect real-time anomalies within\nlarge-scale environments. It aims to detect sequential and quantitative\nanomalies within a multi-source log stream. MoniLog is designed to structure a\nlog stream and perform the monitoring of anomalous sequences. Its output\nclassifier learns from the administrator's actions to label and evaluate the\ncriticality level of anomalies.\n", "date": "2023-04-24", "categories": []}
{"id": "2304.10614", "title": "An Attention Free Conditional Autoencoder For Anomaly Detection in\n  Cryptocurrencies", "abstract": "  It is difficult to identify anomalies in time series, especially when there\nis a lot of noise. Denoising techniques can remove the noise but this technique\ncan cause a significant loss of information. To detect anomalies in the time\nseries we have proposed an attention free conditional autoencoder (AF-CA). We\nstarted from the autoencoder conditional model on which we added an\nAttention-Free LSTM layer \\cite{inzirillo2022attention} in order to make the\nanomaly detection capacity more reliable and to increase the power of anomaly\ndetection. We compared the results of our Attention Free Conditional\nAutoencoder with those of an LSTM Autoencoder and clearly improved the\nexplanatory power of the model and therefore the detection of anomaly in noisy\ntime series.\n", "date": "2023-04-20", "categories": []}
{"id": "2304.10639", "title": "Multi-module based CVAE to predict HVCM faults in the SNS accelerator", "abstract": "  We present a multi-module framework based on Conditional Variational\nAutoencoder (CVAE) to detect anomalies in the power signals coming from\nmultiple High Voltage Converter Modulators (HVCMs). We condition the model with\nthe specific modulator type to capture different representations of the normal\nwaveforms and to improve the sensitivity of the model to identify a specific\ntype of fault when we have limited samples for a given module type. We studied\nseveral neural network (NN) architectures for our CVAE model and evaluated the\nmodel performance by looking at their loss landscape for stability and\ngeneralization. Our results for the Spallation Neutron Source (SNS)\nexperimental data show that the trained model generalizes well to detecting\nmultiple fault types for several HVCM module types. The results of this study\ncan be used to improve the HVCM reliability and overall SNS uptime\n", "date": "2023-04-20", "categories": []}
{"id": "2304.10704", "title": "Interactive System-wise Anomaly Detection", "abstract": "  Anomaly detection, where data instances are discovered containing feature\npatterns different from the majority, plays a fundamental role in various\napplications. However, it is challenging for existing methods to handle the\nscenarios where the instances are systems whose characteristics are not readily\nobserved as data. Appropriate interactions are needed to interact with the\nsystems and identify those with abnormal responses. Detecting system-wise\nanomalies is a challenging task due to several reasons including: how to\nformally define the system-wise anomaly detection problem; how to find the\neffective activation signal for interacting with systems to progressively\ncollect the data and learn the detector; how to guarantee stable training in\nsuch a non-stationary scenario with real-time interactions? To address the\nchallenges, we propose InterSAD (Interactive System-wise Anomaly Detection).\nSpecifically, first, we adopt Markov decision process to model the interactive\nsystems, and define anomalous systems as anomalous transition and anomalous\nreward systems. Then, we develop an end-to-end approach which includes an\nencoder-decoder module that learns system embeddings, and a policy network to\ngenerate effective activation for separating embeddings of normal and anomaly\nsystems. Finally, we design a training method to stabilize the learning\nprocess, which includes a replay buffer to store historical interaction data\nand allow them to be re-sampled. Experiments on two benchmark environments,\nincluding identifying the anomalous robotic systems and detecting user data\npoisoning in recommendation models, demonstrate the superiority of InterSAD\ncompared with state-of-the-art baselines methods.\n", "date": "2023-04-21", "categories": []}
{"id": "2207.07572", "title": "Outlier detection of vital sign trajectories from COVID-19 patients", "abstract": "  In this work, we present a novel trajectory comparison algorithm to identify\nabnormal vital sign trends, with the aim of improving recognition of\ndeteriorating health.\n  There is growing interest in continuous wearable vital sign sensors for\nmonitoring patients remotely at home. These monitors are usually coupled to an\nalerting system, which is triggered when vital sign measurements fall outside a\npredefined normal range. Trends in vital signs, such as increasing heart rate,\nare often indicative of deteriorating health, but are rarely incorporated into\nalerting systems.\n  We introduce a dynamic time warp distance-based measure to compare time\nseries trajectories. We split each multi-variable sign time series into 180\nminute, non-overlapping epochs. We then calculate the distance between all\npairs of epochs. Each epoch is characterized by its mean pairwise distance\n(average link distance) to all other epochs, with clusters forming with nearby\nepochs.\n  We demonstrate in synthetically generated data that this method can identify\nabnormal epochs and cluster epochs with similar trajectories. We then apply\nthis method to a real-world data set of vital signs from 8 patients who had\nrecently been discharged from hospital after contracting COVID-19. We show how\noutlier epochs correspond well with the abnormal vital signs and identify\npatients who were subsequently readmitted to hospital.\n", "date": "2022-07-15", "categories": []}
{"id": "2210.06688", "title": "Overlooked Video Classification in Weakly Supervised Video Anomaly\n  Detection", "abstract": "  Current weakly supervised video anomaly detection algorithms mostly use\nmultiple instance learning (MIL) or their varieties. Almost all recent\napproaches focus on how to select the correct snippets for training to improve\nthe performance. They overlook or do not realize the power of video\nclassification in boosting the performance of anomaly detection. In this paper,\nwe study explicitly the power of video classification supervision using a BERT\nor LSTM. With this BERT or LSTM, CNN features of all snippets of a video can be\naggregated into a single feature which can be used for video classification.\nThis simple yet powerful video classification supervision, combined into the\nMIL framework, brings extraordinary performance improvement on all three major\nvideo anomaly detection datasets. Particularly it improves the mean average\nprecision (mAP) on the XD-Violence from SOTA 78.84\\% to new 82.10\\%. The source\ncode is available at\nhttps://github.com/wjtan99/BERT_Anomaly_Video_Classification.\n", "date": "2022-10-13", "categories": []}
{"id": "2304.09247", "title": "SigSegment: A Signal-Based Segmentation Algorithm for Identifying\n  Anomalous Driving Behaviours in Naturalistic Driving Videos", "abstract": "  In recent years, distracted driving has garnered considerable attention as it\ncontinues to pose a significant threat to public safety on the roads. This has\nincreased the need for innovative solutions that can identify and eliminate\ndistracted driving behavior before it results in fatal accidents. In this\npaper, we propose a Signal-Based anomaly detection algorithm that segments\nvideos into anomalies and non-anomalies using a deep CNN-LSTM classifier to\nprecisely estimate the start and end times of an anomalous driving event. In\nthe phase of anomaly detection and analysis, driver pose background estimation,\nmask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM\nclassifier was applied to candidate anomalies to detect and classify final\nanomalies. The proposed method achieved an overlap score of 0.5424 and ranked\n9th on the public leader board in the AI City Challenge 2023, according to\nexperimental validation results.\n", "date": "2023-04-13", "categories": []}
{"id": "2212.02031", "title": "Prototypical Residual Networks for Anomaly Detection and Localization", "abstract": "  Anomaly detection and localization are widely used in industrial\nmanufacturing for its efficiency and effectiveness. Anomalies are rare and hard\nto collect and supervised models easily over-fit to these seen anomalies with a\nhandful of abnormal samples, producing unsatisfactory performance. On the other\nhand, anomalies are typically subtle, hard to discern, and of various\nappearance, making it difficult to detect anomalies and let alone locate\nanomalous regions. To address these issues, we propose a framework called\nPrototypical Residual Network (PRN), which learns feature residuals of varying\nscales and sizes between anomalous and normal patterns to accurately\nreconstruct the segmentation maps of anomalous regions. PRN mainly consists of\ntwo parts: multi-scale prototypes that explicitly represent the residual\nfeatures of anomalies to normal patterns; a multisize self-attention mechanism\nthat enables variable-sized anomalous feature learning. Besides, we present a\nvariety of anomaly generation strategies that consider both seen and unseen\nappearance variance to enlarge and diversify anomalies. Extensive experiments\non the challenging and widely used MVTec AD benchmark show that PRN outperforms\ncurrent state-of-the-art unsupervised and supervised methods. We further report\nSOTA results on three additional datasets to demonstrate the effectiveness and\ngeneralizability of PRN.\n", "date": "2022-12-05", "categories": []}
{"id": "2207.01472", "title": "Deep Contrastive One-Class Time Series Anomaly Detection", "abstract": "  The accumulation of time-series data and the absence of labels make\ntime-series Anomaly Detection (AD) a self-supervised deep learning task.\nSingle-normality-assumption-based methods, which reveal only a certain aspect\nof the whole normality, are incapable of tasks involved with a large number of\nanomalies. Specifically, Contrastive Learning (CL) methods distance negative\npairs, many of which consist of both normal samples, thus reducing the AD\nperformance. Existing multi-normality-assumption-based methods are usually\ntwo-staged, firstly pre-training through certain tasks whose target may differ\nfrom AD, limiting their performance. To overcome the shortcomings, a deep\nContrastive One-Class Anomaly detection method of time series (COCA) is\nproposed by authors, following the normality assumptions of CL and one-class\nclassification. It treats the original and reconstructed representations as the\npositive pair of negative-sample-free CL, namely \"sequence contrast\". Next,\ninvariance terms and variance terms compose a contrastive one-class loss\nfunction in which the loss of the assumptions is optimized by invariance terms\nsimultaneously and the \"hypersphere collapse\" is prevented by variance terms.\nIn addition, extensive experiments on two real-world time-series datasets show\nthe superior performance of the proposed method achieves state-of-the-art.\n", "date": "2022-07-04", "categories": []}
{"id": "2304.07453", "title": "Context-aware Domain Adaptation for Time Series Anomaly Detection", "abstract": "  Time series anomaly detection is a challenging task with a wide range of\nreal-world applications. Due to label sparsity, training a deep anomaly\ndetector often relies on unsupervised approaches. Recent efforts have been\ndevoted to time series domain adaptation to leverage knowledge from similar\ndomains. However, existing solutions may suffer from negative knowledge\ntransfer on anomalies due to their diversity and sparsity. Motivated by the\nempirical study of context alignment between two domains, we aim to transfer\nknowledge between two domains via adaptively sampling context information for\ntwo domains. This is challenging because it requires simultaneously modeling\nthe complex in-domain temporal dependencies and cross-domain correlations while\nexploiting label information from the source domain. To this end, we propose a\nframework that combines context sampling and anomaly detection into a joint\nlearning procedure. We formulate context sampling into the Markov decision\nprocess and exploit deep reinforcement learning to optimize the time series\ndomain adaptation process via context sampling and design a tailored reward\nfunction to generate domain-invariant features that better align two domains\nfor anomaly detection. Experiments on three public datasets show promise for\nknowledge transfer between two similar domains and two entirely different\ndomains.\n", "date": "2023-04-15", "categories": []}
{"id": "2304.07470", "title": "Few-shot Weakly-supervised Cybersecurity Anomaly Detection", "abstract": "  With increased reliance on Internet based technologies, cyberattacks\ncompromising users' sensitive data are becoming more prevalent. The scale and\nfrequency of these attacks are escalating rapidly, affecting systems and\ndevices connected to the Internet. The traditional defense mechanisms may not\nbe sufficiently equipped to handle the complex and ever-changing new threats.\nThe significant breakthroughs in the machine learning methods including deep\nlearning, had attracted interests from the cybersecurity research community for\nfurther enhancements in the existing anomaly detection methods. Unfortunately,\ncollecting labelled anomaly data for all new evolving and sophisticated attacks\nis not practical. Training and tuning the machine learning model for anomaly\ndetection using only a handful of labelled data samples is a pragmatic\napproach. Therefore, few-shot weakly supervised anomaly detection is an\nencouraging research direction. In this paper, we propose an enhancement to an\nexisting few-shot weakly-supervised deep learning anomaly detection framework.\nThis framework incorporates data augmentation, representation learning and\nordinal regression. We then evaluated and showed the performance of our\nimplemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and\nTON_IoT.\n", "date": "2023-04-15", "categories": []}
{"id": "2304.08058", "title": "One-Class SVM on siamese neural network latent space for Unsupervised\n  Anomaly Detection on brain MRI White Matter Hyperintensities", "abstract": "  Anomaly detection remains a challenging task in neuroimaging when little to\nno supervision is available and when lesions can be very small or with subtle\ncontrast. Patch-based representation learning has shown powerful representation\ncapacities when applied to industrial or medical imaging and outlier detection\nmethods have been applied successfully to these images. In this work, we\npropose an unsupervised anomaly detection (UAD) method based on a latent space\nconstructed by a siamese patch-based auto-encoder and perform the outlier\ndetection with a One-Class SVM training paradigm tailored to the lesion\ndetection task in multi-modality neuroimaging. We evaluate performances of this\nmodel on a public database, the White Matter Hyperintensities (WMH) challenge\nand show in par performance with the two best performing state-of-the-art\nmethods reported so far.\n", "date": "2023-04-17", "categories": []}
{"id": "2304.07147", "title": "Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET\n  Anomaly Detection", "abstract": "  Cancer is a highly heterogeneous condition that can occur almost anywhere in\nthe human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to\ndetect cancer due to its high sensitivity and clear visualisation of the\npattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous,\nit is challenging to train general-purpose discriminative cancer detection\nmodels, with data availability and disease complexity often cited as a limiting\nfactor. Unsupervised anomaly detection models have been suggested as a putative\nsolution. These models learn a healthy representation of tissue and detect\ncancer by predicting deviations from the healthy norm, which requires models\ncapable of accurately learning long-range interactions between organs and their\nimaging patterns with high levels of expressivity. Such characteristics are\nsuitably satisfied by transformers, which have been shown to generate\nstate-of-the-art results in unsupervised anomaly detection by training on\nnormal data. This work expands upon such approaches by introducing multi-modal\nconditioning of the transformer via cross-attention i.e. supplying anatomical\nreference from paired CT. Using 294 whole-body PET/CT samples, we show that our\nanomaly detection method is robust and capable of achieving accurate cancer\nlocalization results even in cases where normal training data is unavailable.\nIn addition, we show the efficacy of this approach on out-of-sample data\nshowcasing the generalizability of this approach with limited training data.\nLastly, we propose to combine model uncertainty with a new kernel density\nestimation approach, and show that it provides clinically and statistically\nsignificant improvements when compared to the classic residual-based anomaly\nmaps. Overall, a superior performance is demonstrated against leading\nstate-of-the-art alternatives, drawing attention to the potential of these\napproaches.\n", "date": "2023-04-14", "categories": []}
{"id": "2211.09224", "title": "Are we certain it's anomalous?", "abstract": "  The progress in modelling time series and, more generally, sequences of\nstructured data has recently revamped research in anomaly detection. The task\nstands for identifying abnormal behaviors in financial series, IT systems,\naerospace measurements, and the medical domain, where anomaly detection may aid\nin isolating cases of depression and attend the elderly. Anomaly detection in\ntime series is a complex task since anomalies are rare due to highly non-linear\ntemporal correlations and since the definition of anomalous is sometimes\nsubjective. Here we propose the novel use of Hyperbolic uncertainty for Anomaly\nDetection (HypAD). HypAD learns self-supervisedly to reconstruct the input\nsignal. We adopt best practices from the state-of-the-art to encode the\nsequence by an LSTM, jointly learned with a decoder to reconstruct the signal,\nwith the aid of GAN critics. Uncertainty is estimated end-to-end by means of a\nhyperbolic neural network. By using uncertainty, HypAD may assess whether it is\ncertain about the input signal but it fails to reconstruct it because this is\nanomalous; or whether the reconstruction error does not necessarily imply\nanomaly, as the model is uncertain, e.g. a complex but regular input signal.\nThe novel key idea is that a detectable anomaly is one where the model is\ncertain but it predicts wrongly. HypAD outperforms the current state-of-the-art\nfor univariate anomaly detection on established benchmarks based on data from\nNASA, Yahoo, Numenta, Amazon, and Twitter. It also yields state-of-the-art\nperformance on a multivariate dataset of anomaly activities in elderly home\nresidences, and it outperforms the baseline on SWaT. Overall, HypAD yields the\nlowest false alarms at the best performance rate, thanks to successfully\nidentifying detectable anomalies.\n", "date": "2022-11-16", "categories": []}
{"id": "2304.02834", "title": "Probing the Purview of Neural Networks via Gradient Analysis", "abstract": "  We analyze the data-dependent capacity of neural networks and assess\nanomalies in inputs from the perspective of networks during inference. The\nnotion of data-dependent capacity allows for analyzing the knowledge base of a\nmodel populated by learned features from training data. We define purview as\nthe additional capacity necessary to characterize inference samples that differ\nfrom the training data. To probe the purview of a network, we utilize gradients\nto measure the amount of change required for the model to characterize the\ngiven inputs more accurately. To eliminate the dependency on ground-truth\nlabels in generating gradients, we introduce confounding labels that are\nformulated by combining multiple categorical labels. We demonstrate that our\ngradient-based approach can effectively differentiate inputs that cannot be\naccurately represented with learned features. We utilize our approach in\napplications of detecting anomalous inputs, including out-of-distribution,\nadversarial, and corrupted samples. Our approach requires no hyperparameter\ntuning or additional data processing and outperforms state-of-the-art methods\nby up to 2.7%, 19.8%, and 35.6% of AUROC scores, respectively.\n", "date": "2023-04-06", "categories": []}
{"id": "2208.10833", "title": "LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph\n  Construction", "abstract": "  Fully supervised log anomaly detection methods suffer the heavy burden of\nannotating massive unlabeled log data. Recently, many semi-supervised methods\nhave been proposed to reduce annotation costs with the help of parsed\ntemplates. However, these methods consider each keyword independently, which\ndisregards the correlation between keywords and the contextual relationships\namong log sequences. In this paper, we propose a novel weakly supervised log\nanomaly detection framework, named LogLG, to explore the semantic connections\namong keywords from sequences. Specifically, we design an end-to-end iterative\nprocess, where the keywords of unlabeled logs are first extracted to construct\na log-event graph. Then, we build a subgraph annotator to generate pseudo\nlabels for unlabeled log sequences. To ameliorate the annotation quality, we\nadopt a self-supervised task to pre-train a subgraph annotator. After that, a\ndetection model is trained with the generated pseudo labels. Conditioned on the\nclassification results, we re-extract the keywords from the log sequences and\nupdate the log-event graph for the next iteration. Experiments on five\nbenchmarks validate the effectiveness of LogLG for detecting anomalies on\nunlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly\nsupervised method, achieves significant performance improvements compared to\nexisting methods.\n", "date": "2022-08-23", "categories": []}
{"id": "2304.05112", "title": "Video Event Restoration Based on Keyframes for Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) is a significant computer vision problem.\nExisting deep neural network (DNN) based VAD methods mostly follow the route of\nframe reconstruction or frame prediction. However, the lack of mining and\nlearning of higher-level visual features and temporal context relationships in\nvideos limits the further performance of these two approaches. Inspired by\nvideo codec theory, we introduce a brand-new VAD paradigm to break through\nthese limitations: First, we propose a new task of video event restoration\nbased on keyframes. Encouraging DNN to infer missing multiple frames based on\nvideo keyframes so as to restore a video event, which can more effectively\nmotivate DNN to mine and learn potential higher-level visual features and\ncomprehensive temporal context relationships in the video. To this end, we\npropose a novel U-shaped Swin Transformer Network with Dual Skip Connections\n(USTN-DSC) for video event restoration, where a cross-attention and a temporal\nupsampling residual skip connection are introduced to further assist in\nrestoring complex static and dynamic motion object features in the video. In\naddition, we propose a simple and effective adjacent frame difference loss to\nconstrain the motion consistency of the video sequence. Extensive experiments\non benchmarks demonstrate that USTN-DSC outperforms most existing methods,\nvalidating the effectiveness of our method.\n", "date": "2023-04-11", "categories": []}
{"id": "2304.05176", "title": "Decoupling anomaly discrimination and representation learning:\n  self-supervised learning for anomaly detection on attributed graph", "abstract": "  Anomaly detection on attributed graphs is a crucial topic for its practical\napplication. Existing methods suffer from semantic mixture and imbalance issue\nbecause they mainly focus on anomaly discrimination, ignoring representation\nlearning. It conflicts with the assortativity assumption that anomalous nodes\ncommonly connect with normal nodes directly. Additionally, there are far fewer\nanomalous nodes than normal nodes, indicating a long-tailed data distribution.\nTo address these challenges, a unique algorithm,Decoupled Self-supervised\nLearning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a\nself-supervised method with anomaly discrimination and representation learning\ndecoupled for anomaly detection. DSLAD employs bilinear pooling and masked\nautoencoder as the anomaly discriminators. By decoupling anomaly discrimination\nand representation learning, a balanced feature space is constructed, in which\nnodes are more semantically discriminative, as well as imbalance issue can be\nresolved. Experiments conducted on various six benchmark datasets reveal the\neffectiveness of DSLAD.\n", "date": "2023-04-11", "categories": []}
{"id": "2304.03588", "title": "Anomalous Sound Detection using Audio Representation with Machine ID\n  based Contrastive Learning Pretraining", "abstract": "  Existing contrastive learning methods for anomalous sound detection refine\nthe audio representation of each audio sample by using the contrast between the\nsamples' augmentations (e.g., with time or frequency masking). However, they\nmight be biased by the augmented data, due to the lack of physical properties\nof machine sound, thereby limiting the detection performance. This paper uses\ncontrastive learning to refine audio representations for each machine ID,\nrather than for each audio sample. The proposed two-stage method uses\ncontrastive learning to pretrain the audio representation model by\nincorporating machine ID and a self-supervised ID classifier to fine-tune the\nlearnt model, while enhancing the relation between audio features from the same\nID. Experiments show that our method outperforms the state-of-the-art methods\nusing contrastive learning or self-supervised classification in overall anomaly\ndetection performance and stability on DCASE 2020 Challenge Task2 dataset.\n", "date": "2023-04-07", "categories": []}
{"id": "2304.04211", "title": "AGAD: Adversarial Generative Anomaly Detection", "abstract": "  Anomaly detection suffered from the lack of anomalies due to the diversity of\nabnormalities and the difficulties of obtaining large-scale anomaly data.\nSemi-supervised anomaly detection methods are often used to solely leverage\nnormal data to detect abnormalities that deviated from the learnt normality\ndistributions. Meanwhile, given the fact that limited anomaly data can be\nobtained with a minor cost in practice, some researches also investigated\nanomaly detection methods under supervised scenarios with limited anomaly data.\nIn order to address the lack of abnormal data for robust anomaly detection, we\npropose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based\nanomaly detection paradigm that learns to detect anomalies by generating\n\\textit{contextual adversarial information} from the massive normal examples.\nEssentially, our method generates pseudo-anomaly data for both supervised and\nsemi-supervised anomaly detection scenarios. Extensive experiments are carried\nout on multiple benchmark datasets and real-world datasets, the results show\nsignificant improvement in both supervised and semi-supervised scenarios.\nImportantly, our approach is data-efficient that can boost up the detection\naccuracy with no more than 5% anomalous training data.\n", "date": "2023-04-09", "categories": []}
{"id": "2207.01463", "title": "Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for\n  Supervised Anomaly Detection", "abstract": "  Most anomaly detection (AD) models are learned using only normal samples in\nan unsupervised way, which may result in ambiguous decision boundary and\ninsufficient discriminability. In fact, a few anomaly samples are often\navailable in real-world applications, the valuable knowledge of known anomalies\nshould also be effectively exploited. However, utilizing a few known anomalies\nduring training may cause another issue that the model may be biased by those\nknown anomalies and fail to generalize to unseen anomalies. In this paper, we\ntackle supervised anomaly detection, i.e., we learn AD models using a few\navailable anomalies with the objective to detect both the seen and unseen\nanomalies. We propose a novel explicit boundary guided semi-push-pull\ncontrastive learning mechanism, which can enhance model's discriminability\nwhile mitigating the bias issue. Our approach is based on two core designs:\nFirst, we find an explicit and compact separating boundary as the guidance for\nfurther feature learning. As the boundary only relies on the normal feature\ndistribution, the bias problem caused by a few known anomalies can be\nalleviated. Second, a boundary guided semi-push-pull loss is developed to only\npull the normal features together while pushing the abnormal features apart\nfrom the separating boundary beyond a certain margin region. In this way, our\nmodel can form a more explicit and discriminative decision boundary to\ndistinguish known and also unseen anomalies from normal samples more\neffectively. Code will be available at https://github.com/xcyao00/BGAD.\n", "date": "2022-07-04", "categories": []}
{"id": "2304.03368", "title": "From Explanation to Action: An End-to-End Human-in-the-loop Framework\n  for Anomaly Reasoning and Management", "abstract": "  Anomalies are often indicators of malfunction or inefficiency in various\nsystems such as manufacturing, healthcare, finance, surveillance, to name a\nfew. While the literature is abundant in effective detection algorithms due to\nthis practical relevance, autonomous anomaly detection is rarely used in\nreal-world scenarios. Especially in high-stakes applications, a\nhuman-in-the-loop is often involved in processes beyond detection such as\nverification and troubleshooting. In this work, we introduce ALARM (for\nAnalyst-in-the-Loop Anomaly Reasoning and Management); an end-to-end framework\nthat supports the anomaly mining cycle comprehensively, from detection to\naction. Besides unsupervised detection of emerging anomalies, it offers anomaly\nexplanations and an interactive GUI for human-in-the-loop processes -- visual\nexploration, sense-making, and ultimately action-taking via designing new\ndetection rules -- that help close ``the loop'' as the new rules complement\nrule-based supervised detection, typical of many deployed systems in practice.\nWe demonstrate \\method's efficacy through a series of case studies with fraud\nanalysts from the financial industry.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.03420", "title": "Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational\n  Autoencoder", "abstract": "  In this paper, we present an end-to-end unsupervised anomaly detection\nframework for 3D point clouds. To the best of our knowledge, this is the first\nwork to tackle the anomaly detection task on a general object represented by a\n3D point cloud. We propose a deep variational autoencoder-based unsupervised\nanomaly detection network adapted to the 3D point cloud and an anomaly score\nspecifically for 3D point clouds. To verify the effectiveness of the model, we\nconducted extensive experiments on the ShapeNet dataset. Through quantitative\nand qualitative evaluation, we demonstrate that the proposed method outperforms\nthe baseline method. Our code is available at\nhttps://github.com/llien30/point_cloud_anomaly_detection.\n", "date": "2023-04-07", "categories": []}
{"id": "2304.02947", "title": "Adaptable and Interpretable Framework for Novelty Detection in Real-Time\n  IoT Systems", "abstract": "  This paper presents the Real-time Adaptive and Interpretable Detection (RAID)\nalgorithm. The novel approach addresses the limitations of state-of-the-art\nanomaly detection methods for multivariate dynamic processes, which are\nrestricted to detecting anomalies within the scope of the model training\nconditions. The RAID algorithm adapts to non-stationary effects such as data\ndrift and change points that may not be accounted for during model development,\nresulting in prolonged service life. A dynamic model based on joint probability\ndistribution handles anomalous behavior detection in a system and the root\ncause isolation based on adaptive process limits. RAID algorithm does not\nrequire changes to existing process automation infrastructures, making it\nhighly deployable across different domains. Two case studies involving real\ndynamic system data demonstrate the benefits of the RAID algorithm, including\nchange point adaptation, root cause isolation, and improved detection accuracy.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.03220", "title": "Anomaly Detection via Gumbel Noise Score Matching", "abstract": "  We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to\ndetect anomalies in categorical data. GNSM accomplishes this by estimating the\nscores, i.e. the gradients of log likelihoods w.r.t.~inputs, of continuously\nrelaxed categorical distributions. We test our method on a suite of anomaly\ndetection tabular datasets. GNSM achieves a consistently high performance\nacross all experiments. We further demonstrate the flexibility of GNSM by\napplying it to image data where the model is tasked to detect poor segmentation\npredictions. Images ranked anomalous by GNSM show clear segmentation failures,\nwith the outputs of GNSM strongly correlating with segmentation metrics\ncomputed on ground-truth. We outline the score matching training objective\nutilized by GNSM and provide an open-source implementation of our work.\n", "date": "2023-04-06", "categories": []}
{"id": "2303.11668", "title": "Focus or Not: A Baseline for Anomaly Event Detection On the Open Public\n  Places with Satellite Images", "abstract": "  In recent years, monitoring the world wide area with satellite images has\nbeen emerged as an important issue.\n  Site monitoring task can be divided into two independent tasks; 1) Change\nDetection and 2) Anomaly Event Detection.\n  Unlike to change detection research is actively conducted based on the\nnumerous datasets(\\eg LEVIR-CD, WHU-CD, S2Looking, xView2 and etc...) to meet\nup the expectations of industries or governments, research on AI models for\ndetecting anomaly events is passively and rarely conducted.\n  In this paper, we introduce a novel satellite imagery dataset(AED-RS) for\ndetecting anomaly events on the open public places.\n  AED-RS Dataset contains satellite images of normal and abnormal situations of\n8 open public places from all over the world.\n  Each places are labeled with different criteria based on the difference of\ncharacteristics of each places.\n  With this dataset, we introduce a baseline model for our dataset TB-FLOW,\nwhich can be trained in weakly-supervised manner and shows reasonable\nperformance on the AED-RS Dataset compared with the other NF(Normalizing-Flow)\nbased anomaly detection models. Our dataset and code will be publicly open in\n\\url{https://github.com/SIAnalytics/RS_AnomalyDetection.git}.\n", "date": "2023-03-21", "categories": []}
{"id": "2303.17802", "title": "Time-series Anomaly Detection based on Difference Subspace between\n  Signal Subspaces", "abstract": "  This paper proposes a new method for anomaly detection in time-series data by\nincorporating the concept of difference subspace into the singular spectrum\nanalysis (SSA). The key idea is to monitor slight temporal variations of the\ndifference subspace between two signal subspaces corresponding to the past and\npresent time-series data, as anomaly score. It is a natural generalization of\nthe conventional SSA-based method which measures the minimum angle between the\ntwo signal subspaces as the degree of changes. By replacing the minimum angle\nwith the difference subspace, our method boosts the performance while using the\nSSA-based framework as it can capture the whole structural difference between\nthe two subspaces in its magnitude and direction. We demonstrate our method's\neffectiveness through performance evaluations on public time-series datasets.\n", "date": "2023-03-31", "categories": []}
{"id": "2304.02189", "title": "A system for exploring big data: an iterative k-means searchlight for\n  outlier detection on open health data", "abstract": "  The interactive exploration of large and evolving datasets is challenging as\nrelationships between underlying variables may not be fully understood. There\nmay be hidden trends and patterns in the data that are worthy of further\nexploration and analysis. We present a system that methodically explores\nmultiple combinations of variables using a searchlight technique and identifies\noutliers. An iterative k-means clustering algorithm is applied to features\nderived through a split-apply-combine paradigm used in the database literature.\nOutliers are identified as singleton or small clusters. This algorithm is swept\nacross the dataset in a searchlight manner. The dimensions that contain\noutliers are combined in pairs with other dimensions using a susbset scan\ntechnique to gain further insight into the outliers. We illustrate this system\nby anaylzing open health care data released by New York State. We apply our\niterative k-means searchlight followed by subset scanning. Several anomalous\ntrends in the data are identified, including cost overruns at specific\nhospitals, and increases in diagnoses such as suicides. These constitute novel\nfindings in the literature, and are of potential use to regulatory agencies,\npolicy makers and concerned citizens.\n", "date": "2023-04-05", "categories": []}
{"id": "2304.02221", "title": "Zero-shot domain adaptation of anomalous samples for semi-supervised\n  anomaly detection", "abstract": "  Semi-supervised anomaly detection~(SSAD) is a task where normal data and a\nlimited number of anomalous data are available for training. In practical\nsituations, SSAD methods suffer adapting to domain shifts, since anomalous data\nare unlikely to be available for the target domain in the training phase. To\nsolve this problem, we propose a domain adaptation method for SSAD where no\nanomalous data are available for the target domain. First, we introduce a\ndomain-adversarial network to a variational auto-encoder-based SSAD model to\nobtain domain-invariant latent variables. Since the decoder cannot reconstruct\nthe original data solely from domain-invariant latent variables, we conditioned\nthe decoder on the domain label. To compensate for the missing anomalous data\nof the target domain, we introduce an importance sampling-based weighted loss\nfunction that approximates the ideal loss function. Experimental results\nindicate that the proposed method helps adapt SSAD models to the target domain\nwhen no anomalous data are available for the target domain.\n", "date": "2023-04-05", "categories": []}
{"id": "2303.18044", "title": "Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly\n  Detection", "abstract": "  Weakly supervised video anomaly detection (WS-VAD) is a challenging problem\nthat aims to learn VAD models only with video-level annotations. In this work,\nwe propose a Long-Short Temporal Co-teaching (LSTC) method to address the\nWS-VAD problem. It constructs two tubelet-based spatio-temporal transformer\nnetworks to learn from short- and long-term video clips respectively. Each\nnetwork is trained with respect to a multiple instance learning (MIL)-based\nranking loss, together with a cross-entropy loss when clip-level pseudo labels\nare available. A co-teaching strategy is adopted to train the two networks.\nThat is, clip-level pseudo labels generated from each network are used to\nsupervise the other one at the next training round, and the two networks are\nlearned alternatively and iteratively. Our proposed method is able to better\ndeal with the anomalies with varying durations as well as subtle anomalies.\nExtensive experiments on three public datasets demonstrate that our method\noutperforms state-of-the-art WS-VAD methods.\n", "date": "2023-03-31", "categories": []}
{"id": "2304.01226", "title": "Abnormal Event Detection via Hypergraph Contrastive Learning", "abstract": "  Abnormal event detection, which refers to mining unusual interactions among\ninvolved entities, plays an important role in many real applications. Previous\nworks mostly over-simplify this task as detecting abnormal pair-wise\ninteractions. However, real-world events may contain multi-typed attributed\nentities and complex interactions among them, which forms an Attributed\nHeterogeneous Information Network (AHIN). With the boom of social networks,\nabnormal event detection in AHIN has become an important, but seldom explored\ntask. In this paper, we firstly study the unsupervised abnormal event detection\nproblem in AHIN. The events are considered as star-schema instances of AHIN and\nare further modeled by hypergraphs. A novel hypergraph contrastive learning\nmethod, named AEHCL, is proposed to fully capture abnormal event patterns.\nAEHCL designs the intra-event and inter-event contrastive modules to exploit\nself-supervised AHIN information. The intra-event contrastive module captures\nthe pair-wise and multivariate interaction anomalies within an event, and the\ninter-event module captures the contextual anomalies among events. These two\nmodules collaboratively boost the performance of each other and improve the\ndetection results. During the testing phase, a contrastive learning-based\nabnormal event score function is further proposed to measure the abnormality\ndegree of events. Extensive experiments on three datasets in different\nscenarios demonstrate the effectiveness of AEHCL, and the results improve\nstate-of-the-art baselines up to 12.0% in Average Precision (AP) and 4.6% in\nArea Under Curve (AUC) respectively.\n", "date": "2023-04-02", "categories": []}
{"id": "2206.15476", "title": "AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly\n  Detection", "abstract": "  Analyzing the distribution shift of data is a growing research direction in\nnowadays Machine Learning (ML), leading to emerging new benchmarks that focus\non providing a suitable scenario for studying the generalization properties of\nML models. The existing benchmarks are focused on supervised learning, and to\nthe best of our knowledge, there is none for unsupervised learning. Therefore,\nwe introduce an unsupervised anomaly detection benchmark with data that shifts\nover time, built over Kyoto-2006+, a traffic dataset for network intrusion\ndetection. This type of data meets the premise of shifting the input\ndistribution: it covers a large time span ($10$ years), with naturally\noccurring changes over time (eg users modifying their behavior patterns, and\nsoftware updates). We first highlight the non-stationary nature of the data,\nusing a basic per-feature analysis, t-SNE, and an Optimal Transport approach\nfor measuring the overall distribution distances between years. Next, we\npropose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing\nsplits. We validate the performance degradation over time with diverse models,\nranging from classical approaches to deep learning. Finally, we show that by\nacknowledging the distribution shift problem and properly addressing it, the\nperformance can be improved compared to the classical training which assumes\nindependent and identically distributed data (on average, by up to $3\\%$ for\nour approach). Dataset and code are available at\nhttps://github.com/bit-ml/AnoShift/.\n", "date": "2022-06-30", "categories": []}
{"id": "2303.17882", "title": "Visual Anomaly Detection via Dual-Attention Transformer and\n  Discriminative Flow", "abstract": "  In this paper, we introduce the novel state-of-the-art Dual-attention\nTransformer and Discriminative Flow (DADF) framework for visual anomaly\ndetection. Based on only normal knowledge, visual anomaly detection has wide\napplications in industrial scenarios and has attracted significant attention.\nHowever, most existing methods fail to meet the requirements. In contrast, the\nproposed DTDF presents a new paradigm: it firstly leverages a pre-trained\nnetwork to acquire multi-scale prior embeddings, followed by the development of\na vision Transformer with dual attention mechanisms, namely self-attention and\nmemorial-attention, to achieve two-level reconstruction for prior embeddings\nwith the sequential and normality association. Additionally, we propose using\nnormalizing flow to establish discriminative likelihood for the joint\ndistribution of prior and reconstructions at each scale. The DADF achieves\n98.3/98.4 of image/pixel AUROC on Mvtec AD; 83.7 of image AUROC and 67.4 of\npixel sPRO on Mvtec LOCO AD benchmarks, demonstrating the effectiveness of our\nproposed approach.\n", "date": "2023-03-31", "categories": []}
{"id": "2303.17949", "title": "Unsupervised Anomaly Detection and Localization of Machine Audio: A\n  GAN-based Approach", "abstract": "  Automatic detection of machine anomaly remains challenging for machine\nlearning. We believe the capability of generative adversarial network (GAN)\nsuits the need of machine audio anomaly detection, yet rarely has this been\ninvestigated by previous work. In this paper, we propose AEGAN-AD, a totally\nunsupervised approach in which the generator (also an autoencoder) is trained\nto reconstruct input spectrograms. It is pointed out that the denoising nature\nof reconstruction deprecates its capacity. Thus, the discriminator is\nredesigned to aid the generator during both training stage and detection stage.\nThe performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2\ndemonstrates the state-of-the-art result on five machine types. A novel anomaly\nlocalization method is also investigated. Source code available at:\nwww.github.com/jianganbai/AEGAN-AD\n", "date": "2023-03-31", "categories": []}
{"id": "2303.17989", "title": "Unsupervised crack detection on complex stone masonry surfaces", "abstract": "  Computer vision for detecting building pathologies has interested researchers\nfor quite some time. Vision-based crack detection is a non-destructive\nassessment technique, which can be useful especially for Cultural Heritage (CH)\nwhere strict regulations apply and, even simple, interventions are not\npermitted. Recently, shallow and deep machine learning architectures applied on\nvarious types of imagery are gaining ground. In this article a crack detection\nmethodology for stone masonry walls is presented. In the proposed approach,\ncrack detection is approached as an unsupervised anomaly detection problem on\nRGB (Red Green Blue) image patches. Towards this direction, some of the most\npopular state of the art CNN (Convolutional Neural Network) architectures are\ndeployed and modified to binary classify the images or image patches by\npredicting a specific class for the tested imagery; 'Crack' or 'No crack', and\ndetect and localize those cracks on the RGB imagery with high accuracy. Testing\nof the model was performed on various test sites and random images retrieved\nfrom the internet and collected by the authors and results suggested the high\nperformance of specific networks compared to the rest, considering also the\nsmall numbers of epochs required for training. Those results met the accuracy\ndelivered by more complex and computationally heavy approaches, requiring a\nlarge amount of data for training. Source code is available on GitHub\nhttps://github.com/pagraf/Crack-detection while datasets are available on\nZenodo https://doi.org/10.5281/zenodo.6516913 .\n", "date": "2023-03-31", "categories": []}
{"id": "2303.18001", "title": "You Only Train Once: Learning a General Anomaly Enhancement Network with\n  Random Masks for Hyperspectral Anomaly Detection", "abstract": "  In this paper, we introduce a new approach to address the challenge of\ngeneralization in hyperspectral anomaly detection (AD). Our method eliminates\nthe need for adjusting parameters or retraining on new test scenes as required\nby most existing methods. Employing an image-level training paradigm, we\nachieve a general anomaly enhancement network for hyperspectral AD that only\nneeds to be trained once. Trained on a set of anomaly-free hyperspectral images\nwith random masks, our network can learn the spatial context characteristics\nbetween anomalies and background in an unsupervised way. Additionally, a\nplug-and-play model selection module is proposed to search for a\nspatial-spectral transform domain that is more suitable for AD task than the\noriginal data. To establish a unified benchmark to comprehensively evaluate our\nmethod and existing methods, we develop a large-scale hyperspectral AD dataset\n(HAD100) that includes 100 real test scenes with diverse anomaly targets. In\ncomparison experiments, we combine our network with a parameter-free detector\nand achieve the optimal balance between detection accuracy and inference speed\namong state-of-the-art AD methods. Experimental results also show that our\nmethod still achieves competitive performance when the training and test set\nare captured by different sensor devices. Our code is available at\nhttps://github.com/ZhaoxuLi123/AETNet.\n", "date": "2023-03-31", "categories": []}
{"id": "2211.12634", "title": "PNI : Industrial Anomaly Detection using Position and Neighborhood\n  Information", "abstract": "  Because anomalous samples cannot be used for training, many anomaly detection\nand localization methods use pre-trained networks and non-parametric modeling\nto estimate encoded feature distribution. However, these methods neglect the\nimpact of position and neighborhood information on the distribution of normal\nfeatures. To overcome this, we propose a new algorithm, \\textbf{PNI}, which\nestimates the normal distribution using conditional probability given\nneighborhood features, modeled with a multi-layer perceptron network. Moreover,\nposition information is utilized by creating a histogram of representative\nfeatures at each position. Instead of simply resizing the anomaly map, the\nproposed method employs an additional refine network trained on synthetic\nanomaly images to better interpolate and account for the shape and edge of the\ninput image. We conducted experiments on the MVTec AD benchmark dataset and\nachieved state-of-the-art performance, with \\textbf{99.56\\%} and\n\\textbf{98.98\\%} AUROC scores in anomaly detection and localization,\nrespectively.\n", "date": "2022-11-22", "categories": []}
{"id": "2303.17334", "title": "GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud\n  Detection", "abstract": "  Along with the rapid evolution of mobile communication technologies, such as\n5G, there has been a drastically increase in telecom fraud, which significantly\ndissipates individual fortune and social wealth. In recent years, graph mining\ntechniques are gradually becoming a mainstream solution for detecting telecom\nfraud. However, the graph imbalance problem, caused by the Pareto principle,\nbrings severe challenges to graph data mining. This is a new and challenging\nproblem, but little previous work has been noticed. In this paper, we propose a\nGraph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph\nimbalance problem. First, we design a GAT-based base classifier to learn the\nembeddings of all nodes in the graph. Then, we feed the embeddings into a\nwell-designed cost-sensitive learner for imbalanced learning. Next, we update\nthe weights according to the misclassification cost to make the model focus\nmore on the minority class. Finally, we sum the node embeddings obtained by\nmultiple cost-sensitive learners to obtain a comprehensive node representation,\nwhich is used for the downstream anomaly detection task. Extensive experiments\non two real-world telecom fraud detection datasets demonstrate that our\nproposed method is effective for the graph imbalance problem, outperforming the\nstate-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is\nalso helpful for solving the widespread over-smoothing problem in GNNs. The\nGAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.\n", "date": "2023-03-29", "categories": []}
{"id": "2303.17505", "title": "Unsupervised Anomaly Detection with Local-Sensitive VQVAE and\n  Global-Sensitive Transformers", "abstract": "  Unsupervised anomaly detection (UAD) has been widely implemented in\nindustrial and medical applications, which reduces the cost of manual\nannotation and improves efficiency in disease diagnosis. Recently, deep\nauto-encoder with its variants has demonstrated its advantages in many UAD\nscenarios. Training on the normal data, these models are expected to locate\nanomalies by producing higher reconstruction error for the abnormal areas than\nthe normal ones. However, this assumption does not always hold because of the\nuncontrollable generalization capability. To solve this problem, we present\nLSGS, a method that builds on Vector Quantised-Variational Autoencoder (VQVAE)\nwith a novel aggregated codebook and transformers with global attention. In\nthis work, the VQVAE focus on feature extraction and reconstruction of images,\nand the transformers fit the manifold and locate anomalies in the latent space.\nThen, leveraging the generated encoding sequences that conform to a normal\ndistribution, we can reconstruct a more accurate image for locating the\nanomalies. Experiments on various datasets demonstrate the effectiveness of the\nproposed method.\n", "date": "2023-03-29", "categories": []}
{"id": "2303.05768", "title": "Learning Global-Local Correspondence with Semantic Bottleneck for\n  Logical Anomaly Detection", "abstract": "  This paper presents a novel framework, named Global-Local Correspondence\nFramework (GLCF), for visual anomaly detection with logical constraints. Visual\nanomaly detection has become an active research area in various real-world\napplications, such as industrial anomaly detection and medical disease\ndiagnosis. However, most existing methods focus on identifying local structural\ndegeneration anomalies and often fail to detect high-level functional anomalies\nthat involve logical constraints. To address this issue, we propose a\ntwo-branch approach that consists of a local branch for detecting structural\nanomalies and a global branch for detecting logical anomalies. To facilitate\nlocal-global feature correspondence, we introduce a novel semantic bottleneck\nenabled by the visual Transformer. Moreover, we develop feature estimation\nnetworks for each branch separately to detect anomalies. Our proposed framework\nis validated using various benchmarks, including industrial datasets, Mvtec AD,\nMvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show\nthat our method outperforms existing methods, particularly in detecting logical\nanomalies.\n", "date": "2023-03-10", "categories": []}
{"id": "2303.15140", "title": "SimpleNet: A Simple Network for Image Anomaly Detection and Localization", "abstract": "  We propose a simple and application-friendly network (called SimpleNet) for\ndetecting and localizing anomalies. SimpleNet consists of four components: (1)\na pre-trained Feature Extractor that generates local features, (2) a shallow\nFeature Adapter that transfers local features towards target domain, (3) a\nsimple Anomaly Feature Generator that counterfeits anomaly features by adding\nGaussian noise to normal features, and (4) a binary Anomaly Discriminator that\ndistinguishes anomaly features from normal features. During inference, the\nAnomaly Feature Generator would be discarded. Our approach is based on three\nintuitions. First, transforming pre-trained features to target-oriented\nfeatures helps avoid domain bias. Second, generating synthetic anomalies in\nfeature space is more effective, as defects may not have much commonality in\nthe image space. Third, a simple discriminator is much efficient and practical.\nIn spite of simplicity, SimpleNet outperforms previous methods quantitatively\nand qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly\ndetection AUROC of 99.6%, reducing the error by 55.5% compared to the next best\nperforming model. Furthermore, SimpleNet is faster than existing methods, with\na high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet\ndemonstrates significant improvements in performance on the One-Class Novelty\nDetection task. Code: https://github.com/DonaldRR/SimpleNet.\n", "date": "2023-03-27", "categories": []}
{"id": "2303.16097", "title": "Attention Boosted Autoencoder for Building Energy Anomaly Detection", "abstract": "  Leveraging data collected from smart meters in buildings can aid in\ndeveloping policies towards energy conservation. Significant energy savings\ncould be realised if deviations in the building operating conditions are\ndetected early, and appropriate measures are taken. Towards this end, machine\nlearning techniques can be used to automate the discovery of these abnormal\npatterns in the collected data. Current methods in anomaly detection rely on an\nunderlying model to capture the usual or acceptable operating behaviour. In\nthis paper, we propose a novel attention mechanism to model the consumption\nbehaviour of a building and demonstrate the effectiveness of the model in\ncapturing the relations using sample case studies. A real-world dataset is\nmodelled using the proposed architecture, and the results are presented. A\nvisualisation approach towards understanding the relations captured by the\nmodel is also presented.\n", "date": "2023-03-28", "categories": []}
{"id": "2210.09693", "title": "TFAD: A Decomposition Time Series Anomaly Detection Architecture with\n  Time-Frequency Analysis", "abstract": "  Time series anomaly detection is a challenging problem due to the complex\ntemporal dependencies and the limited label data. Although some algorithms\nincluding both traditional and deep models have been proposed, most of them\nmainly focus on time-domain modeling, and do not fully utilize the information\nin the frequency domain of the time series data. In this paper, we propose a\nTime-Frequency analysis based time series Anomaly Detection model, or TFAD for\nshort, to exploit both time and frequency domains for performance improvement.\nBesides, we incorporate time series decomposition and data augmentation\nmechanisms in the designed time-frequency architecture to further boost the\nabilities of performance and interpretability. Empirical studies on widely used\nbenchmark datasets show that our approach obtains state-of-the-art performance\nin univariate and multivariate time series anomaly detection tasks. Code is\nprovided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.\n", "date": "2022-10-18", "categories": []}
{"id": "2303.14814", "title": "WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation", "abstract": "  Visual anomaly classification and segmentation are vital for automating\nindustrial quality inspection. The focus of prior research in the field has\nbeen on training custom models for each quality inspection task, which requires\ntask-specific images and annotation. In this paper we move away from this\nregime, addressing zero-shot and few-normal-shot anomaly classification and\nsegmentation. Recently CLIP, a vision-language model, has shown revolutionary\ngenerality with competitive zero-/few-shot performance in comparison to\nfull-supervision. But CLIP falls short on anomaly classification and\nsegmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a\ncompositional ensemble on state words and prompt templates and (2) efficient\nextraction and aggregation of window/patch/image-level features aligned with\ntext. We also propose its few-normal-shot extension WinCLIP+, which uses\ncomplementary information from normal images. In MVTec-AD (and VisA), without\nfurther tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot\nanomaly classification and segmentation while WinCLIP+ does 93.1%/95.2%\n(83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.\n", "date": "2023-03-26", "categories": []}
{"id": "2303.15167", "title": "Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep\n  Skeleton Features", "abstract": "  This study investigates unsupervised anomaly action recognition, which\nidentifies video-level abnormal-human-behavior events in an unsupervised manner\nwithout abnormal samples, and simultaneously addresses three limitations in the\nconventional skeleton-based approaches: target domain-dependent DNN training,\nrobustness against skeleton errors, and a lack of normal samples. We present a\nunified, user prompt-guided zero-shot learning framework using a target\ndomain-independent skeleton feature extractor, which is pretrained on a\nlarge-scale action recognition dataset. Particularly, during the training phase\nusing normal samples, the method models the distribution of skeleton features\nof the normal actions while freezing the weights of the DNNs and estimates the\nanomaly score using this distribution in the inference phase. Additionally, to\nincrease robustness against skeleton errors, we introduce a DNN architecture\ninspired by a point cloud deep learning paradigm, which sparsely propagates the\nfeatures between joints. Furthermore, to prevent the unobserved normal actions\nfrom being misidentified as abnormal actions, we incorporate a similarity score\nbetween the user prompt embeddings and skeleton features aligned in the common\nspace into the anomaly score, which indirectly supplements normal actions. On\ntwo publicly available datasets, we conduct experiments to test the\neffectiveness of the proposed method with respect to abovementioned\nlimitations.\n", "date": "2023-03-27", "categories": []}
{"id": "2303.15319", "title": "Automated visual inspection of CMS HGCAL silicon sensor surface using an\n  ensemble of a deep convolutional autoencoder and classifier", "abstract": "  More than a thousand 8\" silicon sensors will be visually inspected to look\nfor anomalies on their surface during the quality control preceding assembly\ninto the High-Granularity Calorimeter for the CMS experiment at CERN. A deep\nlearning-based algorithm that pre-selects potentially anomalous images of the\nsensor surface in real time has been developed to automate the visual\ninspection. The anomaly detection is done by an ensemble of independent deep\nconvolutional neural networks: an autoencoder and a classifier. The performance\nis evaluated on images acquired in production. The pre-selection reduces the\nnumber of images requiring human inspection by 85%, with recall of 97%. Data\ngathered in production can be used for continuous learning to improve the\naccuracy incrementally.\n", "date": "2023-03-09", "categories": []}
{"id": "2211.07381", "title": "FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly\n  Detection", "abstract": "  Feature embedding-based methods have shown exceptional performance in\ndetecting industrial anomalies by comparing features of target images with\nnormal images. However, some methods do not meet the speed requirements of\nreal-time inference, which is crucial for real-world applications. To address\nthis issue, we propose a new method called Fast Adaptive Patch Memory (FAPM)\nfor real-time industrial anomaly detection. FAPM utilizes patch-wise and\nlayer-wise memory banks that store the embedding features of images at the\npatch and layer level, respectively, which eliminates unnecessary repetitive\ncomputations. We also propose patch-wise adaptive coreset sampling for faster\nand more accurate detection. FAPM performs well in both accuracy and speed\ncompared to other state-of-the-art methods\n", "date": "2022-11-14", "categories": []}
{"id": "2303.12952", "title": "TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional\n  Cycle-Consistent Generative Adversarial Networks", "abstract": "  Anomaly detection is widely used in network intrusion detection, autonomous\ndriving, medical diagnosis, credit card frauds, etc. However, several key\nchallenges remain open, such as lack of ground truth labels, presence of\ncomplex temporal patterns, and generalizing over different datasets. This paper\nproposes TSI-GAN, an unsupervised anomaly detection model for time-series that\ncan learn complex temporal patterns automatically and generalize well, i.e., no\nneed for choosing dataset-specific parameters, making statistical assumptions\nabout underlying data, or changing model architectures. To achieve these goals,\nwe convert each input time-series into a sequence of 2D images using two\nencoding techniques with the intent of capturing temporal patterns and various\ntypes of deviance. Moreover, we design a reconstructive GAN that uses\nconvolutional layers in an encoder-decoder network and employs\ncycle-consistency loss during training to ensure that inverse mappings are\naccurate as well. In addition, we also instrument a Hodrick-Prescott filter in\npost-processing to mitigate false positives. We evaluate TSI-GAN using 250\nwell-curated and harder-than-usual datasets and compare with 8 state-of-the-art\nbaseline methods. The results demonstrate the superiority of TSI-GAN to all the\nbaselines, offering an overall performance improvement of 13% and 31% over the\nsecond-best performer MERLIN and the third-best performer LSTM-AE,\nrespectively.\n", "date": "2023-03-22", "categories": []}
{"id": "2303.13051", "title": "Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection", "abstract": "  Increasing scene-awareness is a key challenge in video anomaly detection\n(VAD). In this work, we propose a hierarchical semantic contrast (HSC) method\nto learn a scene-aware VAD model from normal videos. We first incorporate\nforeground object and background scene features with high-level semantics by\ntaking advantage of pre-trained video parsing models. Then, building upon the\nautoencoder-based reconstruction framework, we introduce both scene-level and\nobject-level contrastive learning to enforce the encoded latent features to be\ncompact within the same semantic classes while being separable across different\nclasses. This hierarchical semantic contrast strategy helps to deal with the\ndiversity of normal patterns and also increases their discrimination ability.\nMoreover, for the sake of tackling rare normal activities, we design a\nskeleton-based motion augmentation to increase samples and refine the model\nfurther. Extensive experiments on three public datasets and scene-dependent\nmixture datasets validate the effectiveness of our proposed method.\n", "date": "2023-03-23", "categories": []}
{"id": "2303.12302", "title": "Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete\n  Deep Generative Model", "abstract": "  Deep generative learning cannot only be used for generating new data with\nstatistical characteristics derived from input data but also for anomaly\ndetection, by separating nominal and anomalous instances based on their\nreconstruction quality. In this paper, we explore the performance of three\nunsupervised deep generative models -- variational autoencoders (VAEs) with\nGaussian, Bernoulli, and Boltzmann priors -- in detecting anomalies in\nflight-operations data of commercial flights consisting of multivariate time\nseries. We devised two VAE models with discrete latent variables (DVAEs), one\nwith a factorized Bernoulli prior and one with a restricted Boltzmann machine\n(RBM) as prior, because of the demand for discrete-variable models in\nmachine-learning applications and because the integration of quantum devices\nbased on two-level quantum systems requires such models. The DVAE with RBM\nprior, using a relatively simple -- and classically or quantum-mechanically\nenhanceable -- sampling technique for the evolution of the RBM's negative\nphase, performed better than the Bernoulli DVAE and on par with the Gaussian\nmodel, which has a continuous latent space. Our studies demonstrate the\ncompetitiveness of a discrete deep generative model with its Gaussian\ncounterpart on anomaly-detection tasks. Moreover, the DVAE model with RBM prior\ncan be easily integrated with quantum sampling by outsourcing its generative\nprocess to measurements of quantum states obtained from a quantum annealer or\ngate-model device.\n", "date": "2023-03-22", "categories": []}
{"id": "2303.12369", "title": "Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly\n  Detection", "abstract": "  Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the\nbinary anomaly label is only given on the video level, but the output requires\nsnippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing\nin WSVAD. However, MIL is notoriously known to suffer from many false alarms\nbecause the snippet-level detector is easily biased towards the abnormal\nsnippets with simple context, confused by the normality with the same bias, and\nmissing the anomaly with a different pattern. To this end, we propose a new MIL\nframework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve\nWSVAD. At each MIL training iteration, we use the current detector to divide\nthe samples into two groups with different context biases: the most confident\nabnormal/normal snippets and the rest ambiguous ones. Then, by seeking the\ninvariant features across the two sample groups, we can remove the variant\ncontext biases. Extensive experiments on benchmarks UCF-Crime and TAD\ndemonstrate the effectiveness of our UMIL. Our code is provided at\nhttps://github.com/ktr-hubrt/UMIL.\n", "date": "2023-03-22", "categories": []}
{"id": "2211.11317", "title": "DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly\n  Detection", "abstract": "  Visual anomaly detection, an important problem in computer vision, is usually\nformulated as a one-class classification and segmentation task. The\nstudent-teacher (S-T) framework has proved to be effective in solving this\nchallenge. However, previous works based on S-T only empirically applied\nconstraints on normal data and fused multi-level information. In this study, we\npropose an improved model called DeSTSeg, which integrates a pre-trained\nteacher network, a denoising student encoder-decoder, and a segmentation\nnetwork into one framework. First, to strengthen the constraints on anomalous\ndata, we introduce a denoising procedure that allows the student network to\nlearn more robust representations. From synthetically corrupted normal images,\nwe train the student network to match the teacher network feature of the same\nimages without corruption. Second, to fuse the multi-level S-T features\nadaptively, we train a segmentation network with rich supervision from\nsynthetic anomaly masks, achieving a substantial performance improvement.\nExperiments on the industrial inspection benchmark dataset demonstrate that our\nmethod achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8%\non pixel-level average precision, and 76.4% on instance-level average\nprecision.\n", "date": "2022-11-21", "categories": []}
{"id": "2206.04803", "title": "Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application\n  of Machine Learning-based Forensics", "abstract": "  In shaping the Internet of Money, the application of blockchain and\ndistributed ledger technologies (DLTs) to the financial sector triggered\nregulatory concerns. Notably, while the user anonymity enabled in this field\nmay safeguard privacy and data protection, the lack of identifiability hinders\naccountability and challenges the fight against money laundering and the\nfinancing of terrorism and proliferation (AML/CFT). As law enforcement agencies\nand the private sector apply forensics to track crypto transfers across\necosystems that are socio-technical in nature, this paper focuses on the\ngrowing relevance of these techniques in a domain where their deployment\nimpacts the traits and evolution of the sphere. In particular, this work offers\ncontextualized insights into the application of methods of machine learning and\ntransaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin\ntransactions represented as a directed graph network through various\ntechniques. The modeling of blockchain transactions as a complex network\nsuggests that the use of graph-based data analysis methods can help classify\ntransactions and identify illicit ones. Indeed, this work shows that the neural\nnetwork types known as Graph Convolutional Networks (GCN) and Graph Attention\nNetworks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN\noutperform other classic approaches and GAT are applied for the first time to\ndetect anomalies in Bitcoin. Ultimately, the paper upholds the value of\npublic-private synergies to devise forensic strategies conscious of the spirit\nof explainability and data openness.\n", "date": "2022-06-07", "categories": []}
{"id": "2210.04227", "title": "Dual-distribution discrepancy with self-supervised refinement for\n  anomaly detection in medical images", "abstract": "  Medical anomaly detection is a crucial yet challenging task aimed at\nrecognizing abnormal images to assist in diagnosis. Due to the high-cost\nannotations of abnormal images, most methods utilize only known normal images\nduring training and identify samples deviating from the normal profile as\nanomalies in the testing phase. Many readily available unlabeled images\ncontaining anomalies are thus ignored in the training phase, restricting the\nperformance. To solve this problem, we introduce one-class semi-supervised\nlearning (OC-SSL) to utilize known normal and unlabeled images for training,\nand propose Dual-distribution Discrepancy for Anomaly Detection (DDAD) based on\nthis setting. Ensembles of reconstruction networks are designed to model the\ndistribution of normal images and the distribution of both normal and unlabeled\nimages, deriving the normative distribution module (NDM) and unknown\ndistribution module (UDM). Subsequently, the intra-discrepancy of NDM and\ninter-discrepancy between the two modules are designed as anomaly scores.\nFurthermore, we propose a new perspective on self-supervised learning, which is\ndesigned to refine the anomaly scores rather than detect anomalies directly.\nFive medical datasets, including chest X-rays, brain MRIs and retinal fundus\nimages, are organized as benchmarks for evaluation. Experiments on these\nbenchmarks comprehensively compare a wide range of anomaly detection methods\nand demonstrate that our method achieves significant gains and outperforms the\nstate-of-the-art. Code and organized benchmarks are available at\nhttps://github.com/caiyu6666/DDAD-ASR.\n", "date": "2022-10-09", "categories": []}
{"id": "2303.10704", "title": "PseudoBound: Limiting the anomaly reconstruction capability of one-class\n  classifiers using pseudo anomalies", "abstract": "  Due to the rarity of anomalous events, video anomaly detection is typically\napproached as one-class classification (OCC) problem. Typically in OCC, an\nautoencoder (AE) is trained to reconstruct the normal only training data with\nthe expectation that, in test time, it can poorly reconstruct the anomalous\ndata. However, previous studies have shown that, even trained with only normal\ndata, AEs can often reconstruct anomalous data as well, resulting in a\ndecreased performance. To mitigate this problem, we propose to limit the\nanomaly reconstruction capability of AEs by incorporating pseudo anomalies\nduring the training of an AE. Extensive experiments using five types of pseudo\nanomalies show the robustness of our training mechanism towards any kind of\npseudo anomaly. Moreover, we demonstrate the effectiveness of our proposed\npseudo anomaly based training approach against several existing state-ofthe-art\n(SOTA) methods on three benchmark video anomaly datasets, outperforming all the\nother reconstruction-based approaches in two datasets and showing the second\nbest performance in the other dataset.\n", "date": "2023-03-19", "categories": []}
{"id": "2303.09703", "title": "A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of\n  a Wind Power Dataset", "abstract": "  Anomalies refer to data points or events that deviate from normal and\nhomogeneous events, which can include fraudulent activities, network\ninfiltrations, equipment malfunctions, process changes, or other significant\nbut infrequent events. Prompt detection of such events can prevent potential\nlosses in terms of finances, information, and human resources. With the\nadvancement of computational capabilities and the availability of large\ndatasets, anomaly detection has become a major area of research. Among these,\nanomaly detection in time series has gained more attention recently due to the\nadded complexity imposed by the time dimension. This study presents a novel\nframework for time series anomaly detection using a combination of\nBidirectional Long Short Term Memory (Bi-LSTM) architecture and Autoencoder.\nThe Bi-LSTM network, which comprises two unidirectional LSTM networks, can\nanalyze the time series data from both directions and thus effectively discover\nthe long-term dependencies hidden in the sequential data. Meanwhile, the\nAutoencoder mechanism helps to establish the optimal threshold beyond which an\nevent can be classified as an anomaly. To demonstrate the effectiveness of the\nproposed framework, it is applied to a real-world multivariate time series\ndataset collected from a wind farm. The Bi-LSTM Autoencoder model achieved a\nclassification accuracy of 96.79% and outperformed more commonly used LSTM\nAutoencoder models.\n", "date": "2023-03-17", "categories": []}
{"id": "2303.09746", "title": "Detecting Out-of-distribution Examples via Class-conditional Impressions\n  Reappearing", "abstract": "  Out-of-distribution (OOD) detection aims at enhancing standard deep neural\nnetworks to distinguish anomalous inputs from original training data. Previous\nprogress has introduced various approaches where the in-distribution training\ndata and even several OOD examples are prerequisites. However, due to privacy\nand security, auxiliary data tends to be impractical in a real-world scenario.\nIn this paper, we propose a data-free method without training on natural data,\ncalled Class-Conditional Impressions Reappearing (C2IR), which utilizes image\nimpressions from the fixed model to recover class-conditional feature\nstatistics. Based on that, we introduce Integral Probability Metrics to\nestimate layer-wise class-conditional deviations and obtain layer weights by\nMeasuring Gradient-based Importance (MGI). The experiments verify the\neffectiveness of our method and indicate that C2IR outperforms other post-hoc\nmethods and reaches comparable performance to the full access (ID and OOD)\ndetection method, especially in the far-OOD dataset (SVHN).\n", "date": "2023-03-17", "categories": []}
{"id": "2303.09972", "title": "Neighborhood Averaging for Improving Outlier Detectors", "abstract": "  We hypothesize that similar objects should have similar outlier scores. To\nour knowledge, all existing outlier detectors calculate the outlier score for\neach object independently regardless of the outlier scores of the other\nobjects. Therefore, they do not guarantee that similar objects have similar\noutlier scores. To verify our proposed hypothesis, we propose an outlier score\npost-processing technique for outlier detectors, called neighborhood\naveraging(NA), which pays attention to objects and their neighbors and\nguarantees them to have more similar outlier scores than their original scores.\nGiven an object and its outlier score from any outlier detector, NA modifies\nits outlier score by combining it with its k nearest neighbors' scores. We\ndemonstrate the effectivity of NA by using the well-known k-nearest neighbors\n(k-NN). Experimental results show that NA improves all 10 tested baseline\ndetectors by 13% (from 0.70 to 0.79 AUC) on average evaluated on nine\nreal-world datasets. Moreover, even outlier detectors that are already based on\nk-NN are also improved. The experiments also show that in some applications,\nthe choice of detector is no more significant when detectors are jointly used\nwith NA, which may pose a challenge to the generally considered idea that the\ndata model is the most important factor. We open our code on www.outlierNet.com\nfor reproducibility.\n", "date": "2023-03-17", "categories": []}
{"id": "2210.02226", "title": "Null Hypothesis Test for Anomaly Detection", "abstract": "  We extend the use of Classification Without Labels for anomaly detection with\na hypothesis test designed to exclude the background-only hypothesis. By\ntesting for statistical independence of the two discriminating dataset regions,\nwe are able to exclude the background-only hypothesis without relying on fixed\nanomaly score cuts or extrapolations of background estimates between regions.\nThe method relies on the assumption of conditional independence of anomaly\nscore features and dataset regions, which can be ensured using existing\ndecorrelation techniques. As a benchmark example, we consider the LHC Olympics\ndataset where we show that mutual information represents a suitable test for\nstatistical independence and our method exhibits excellent and robust\nperformance at different signal fractions even in presence of realistic feature\ncorrelations.\n", "date": "2022-10-05", "categories": []}
{"id": "2303.08193", "title": "RODD: Robust Outlier Detection in Data Cubes", "abstract": "  Data cubes are multidimensional databases, often built from several separate\ndatabases, that serve as flexible basis for data analysis. Surprisingly,\noutlier detection on data cubes has not yet been treated extensively. In this\nwork, we provide the first framework to evaluate robust outlier detection\nmethods in data cubes (RODD). We introduce a novel random forest-based outlier\ndetection approach (RODD-RF) and compare it with more traditional methods based\non robust location estimators. We propose a general type of test data and\nexamine all methods in a simulation study. Moreover, we apply ROOD-RF to real\nworld data. The results show that RODD-RF can lead to improved outlier\ndetection.\n", "date": "2023-03-14", "categories": []}
{"id": "2303.08452", "title": "Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly\n  Detection", "abstract": "  Early and accurate disease detection is crucial for patient management and\nsuccessful treatment outcomes. However, the automatic identification of\nanomalies in medical images can be challenging. Conventional methods rely on\nlarge labeled datasets which are difficult to obtain. To overcome these\nlimitations, we introduce a novel unsupervised approach, called PHANES (Pseudo\nHealthy generative networks for ANomaly Segmentation). Our method has the\ncapability of reversing anomalies, i.e., preserving healthy tissue and\nreplacing anomalous regions with pseudo-healthy (PH) reconstructions. Unlike\nrecent diffusion models, our method does not rely on a learned noise\ndistribution nor does it introduce random alterations to the entire image.\nInstead, we use latent generative networks to create masks around possible\nanomalies, which are refined using inpainting generative networks. We\ndemonstrate the effectiveness of PHANES in detecting stroke lesions in T1w\nbrain MRI datasets and show significant improvements over state-of-the-art\n(SOTA) methods. We believe that our proposed framework will open new avenues\nfor interpretable, fast, and accurate anomaly segmentation with the potential\nto support various clinical-oriented downstream tasks.\n", "date": "2023-03-15", "categories": []}
{"id": "2303.07452", "title": "Network Anomaly Detection Using Federated Learning", "abstract": "  Due to the veracity and heterogeneity in network traffic, detecting anomalous\nevents is challenging. The computational load on global servers is a\nsignificant challenge in terms of efficiency, accuracy, and scalability. Our\nprimary motivation is to introduce a robust and scalable framework that enables\nefficient network anomaly detection. We address the issue of scalability and\nefficiency for network anomaly detection by leveraging federated learning, in\nwhich multiple participants train a global model jointly. Unlike centralized\ntraining architectures, federated learning does not require participants to\nupload their training data to the server, preventing attackers from exploiting\nthe training data. Moreover, most prior works have focused on traditional\ncentralized machine learning, making federated machine learning under-explored\nin network anomaly detection. Therefore, we propose a deep neural network\nframework that could work on low to mid-end devices detecting network anomalies\nwhile checking if a request from a specific IP address is malicious or not.\nCompared to multiple traditional centralized machine learning models, the deep\nneural federated model reduces training time overhead. The proposed method\nperforms better than baseline machine learning techniques on the UNSW-NB15 data\nset as measured by experiments conducted with an accuracy of 97.21% and a\nfaster computation time.\n", "date": "2023-03-13", "categories": []}
{"id": "2210.01078", "title": "Unsupervised Model Selection for Time-series Anomaly Detection", "abstract": "  Anomaly detection in time-series has a wide range of practical applications.\nWhile numerous anomaly detection methods have been proposed in the literature,\na recent survey concluded that no single method is the most accurate across\nvarious datasets. To make matters worse, anomaly labels are scarce and rarely\navailable in practice. The practical problem of selecting the most accurate\nmodel for a given dataset without labels has received little attention in the\nliterature. This paper answers this question i.e. Given an unlabeled dataset\nand a set of candidate anomaly detectors, how can we select the most accurate\nmodel? To this end, we identify three classes of surrogate (unsupervised)\nmetrics, namely, prediction error, model centrality, and performance on\ninjected synthetic anomalies, and show that some metrics are highly correlated\nwith standard supervised anomaly detection performance metrics such as the\n$F_1$ score, but to varying degrees. We formulate metric combination with\nmultiple imperfect surrogate metrics as a robust rank aggregation problem. We\nthen provide theoretical justification behind the proposed approach.\nLarge-scale experiments on multiple real-world datasets demonstrate that our\nproposed unsupervised approach is as effective as selecting the most accurate\nmodel based on partially labeled data.\n", "date": "2022-10-03", "categories": []}
{"id": "2303.06431", "title": "Anomaly Detection with Ensemble of Encoder and Decoder", "abstract": "  Hacking and false data injection from adversaries can threaten power grids'\neveryday operations and cause significant economic loss. Anomaly detection in\npower grids aims to detect and discriminate anomalies caused by cyber attacks\nagainst the power system, which is essential for keeping power grids working\ncorrectly and efficiently. Different methods have been applied for anomaly\ndetection, such as statistical methods and machine learning-based methods.\nUsually, machine learning-based methods need to model the normal data\ndistribution. In this work, we propose a novel anomaly detection method by\nmodeling the data distribution of normal samples via multiple encoders and\ndecoders. Specifically, the proposed method maps input samples into a latent\nspace and then reconstructs output samples from latent vectors. The extra\nencoder finally maps reconstructed samples to latent representations. During\nthe training phase, we optimize parameters by minimizing the reconstruction\nloss and encoding loss. Training samples are re-weighted to focus more on\nmissed correlations between features of normal data. Furthermore, we employ the\nlong short-term memory model as encoders and decoders to test its\neffectiveness. We also investigate a meta-learning-based framework for\nhyper-parameter tuning of our approach. Experiment results on network intrusion\nand power system datasets demonstrate the effectiveness of our proposed method,\nwhere our models consistently outperform all baselines.\n", "date": "2023-03-11", "categories": []}
{"id": "2303.06879", "title": "Spacecraft Anomaly Detection with Attention Temporal Convolution Network", "abstract": "  Spacecraft faces various situations when carrying out exploration missions in\ncomplex space, thus monitoring the anomaly status of spacecraft is crucial to\nthe development of \\textcolor{blue}{the} aerospace industry. The time series\ntelemetry data generated by on-orbit spacecraft \\textcolor{blue}{contains}\nimportant information about the status of spacecraft. However, traditional\ndomain knowledge-based spacecraft anomaly detection methods are not effective\ndue to high dimensionality and complex correlation among variables. In this\nwork, we propose an anomaly detection framework for spacecraft multivariate\ntime-series data based on temporal convolution networks (TCNs). First, we\nemploy dynamic graph attention to model the complex correlation among variables\nand time series. Second, temporal convolution networks with parallel processing\nability are used to extract multidimensional \\textcolor{blue}{features} for\n\\textcolor{blue}{the} downstream prediction task. Finally, many potential\nanomalies are detected by the best threshold. Experiments on real NASA SMAP/MSL\nspacecraft datasets show the superiority of our proposed model with respect to\nstate-of-the-art methods.\n", "date": "2023-03-13", "categories": []}
{"id": "2303.05904", "title": "Deep Anomaly Detection on Tennessee Eastman Process Data", "abstract": "  This paper provides the first comprehensive evaluation and analysis of modern\n(deep-learning) unsupervised anomaly detection methods for chemical process\ndata. We focus on the Tennessee Eastman process dataset, which has been a\nstandard litmus test to benchmark anomaly detection methods for nearly three\ndecades. Our extensive study will facilitate choosing appropriate anomaly\ndetection methods in industrial applications.\n", "date": "2023-03-10", "categories": []}
{"id": "2211.09041", "title": "Anomaly Detection via Multi-Scale Contrasted Memory", "abstract": "  Deep anomaly detection (AD) aims to provide robust and efficient classifiers\nfor one-class and unbalanced settings. However current AD models still struggle\non edge-case normal samples and are often unable to keep high performance over\ndifferent scales of anomalies. Moreover, there currently does not exist a\nunified framework efficiently covering both one-class and unbalanced learnings.\nIn the light of these limitations, we introduce a new two-stage anomaly\ndetector which memorizes during training multi-scale normal prototypes to\ncompute an anomaly deviation score. First, we simultaneously learn\nrepresentations and memory modules on multiple scales using a novel\nmemory-augmented contrastive learning. Then, we train an anomaly distance\ndetector on the spatial deviation maps between prototypes and observations. Our\nmodel highly improves the state-of-the-art performance on a wide range of\nobject, style and local anomalies with up to 50% error relative improvement on\nCIFAR-100. It is also the first model to keep high performance across the\none-class and unbalanced settings.\n", "date": "2022-11-16", "categories": []}
{"id": "2303.05047", "title": "Diversity-Measurable Anomaly Detection", "abstract": "  Reconstruction-based anomaly detection models achieve their purpose by\nsuppressing the generalization ability for anomaly. However, diverse normal\npatterns are consequently not well reconstructed as well. Although some efforts\nhave been made to alleviate this problem by modeling sample diversity, they\nsuffer from shortcut learning due to undesired transmission of abnormal\ninformation. In this paper, to better handle the tradeoff problem, we propose\nDiversity-Measurable Anomaly Detection (DMAD) framework to enhance\nreconstruction diversity while avoid the undesired generalization on anomalies.\nTo this end, we design Pyramid Deformation Module (PDM), which models diverse\nnormals and measures the severity of anomaly by estimating multi-scale\ndeformation fields from reconstructed reference to original input. Integrated\nwith an information compression module, PDM essentially decouples deformation\nfrom prototypical embedding and makes the final anomaly score more reliable.\nExperimental results on both surveillance videos and industrial images\ndemonstrate the effectiveness of our method. In addition, DMAD works equally\nwell in front of contaminated data and anomaly-like normal samples.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05109", "title": "Updated version: A Video Anomaly Detection Framework based on\n  Appearance-Motion Semantics Representation Consistency", "abstract": "  Video anomaly detection is an essential but challenging task. The prevalent\nmethods mainly investigate the reconstruction difference between normal and\nabnormal patterns but ignore the semantics consistency between appearance and\nmotion information of behavior patterns, making the results highly dependent on\nthe local context of frame sequences and lacking the understanding of behavior\nsemantics. To address this issue, we propose a framework of Appearance-Motion\nSemantics Representation Consistency that uses the gap of appearance and motion\nsemantic representation consistency between normal and abnormal data. The\ntwo-stream structure is designed to encode the appearance and motion\ninformation representation of normal samples, and a novel consistency loss is\nproposed to enhance the consistency of feature semantics so that anomalies with\nlow consistency can be identified. Moreover, the lower consistency features of\nanomalies can be used to deteriorate the quality of the predicted frame, which\nmakes anomalies easier to spot. Experimental results demonstrate the\neffectiveness of the proposed method.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05112", "title": "Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A\n  Simple yet Efficient Framework based on Masked Autoencoder", "abstract": "  Due to the limited availability of anomalous samples for training, video\nanomaly detection is commonly viewed as a one-class classification problem.\nMany prevalent methods investigate the reconstruction difference produced by\nAutoEncoders (AEs) under the assumption that the AEs would reconstruct the\nnormal data well while reconstructing anomalies poorly. However, even with only\nnormal data training, the AEs often reconstruct anomalies well, which depletes\ntheir anomaly detection performance. To alleviate this issue, we propose a\nsimple yet efficient framework for video anomaly detection. The pseudo anomaly\nsamples are introduced, which are synthesized from only normal data by\nembedding random mask tokens without extra data processing. We also propose a\nnormalcy consistency training strategy that encourages the AEs to better learn\nthe regular knowledge from normal and corresponding pseudo anomaly data. This\nway, the AEs learn more distinct reconstruction boundaries between normal and\nabnormal data, resulting in superior anomaly discrimination capability.\nExperimental results demonstrate the effectiveness of the proposed method.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05116", "title": "Multi-level Memory-augmented Appearance-Motion Correspondence Framework\n  for Video Anomaly Detection", "abstract": "  Frame prediction based on AutoEncoder plays a significant role in\nunsupervised video anomaly detection. Ideally, the models trained on the normal\ndata could generate larger prediction errors of anomalies. However, the\ncorrelation between appearance and motion information is underutilized, which\nmakes the models lack an understanding of normal patterns. Moreover, the models\ndo not work well due to the uncontrollable generalizability of deep\nAutoEncoder. To tackle these problems, we propose a multi-level\nmemory-augmented appearance-motion correspondence framework. The latent\ncorrespondence between appearance and motion is explored via appearance-motion\nsemantics alignment and semantics replacement training. Besides, we also\nintroduce a Memory-Guided Suppression Module, which utilizes the difference\nfrom normal prototype features to suppress the reconstruction capacity caused\nby skip-connection, achieving the tradeoff between the good reconstruction of\nnormal data and the poor reconstruction of abnormal data. Experimental results\nshow that our framework outperforms the state-of-the-art methods, achieving\nAUCs of 99.6\\%, 93.8\\%, and 76.3\\% on UCSD Ped2, CUHK Avenue, and ShanghaiTech\ndatasets.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05413", "title": "Fast kernel methods for Data Quality Monitoring as a goodness-of-fit\n  test", "abstract": "  We here propose a machine learning approach for monitoring particle detectors\nin real-time. The goal is to assess the compatibility of incoming experimental\ndata with a reference dataset, characterising the data behaviour under normal\ncircumstances, via a likelihood-ratio hypothesis test. The model is based on a\nmodern implementation of kernel methods, nonparametric algorithms that can\nlearn any continuous function given enough data. The resulting approach is\nefficient and agnostic to the type of anomaly that may be present in the data.\nOur study demonstrates the effectiveness of this strategy on multivariate data\nfrom drift tube chamber muon detectors.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.05463", "title": "Understanding the Challenges and Opportunities of Pose-based Anomaly\n  Detection", "abstract": "  Pose-based anomaly detection is a video-analysis technique for detecting\nanomalous events or behaviors by examining human pose extracted from the video\nframes. Utilizing pose data alleviates privacy and ethical issues. Also,\ncomputation-wise, the complexity of pose-based models is lower than pixel-based\napproaches. However, it introduces more challenges, such as noisy skeleton\ndata, losing important pixel information, and not having enriched enough\nfeatures. These problems are exacerbated by a lack of anomaly detection\ndatasets that are good enough representatives of real-world scenarios. In this\nwork, we analyze and quantify the characteristics of two well-known video\nanomaly datasets to better understand the difficulties of pose-based anomaly\ndetection. We take a step forward, exploring the discriminating power of pose\nand trajectory for video anomaly detection and their effectiveness based on\ncontext. We believe these experiments are beneficial for a better comprehension\nof pose-based anomaly detection and the datasets currently available. This will\naid researchers in tackling the task of anomaly detection with a more lucid\nperspective, accelerating the development of robust models with better\nperformance.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.03611", "title": "TinyAD: Memory-efficient anomaly detection for time series data in\n  Industrial IoT", "abstract": "  Monitoring and detecting abnormal events in cyber-physical systems is crucial\nto industrial production. With the prevalent deployment of the Industrial\nInternet of Things (IIoT), an enormous amount of time series data is collected\nto facilitate machine learning models for anomaly detection, and it is of the\nutmost importance to directly deploy the trained models on the IIoT devices.\nHowever, it is most challenging to deploy complex deep learning models such as\nConvolutional Neural Networks (CNNs) on these memory-constrained IIoT devices\nembedded with microcontrollers (MCUs). To alleviate the memory constraints of\nMCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to\nefficiently facilitate onboard inference of CNNs for real-time anomaly\ndetection. First, we conduct a comprehensive analysis of depthwise separable\nCNNs and regular CNNs for anomaly detection and find that the depthwise\nseparable convolution operation can reduce the model size by 50-90% compared\nwith the traditional CNNs. Then, to reduce the peak memory consumption of CNNs,\nwe explore two complementary strategies, in-place, and patch-by-patch memory\nrescheduling, and integrate them into a unified framework. The in-place method\ndecreases the peak memory of the depthwise convolution by sparing a temporary\nbuffer to transfer the activation results, while the patch-by-patch method\nfurther reduces the peak memory of layer-wise execution by slicing the input\ndata into corresponding receptive fields and executing in order. Furthermore,\nby adjusting the dimension of convolution filters, these strategies apply to\nboth univariate time series and multidomain time series features. Extensive\nexperiments on real-world industrial datasets show that our framework can\nreduce peak memory consumption by 2-5x with negligible computation overhead.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.03758", "title": "Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI", "abstract": "  The use of supervised deep learning techniques to detect pathologies in brain\nMRI scans can be challenging due to the diversity of brain anatomy and the need\nfor annotated data sets. An alternative approach is to use unsupervised anomaly\ndetection, which only requires sample-level labels of healthy brains to create\na reference representation. This reference representation can then be compared\nto unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To\naccomplish this, generative models are needed to create anatomically consistent\nMRI scans of healthy brains. While recent diffusion models have shown promise\nin this task, accurately generating the complex structure of the human brain\nremains a challenge. In this paper, we propose a method that reformulates the\ngeneration task of diffusion models as a patch-based estimation of healthy\nbrain anatomy, using spatial context to guide and improve reconstruction. We\nevaluate our approach on data of tumors and multiple sclerosis lesions and\ndemonstrate a relative improvement of 25.1% compared to existing baselines.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.03925", "title": "Robust Semi-Supervised Anomaly Detection via Adversarially Learned\n  Continuous Noise Corruption", "abstract": "  Anomaly detection is the task of recognising novel samples which deviate\nsignificantly from pre-establishednormality. Abnormal classes are not present\nduring training meaning that models must learn effective rep-resentations\nsolely across normal class data samples. Deep Autoencoders (AE) have been\nwidely used foranomaly detection tasks, but suffer from overfitting to a null\nidentity function. To address this problem, weimplement a training scheme\napplied to a Denoising Autoencoder (DAE) which introduces an efficient methodof\nproducing Adversarially Learned Continuous Noise (ALCN) to maximally globally\ncorrupt the input priorto denoising. Prior methods have applied similar\napproaches of adversarial training to increase the robustnessof DAE, however\nthey exhibit limitations such as slow inference speed reducing their real-world\napplicabilityor producing generalised obfuscation which is more trivial to\ndenoise. We show through rigorous evaluationthat our ALCN method of\nregularisation during training improves AUC performance during inference\nwhileremaining efficient over both classical, leave-one-out novelty detection\ntasks with the variations-: 9 (normal)vs. 1 (abnormal) & 1 (normal) vs. 9\n(abnormal); MNIST - AUCavg: 0.890 & 0.989, CIFAR-10 - AUCavg: 0.670& 0.742, in\naddition to challenging real-world anomaly detection tasks: industrial\ninspection (MVTEC-AD -AUCavg: 0.780) and plant disease detection (Plant Village\n- AUC: 0.770) when compared to prior approaches.\n", "date": "2023-03-02", "categories": []}
{"id": "2303.02318", "title": "Achieving Counterfactual Fairness for Anomaly Detection", "abstract": "  Ensuring fairness in anomaly detection models has received much attention\nrecently as many anomaly detection applications involve human beings. However,\nexisting fair anomaly detection approaches mainly focus on association-based\nfairness notions. In this work, we target counterfactual fairness, which is a\nprevalent causation-based fairness notion. The goal of counterfactually fair\nanomaly detection is to ensure that the detection outcome of an individual in\nthe factual world is the same as that in the counterfactual world where the\nindividual had belonged to a different group. To this end, we propose a\ncounterfactually fair anomaly detection (CFAD) framework which consists of two\nphases, counterfactual data generation and fair anomaly detection. Experimental\nresults on a synthetic dataset and two real datasets show that CFAD can\neffectively detect anomalies as well as ensure counterfactual fairness.\n", "date": "2023-03-04", "categories": []}
{"id": "2303.02595", "title": "PyramidFlow: High-Resolution Defect Contrastive Localization using\n  Pyramid Normalizing Flow", "abstract": "  During industrial processing, unforeseen defects may arise in products due to\nuncontrollable factors. Although unsupervised methods have been successful in\ndefect localization, the usual use of pre-trained models results in\nlow-resolution outputs, which damages visual performance. To address this\nissue, we propose PyramidFlow, the first fully normalizing flow method without\npre-trained models that enables high-resolution defect localization.\nSpecifically, we propose a latent template-based defect contrastive\nlocalization paradigm to reduce intra-class variance, as the pre-trained models\ndo. In addition, PyramidFlow utilizes pyramid-like normalizing flows for\nmulti-scale fusing and volume normalization to help generalization. Our\ncomprehensive studies on MVTecAD demonstrate the proposed method outperforms\nthe comparable algorithms that do not use external priors, even achieving\nstate-of-the-art performance in more challenging BTAD scenarios.\n", "date": "2023-03-05", "categories": []}
{"id": "2303.00870", "title": "Implementing Active Learning in Cybersecurity: Detecting Anomalies in\n  Redacted Emails", "abstract": "  Research on email anomaly detection has typically relied on specially\nprepared datasets that may not adequately reflect the type of data that occurs\nin industry settings. In our research, at a major financial services company,\nprivacy concerns prevented inspection of the bodies of emails and attachment\ndetails (although subject headings and attachment filenames were available).\nThis made labeling possible anomalies in the resulting redacted emails more\ndifficult. Another source of difficulty is the high volume of emails combined\nwith the scarcity of resources making machine learning (ML) a necessity, but\nalso creating a need for more efficient human training of ML models. Active\nlearning (AL) has been proposed as a way to make human training of ML models\nmore efficient. However, the implementation of Active Learning methods is a\nhuman-centered AI challenge due to potential human analyst uncertainty, and the\nlabeling task can be further complicated in domains such as the cybersecurity\ndomain (or healthcare, aviation, etc.) where mistakes in labeling can have\nhighly adverse consequences. In this paper we present research results\nconcerning the application of Active Learning to anomaly detection in redacted\nemails, comparing the utility of different methods for implementing active\nlearning in this context. We evaluate different AL strategies and their impact\non resulting model performance. We also examine how ratings of confidence that\nexperts have in their labels can inform AL. The results obtained are discussed\nin terms of their implications for AL methodology and for the role of experts\nin model-assisted email anomaly screening.\n", "date": "2023-03-01", "categories": []}
{"id": "2303.02052", "title": "Interruptions detection in video conferences", "abstract": "  In recent years, video conferencing (VC) popularity has skyrocketed for a\nwide range of activities. As a result, the number of VC users surged sharply.\nThe sharp increase in VC usage has been accompanied by various newly emerging\nprivacy and security challenges. VC meetings became a target for various\nsecurity attacks, such as Zoombombing. Other VC-related challenges also\nemerged. For example, during COVID lockdowns, educators had to teach in online\nenvironments struggling with keeping students engaged for extended periods. In\nparallel, the amount of available VC videos has grown exponentially. Thus,\nusers and companies are limited in finding abnormal segments in VC meetings\nwithin the converging volumes of data. Such abnormal events that affect most\nmeeting participants may be indicators of interesting points in time, including\nsecurity attacks or other changes in meeting climate, like someone joining a\nmeeting or sharing a dramatic content. Here, we present a novel algorithm for\ndetecting abnormal events in VC data. We curated VC publicly available\nrecordings, including meetings with interruptions. We analyzed the videos using\nour algorithm, extracting time windows where abnormal occurrences were\ndetected. Our algorithm is a pipeline that combines multiple methods in several\nsteps to detect users' faces in each video frame, track face locations during\nthe meeting and generate vector representations of a facial expression for each\nface in each frame. Vector representations are used to monitor changes in\nfacial expressions throughout the meeting for each participant. The overall\nchange in meeting climate is quantified using those parameters across all\nparticipants, and translating them into event anomaly detection. This is the\nfirst open pipeline for automatically detecting anomaly events in VC meetings.\nOur model detects abnormal events with 92.3% precision over the collected\ndataset.\n", "date": "2023-02-25", "categories": []}
{"id": "2302.12245", "title": "Set Features for Fine-grained Anomaly Detection", "abstract": "  Fine-grained anomaly detection has recently been dominated by segmentation\nbased approaches. These approaches first classify each element of the sample\n(e.g., image patch) as normal or anomalous and then classify the entire sample\nas anomalous if it contains anomalous elements. However, such approaches do not\nextend to scenarios where the anomalies are expressed by an unusual combination\nof normal elements. In this paper, we overcome this limitation by proposing set\nfeatures that model each sample by the distribution its elements. We compute\nthe anomaly score of each sample using a simple density estimation method. Our\nsimple-to-implement approach outperforms the state-of-the-art in image-level\nlogical anomaly detection (+3.4%) and sequence-level time-series anomaly\ndetection (+2.4%).\n", "date": "2023-02-23", "categories": []}
{"id": "2303.00409", "title": "RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for\n  Open-Ended Time Series", "abstract": "  An open-ended time series refers to a series of data points indexed in time\norder without an end. Such a time series can be found everywhere due to the\nprevalence of Internet of Things. Providing lightweight and real-time anomaly\ndetection for open-ended time series is highly desirable to industry and\norganizations since it allows immediate response and avoids potential financial\nloss. In the last few years, several real-time time series anomaly detection\napproaches have been introduced. However, they might exhaust system resources\nwhen they are applied to open-ended time series for a long time. To address\nthis issue, in this paper we propose RePAD2, a lightweight real-time anomaly\ndetection approach for open-ended time series by improving its predecessor\nRePAD, which is one of the state-of-the-art anomaly detection approaches. We\nconducted a series of experiments to compare RePAD2 with RePAD and another\nsimilar detection approach based on real-world time series datasets, and\ndemonstrated that RePAD2 can address the mentioned resource exhaustion issue\nwhile offering comparable detection accuracy and slightly less time\nconsumption.\n", "date": "2023-03-01", "categories": []}
{"id": "2303.01272", "title": "Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly\n  Detection in Time Series", "abstract": "  The field of time series anomaly detection is constantly advancing, with\nseveral methods available, making it a challenge to determine the most\nappropriate method for a specific domain. The evaluation of these methods is\nfacilitated by the use of metrics, which vary widely in their properties.\nDespite the existence of new evaluation metrics, there is limited agreement on\nwhich metrics are best suited for specific scenarios and domain, and the most\ncommonly used metrics have faced criticism in the literature. This paper\nprovides a comprehensive overview of the metrics used for the evaluation of\ntime series anomaly detection methods, and also defines a taxonomy of these\nbased on how they are calculated. By defining a set of properties for\nevaluation metrics and a set of specific case studies and experiments, twenty\nmetrics are analyzed and discussed in detail, highlighting the unique\nsuitability of each for specific tasks. Through extensive experimentation and\nanalysis, this paper argues that the choice of evaluation metric must be made\nwith care, taking into account the specific requirements of the task at hand.\n", "date": "2023-03-02", "categories": []}
{"id": "2302.14781", "title": "Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach", "abstract": "  Fixing energy leakage caused by different anomalies can result in significant\nenergy savings and extended appliance life. Further, it assists grid operators\nin scheduling their resources to meet the actual needs of end users, while\nhelping end users reduce their energy costs. In this paper, we analyze the\npatterns pertaining to the power consumption of dishwashers used in two houses\nof the REFIT dataset. Then two autoencoder (AEs) with 1D-CNN and TCN as\nbackbones are trained to differentiate the normal patterns from the abnormal\nones. Our results indicate that TCN outperforms CNN1D in detecting anomalies in\nenergy consumption. Finally, the data from the Fridge_Freezer and the Freezer\nof house No. 3 in REFIT is also used to evaluate our approach.\n", "date": "2023-02-28", "categories": []}
{"id": "2302.12918", "title": "Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems", "abstract": "  Our work focuses on anomaly detection in cyber-physical systems. Prior\nliterature has three limitations: (1) Failing to capture long-delayed patterns\nin system anomalies; (2) Ignoring dynamic changes in sensor connections; (3)\nThe curse of high-dimensional data samples. These limit the detection\nperformance and usefulness of existing works. To address them, we propose a new\napproach called deep graph stream support vector data description (SVDD) for\nanomaly detection. Specifically, we first use a transformer to preserve both\nshort and long temporal patterns of monitoring data in temporal embeddings.\nThen we cluster these embeddings according to sensor type and utilize them to\nestimate the change in connectivity between various sensors to construct a new\nweighted graph. The temporal embeddings are mapped to the new graph as node\nattributes to form weighted attributed graph. We input the graph into a\nvariational graph auto-encoder model to learn final spatio-temporal\nrepresentation. Finally, we learn a hypersphere that encompasses normal\nembeddings and predict the system status by calculating the distances between\nthe hypersphere and data samples. Extensive experiments validate the\nsuperiority of our model, which improves F1-score by 35.87%, AUC by 19.32%,\nwhile being 32 times faster than the best baseline at training and inference.\n", "date": "2023-02-24", "categories": []}
{"id": "2302.13593", "title": "Brain subtle anomaly detection based on auto-encoders latent space\n  analysis : application to de novo parkinson patients", "abstract": "  Neural network-based anomaly detection remains challenging in clinical\napplications with little or no supervised information and subtle anomalies such\nas hardly visible brain lesions. Among unsupervised methods, patch-based\nauto-encoders with their efficient representation power provided by their\nlatent space, have shown good results for visible lesion detection. However,\nthe commonly used reconstruction error criterion may limit their performance\nwhen facing less obvious lesions. In this work, we design two alternative\ndetection criteria. They are derived from multivariate analysis and can more\ndirectly capture information from latent space representations. Their\nperformance compares favorably with two additional supervised learning methods,\non a difficult de novo Parkinson Disease (PD) classification task.\n", "date": "2023-02-27", "categories": []}
{"id": "2302.11135", "title": "Semi-Supervised Approach for Early Stuck Sign Detection in Drilling\n  Operations", "abstract": "  A real-time stuck pipe prediction methodology is proposed in this paper. We\nassume early signs of stuck pipe to be apparent when the drilling data behavior\ndeviates from that from normal drilling operations. The definition of normalcy\nchanges with drill string configuration or geological conditions. Here, a\ndepth-domain data representation is adopted to capture the localized normal\nbehavior. Several models, based on auto-encoder and variational auto-encoders,\nare trained on regular drilling data extracted from actual drilling data. When\nthe trained model is applied to data sets before stuck incidents, eight\nincidents showed large reconstruction errors. These results suggest better\nperformance than the previously reported supervised approach. Inter-comparison\nof various models reveals the robustness of our approach. The model performance\ndepends on the featured parameter suggesting the need for multiple models in\nactual operation.\n", "date": "2023-02-22", "categories": []}
{"id": "2302.11019", "title": "Using Semantic Information for Defining and Detecting OOD Inputs", "abstract": "  As machine learning models continue to achieve impressive performance across\ndifferent tasks, the importance of effective anomaly detection for such models\nhas increased as well. It is common knowledge that even well-trained models\nlose their ability to function effectively on out-of-distribution inputs. Thus,\nout-of-distribution (OOD) detection has received some attention recently. In\nthe vast majority of cases, it uses the distribution estimated by the training\ndataset for OOD detection. We demonstrate that the current detectors inherit\nthe biases in the training dataset, unfortunately. This is a serious\nimpediment, and can potentially restrict the utility of the trained model. This\ncan render the current OOD detectors impermeable to inputs lying outside the\ntraining distribution but with the same semantic information (e.g. training\nclass labels). To remedy this situation, we begin by defining what should\nideally be treated as an OOD, by connecting inputs with their semantic\ninformation content. We perform OOD detection on semantic information extracted\nfrom the training data of MNIST and COCO datasets and show that it not only\nreduces false alarms but also significantly improves the detection of OOD\ninputs with spurious features from the training data.\n", "date": "2023-02-21", "categories": []}
{"id": "2302.11234", "title": "Cluster Purging: Efficient Outlier Detection based on Rate-Distortion\n  Theory", "abstract": "  Rate-distortion theory-based outlier detection builds upon the rationale that\na good data compression will encode outliers with unique symbols. Based on this\nrationale, we propose Cluster Purging, which is an extension of\nclustering-based outlier detection. This extension allows one to assess the\nrepresentivity of clusterings, and to find data that are best represented by\nindividual unique clusters. We propose two efficient algorithms for performing\nCluster Purging, one being parameter-free, while the other algorithm has a\nparameter that controls representivity estimations, allowing it to be tuned in\nsupervised setups. In an experimental evaluation, we show that Cluster Purging\nimproves upon outliers detected from raw clusterings, and that Cluster Purging\ncompetes strongly against state-of-the-art alternatives.\n", "date": "2023-02-22", "categories": []}
{"id": "2302.10601", "title": "Few-shot Detection of Anomalies in Industrial Cyber-Physical System via\n  Prototypical Network and Contrastive Learning", "abstract": "  The rapid development of Industry 4.0 has amplified the scope and\ndestructiveness of industrial Cyber-Physical System (CPS) by network attacks.\nAnomaly detection techniques are employed to identify these attacks and\nguarantee the normal operation of industrial CPS. However, it is still a\nchallenging problem to cope with scenarios with few labeled samples. In this\npaper, we propose a few-shot anomaly detection model (FSL-PN) based on\nprototypical network and contrastive learning for identifying anomalies with\nlimited labeled data from industrial CPS. Specifically, we design a contrastive\nloss to assist the training process of the feature extractor and learn more\nfine-grained features to improve the discriminative performance. Subsequently,\nto tackle the overfitting issue during classifying, we construct a robust cost\nfunction with a specific regularizer to enhance the generalization capability.\nExperimental results based on two public imbalanced datasets with few-shot\nsettings show that the FSL-PN model can significantly improve F1 score and\nreduce false alarm rate (FAR) for identifying anomalous signals to guarantee\nthe security of industrial CPS.\n", "date": "2023-02-21", "categories": []}
{"id": "2302.09320", "title": "Anomaly Detection of UAV State Data Based on Single-class Triangular\n  Global Alignment Kernel Extreme Learning Machine", "abstract": "  Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in\nmilitary and civilian fields. With the continuous enrichment and extensive\nexpansion of application scenarios, the safety of UAVs is constantly being\nchallenged. To address this challenge, we propose algorithms to detect\nanomalous data collected from drones to improve drone safety. We deployed a\none-class kernel extreme learning machine (OCKELM) to detect anomalies in drone\ndata. By default, OCKELM uses the radial basis (RBF) kernel function as the\nkernel function of the model. To improve the performance of OCKELM, we choose a\nTriangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and\nintroduce the Fast Independent Component Analysis (FastICA) algorithm to\nreconstruct UAV data. Based on the above improvements, we create a novel\nanomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated\non the UCI dataset and detected on the Aeronautical Laboratory Failures and\nAnomalies (ALFA) dataset. The experimental results show that compared with\nother methods, the accuracy of this method is improved by more than 30%, and\npoint anomalies are effectively detected.\n", "date": "2023-02-18", "categories": []}
{"id": "2302.09794", "title": "Two-stream Decoder Feature Normality Estimating Network for Industrial\n  Anomaly Detection", "abstract": "  Image reconstruction-based anomaly detection has recently been in the\nspotlight because of the difficulty of constructing anomaly datasets. These\napproaches work by learning to model normal features without seeing abnormal\nsamples during training and then discriminating anomalies at test time based on\nthe reconstructive errors. However, these models have limitations in\nreconstructing the abnormal samples due to their indiscriminate conveyance of\nfeatures. Moreover, these approaches are not explicitly optimized for\ndistinguishable anomalies. To address these problems, we propose a two-stream\ndecoder network (TSDN), designed to learn both normal and abnormal features.\nAdditionally, we propose a feature normality estimator (FNE) to eliminate\nabnormal features and prevent high-quality reconstruction of abnormal regions.\nEvaluation on a standard benchmark demonstrated performance better than\nstate-of-the-art models.\n", "date": "2023-02-20", "categories": []}
{"id": "2302.09800", "title": "CNTS: Cooperative Network for Time Series", "abstract": "  The use of deep learning techniques in detecting anomalies in time series\ndata has been an active area of research with a long history of development and\na variety of approaches. In particular, reconstruction-based unsupervised\nanomaly detection methods have gained popularity due to their intuitive\nassumptions and low computational requirements. However, these methods are\noften susceptible to outliers and do not effectively model anomalies, leading\nto suboptimal results. This paper presents a novel approach for unsupervised\nanomaly detection, called the Cooperative Network Time Series (CNTS) approach.\nThe CNTS system consists of two components: a detector and a reconstructor. The\ndetector is responsible for directly detecting anomalies, while the\nreconstructor provides reconstruction information to the detector and updates\nits learning based on anomalous information received from the detector. The\ncentral aspect of CNTS is a multi-objective optimization problem, which is\nsolved through a cooperative solution strategy. Experiments on three real-world\ndatasets demonstrate the state-of-the-art performance of CNTS and confirm the\ncooperative effectiveness of the detector and reconstructor. The source code\nfor this study is publicly available on GitHub.\n", "date": "2023-02-20", "categories": []}
{"id": "2302.08769", "title": "Collaborative Discrepancy Optimization for Reliable Image Anomaly\n  Localization", "abstract": "  Most unsupervised image anomaly localization methods suffer from\novergeneralization because of the high generalization abilities of\nconvolutional neural networks, leading to unreliable predictions. To mitigate\nthe overgeneralization, this study proposes to collaboratively optimize normal\nand abnormal feature distributions with the assistance of synthetic anomalies,\nnamely collaborative discrepancy optimization (CDO). CDO introduces a margin\noptimization module and an overlap optimization module to optimize the two key\nfactors determining the localization performance, i.e., the margin and the\noverlap between the discrepancy distributions (DDs) of normal and abnormal\nsamples. With CDO, a large margin and a small overlap between normal and\nabnormal DDs are obtained, and the prediction reliability is boosted.\nExperiments on MVTec2D and MVTec3D show that CDO effectively mitigates the\novergeneralization and achieves great anomaly localization performance with\nreal-time computation efficiency. A real-world automotive plastic parts\ninspection application further demonstrates the capability of the proposed CDO.\nCode is available on https://github.com/caoyunkang/CDO.\n", "date": "2023-02-17", "categories": []}
{"id": "2302.07643", "title": "A Subspace Projection Approach to Autoencoder-based Anomaly Detection", "abstract": "  Autoencoder (AE) is a neural network (NN) architecture that is trained to\nreconstruct an input at its output. By measuring the reconstruction errors of\nnew input samples, AE can detect anomalous samples deviated from the trained\ndata distribution. The key to success is to achieve high-fidelity\nreconstruction (HFR) while restricting AE's capability of generalization beyond\ntraining data, which should be balanced commonly via iterative re-training.\nAlternatively, we propose a novel framework of AE-based anomaly detection,\ncoined HFR-AE, by projecting new inputs into a subspace wherein the trained AE\nachieves HFR, thereby increasing the gap between normal and anomalous sample\nreconstruction errors. Simulation results corroborate that HFR-AE improves the\narea under receiver operating characteristic curve (AUROC) under different AE\narchitectures and settings by up to 13.4% compared to Vanilla AE-based anomaly\ndetection.\n", "date": "2023-02-15", "categories": []}
{"id": "2302.06914", "title": "Heterogeneous Anomaly Detection for Software Systems via Semi-supervised\n  Cross-modal Attention", "abstract": "  Prompt and accurate detection of system anomalies is essential to ensure the\nreliability of software systems. Unlike manual efforts that exploit all\navailable run-time information, existing approaches usually leverage only a\nsingle type of monitoring data (often logs or metrics) or fail to make\neffective use of the joint information among different types of data.\nConsequently, many false predictions occur. To better understand the\nmanifestations of system anomalies, we conduct a systematical study on a large\namount of heterogeneous data, i.e., logs and metrics. Our study demonstrates\nthat logs and metrics can manifest system anomalies collaboratively and\ncomplementarily, and neither of them only is sufficient. Thus, integrating\nheterogeneous data can help recover the complete picture of a system's health\nstatus. In this context, we propose Hades, the first end-to-end semi-supervised\napproach to effectively identify system anomalies based on heterogeneous data.\nOur approach employs a hierarchical architecture to learn a global\nrepresentation of the system status by fusing log semantics and metric\npatterns. It captures discriminative features and meaningful interactions from\nheterogeneous data via a cross-modal attention module, trained in a\nsemi-supervised manner. We evaluate Hades extensively on large-scale simulated\ndata and datasets from Huawei Cloud. The experimental results present the\neffectiveness of our model in detecting system anomalies. We also release the\ncode and the annotated dataset for replication and future research.\n", "date": "2023-02-14", "categories": []}
{"id": "2302.07187", "title": "Lessons from the Development of an Anomaly Detection Interface on the\n  Mars Perseverance Rover using the ISHMAP Framework", "abstract": "  While anomaly detection stands among the most important and valuable problems\nacross many scientific domains, anomaly detection research often focuses on AI\nmethods that can lack the nuance and interpretability so critical to conducting\nscientific inquiry. In this application paper we present the results of\nutilizing an alternative approach that situates the mathematical framing of\nmachine learning based anomaly detection within a participatory design\nframework. In a collaboration with NASA scientists working with the PIXL\ninstrument studying Martian planetary geochemistry as a part of the search for\nextra-terrestrial life; we report on over 18 months of in-context user research\nand co-design to define the key problems NASA scientists face when looking to\ndetect and interpret spectral anomalies. We address these problems and develop\na novel spectral anomaly detection toolkit for PIXL scientists that is highly\naccurate while maintaining strong transparency to scientific interpretation. We\nalso describe outcomes from a yearlong field deployment of the algorithm and\nassociated interface. Finally we introduce a new design framework which we\ndeveloped through the course of this collaboration for co-creating anomaly\ndetection algorithms: Iterative Semantic Heuristic Modeling of Anomalous\nPhenomena (ISHMAP), which provides a process for scientists and researchers to\nproduce natively interpretable anomaly detection models. This work showcases an\nexample of successfully bridging methodologies from AI and HCI within a\nscientific domain, and provides a resource in ISHMAP which may be used by other\nresearchers and practitioners looking to partner with other scientific teams to\nachieve better science through more effective and interpretable anomaly\ndetection tools.\n", "date": "2023-02-14", "categories": []}
{"id": "2207.08003", "title": "SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video\n  Anomaly Detection", "abstract": "  A self-supervised multi-task learning (SSMTL) framework for video anomaly\ndetection was recently introduced in literature. Due to its highly accurate\nresults, the method attracted the attention of many researchers. In this work,\nwe revisit the self-supervised multi-task learning framework, proposing several\nupdates to the original method. First, we study various detection methods, e.g.\nbased on detecting high-motion regions using optical flow or background\nsubtraction, since we believe the currently used pre-trained YOLOv3 is\nsuboptimal, e.g. objects in motion or objects from unknown classes are never\ndetected. Second, we modernize the 3D convolutional backbone by introducing\nmulti-head self-attention modules, inspired by the recent success of vision\ntransformers. As such, we alternatively introduce both 2D and 3D convolutional\nvision transformer (CvT) blocks. Third, in our attempt to further improve the\nmodel, we study additional self-supervised learning tasks, such as predicting\nsegmentation maps through knowledge distillation, solving jigsaw puzzles,\nestimating body pose through knowledge distillation, predicting masked regions\n(inpainting), and adversarial learning with pseudo-anomalies. We conduct\nexperiments to assess the performance impact of the introduced changes. Upon\nfinding more promising configurations of the framework, dubbed SSMTL++v1 and\nSSMTL++v2, we extend our preliminary experiments to more data sets,\ndemonstrating that our performance gains are consistent across all data sets.\nIn most cases, our results on Avenue, ShanghaiTech and UBnormal raise the\nstate-of-the-art performance bar to a new level.\n", "date": "2022-07-16", "categories": []}
{"id": "2208.03879", "title": "Clear Memory-Augmented Auto-Encoder for Surface Defect Detection", "abstract": "  In surface defect detection, due to the extreme imbalance in the number of\npositive and negative samples, positive-samples-based anomaly detection methods\nhave received more and more attention. Specifically, reconstruction-based\nmethods are the most popular. However, existing methods are either difficult to\nrepair abnormal foregrounds or reconstruct clear backgrounds. Therefore, we\npropose a clear memory-augmented auto-encoder (CMA-AE). At first, we propose a\nnovel clear memory-augmented module (CMAM), which combines the encoding and\nmemoryencoding in a way of forgetting and inputting, thereby repairing abnormal\nforegrounds and preserving clear backgrounds. Secondly, a general artificial\nanomaly generation algorithm (GAAGA) is proposed to simulate anomalies that are\nas realistic and feature-rich as possible. At last, we propose a novel multi\nscale feature residual detection method (MSFR) for defect segmentation, which\nmakes the defect location more accurate. Extensive comparison experiments\ndemonstrate that CMA-AE achieves state-of-the-art detection accuracy and shows\ngreat potential in industrial applications.\n", "date": "2022-08-08", "categories": []}
{"id": "2302.05525", "title": "Satellite Anomaly Detection Using Variance Based Genetic Ensemble of\n  Neural Networks", "abstract": "  In this paper, we use a variance-based genetic ensemble (VGE) of Neural\nNetworks (NNs) to detect anomalies in the satellite's historical data. We use\nan efficient ensemble of the predictions from multiple Recurrent Neural\nNetworks (RNNs) by leveraging each model's uncertainty level (variance). For\nprediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the\noptimal structure for each RNN model. However, finding the model uncertainty\nlevel is challenging in many cases. Although the Bayesian NNs (BNNs)-based\nmethods are popular for providing the confidence bound of the models, they\ncannot be employed in complex NN structures as they are computationally\nintractable. This paper uses the Monte Carlo (MC) dropout as an approximation\nversion of BNNs. Then these uncertainty levels and each predictive model\nsuggested by GA are used to generate a new model, which is then used for\nforecasting the TS and AD. Simulation results show that the forecasting and AD\ncapability of the ensemble model outperforms existing approaches.\n", "date": "2023-02-10", "categories": []}
{"id": "2302.06048", "title": "Unsupervised Deep One-Class Classification with Adaptive Threshold based\n  on Training Dynamics", "abstract": "  One-class classification has been a prevailing method in building deep\nanomaly detection models under the assumption that a dataset consisting of\nnormal samples is available. In practice, however, abnormal samples are often\nmixed in a training dataset, and they detrimentally affect the training of deep\nmodels, which limits their applicability. For robust normality learning of deep\npractical models, we propose an unsupervised deep one-class classification that\nlearns normality from pseudo-labeled normal samples, i.e., outlier detection in\nsingle cluster scenarios. To this end, we propose a pseudo-labeling method by\nan adaptive threshold selected by ranking-based training dynamics. The\nexperiments on 10 anomaly detection benchmarks show that our method effectively\nimproves performance on anomaly detection by sizable margins.\n", "date": "2023-02-13", "categories": []}
{"id": "2302.05160", "title": "Dual Memory Units with Uncertainty Regulation for Weakly Supervised\n  Video Anomaly Detection", "abstract": "  Learning discriminative features for effectively separating abnormal events\nfrom normality is crucial for weakly supervised video anomaly detection\n(WS-VAD) tasks. Existing approaches, both video and segment-level label\noriented, mainly focus on extracting representations for anomaly data while\nneglecting the implication of normal data. We observe that such a scheme is\nsub-optimal, i.e., for better distinguishing anomaly one needs to understand\nwhat is a normal state, and may yield a higher false alarm rate. To address\nthis issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU)\nmodel to learn both the representations of normal data and discriminative\nfeatures of abnormal data. To be specific, inspired by the traditional global\nand local structure on graph convolutional networks, we introduce a Global and\nLocal Multi-Head Self Attention (GL-MHSA) module for the Transformer network to\nobtain more expressive embeddings for capturing associations in videos. Then,\nwe use two memory banks, one additional abnormal memory for tackling hard\nsamples, to store and separate abnormal and normal prototypes and maximize the\nmargins between the two representations. Finally, we propose an uncertainty\nlearning scheme to learn the normal data latent space, that is robust to noise\nfrom camera switching, object changing, scene transforming, etc. Extensive\nexperiments on XD-Violence and UCF-Crime datasets demonstrate that our method\noutperforms the state-of-the-art methods by a sizable margin.\n", "date": "2023-02-10", "categories": []}
{"id": "2207.06819", "title": "Anomal-E: A Self-Supervised Network Intrusion Detection System based on\n  Graph Neural Networks", "abstract": "  This paper investigates Graph Neural Networks (GNNs) application for\nself-supervised network intrusion and anomaly detection. GNNs are a deep\nlearning approach for graph-based data that incorporate graph structures into\nlearning to generalise graph representations and output embeddings. As network\nflows are naturally graph-based, GNNs are a suitable fit for analysing and\nlearning network behaviour. The majority of current implementations of\nGNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled\nnetwork traffic which can not only restrict the amount and structure of input\ntraffic, but also the NIDSs potential to adapt to unseen attacks. To overcome\nthese restrictions, we present Anomal-E, a GNN approach to intrusion and\nanomaly detection that leverages edge features and graph topological structure\nin a self-supervised process. This approach is, to the best our knowledge, the\nfirst successful and practical approach to network intrusion detection that\nutilises network flows in a self-supervised, edge leveraging GNN. Experimental\nresults on two modern benchmark NIDS datasets not only clearly display the\nimprovement of using Anomal-E embeddings rather than raw features, but also the\npotential Anomal-E has for detection on wild network traffic.\n", "date": "2022-07-14", "categories": []}
{"id": "2302.04549", "title": "Weakly Supervised Anomaly Detection: A Survey", "abstract": "  Anomaly detection (AD) is a crucial task in machine learning with various\napplications, such as detecting emerging diseases, identifying financial\nfrauds, and detecting fake news. However, obtaining complete, accurate, and\nprecise labels for AD tasks can be expensive and challenging due to the cost\nand difficulties in data annotation. To address this issue, researchers have\ndeveloped AD methods that can work with incomplete, inexact, and inaccurate\nsupervision, collectively summarized as weakly supervised anomaly detection\n(WSAD) methods. In this study, we present the first comprehensive survey of\nWSAD methods by categorizing them into the above three weak supervision\nsettings across four data modalities (i.e., tabular, graph, time-series, and\nimage/video data). For each setting, we provide formal definitions, key\nalgorithms, and potential future directions. To support future research, we\nconduct experiments on a selected setting and release the source code, along\nwith a collection of WSAD methods and data.\n", "date": "2023-02-09", "categories": []}
{"id": "2206.03698", "title": "What do we learn? Debunking the Myth of Unsupervised Outlier Detection", "abstract": "  Even though auto-encoders (AEs) have the desirable property of learning\ncompact representations without labels and have been widely applied to\nout-of-distribution (OoD) detection, they are generally still poorly understood\nand are used incorrectly in detecting outliers where the normal and abnormal\ndistributions are strongly overlapping. In general, the learned manifold is\nassumed to contain key information that is only important for describing\nsamples within the training distribution, and that the reconstruction of\noutliers leads to high residual errors. However, recent work suggests that AEs\nare likely to be even better at reconstructing some types of OoD samples. In\nthis work, we challenge this assumption and investigate what auto-encoders\nactually learn when they are posed to solve two different tasks. First, we\npropose two metrics based on the Fr\\'echet inception distance (FID) and\nconfidence scores of a trained classifier to assess whether AEs can learn the\ntraining distribution and reliably recognize samples from other domains.\nSecond, we investigate whether AEs are able to synthesize normal images from\nsamples with abnormal regions, on a more challenging lung pathology detection\ntask. We have found that state-of-the-art (SOTA) AEs are either unable to\nconstrain the latent manifold and allow reconstruction of abnormal patterns, or\nthey are failing to accurately restore the inputs from their latent\ndistribution, resulting in blurred or misaligned reconstructions. We propose\nnovel deformable auto-encoders (MorphAEus) to learn perceptually aware global\nimage priors and locally adapt their morphometry based on estimated dense\ndeformation fields. We demonstrate superior performance over unsupervised\nmethods in detecting OoD and pathology.\n", "date": "2022-06-08", "categories": []}
{"id": "2302.03302", "title": "Towards Meaningful Anomaly Detection: The Effect of Counterfactual\n  Explanations on the Investigation of Anomalies in Multivariate Time Series", "abstract": "  Detecting rare events is essential in various fields, e.g., in cyber security\nor maintenance. Often, human experts are supported by anomaly detection systems\nas continuously monitoring the data is an error-prone and tedious task.\nHowever, among the anomalies detected may be events that are rare, e.g., a\nplanned shutdown of a machine, but are not the actual event of interest, e.g.,\nbreakdowns of a machine. Therefore, human experts are needed to validate\nwhether the detected anomalies are relevant. We propose to support this anomaly\ninvestigation by providing explanations of anomaly detection. Related work only\nfocuses on the technical implementation of explainable anomaly detection and\nneglects the subsequent human anomaly investigation. To address this research\ngap, we conduct a behavioral experiment using records of taxi rides in New York\nCity as a testbed. Participants are asked to differentiate extreme weather\nevents from other anomalous events such as holidays or sporting events. Our\nresults show that providing counterfactual explanations do improve the\ninvestigation of anomalies, indicating potential for explainable anomaly\ndetection in general.\n", "date": "2023-02-07", "categories": []}
{"id": "2206.02704", "title": "Perturbation Learning Based Anomaly Detection", "abstract": "  This paper presents a simple yet effective method for anomaly detection. The\nmain idea is to learn small perturbations to perturb normal data and learn a\nclassifier to classify the normal data and the perturbed data into two\ndifferent classes. The perturbator and classifier are jointly learned using\ndeep neural networks. Importantly, the perturbations should be as small as\npossible but the classifier is still able to recognize the perturbed data from\nunperturbed data. Therefore, the perturbed data are regarded as abnormal data\nand the classifier provides a decision boundary between the normal data and\nabnormal data, although the training data do not include any abnormal data.\nCompared with the state-of-the-art of anomaly detection, our method does not\nrequire any assumption about the shape (e.g. hypersphere) of the decision\nboundary and has fewer hyper-parameters to determine. Empirical studies on\nbenchmark datasets verify the effectiveness and superiority of our method.\n", "date": "2022-06-06", "categories": []}
{"id": "2302.02051", "title": "Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting", "abstract": "  Anomalies in univariate time series often refer to abnormal values and\ndeviations from the temporal patterns from majority of historical observations.\nIn multivariate time series, anomalies also refer to abnormal changes in the\ninter-series relationship, such as correlation, over time. Existing studies\nhave been able to model such inter-series relationships through graph neural\nnetworks. However, most works settle on learning a static graph globally or\nwithin a context window to assist a time series forecasting task or a\nreconstruction task, whose objective is not tailored to explicitly detect the\nabnormal relationship. Some other works detect anomalies based on\nreconstructing or forecasting a list of inter-series graphs, which\ninadvertently weakens their power to capture temporal patterns within the data\ndue to the discrete nature of graphs. In this study, we propose DyGraphAD, a\nmultivariate time series anomaly detection framework based upon a list of\ndynamic inter-series graphs. The core idea is to detect anomalies based on the\ndeviation of inter-series relationships and intra-series temporal patterns from\nnormal to anomalous states, by leveraging the evolving nature of the graphs in\norder to assist a graph forecasting task and a time series forecasting task\nsimultaneously. Our numerical experiments on real-world datasets demonstrate\nthat DyGraphAD has superior performance than baseline anomaly detection\napproaches.\n", "date": "2023-02-04", "categories": []}
{"id": "2302.02896", "title": "Label Assisted Autoencoder for Anomaly Detection in Power Generation\n  Plants", "abstract": "  One of the critical factors that drive the economic development of a country\nand guarantee the sustainability of its industries is the constant availability\nof electricity. This is usually provided by the national electric grid.\nHowever, in developing countries where companies are emerging on a constant\nbasis including telecommunication industries, those are still experiencing a\nnon-stable electricity supply. Therefore, they have to rely on generators to\nguarantee their full functionality. Those generators depend on fuel to function\nand the rate of consumption gets usually high, if not monitored properly.\nMonitoring operation is usually carried out by a (non-expert) human. In some\ncases, this could be a tedious process, as some companies have reported an\nexaggerated high consumption rate. This work proposes a label assisted\nautoencoder for anomaly detection in the fuel consumed by power generating\nplants. In addition to the autoencoder model, we added a labelling assistance\nmodule that checks if an observation is labelled, the label is used to check\nthe veracity of the corresponding anomaly classification given a threshold. A\nconsensus is then reached on whether training should stop or whether the\nthreshold should be updated or the training should continue with the search for\nhyper-parameters. Results show that the proposed model is highly efficient for\nreading anomalies with a detection accuracy of $97.20\\%$ which outperforms the\nexisting model of $96.1\\%$ accuracy trained on the same dataset. In addition,\nthe proposed model is able to classify the anomalies according to their degree\nof severity.\n", "date": "2023-02-06", "categories": []}
{"id": "2302.01759", "title": "Leveraging a Probabilistic PCA Model to Understand the Multivariate\n  Statistical Network Monitoring Framework for Network Security Anomaly\n  Detection", "abstract": "  Network anomaly detection is a very relevant research area nowadays,\nespecially due to its multiple applications in the field of network security.\nThe boost of new models based on variational autoencoders and generative\nadversarial networks has motivated a reevaluation of traditional techniques for\nanomaly detection. It is, however, essential to be able to understand these new\nmodels from the perspective of the experience attained from years of evaluating\nnetwork security data for anomaly detection. In this paper, we revisit anomaly\ndetection techniques based on PCA from a probabilistic generative model point\nof view, and contribute a mathematical model that relates them. Specifically,\nwe start with the probabilistic PCA model and explain its connection to the\nMultivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently\nsuccessfully proposed as a means of incorporating industrial process anomaly\ndetection experience into the field of networking. We have evaluated the\nmathematical model using two different datasets. The first, a synthetic dataset\ncreated to better understand the analysis proposed, and the second, UGR'16, is\na specifically designed real-traffic dataset for network security anomaly\ndetection. We have drawn conclusions that we consider to be useful when\napplying generative models to network security detection.\n", "date": "2023-02-02", "categories": []}
{"id": "2212.11080", "title": "Is it worth it? Comparing six deep and classical methods for\n  unsupervised anomaly detection in time series", "abstract": "  Detecting anomalies in time series data is important in a variety of fields,\nincluding system monitoring, healthcare, and cybersecurity. While the abundance\nof available methods makes it difficult to choose the most appropriate method\nfor a given application, each method has its strengths in detecting certain\ntypes of anomalies. In this study, we compare six unsupervised anomaly\ndetection methods of varying complexity to determine whether more complex\nmethods generally perform better and if certain methods are better suited to\ncertain types of anomalies. We evaluated the methods using the UCR anomaly\narchive, a recent benchmark dataset for anomaly detection. We analyzed the\nresults on a dataset and anomaly type level after adjusting the necessary\nhyperparameters for each method. Additionally, we assessed the ability of each\nmethod to incorporate prior knowledge about anomalies and examined the\ndifferences between point-wise and sequence-wise features. Our experiments show\nthat classical machine learning methods generally outperform deep learning\nmethods across a range of anomaly types.\n", "date": "2022-12-21", "categories": []}
{"id": "2302.00939", "title": "An optimization method for out-of-distribution anomaly detection models", "abstract": "  Frequent false alarms impede the promotion of unsupervised anomaly detection\nalgorithms in industrial applications. Potential characteristics of false\nalarms depending on the trained detector are revealed by investigating density\nprobability distributions of prediction scores in the out-of-distribution\nanomaly detection tasks. An SVM-based classifier is exploited as a\npost-processing module to identify false alarms from the anomaly map at the\nobject level. Besides, a sample synthesis strategy is devised to incorporate\nfuzzy prior knowledge on the specific application in the anomaly-free training\ndataset. Experimental results illustrate that the proposed method\ncomprehensively improves the performances of two segmentation models at both\nimage and pixel levels on two industrial applications.\n", "date": "2023-02-02", "categories": []}
{"id": "2302.01204", "title": "Laplacian Change Point Detection for Single and Multi-view Dynamic\n  Graphs", "abstract": "  Dynamic graphs are rich data structures that are used to model complex\nrelationships between entities over time. In particular, anomaly detection in\ntemporal graphs is crucial for many real world applications such as intrusion\nidentification in network systems, detection of ecosystem disturbances and\ndetection of epidemic outbreaks. In this paper, we focus on change point\ndetection in dynamic graphs and address three main challenges associated with\nthis problem: i). how to compare graph snapshots across time, ii). how to\ncapture temporal dependencies, and iii). how to combine different views of a\ntemporal graph. To solve the above challenges, we first propose Laplacian\nAnomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low\ndimensional embedding of the graph structure at each snapshot. LAD explicitly\nmodels short term and long term dependencies by applying two sliding windows.\nNext, we propose MultiLAD, a simple and effective generalization of LAD to\nmulti-view graphs. MultiLAD provides the first change point detection method\nfor multi-view dynamic graphs. It aggregates the singular values of the\nnormalized graph Laplacian from different views through the scalar power mean\noperation. Through extensive synthetic experiments, we show that i). LAD and\nMultiLAD are accurate and outperforms state-of-the-art baselines and their\nmulti-view extensions by a large margin, ii). MultiLAD's advantage over\ncontenders significantly increases when additional views are available, and\niii). MultiLAD is highly robust to noise from individual views. In five real\nworld dynamic graphs, we demonstrate that LAD and MultiLAD identify significant\nevents as top anomalies such as the implementation of government COVID-19\ninterventions which impacted the population mobility in multi-view traffic\nnetworks.\n", "date": "2023-02-02", "categories": []}
{"id": "2301.13527", "title": "Real-Time Outlier Detection with Dynamic Process Limits", "abstract": "  Anomaly detection methods are part of the systems where rare events may\nendanger an operation's profitability, safety, and environmental aspects.\nAlthough many state-of-the-art anomaly detection methods were developed to\ndate, their deployment is limited to the operation conditions present during\nthe model training. Online anomaly detection brings the capability to adapt to\ndata drifts and change points that may not be represented during model\ndevelopment resulting in prolonged service life. This paper proposes an online\nanomaly detection algorithm for existing real-time infrastructures where\nlow-latency detection is required and novel patterns in data occur\nunpredictably. The online inverse cumulative distribution-based approach is\nintroduced to eliminate common problems of offline anomaly detectors, meanwhile\nproviding dynamic process limits to normal operation. The benefit of the\nproposed method is the ease of use, fast computation, and deployability as\nshown in two case studies of real microgrid operation data.\n", "date": "2023-01-31", "categories": []}
{"id": "2301.12048", "title": "Making Reconstruction-based Method Great Again for Video Anomaly\n  Detection", "abstract": "  Anomaly detection in videos is a significant yet challenging problem.\nPrevious approaches based on deep neural networks employ either\nreconstruction-based or prediction-based approaches. Nevertheless, existing\nreconstruction-based methods 1) rely on old-fashioned convolutional\nautoencoders and are poor at modeling temporal dependency; 2) are prone to\noverfit the training samples, leading to indistinguishable reconstruction\nerrors of normal and abnormal frames during the inference phase. To address\nsuch issues, firstly, we get inspiration from transformer and propose ${\\textbf\nS}$patio-${\\textbf T}$emporal ${\\textbf A}$uto-${\\textbf T}$rans-${\\textbf\nE}$ncoder, dubbed as $\\textbf{STATE}$, as a new autoencoder model for enhanced\nconsecutive frame reconstruction. Our STATE is equipped with a specifically\ndesigned learnable convolutional attention module for efficient temporal\nlearning and reasoning. Secondly, we put forward a novel reconstruction-based\ninput perturbation technique during testing to further differentiate anomalous\nframes. With the same perturbation magnitude, the testing reconstruction error\nof the normal frames lowers more than that of the abnormal frames, which\ncontributes to mitigating the overfitting problem of reconstruction. Owing to\nthe high relevance of the frame abnormality and the objects in the frame, we\nconduct object-level reconstruction using both the raw frame and the\ncorresponding optical flow patches. Finally, the anomaly score is designed\nbased on the combination of the raw and motion reconstruction errors using\nperturbed inputs. Extensive experiments on benchmark video anomaly detection\ndatasets demonstrate that our approach outperforms previous\nreconstruction-based methods by a notable margin, and achieves state-of-the-art\nanomaly detection performance consistently. The code is available at\nhttps://github.com/wyzjack/MRMGA4VAD.\n", "date": "2023-01-28", "categories": []}
{"id": "2301.13031", "title": "BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly\n  Detection in Multivariate Time Series", "abstract": "  Detecting anomalies in multivariate time series(MTS) data plays an important\nrole in many domains. The abnormal values could indicate events, medical\nabnormalities,cyber-attacks, or faulty devices which if left undetected could\nlead to significant loss of resources, capital, or human lives. In this paper,\nwe propose a novel and innovative approach to anomaly detection called Bayesian\nState-Space Anomaly Detection(BSSAD). The BSSAD consists of two modules: the\nneural network module and the Bayesian state-space module. The design of our\napproach combines the strength of Bayesian state-space algorithms in predicting\nthe next state and the effectiveness of recurrent neural networks and\nautoencoders in understanding the relationship between the data to achieve high\naccuracy in detecting anomalies. The modular design of our approach allows\nflexibility in implementation with the option of changing the parameters of the\nBayesian state-space models or swap-ping neural network algorithms to achieve\ndifferent levels of performance. In particular, we focus on using Bayesian\nstate-space models of particle filters and ensemble Kalman filters. We\nconducted extensive experiments on five different datasets. The experimental\nresults show the superior performance of our model over baselines, achieving an\nF1-score greater than 0.95. In addition, we also propose using a metric called\nMatthewCorrelation Coefficient (MCC) to obtain more comprehensive information\nabout the accuracy of anomaly detection.\n", "date": "2023-01-30", "categories": []}
{"id": "2301.13199", "title": "Streaming Anomaly Detection", "abstract": "  Anomaly detection is critical for finding suspicious behavior in innumerable\nsystems. We need to detect anomalies in real-time, i.e. determine if an\nincoming entity is anomalous or not, as soon as we receive it, to minimize the\neffects of malicious activities and start recovery as soon as possible.\nTherefore, online algorithms that can detect anomalies in a streaming manner\nare essential.\n  We first propose MIDAS which uses a count-min sketch to detect anomalous\nedges in dynamic graphs in an online manner, using constant time and memory. We\nthen propose two variants, MIDAS-R which incorporates temporal and spatial\nrelations, and MIDAS-F which aims to filter away anomalous edges to prevent\nthem from negatively affecting the internal data structures.\n  We then extend the count-min sketch to a Higher-Order sketch to capture\ncomplex relations in graph data, and to reduce detecting suspicious dense\nsubgraph problem to finding a dense submatrix in constant time. Using this\nsketch, we propose four streaming methods to detect edge and subgraph\nanomalies.\n  Next, we broaden the graph setting to multi-aspect data. We propose MStream\nwhich detects explainable anomalies in multi-aspect data streams. We further\npropose MStream-PCA, MStream-IB, and MStream-AE to incorporate correlation\nbetween features.\n  Finally, we consider multi-dimensional data streams with concept drift and\npropose MemStream. MemStream leverages the power of a denoising autoencoder to\nlearn representations and a memory module to learn the dynamically changing\ntrend in data without the need for labels. We prove a theoretical bound on the\nsize of memory for effective drift handling. In addition, we allow quick\nretraining when the arriving stream becomes sufficiently different from the\ntraining data. Furthermore, MemStream makes use of two architecture design\nchoices to be robust to memory poisoning.\n", "date": "2023-01-30", "categories": []}
{"id": "2301.10441", "title": "Learning Trustworthy Model from Noisy Labels based on Rough Set for\n  Surface Defect Detection", "abstract": "  In the surface defect detection, there are some suspicious regions that\ncannot be uniquely classified as abnormal or normal. The annotating of\nsuspicious regions is easily affected by factors such as workers' emotional\nfluctuations and judgment standard, resulting in noisy labels, which in turn\nleads to missing and false detections, and ultimately leads to inconsistent\njudgments of product quality. Unlike the usual noisy labels, the ones used for\nsurface defect detection appear to be inconsistent rather than mislabeled. The\nnoise occurs in almost every label and is difficult to correct or evaluate. In\nthis paper, we proposed a framework that learns trustworthy models from noisy\nlabels for surface defect defection. At first, to avoid the negative impact of\nnoisy labels on the model, we represent the suspicious regions with consistent\nand precise elements at the pixel-level and redesign the loss function.\nSecondly, without changing network structure and adding any extra labels,\npluggable spatially correlated Bayesian module is proposed. Finally, the defect\ndiscrimination confidence is proposed to measure the uncertainty, with which\nanomalies can be identified as defects. Our results indicate not only the\neffectiveness of the proposed method in learning from noisy labels, but also\nrobustness and real-time performance.\n", "date": "2023-01-25", "categories": []}
{"id": "2301.10681", "title": "PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning", "abstract": "  Due to the complexity of modern IT services, failures can be manifold, occur\nat any stage, and are hard to detect. For this reason, anomaly detection\napplied to monitoring data such as logs allows gaining relevant insights to\nimprove IT services steadily and eradicate failures. However, existing anomaly\ndetection methods that provide high accuracy often rely on labeled training\ndata, which are time-consuming to obtain in practice. Therefore, we propose\nPULL, an iterative log analysis method for reactive anomaly detection based on\nestimated failure time windows provided by monitoring systems instead of\nlabeled data. Our attention-based model uses a novel objective function for\nweak supervision deep learning that accounts for imbalanced data and applies an\niterative learning strategy for positive and unknown samples (PU learning) to\nidentify anomalous logs. Our evaluation shows that PULL consistently\noutperforms ten benchmark baselines across three different datasets and detects\nanomalous log messages with an F1-score of more than 0.99 even within imprecise\nfailure time windows.\n", "date": "2023-01-25", "categories": []}
{"id": "2207.13798", "title": "Look at Adjacent Frames: Video Anomaly Detection without Offline\n  Training", "abstract": "  We propose a solution to detect anomalous events in videos without the need\nto train a model offline. Specifically, our solution is based on a\nrandomly-initialized multilayer perceptron that is optimized online to\nreconstruct video frames, pixel-by-pixel, from their frequency information.\nBased on the information shifts between adjacent frames, an incremental learner\nis used to update parameters of the multilayer perceptron after observing each\nframe, thus allowing to detect anomalous events along the video stream.\nTraditional solutions that require no offline training are limited to operating\non videos with only a few abnormal frames. Our solution breaks this limit and\nachieves strong performance on benchmark datasets.\n", "date": "2022-07-27", "categories": []}
{"id": "2301.09030", "title": "Condition monitoring and anomaly detection in cyber-physical systems", "abstract": "  The modern industrial environment is equipping myriads of smart manufacturing\nmachines where the state of each device can be monitored continuously. Such\nmonitoring can help identify possible future failures and develop a\ncost-effective maintenance plan. However, it is a daunting task to perform\nearly detection with low false positives and negatives from the huge volume of\ncollected data. This requires developing a holistic machine learning framework\nto address the issues in condition monitoring of high-priority components and\ndevelop efficient techniques to detect anomalies that can detect and possibly\nlocalize the faulty components. This paper presents a comparative analysis of\nrecent machine learning approaches for robust, cost-effective anomaly detection\nin cyber-physical systems. While detection has been extensively studied, very\nfew researchers have analyzed the localization of the anomalies. We show that\nsupervised learning outperforms unsupervised algorithms. For supervised cases,\nwe achieve near-perfect accuracy of 98 percent (specifically for tree-based\nalgorithms). In contrast, the best-case accuracy in the unsupervised cases was\n63 percent :the area under the receiver operating characteristic curve (AUC)\nexhibits similar outcomes as an additional metric.\n", "date": "2023-01-22", "categories": []}
{"id": "2301.08330", "title": "The role of noise in denoising models for anomaly detection in medical\n  images", "abstract": "  Pathological brain lesions exhibit diverse appearance in brain images, in\nterms of intensity, texture, shape, size, and location. Comprehensive sets of\ndata and annotations are difficult to acquire. Therefore, unsupervised anomaly\ndetection approaches have been proposed using only normal data for training,\nwith the aim of detecting outlier anomalous voxels at test time. Denoising\nmethods, for instance classical denoising autoencoders (DAEs) and more recently\nemerging diffusion models, are a promising approach, however naive application\nof pixelwise noise leads to poor anomaly detection performance. We show that\noptimization of the spatial resolution and magnitude of the noise improves the\nperformance of different model training regimes, with similar noise parameter\nadjustments giving good performance for both DAEs and diffusion models. Visual\ninspection of the reconstructions suggests that the training noise influences\nthe trade-off between the extent of the detail that is reconstructed and the\nextent of erasure of anomalies, both of which contribute to better anomaly\ndetection performance. We validate our findings on two real-world datasets\n(tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain\nCT), showing good detection on diverse anomaly appearances. Overall, we find\nthat a DAE trained with coarse noise is a fast and simple method that gives\nstate-of-the-art accuracy. Diffusion models applied to anomaly detection are as\nyet in their infancy and provide a promising avenue for further research.\n", "date": "2023-01-19", "categories": []}
{"id": "2301.07846", "title": "ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection", "abstract": "  With the increasing prevalence of scalable file systems in the context of\nHigh Performance Computing (HPC), the importance of accurate anomaly detection\non runtime logs is increasing. But as it currently stands, many\nstate-of-the-art methods for log-based anomaly detection, such as DeepLog, have\nencountered numerous challenges when applied to logs from many parallel file\nsystems (PFSes), often due to their irregularity and ambiguity in time-based\nlog sequences. To circumvent these problems, this study proposes ClusterLog, a\nlog pre-processing method that clusters the temporal sequence of log keys based\non their semantic similarity. By grouping semantically and sentimentally\nsimilar logs, this approach aims to represent log sequences with the smallest\namount of unique log keys, intending to improve the ability of a downstream\nsequence-based model to effectively learn the log patterns. The preliminary\nresults of ClusterLog indicate not only its effectiveness in reducing the\ngranularity of log sequences without the loss of important sequence information\nbut also its generalizability to different file systems' logs.\n", "date": "2023-01-19", "categories": []}
{"id": "2301.07923", "title": "Human-Scene Network: A Novel Baseline with Self-rectifying Loss for\n  Weakly supervised Video Anomaly Detection", "abstract": "  Video anomaly detection in surveillance systems with only video-level labels\n(i.e. weakly-supervised) is challenging. This is due to, (i) the complex\nintegration of human and scene based anomalies comprising of subtle and sharp\nspatio-temporal cues in real-world scenarios, (ii) non-optimal optimization\nbetween normal and anomaly instances under weak supervision. In this paper, we\npropose a Human-Scene Network to learn discriminative representations by\ncapturing both subtle and strong cues in a dissociative manner. In addition, a\nself-rectifying loss is also proposed that dynamically computes the pseudo\ntemporal annotations from video-level labels for optimizing the Human-Scene\nNetwork effectively. The proposed Human-Scene Network optimized with\nself-rectifying loss is validated on three publicly available datasets i.e.\nUCF-Crime, ShanghaiTech and IITB-Corridor, outperforming recently reported\nstate-of-the-art approaches on five out of the six scenarios considered.\n", "date": "2023-01-19", "categories": []}
{"id": "2301.08064", "title": "Position Regression for Unsupervised Anomaly Detection", "abstract": "  In recent years, anomaly detection has become an essential field in medical\nimage analysis. Most current anomaly detection methods for medical images are\nbased on image reconstruction. In this work, we propose a novel anomaly\ndetection approach based on coordinate regression. Our method estimates the\nposition of patches within a volume, and is trained only on data of healthy\nsubjects. During inference, we can detect and localize anomalies by considering\nthe error of the position estimate of a given patch. We apply our method to 3D\nCT volumes and evaluate it on patients with intracranial haemorrhages and\ncranial fractures. The results show that our method performs well in detecting\nthese anomalies. Furthermore, we show that our method requires less memory than\ncomparable approaches that involve image reconstruction. This is highly\nrelevant for processing large 3D volumes, for instance, CT or MRI scans.\n", "date": "2023-01-19", "categories": []}
{"id": "2212.10682", "title": "Privacy-Protecting Behaviours of Risk Detection in People with Dementia\n  using Videos", "abstract": "  People living with dementia often exhibit behavioural and psychological\nsymptoms of dementia that can put their and others' safety at risk. Existing\nvideo surveillance systems in long-term care facilities can be used to monitor\nsuch behaviours of risk to alert the staff to prevent potential injuries or\ndeath in some cases. However, these behaviours of risk events are heterogeneous\nand infrequent in comparison to normal events. Moreover, analyzing raw videos\ncan also raise privacy concerns. In this paper, we present two novel\nprivacy-protecting video-based anomaly detection approaches to detect\nbehaviours of risks in people with dementia. We either extracted body pose\ninformation as skeletons or used semantic segmentation masks to replace\nmultiple humans in the scene with their semantic boundaries. Our work differs\nfrom most existing approaches for video anomaly detection that focus on\nappearance-based features, which can put the privacy of a person at risk and is\nalso susceptible to pixel-based noise, including illumination and viewing\ndirection. We used anonymized videos of normal activities to train customized\nspatio-temporal convolutional autoencoders and identify behaviours of risk as\nanomalies. We showed our results on a real-world study conducted in a dementia\ncare unit with patients with dementia, containing approximately 21 hours of\nnormal activities data for training and 9 hours of data containing normal and\nbehaviours of risk events for testing. We compared our approaches with the\noriginal RGB videos and obtained a similar area under the receiver operating\ncharacteristic curve performance of 0.807 for the skeleton-based approach and\n0.823 for the segmentation mask-based approach.\n", "date": "2022-12-20", "categories": []}
{"id": "2301.06035", "title": "Efficient anomaly detection method for rooftop PV systems using big data\n  and permutation entropy", "abstract": "  The number of rooftop photovoltaic (PV) systems has significantly increased\nin recent years around the globe, including in Australia. This trend is\nanticipated to continue in the next few years. Given their high share of\ngeneration in power systems, detecting malfunctions and abnormalities in\nrooftop PV systems is essential for ensuring their high efficiency and safety.\nIn this paper, we present a novel anomaly detection method for a large number\nof rooftop PV systems installed in a region using big data and a time series\ncomplexity measure called weighted permutation entropy (WPE). This efficient\nmethod only uses the historical PV generation data in a given region to\nidentify anomalous PV systems and requires no new sensor or smart device. Using\na real-world PV generation dataset, we discuss how the hyperparameters of WPE\nshould be tuned for the purpose. The proposed PV anomaly detection method is\nthen tested on rooftop PV generation data from over 100 South Australian\nhouseholds. The results demonstrate that anomalous systems detected by our\nmethod have indeed encountered problems and require a close inspection. The\ndetection and resolution of potential faults would result in better rooftop PV\nsystems, longer lifetimes, and higher returns on investment.\n", "date": "2023-01-15", "categories": []}
{"id": "2301.06794", "title": "Subgraph Centralization: A Necessary Step for Graph Anomaly Detection", "abstract": "  Graph anomaly detection has attracted a lot of interest recently. Despite\ntheir successes, existing detectors have at least two of the three weaknesses:\n(a) high computational cost which limits them to small-scale networks only; (b)\nexisting treatment of subgraphs produces suboptimal detection accuracy; and (c)\nunable to provide an explanation as to why a node is anomalous, once it is\nidentified. We identify that the root cause of these weaknesses is a lack of a\nproper treatment for subgraphs. A treatment called Subgraph Centralization for\ngraph anomaly detection is proposed to address all the above weaknesses. Its\nimportance is shown in two ways. First, we present a simple yet effective new\nframework called Graph-Centric Anomaly Detection (GCAD). The key advantages of\nGCAD over existing detectors including deep-learning detectors are: (i) better\nanomaly detection accuracy; (ii) linear time complexity with respect to the\nnumber of nodes; and (iii) it is a generic framework that admits an existing\npoint anomaly detector to be used to detect node anomalies in a network.\nSecond, we show that Subgraph Centralization can be incorporated into two\nexisting detectors to overcome the above-mentioned weaknesses.\n", "date": "2023-01-17", "categories": []}
{"id": "2211.10060", "title": "Normal Reference Attention and Defective Feature Perception Network for\n  Surface Defect Detection", "abstract": "  Visual anomaly detection plays a significant role in the development of\nindustrial automatic product quality inspection. As a result of the utmost\nimbalance in the amount of normal and abnormal data, growing attention has been\ngiven to unsupervised methods for defect detection. Although existing\nreconstruction-based methods have been widely studied recently, establishing a\nrobust reconstruction model for various textured surface defect detection\nremains a challenging task due to homogeneous and nonregular surface textures.\nIn this paper, we propose a novel unsupervised reconstruction-based method\ncalled the normal reference attention and defective feature perception network\n(NDP-Net) to accurately inspect a variety of textured defects. Unlike most\nreconstruction-based methods, our NDP-Net first employs an encoding module that\nextracts multi scale discriminative features of the surface textures, which is\naugmented with the defect discriminative ability by the proposed artificial\ndefects and the novel pixel-level defect perception loss. Subsequently, a novel\nreference-based attention module (RBAM) is proposed to leverage the normal\nfeatures of the fixed reference image to repair the defective features and\nrestrain the reconstruction of the defects. Next, the repaired features are fed\ninto a decoding module to reconstruct the normal textured background. Finally,\nthe novel multi scale defect segmentation module (MSDSM) is introduced for\nprecise defect detection and segmentation. In addition, a two-stage training\nstrategy is utilized to enhance the inspection performance.\n", "date": "2022-11-18", "categories": []}
{"id": "2211.13577", "title": "Learning Invariant Rules from Data for Interpretable Anomaly Detection", "abstract": "  In the research area of anomaly detection, novel and promising methods are\nfrequently developed. However, most existing studies exclusively focus on the\ndetection task only and ignore the interpretability of the underlying models as\nwell as their detection results. Nevertheless, anomaly interpretation, which\naims to provide explanation of why specific data instances are identified as\nanomalies, is an equally important task in many real-world applications. In\nthis work, we propose a novel framework which synergizes several machine\nlearning and data mining techniques to automatically learn invariant rules that\nare consistently satisfied in a given dataset. The learned invariant rules can\nprovide explicit explanation of anomaly detection results in the inference\nphase and thus are extremely useful for subsequent decision-making regarding\nreported anomalies. Furthermore, our empirical evaluation shows that the\nproposed method can also achieve comparable or even better performance in terms\nof AUC and partial AUC on public benchmark datasets across various application\ndomains compared with start-of-the-art anomaly detection models.\n", "date": "2022-11-24", "categories": []}
{"id": "2212.12121", "title": "Federated PCA on Grassmann Manifold for Anomaly Detection in IoT\n  Networks", "abstract": "  In the era of Internet of Things (IoT), network-wide anomaly detection is a\ncrucial part of monitoring IoT networks due to the inherent security\nvulnerabilities of most IoT devices. Principal Components Analysis (PCA) has\nbeen proposed to separate network traffics into two disjoint subspaces\ncorresponding to normal and malicious behaviors for anomaly detection. However,\nthe privacy concerns and limitations of devices' computing resources compromise\nthe practical effectiveness of PCA. We propose a federated PCA-based\nGrassmannian optimization framework that coordinates IoT devices to aggregate a\njoint profile of normal network behaviors for anomaly detection. First, we\nintroduce a privacy-preserving federated PCA framework to simultaneously\ncapture the profile of various IoT devices' traffic. Then, we investigate the\nalternating direction method of multipliers gradient-based learning on the\nGrassmann manifold to guarantee fast training and the absence of detecting\nlatency using limited computational resources. Empirical results on the NSL-KDD\ndataset demonstrate that our method outperforms baseline approaches. Finally,\nwe show that the Grassmann manifold algorithm is highly adapted for IoT anomaly\ndetection, which permits drastically reducing the analysis time of the system.\nTo the best of our knowledge, this is the first federated PCA algorithm for\nanomaly detection meeting the requirements of IoT networks.\n", "date": "2022-12-23", "categories": []}
{"id": "2211.07454", "title": "LGN-Net: Local-Global Normality Network for Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) has been intensively studied for years because\nof its potential applications in intelligent video systems. Existing\nunsupervised VAD methods tend to learn normality from training sets consisting\nof only normal videos and regard instances deviating from such normality as\nanomalies. However, they often consider only local or global normality in the\ntemporal dimension. Some of them focus on learning local spatiotemporal\nrepresentations from consecutive frames to enhance the representation for\nnormal events. But powerful representation allows these methods to represent\nsome anomalies and causes miss detection. In contrast, the other methods are\ndevoted to memorizing prototypical normal patterns of whole training videos to\nweaken the generalization for anomalies, which also restricts them from\nrepresenting diverse normal patterns and causes false alarm. To this end, we\npropose a two-branch model, Local-Global Normality Network (LGN-Net), to\nsimultaneously learn local and global normality. Specifically, one branch\nlearns the evolution regularities of appearance and motion from consecutive\nframes as local normality utilizing a spatiotemporal prediction network, while\nthe other branch memorizes prototype features of the whole videos as global\nnormality by a memory module. LGN-Net achieves a balance of representing normal\nand abnormal instances by fusing local and global normality. In addition, the\nfused normality enables LGN-Net to generalize to various scenes more than\nexploiting single normality. Experiments demonstrate the effectiveness and\nsuperior performance of our method. The code is available online:\nhttps://github.com/Myzhao1999/LGN-Net.\n", "date": "2022-11-14", "categories": []}
{"id": "2301.02909", "title": "How to Allocate your Label Budget? Choosing between Active Learning and\n  Learning to Reject in Anomaly Detection", "abstract": "  Anomaly detection attempts at finding examples that deviate from the expected\nbehaviour. Usually, anomaly detection is tackled from an unsupervised\nperspective because anomalous labels are rare and difficult to acquire.\nHowever, the lack of labels makes the anomaly detector have high uncertainty in\nsome regions, which usually results in poor predictive performance or low user\ntrust in the predictions. One can reduce such uncertainty by collecting\nspecific labels using Active Learning (AL), which targets examples close to the\ndetector's decision boundary. Alternatively, one can increase the user trust by\nallowing the detector to abstain from making highly uncertain predictions,\nwhich is called Learning to Reject (LR). One way to do this is by thresholding\nthe detector's uncertainty based on where its performance is low, which\nrequires labels to be evaluated. Although both AL and LR need labels, they work\nwith different types of labels: AL seeks strategic labels, which are evidently\nbiased, while LR requires i.i.d. labels to evaluate the detector's performance\nand set the rejection threshold. Because one usually has a unique label budget,\ndeciding how to optimally allocate it is challenging. In this paper, we propose\na mixed strategy that, given a budget of labels, decides in multiple rounds\nwhether to use the budget to collect AL labels or LR labels. The strategy is\nbased on a reward function that measures the expected gain when allocating the\nbudget to either side. We evaluate our strategy on 18 benchmark datasets and\ncompare it to some baselines.\n", "date": "2023-01-07", "categories": []}
{"id": "2301.03470", "title": "Unsupervised Multivariate Time-Series Transformers for Seizure\n  Identification on EEG", "abstract": "  Epilepsy is one of the most common neurological disorders, typically observed\nvia seizure episodes. Epileptic seizures are commonly monitored through\nelectroencephalogram (EEG) recordings due to their routine and low expense\ncollection. The stochastic nature of EEG makes seizure identification via\nmanual inspections performed by highly-trained experts a tedious endeavor,\nmotivating the use of automated identification. The literature on automated\nidentification focuses mostly on supervised learning methods requiring expert\nlabels of EEG segments that contain seizures, which are difficult to obtain.\nMotivated by these observations, we pose seizure identification as an\nunsupervised anomaly detection problem. To this end, we employ the first\nunsupervised transformer-based model for seizure identification on raw EEG. We\ntrain an autoencoder involving a transformer encoder via an unsupervised loss\nfunction, incorporating a novel masking strategy uniquely designed for\nmultivariate time-series data such as EEG. Training employs EEG recordings that\ndo not contain any seizures, while seizures are identified with respect to\nreconstruction errors at inference time. We evaluate our method on three\npublicly available benchmark EEG datasets for distinguishing seizure vs.\nnon-seizure windows. Our method leads to significantly better seizure\nidentification performance than supervised learning counterparts, by up to 16%\nrecall, 9% accuracy, and 9% Area under the Receiver Operating Characteristics\nCurve (AUC), establishing particular benefits on highly imbalanced data.\nThrough accurate seizure identification, our method could facilitate widely\naccessible and early detection of epilepsy development, without needing\nexpensive label collection or manual feature extraction.\n", "date": "2023-01-03", "categories": []}
{"id": "2301.02126", "title": "CRADL: Contrastive Representations for Unsupervised Anomaly Detection\n  and Localization", "abstract": "  Unsupervised anomaly detection in medical imaging aims to detect and localize\narbitrary anomalies without requiring annotated anomalous data during training.\nOften, this is achieved by learning a data distribution of normal samples and\ndetecting anomalies as regions in the image which deviate from this\ndistribution. Most current state-of-the-art methods use latent variable\ngenerative models operating directly on the images. However, generative models\nhave been shown to mostly capture low-level features, s.a. pixel-intensities,\ninstead of rich semantic features, which also applies to their representations.\nWe circumvent this problem by proposing CRADL whose core idea is to model the\ndistribution of normal samples directly in the low-dimensional representation\nspace of an encoder trained with a contrastive pretext-task. By utilizing the\nrepresentations of contrastive learning, we aim to fix the over-fixation on\nlow-level features and learn more semantic-rich representations. Our\nexperiments on anomaly detection and localization tasks using three distinct\nevaluation datasets show that 1) contrastive representations are superior to\nrepresentations of generative latent variable models and 2) the CRADL framework\nshows competitive or superior performance to state-of-the-art.\n", "date": "2023-01-05", "categories": []}
{"id": "2207.06055", "title": "Experiments on Anomaly Detection in Autonomous Driving by\n  Forward-Backward Style Transfers", "abstract": "  Great progress has been achieved in the community of autonomous driving in\nthe past few years. As a safety-critical problem, however, anomaly detection is\na huge hurdle towards a large-scale deployment of autonomous vehicles in the\nreal world. While many approaches, such as uncertainty estimation or\nsegmentation-based image resynthesis, are extremely promising, there is more to\nbe explored. Especially inspired by works on anomaly detection based on image\nresynthesis, we propose a novel approach for anomaly detection through style\ntransfer. We leverage generative models to map an image from its original style\ndomain of road traffic to an arbitrary one and back to generate pixelwise\nanomaly scores. However, our experiments have proven our hypothesis wrong, and\nwe were unable to produce significant results. Nevertheless, we want to share\nour findings, so that others can learn from our experiments.\n", "date": "2022-07-13", "categories": []}
{"id": "2301.00014", "title": "Time series Forecasting to detect anomalous behaviours in Multiphase\n  Flow Meters", "abstract": "  An Anomaly Detection (AD) System for Self-diagnosis has been developed for\nMultiphase Flow Meter (MPFM). The system relies on machine learning algorithms\nfor time series forecasting, historical data have been used to train a model\nand to predict the behavior of a sensor and, thus, to detect anomalies.\n", "date": "2022-12-30", "categories": []}
{"id": "2301.00134", "title": "Exploring the Use of Data-Driven Approaches for Anomaly Detection in the\n  Internet of Things (IoT) Environment", "abstract": "  The Internet of Things (IoT) is a system that connects physical computing\ndevices, sensors, software, and other technologies. Data can be collected,\ntransferred, and exchanged with other devices over the network without\nrequiring human interactions. One challenge the development of IoT faces is the\nexistence of anomaly data in the network. Therefore, research on anomaly\ndetection in the IoT environment has become popular and necessary in recent\nyears. This survey provides an overview to understand the current progress of\nthe different anomaly detection algorithms and how they can be applied in the\ncontext of the Internet of Things. In this survey, we categorize the widely\nused anomaly detection machine learning and deep learning techniques in IoT\ninto three types: clustering-based, classification-based, and deep learning\nbased. For each category, we introduce some state-of-the-art anomaly detection\nmethods and evaluate the advantages and limitations of each technique.\n", "date": "2022-12-31", "categories": []}
{"id": "2212.14621", "title": "Label-Efficient Interactive Time-Series Anomaly Detection", "abstract": "  Time-series anomaly detection is an important task and has been widely\napplied in the industry. Since manual data annotation is expensive and\ninefficient, most applications adopt unsupervised anomaly detection methods,\nbut the results are usually sub-optimal and unsatisfactory to end customers.\nWeak supervision is a promising paradigm for obtaining considerable labels in a\nlow-cost way, which enables the customers to label data by writing heuristic\nrules rather than annotating each instance individually. However, in the\ntime-series domain, it is hard for people to write reasonable labeling\nfunctions as the time-series data is numerically continuous and difficult to be\nunderstood. In this paper, we propose a Label-Efficient Interactive Time-Series\nAnomaly Detection (LEIAD) system, which enables a user to improve the results\nof unsupervised anomaly detection by performing only a small amount of\ninteractions with the system. To achieve this goal, the system integrates weak\nsupervision and active learning collaboratively while generating labeling\nfunctions automatically using only a few labeled data. All of these techniques\nare complementary and can promote each other in a reinforced manner. We conduct\nexperiments on three time-series anomaly detection datasets, demonstrating that\nthe proposed system is superior to existing solutions in both weak supervision\nand active learning areas. Also, the system has been tested in a real scenario\nin industry to show its practicality.\n", "date": "2022-12-30", "categories": []}
{"id": "2212.13513", "title": "Anomaly detection in laser-guided vehicles' batteries: a case study", "abstract": "  Detecting anomalous data within time series is a very relevant task in\npattern recognition and machine learning, with many possible applications that\nrange from disease prevention in medicine, e.g., detecting early alterations of\nthe health status before it can clearly be defined as \"illness\" up to\nmonitoring industrial plants. Regarding this latter application, detecting\nanomalies in an industrial plant's status firstly prevents serious damages that\nwould require a long interruption of the production process. Secondly, it\npermits optimal scheduling of maintenance interventions by limiting them to\nurgent situations. At the same time, they typically follow a fixed prudential\nschedule according to which components are substituted well before the end of\ntheir expected lifetime. This paper describes a case study regarding the\nmonitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we\nworked as our contribution to project SUPER (Supercomputing Unified Platform,\nEmilia Romagna) aimed at establishing and demonstrating a regional\nHigh-Performance Computing platform that is going to represent the main Italian\nsupercomputing environment for both computing power and data volume.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.13520", "title": "Challenges in anomaly and change point detection", "abstract": "  This paper presents an introduction to the state-of-the-art in anomaly and\nchange-point detection. On the one hand, the main concepts needed to understand\nthe vast scientific literature on those subjects are introduced. On the other,\na selection of important surveys and books, as well as two selected active\nresearch topics in the field, are presented.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.13558", "title": "AER: Auto-Encoder with Regression for Time Series Anomaly Detection", "abstract": "  Anomaly detection on time series data is increasingly common across various\nindustrial domains that monitor metrics in order to prevent potential accidents\nand economic losses. However, a scarcity of labeled data and ambiguous\ndefinitions of anomalies can complicate these efforts. Recent unsupervised\nmachine learning methods have made remarkable progress in tackling this problem\nusing either single-timestamp predictions or time series reconstructions. While\ntraditionally considered separately, these methods are not mutually exclusive\nand can offer complementary perspectives on anomaly detection. This paper first\nhighlights the successes and limitations of prediction-based and\nreconstruction-based methods with visualized time series signals and anomaly\nscores. We then propose AER (Auto-encoder with Regression), a joint model that\ncombines a vanilla auto-encoder and an LSTM regressor to incorporate the\nsuccesses and address the limitations of each method. Our model can produce\nbi-directional predictions while simultaneously reconstructing the original\ntime series by optimizing a joint objective function. Furthermore, we propose\nseveral ways of combining the prediction and reconstruction errors through a\nseries of ablation studies. Finally, we compare the performance of the AER\narchitecture against two prediction-based methods and three\nreconstruction-based methods on 12 well-known univariate time series datasets\nfrom NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest\naveraged F1 score across all datasets (a 23.5% improvement compared to ARIMA)\nwhile retaining a runtime similar to its vanilla auto-encoder and regressor\ncomponents. Our model is available in Orion, an open-source benchmarking tool\nfor time series anomaly detection.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.13596", "title": "Semi-supervised multiscale dual-encoding method for faulty traffic data\n  detection", "abstract": "  Inspired by the recent success of deep learning in multiscale information\nencoding, we introduce a variational autoencoder (VAE) based semi-supervised\nmethod for detection of faulty traffic data, which is cast as a classification\nproblem. Continuous wavelet transform (CWT) is applied to the time series of\ntraffic volume data to obtain rich features embodied in time-frequency\nrepresentation, followed by a twin of VAE models to separately encode normal\ndata and faulty data. The resulting multiscale dual encodings are concatenated\nand fed to an attention-based classifier, consisting of a self-attention module\nand a multilayer perceptron. For comparison, the proposed architecture is\nevaluated against five different encoding schemes, including (1) VAE with only\nnormal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both\nnormal and faulty data encodings, but without attention module in the\nclassifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)\nencoding. The first four encoding schemes adopted the same convolutional neural\nnetwork (CNN) architecture while the fifth encoding scheme follows the\ntransformer architecture of CViT. Our experiments show that the proposed\narchitecture with the dual encoding scheme, coupled with attention module,\noutperforms other encoding schemes and results in classification accuracy of\n96.4%, precision of 95.5%, and recall of 97.7%.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.13626", "title": "LOSDD: Leave-Out Support Vector Data Description for Outlier Detection", "abstract": "  Support Vector Machines have been successfully used for one-class\nclassification (OCSVM, SVDD) when trained on clean data, but they work much\nworse on dirty data: outliers present in the training data tend to become\nsupport vectors, and are hence considered \"normal\". In this article, we improve\nthe effectiveness to detect outliers in dirty training data with a leave-out\nstrategy: by temporarily omitting one candidate at a time, this point can be\njudged using the remaining data only. We show that this is more effective at\nscoring the outlierness of points than using the slack term of existing\nSVM-based approaches. Identified outliers can then be removed from the data,\nsuch that outliers hidden by other outliers can be identified, to reduce the\nproblem of masking. Naively, this approach would require training N individual\nSVMs (and training $O(N^2)$ SVMs when iteratively removing the worst outliers\none at a time), which is prohibitively expensive. We will discuss that only\nsupport vectors need to be considered in each step and that by reusing SVM\nparameters and weights, this incremental retraining can be accelerated\nsubstantially. By removing candidates in batches, we can further improve the\nprocessing time, although it obviously remains more costly than training a\nsingle SVM.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.13904", "title": "A Novel Self-Supervised Learning-Based Anomaly Node Detection Method\n  Based on an Autoencoder in Wireless Sensor Networks", "abstract": "  Due to the issue that existing wireless sensor network (WSN)-based anomaly\ndetection methods only consider and analyze temporal features, in this paper, a\nself-supervised learning-based anomaly node detection method based on an\nautoencoder is designed. This method integrates temporal WSN data flow feature\nextraction, spatial position feature extraction and intermodal WSN correlation\nfeature extraction into the design of the autoencoder to make full use of the\nspatial and temporal information of the WSN for anomaly detection. First, a\nfully connected network is used to extract the temporal features of nodes by\nconsidering a single mode from a local spatial perspective. Second, a graph\nneural network (GNN) is used to introduce the WSN topology from a global\nspatial perspective for anomaly detection and extract the spatial and temporal\nfeatures of the data flows of nodes and their neighbors by considering a single\nmode. Then, the adaptive fusion method involving weighted summation is used to\nextract the relevant features between different models. In addition, this paper\nintroduces a gated recurrent unit (GRU) to solve the long-term dependence\nproblem of the time dimension. Eventually, the reconstructed output of the\ndecoder and the hidden layer representation of the autoencoder are fed into a\nfully connected network to calculate the anomaly probability of the current\nsystem. Since the spatial feature extraction operation is advanced, the\ndesigned method can be applied to the task of large-scale network anomaly\ndetection by adding a clustering operation. Experiments show that the designed\nmethod outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2%\nhigher than those of the existing anomaly detection methods based on\nunsupervised reconstruction and prediction. Code and model are available at\nhttps://github.com/GuetYe/anomaly_detection/GLSL\n", "date": "2022-12-26", "categories": []}
{"id": "2212.12878", "title": "A Lightweight Reconstruction Network for Surface Defect Inspection", "abstract": "  Currently, most deep learning methods cannot solve the problem of scarcity of\nindustrial product defect samples and significant differences in\ncharacteristics. This paper proposes an unsupervised defect detection algorithm\nbased on a reconstruction network, which is realized using only a large number\nof easily obtained defect-free sample data. The network includes two parts:\nimage reconstruction and surface defect area detection. The reconstruction\nnetwork is designed through a fully convolutional autoencoder with a\nlightweight structure. Only a small number of normal samples are used for\ntraining so that the reconstruction network can be A defect-free reconstructed\nimage is generated. A function combining structural loss and $\\mathit{L}1$ loss\nis proposed as the loss function of the reconstruction network to solve the\nproblem of poor detection of irregular texture surface defects. Further, the\nresidual of the reconstructed image and the image to be tested is used as the\npossible region of the defect, and conventional image operations can realize\nthe location of the fault. The unsupervised defect detection algorithm of the\nproposed reconstruction network is used on multiple defect image sample sets.\nCompared with other similar algorithms, the results show that the unsupervised\ndefect detection algorithm of the reconstructed network has strong robustness\nand accuracy.\n", "date": "2022-12-25", "categories": []}
{"id": "2212.10939", "title": "Joint Embedding of 2D and 3D Networks for Medical Image Anomaly\n  Detection", "abstract": "  Obtaining ground truth data in medical imaging has difficulties due to the\nfact that it requires a lot of annotating time from the experts in the field.\nAlso, when trained with supervised learning, it detects only the cases included\nin the labels. In real practice, we want to also open to other possibilities\nthan the named cases while examining the medical images. As a solution, the\nneed for anomaly detection that can detect and localize abnormalities by\nlearning the normal characteristics using only normal images is emerging. With\nmedical image data, we can design either 2D or 3D networks of self-supervised\nlearning for anomaly detection task. Although 3D networks, which learns 3D\nstructures of the human body, show good performance in 3D medical image anomaly\ndetection, they cannot be stacked in deeper layers due to memory problems.\nWhile 2D networks have advantage in feature detection, they lack 3D context\ninformation. In this paper, we develop a method for combining the strength of\nthe 3D network and the strength of the 2D network through joint embedding. We\nalso propose the pretask of self-supervised learning to make it possible for\nthe networks to learn efficiently. Through the experiments, we show that the\nproposed method achieves better performance in both classification and\nsegmentation tasks compared to the SoTA method.\n", "date": "2022-12-21", "categories": []}
{"id": "2212.11507", "title": "Supervised Anomaly Detection Method Combining Generative Adversarial\n  Networks and Three-Dimensional Data in Vehicle Inspections", "abstract": "  The external visual inspections of rolling stock's underfloor equipment are\ncurrently being performed via human visual inspection. In this study, we\nattempt to partly automate visual inspection by investigating anomaly\ninspection algorithms that use image processing technology. As the railroad\nmaintenance studies tend to have little anomaly data, unsupervised learning\nmethods are usually preferred for anomaly detection; however, training cost and\naccuracy is still a challenge. Additionally, a researcher created anomalous\nimages from normal images by adding noise, etc., but the anomalous targeted in\nthis study is the rotation of piping cocks that was difficult to create using\nnoise. Therefore, in this study, we propose a new method that uses style\nconversion via generative adversarial networks on three-dimensional computer\ngraphics and imitates anomaly images to apply anomaly detection based on\nsupervised learning. The geometry-consistent style conversion model was used to\nconvert the image, and because of this the color and texture of the image were\nsuccessfully made to imitate the real image while maintaining the anomalous\nshape. Using the generated anomaly images as supervised data, the anomaly\ndetection model can be easily trained without complex adjustments and\nsuccessfully detects anomalies.\n", "date": "2022-12-22", "categories": []}
{"id": "2212.10992", "title": "LogAnMeta: Log Anomaly Detection Using Meta Learning", "abstract": "  Modern telecom systems are monitored with performance and system logs from\nmultiple application layers and components. Detecting anomalous events from\nthese logs is key to identify security breaches, resource over-utilization,\ncritical/fatal errors, etc. Current supervised log anomaly detection frameworks\ntend to perform poorly on new types or signatures of anomalies with few or\nunseen samples in the training data. In this work, we propose a\nmeta-learning-based log anomaly detection framework (LogAnMeta) for detecting\nanomalies from sequence of log events with few samples. LoganMeta train a\nhybrid few-shot classifier in an episodic manner. The experimental results\ndemonstrate the efficacy of our proposed method\n", "date": "2022-12-21", "categories": []}
{"id": "2212.11122", "title": "Diamond Abrasive Electroplated Surface Anomaly Detection using\n  Convolutional Neural Networks for Industrial Quality Inspection", "abstract": "  Electroplated diamond abrasive tools require nickel coating on a metal\nsurface for abrasive bonding and part functionality. The electroplated\nnickel-coated abrasive tool is expected to have a high-quality part performance\nby having a nickel coating thickness of between 50% to 60% of the abrasive\nmedian diameter, uniformity of the nickel layer, abrasive distribution over the\nelectroplated surface, and bright gloss. Electroplating parameters are set\naccordingly for this purpose. Industrial quality inspection for defects of\nthese abrasive electroplated parts with optical inspection instruments is\nextremely challenging due to the diamond's light refraction, dispersion nature,\nand reflective bright nickel surface. The difficulty posed by this challenge\nrequires parts to be quality inspected manually with an eye loupe that is\nsubjective and costly. In this study, we use a Convolutional Neural Network\n(CNN) model in the production line to detect abrasive electroplated part\nanomalies allowing us to fix or eliminate those parts or elements that are in\nbad condition from the production chain and ultimately reduce manual quality\ninspection cost. We used 744 samples to train our model. Our model successfully\nidentified over 99% of the parts with an anomaly. Keywords: Artificial\nIntelligence, Anomaly Detection, Industrial Quality Inspection, Electroplating,\nDiamond Abrasive Tool\n", "date": "2022-12-11", "categories": []}
{"id": "2208.09440", "title": "Feature Selection for Fault Detection and Prediction based on Event Log\n  Analysis", "abstract": "  Event logs are widely used for anomaly detection and prediction in complex\nsystems. Existing log-based anomaly detection methods usually consist of four\nmain steps: log collection, log parsing, feature extraction, and anomaly\ndetection, wherein the feature extraction step extracts useful features for\nanomaly detection by counting log events. For a complex system, such as a\nlithography machine consisting of a large number of subsystems, its log may\ncontain thousands of different events, resulting in abounding extracted\nfeatures. However, when anomaly detection is performed at the subsystem level,\nanalyzing all features becomes expensive and unnecessary. To mitigate this\nproblem, we develop a feature selection method for log-based anomaly detection\nand prediction, largely improving the effectiveness and efficiency.\n", "date": "2022-08-19", "categories": []}
{"id": "2212.02602", "title": "Automatic Anomalies Detection in Hydraulic Devices", "abstract": "  Nowadays, the applications of hydraulic systems are present in a wide variety\nof devices in both industrial and everyday environments. The implementation and\nusage of hydraulic systems have been well documented; however, today, this\nstill faces a challenge, the integration of tools that allow more accurate\ninformation about the functioning and operation of these systems for proactive\ndecision-making. In industrial applications, many sensors and methods exist to\nmeasure and determine the status of process variables (e.g., flow, pressure,\nforce). Nevertheless, little has been done to have systems that can provide\nusers with device-health information related to hydraulic devices integrated\ninto the machinery. Implementing artificial intelligence (AI) technologies and\nmachine learning (ML) models in hydraulic system components has been identified\nas a solution to the challenge many industries currently face: optimizing\nprocesses and carrying them out more safely and efficiently. This paper\npresents a solution for the characterization and estimation of anomalies in one\nof the most versatile and used devices in hydraulic systems, cylinders. AI and\nML models were implemented to determine the current operating status of these\nhydraulic components and whether they are working correctly or if a failure\nmode or abnormal condition is present.\n", "date": "2022-12-05", "categories": []}
{"id": "2212.09352", "title": "Robust Anomaly Map Assisted Multiple Defect Detection with Supervised\n  Classification Techniques", "abstract": "  Industry 4.0 aims to optimize the manufacturing environment by leveraging new\ntechnological advances, such as new sensing capabilities and artificial\nintelligence. The DRAEM technique has shown state-of-the-art performance for\nunsupervised classification. The ability to create anomaly maps highlighting\nareas where defects probably lie can be leveraged to provide cues to supervised\nclassification models and enhance their performance. Our research shows that\nthe best performance is achieved when training a defect detection model by\nproviding an image and the corresponding anomaly map as input. Furthermore,\nsuch a setting provides consistent performance when framing the defect\ndetection as a binary or multiclass classification problem and is not affected\nby class balancing policies. We performed the experiments on three datasets\nwith real-world data provided by Philips Consumer Lifestyle BV.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.09518", "title": "FedTADBench: Federated Time-Series Anomaly Detection Benchmark", "abstract": "  Time series anomaly detection strives to uncover potential abnormal behaviors\nand patterns from temporal data, and has fundamental significance in diverse\napplication scenarios. Constructing an effective detection model usually\nrequires adequate training data stored in a centralized manner, however, this\nrequirement sometimes could not be satisfied in realistic scenarios. As a\nprevailing approach to address the above problem, federated learning has\ndemonstrated its power to cooperate with the distributed data available while\nprotecting the privacy of data providers. However, it is still unclear that how\nexisting time series anomaly detection algorithms perform with decentralized\ndata storage and privacy protection through federated learning. To study this,\nwe conduct a federated time series anomaly detection benchmark, named\nFedTADBench, which involves five representative time series anomaly detection\nalgorithms and four popular federated learning methods. We would like to answer\nthe following questions: (1)How is the performance of time series anomaly\ndetection algorithms when meeting federated learning? (2) Which federated\nlearning method is the most appropriate one for time series anomaly detection?\n(3) How do federated time series anomaly detection approaches perform on\ndifferent partitions of data in clients? Numbers of results as well as\ncorresponding analysis are provided from extensive experiments with various\nsettings. The source code of our benchmark is publicly available at\nhttps://github.com/fanxingliu2020/FedTADBench.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.08506", "title": "Weakly Supervised Video Anomaly Detection Based on Cross-Batch\n  Clustering Guidance", "abstract": "  Weakly supervised video anomaly detection (WSVAD) is a challenging task since\nonly video-level labels are available for training. In previous studies, the\ndiscriminative power of the learned features is not strong enough, and the data\nimbalance resulting from the mini-batch training strategy is ignored. To\naddress these two issues, we propose a novel WSVAD method based on cross-batch\nclustering guidance. To enhance the discriminative power of features, we\npropose a batch clustering based loss to encourage a clustering branch to\ngenerate distinct normal and abnormal clusters based on a batch of data.\nMeanwhile, we design a cross-batch learning strategy by introducing clustering\nresults from previous mini-batches to reduce the impact of data imbalance. In\naddition, we propose to generate more accurate segment-level anomaly scores\nbased on batch clustering guidance further improving the performance of WSVAD.\nExtensive experiments on two public datasets demonstrate the effectiveness of\nour approach.\n", "date": "2022-12-16", "categories": []}
{"id": "2206.01992", "title": "CAINNFlow: Convolutional block Attention modules and Invertible Neural\n  Networks Flow for anomaly detection and localization tasks", "abstract": "  Detection of object anomalies is crucial in industrial processes, but\nunsupervised anomaly detection and localization is particularly important due\nto the difficulty of obtaining a large number of defective samples and the\nunpredictable types of anomalies in real life. Among the existing unsupervised\nanomaly detection and localization methods, the NF-based scheme has achieved\nbetter results. However, the two subnets (complex functions) $s_{i}(u_{i})$ and\n$t_{i}(u_{i})$ in NF are usually multilayer perceptrons, which need to squeeze\nthe input visual features from 2D flattening to 1D, destroying the spatial\nlocation relationship in the feature map and losing the spatial structure\ninformation. In order to retain and effectively extract spatial structure\ninformation, we design in this study a complex function model with alternating\nCBAM embedded in a stacked $3\\times3$ full convolution, which is able to retain\nand effectively extract spatial structure information in the normalized flow\nmodel. Extensive experimental results on the MVTec AD dataset show that\nCAINNFlow achieves advanced levels of accuracy and inference efficiency based\non CNN and Transformer backbone networks as feature extractors, and CAINNFlow\nachieves a pixel-level AUC of $98.64\\%$ for anomaly detection in MVTec AD.\n", "date": "2022-06-04", "categories": []}
{"id": "2212.07558", "title": "DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly\n  Detection", "abstract": "  Machine Learning (ML) approaches have been used to enhance the detection\ncapabilities of Network Intrusion Detection Systems (NIDSs). Recent work has\nachieved near-perfect performance by following binary- and multi-class network\nanomaly detection tasks. Such systems depend on the availability of both\n(benign and malicious) network data classes during the training phase. However,\nattack data samples are often challenging to collect in most organisations due\nto security controls preventing the penetration of known malicious traffic to\ntheir networks. Therefore, this paper proposes a Deep One-Class (DOC)\nclassifier for network intrusion detection by only training on benign network\ndata samples. The novel one-class classification architecture consists of a\nhistogram-based deep feed-forward classifier to extract useful network data\nfeatures and use efficient outlier detection. The DOC classifier has been\nextensively evaluated using two benchmark NIDS datasets. The results\ndemonstrate its superiority over current state-of-the-art one-class classifiers\nin terms of detection and false positive rates.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.07691", "title": "Anomaly Detection in Driving by Cluster Analysis Twice", "abstract": "  Events deviating from normal traffic patterns in driving, anomalies, such as\naggressive driving or bumpy roads, may harm delivery efficiency for\ntransportation and logistics (T&L) business. Thus, detecting anomalies in\ndriving is critical for the T&L industry. So far numerous researches have used\nvehicle sensor data to identify anomalies. Most previous works captured\nanomalies by using deep learning or machine learning algorithms, which require\nprior training processes and huge computational costs. This study proposes a\nmethod namely Anomaly Detection in Driving by Cluster Analysis Twice (ADDCAT)\nwhich clusters the processed sensor data in different physical properties. An\nevent is said to be an anomaly if it never fits with the major cluster, which\nis considered as the pattern of normality in driving. This method provides a\nway to detect anomalies in driving with no prior training processes and huge\ncomputational costs needed. This paper validated the performance of the method\non an open dataset.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.07757", "title": "Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous\n  Vehicles", "abstract": "  Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR\nand radar are widely used in autonomous vehicles for environmental perception,\nnavigation and assisted braking control. Despite their relative importance in\nmaking safer driving decisions, these devices are vulnerable to multiple attack\ntypes including spoofing, triggering and false data injection. When these\nattacks are successful they can compromise the security of autonomous vehicles\nleading to severe consequences for the driver, nearby vehicles and pedestrians.\nTo handle these attacks and protect the measurement devices, we propose a\nspatial-temporal anomaly detection model \\textit{STAnDS} which incorporates a\nresidual error spatial detector, with a time-based expected change detection.\nThis approach is evaluated using a simulated quantitative environment and the\nresults show that \\textit{STAnDS} is effective at detecting multiple attack\ntypes.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.07802", "title": "Chaotic Variational Auto Encoder based One Class Classifier for\n  Insurance Fraud Detection", "abstract": "  Of late, insurance fraud detection has assumed immense significance owing to\nthe huge financial & reputational losses fraud entails and the phenomenal\nsuccess of the fraud detection techniques. Insurance is majorly divided into\ntwo categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes\nhealth insurance and auto insurance among other things. In either of the\ncategories, the fraud detection techniques should be designed in such a way\nthat they capture as many fraudulent transactions as possible. Owing to the\nrarity of fraudulent transactions, in this paper, we propose a chaotic\nvariational autoencoder (C-VAE to perform one-class classification (OCC) on\ngenuine transactions. Here, we employed the logistic chaotic map to generate\nrandom noise in the latent space. The effectiveness of C-VAE is demonstrated on\nthe health insurance fraud and auto insurance datasets. We considered vanilla\nVariational Auto Encoder (VAE) as the baseline. It is observed that C-VAE\noutperformed VAE in both datasets. C-VAE achieved a classification rate of\n77.9% and 87.25% in health and automobile insurance datasets respectively.\nFurther, the t-test conducted at 1% level of significance and 18 degrees of\nfreedom infers that C-VAE is statistically significant than the VAE.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.07900", "title": "EVAL: Explainable Video Anomaly Localization", "abstract": "  We develop a novel framework for single-scene video anomaly localization that\nallows for human-understandable reasons for the decisions the system makes. We\nfirst learn general representations of objects and their motions (using deep\nnetworks) and then use these representations to build a high-level,\nlocation-dependent model of any particular scene. This model can be used to\ndetect anomalies in new videos of the same scene. Importantly, our approach is\nexplainable - our high-level appearance and motion features can provide\nhuman-understandable reasons for why any part of a video is classified as\nnormal or anomalous. We conduct experiments on standard video anomaly detection\ndatasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show\nsignificant improvements over the previous state-of-the-art.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.07010", "title": "Cross-Domain Video Anomaly Detection without Target Domain Adaptation", "abstract": "  Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume\nthat at least few task-relevant target domain training data are available for\nadaptation from the source to the target domain. However, this requires\nlaborious model-tuning by the end-user who may prefer to have a system that\nworks ``out-of-the-box.\" To address such practical scenarios, we identify a\nnovel target domain (inference-time) VAD task where no target domain training\ndata are available. To this end, we propose a new `Zero-shot Cross-domain Video\nAnomaly Detection (zxvad)' framework that includes a future-frame prediction\ngenerative model setup. Different from prior future-frame prediction models,\nour model uses a novel Normalcy Classifier module to learn the features of\nnormal event videos by learning how such features are different ``relatively\"\nto features in pseudo-abnormal examples. A novel Untrained Convolutional Neural\nNetwork based Anomaly Synthesis module crafts these pseudo-abnormal examples by\nadding foreign objects in normal video frames with no extra training cost. With\nour novel relative normalcy feature learning strategy, zxvad generalizes and\nlearns to distinguish between normal and abnormal frames in a new target domain\nwithout adaptation during inference. Through evaluations on common datasets, we\nshow that zxvad outperforms the state-of-the-art (SOTA), regardless of whether\ntask-relevant (i.e., VAD) source training data are available or not. Lastly,\nzxvad also beats the SOTA methods in inference-time efficiency metrics\nincluding the model size, total parameters, GPU energy consumption, and GMACs.\n", "date": "2022-12-14", "categories": []}
{"id": "2212.05786", "title": "Multi-scale Feature Imitation for Unsupervised Anomaly Localization", "abstract": "  The unsupervised anomaly localization task faces the challenge of missing\nanomaly sample training, detecting multiple types of anomalies, and dealing\nwith the proportion of the area of multiple anomalies. A separate\nteacher-student feature imitation network structure and a multi-scale\nprocessing strategy combining an image and feature pyramid are proposed to\nsolve these problems. A network module importance search method based on\ngradient descent optimization is proposed to simplify the network structure.\nThe experimental results show that the proposed algorithm performs better than\nthe feature modeling anomaly localization method on the real industrial product\ndetection dataset in the same period. The multi-scale strategy can effectively\nimprove the effect compared with the benchmark method.\n", "date": "2022-12-12", "categories": []}
{"id": "2212.06154", "title": "Zero-Shot Motor Health Monitoring by Blind Domain Transition", "abstract": "  Continuous long-term monitoring of motor health is crucial for the early\ndetection of abnormalities such as bearing faults (up to 51% of motor failures\nare attributed to bearing faults). Despite numerous methodologies proposed for\nbearing fault detection, most of them require normal (healthy) and abnormal\n(faulty) data for training. Even with the recent deep learning (DL)\nmethodologies trained on the labeled data from the same machine, the\nclassification accuracy significantly deteriorates when one or few conditions\nare altered. Furthermore, their performance suffers significantly or may\nentirely fail when they are tested on another machine with entirely different\nhealthy and faulty signal patterns. To address this need, in this pilot study,\nwe propose a zero-shot bearing fault detection method that can detect any fault\non a new (target) machine regardless of the working conditions, sensor\nparameters, or fault characteristics. To accomplish this objective, a 1D\nOperational Generative Adversarial Network (Op-GAN) first characterizes the\ntransition between normal and fault vibration signals of (a) source machine(s)\nunder various conditions, sensor parameters, and fault types. Then for a target\nmachine, the potential faulty signals can be generated, and over its actual\nhealthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN\nfault detector can then be trained to detect the real faulty condition in real\ntime whenever it occurs. To validate the proposed approach, a new benchmark\ndataset is created using two different motors working under different\nconditions and sensor locations. Experimental results demonstrate that this\nnovel approach can accurately detect any bearing fault achieving an average\nrecall rate of around 89% and 95% on two target machines regardless of its\ntype, severity, and location.\n", "date": "2022-12-12", "categories": []}
{"id": "2212.05478", "title": "Mul-GAD: a semi-supervised graph anomaly detection framework via\n  aggregating multi-view information", "abstract": "  Anomaly detection is defined as discovering patterns that do not conform to\nthe expected behavior. Previously, anomaly detection was mostly conducted using\ntraditional shallow learning techniques, but with little improvement. As the\nemergence of graph neural networks (GNN), graph anomaly detection has been\ngreatly developed. However, recent studies have shown that GNN-based methods\nencounter challenge, in that no graph anomaly detection algorithm can perform\ngeneralization on most datasets. To bridge the tap, we propose a multi-view\nfusion approach for graph anomaly detection (Mul-GAD). The view-level fusion\ncaptures the extent of significance between different views, while the\nfeature-level fusion makes full use of complementary information. We\ntheoretically and experimentally elaborate the effectiveness of the fusion\nstrategies. For a more comprehensive conclusion, we further investigate the\neffect of the objective function and the number of fused views on detection\nperformance. Exploiting these findings, our Mul-GAD is proposed equipped with\nfusion strategies and the well-performed objective function. Compared with\nother state-of-the-art detection methods, we achieve a better detection\nperformance and generalization in most scenarios via a series of experiments\nconducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code\nis available at https://github.com/liuyishoua/Mul-Graph-Fusion.\n", "date": "2022-12-11", "categories": []}
{"id": "2212.05532", "title": "Graph Learning for Anomaly Analytics: Algorithms, Applications, and\n  Challenges", "abstract": "  Anomaly analytics is a popular and vital task in various research contexts,\nwhich has been studied for several decades. At the same time, deep learning has\nshown its capacity in solving many graph-based tasks like, node classification,\nlink prediction, and graph classification. Recently, many studies are extending\ngraph learning models for solving anomaly analytics problems, resulting in\nbeneficial advances in graph-based anomaly analytics techniques. In this\nsurvey, we provide a comprehensive overview of graph learning methods for\nanomaly analytics tasks. We classify them into four categories based on their\nmodel architectures, namely graph convolutional network (GCN), graph attention\nnetwork (GAT), graph autoencoder (GAE), and other graph learning models. The\ndifferences between these methods are also compared in a systematic manner.\nFurthermore, we outline several graph-based anomaly analytics applications\nacross various domains in the real world. Finally, we discuss five potential\nfuture research directions in this rapidly growing field.\n", "date": "2022-12-11", "categories": []}
{"id": "2212.05912", "title": "A machine learning approach to support decision in insider trading\n  detection", "abstract": "  Identifying market abuse activity from data on investors' trading activity is\nvery challenging both for the data volume and for the low signal to noise\nratio. Here we propose two complementary unsupervised machine learning methods\nto support market surveillance aimed at identifying potential insider trading\nactivities. The first one uses clustering to identify, in the vicinity of a\nprice sensitive event such as a takeover bid, discontinuities in the trading\nactivity of an investor with respect to his/her own past trading history and on\nthe present trading activity of his/her peers. The second unsupervised approach\naims at identifying (small) groups of investors that act coherently around\nprice sensitive events, pointing to potential insider rings, i.e. a group of\nsynchronised traders displaying strong directional trading in rewarding\nposition in a period before the price sensitive event. As a case study, we\napply our methods to investor resolved data of Italian stocks around takeover\nbids.\n", "date": "2022-12-06", "categories": []}
{"id": "2209.01816", "title": "ADTR: Anomaly Detection Transformer with Feature Reconstruction", "abstract": "  Anomaly detection with only prior knowledge from normal samples attracts more\nattention because of the lack of anomaly samples. Existing CNN-based pixel\nreconstruction approaches suffer from two concerns. First, the reconstruction\nsource and target are raw pixel values that contain indistinguishable semantic\ninformation. Second, CNN tends to reconstruct both normal samples and anomalies\nwell, making them still hard to distinguish. In this paper, we propose Anomaly\nDetection TRansformer (ADTR) to apply a transformer to reconstruct pre-trained\nfeatures. The pre-trained features contain distinguishable semantic\ninformation. Also, the adoption of transformer limits to reconstruct anomalies\nwell such that anomalies could be detected easily once the reconstruction\nfails. Moreover, we propose novel loss functions to make our approach\ncompatible with the normal-sample-only case and the anomaly-available case with\nboth image-level and pixel-level labeled anomalies. The performance could be\nfurther improved by adding simple synthetic or external irrelevant anomalies.\nExtensive experiments are conducted on anomaly detection datasets including\nMVTec-AD and CIFAR-10. Our method achieves superior performance compared with\nall baselines.\n", "date": "2022-09-05", "categories": []}
{"id": "2209.05899", "title": "A Meta-level Analysis of Online Anomaly Detectors", "abstract": "  Real-time detection of anomalies in streaming data is receiving increasing\nattention as it allows us to raise alerts, predict faults, and detect\nintrusions or threats across industries. Yet, little attention has been given\nto compare the effectiveness and efficiency of anomaly detectors for streaming\ndata (i.e., of online algorithms). In this paper, we present a qualitative,\nsynthetic overview of major online detectors from different algorithmic\nfamilies (i.e., distance, density, tree or projection-based) and highlight\ntheir main ideas for constructing, updating and testing detection models. Then,\nwe provide a thorough analysis of the results of a quantitative experimental\nevaluation of online detection algorithms along with their offline\ncounterparts. The behavior of the detectors is correlated with the\ncharacteristics of different datasets (i.e., meta-features), thereby providing\na meta-level analysis of their performance. Our study addresses several missing\ninsights from the literature such as (a) how reliable are detectors against a\nrandom classifier and what dataset characteristics make them perform randomly;\n(b) to what extent online detectors approximate the performance of offline\ncounterparts; (c) which sketch strategy and update primitives of detectors are\nbest to detect anomalies visible only within a feature subspace of a dataset;\n(d) what are the tradeoffs between the effectiveness and the efficiency of\ndetectors belonging to different algorithmic families; (e) which specific\ncharacteristics of datasets yield an online algorithm to outperform all others.\n", "date": "2022-09-13", "categories": []}
{"id": "2210.06188", "title": "Anomaly Detection using Generative Models and Sum-Product Networks in\n  Mammography Scans", "abstract": "  Unsupervised anomaly detection models which are trained solely by healthy\ndata, have gained importance in the recent years, as the annotation of medical\ndata is a tedious task. Autoencoders and generative adversarial networks are\nthe standard anomaly detection methods that are utilized to learn the data\ndistribution. However, they fall short when it comes to inference and\nevaluation of the likelihood of test samples. We propose a novel combination of\ngenerative models and a probabilistic graphical model. After encoding image\nsamples by autoencoders, the distribution of data is modeled by Random and\nTensorized Sum-Product Networks ensuring exact and efficient inference at test\ntime. We evaluate different autoencoder architectures in combination with\nRandom and Tensorized Sum-Product Networks on mammography images using\npatch-wise processing and observe superior performance over utilizing the\nmodels standalone and state-of-the-art in anomaly detection for medical data.\n", "date": "2022-10-12", "categories": []}
{"id": "2212.04984", "title": "Transformer-based normative modelling for anomaly detection of early\n  schizophrenia", "abstract": "  Despite the impact of psychiatric disorders on clinical health, early-stage\ndiagnosis remains a challenge. Machine learning studies have shown that\nclassifiers tend to be overly narrow in the diagnosis prediction task. The\noverlap between conditions leads to high heterogeneity among participants that\nis not adequately captured by classification models. To address this issue,\nnormative approaches have surged as an alternative method. By using a\ngenerative model to learn the distribution of healthy brain data patterns, we\ncan identify the presence of pathologies as deviations or outliers from the\ndistribution learned by the model. In particular, deep generative models showed\ngreat results as normative models to identify neurological lesions in the\nbrain. However, unlike most neurological lesions, psychiatric disorders present\nsubtle changes widespread in several brain regions, making these alterations\nchallenging to identify. In this work, we evaluate the performance of\ntransformer-based normative models to detect subtle brain changes expressed in\nadolescents and young adults. We trained our model on 3D MRI scans of\nneurotypical individuals (N=1,765). Then, we obtained the likelihood of\nneurotypical controls and psychiatric patients with early-stage schizophrenia\nfrom an independent dataset (N=93) from the Human Connectome Project. Using the\npredicted likelihood of the scans as a proxy for a normative score, we obtained\nan AUROC of 0.82 when assessing the difference between controls and individuals\nwith early-stage schizophrenia. Our approach surpassed recent normative methods\nbased on brain age and Gaussian Process, showing the promising use of deep\ngenerative models to help in individualised analyses.\n", "date": "2022-12-08", "categories": []}
{"id": "2212.02645", "title": "AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm", "abstract": "  We combine the metrics of distance and isolation to develop the Analytic\nIsolation and Distance-based Anomaly (AIDA) detection algorithm. AIDA is the\nfirst distance-based method that does not rely on the concept of\nnearest-neighbours, making it a parameter-free model.\n  Differently from the prevailing literature, in which the isolation metric is\nalways computed via simulations, we show that AIDA admits an analytical\nexpression for the outlier score, providing new insights into the isolation\nmetric. Additionally, we present an anomaly explanation method based on AIDA,\nthe Tempered Isolation-based eXplanation (TIX) algorithm, which finds the most\nrelevant outlier features even in data sets with hundreds of dimensions. We\ntest both algorithms on synthetic and empirical data: we show that AIDA is\ncompetitive when compared to other state-of-the-art methods, and it is superior\nin finding outliers hidden in multidimensional feature subspaces. Finally, we\nillustrate how the TIX algorithm is able to find outliers in multidimensional\nfeature subspaces, and use these explanations to analyze common benchmarks used\nin anomaly detection.\n", "date": "2022-12-05", "categories": []}
{"id": "2212.04090", "title": "Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly\n  Supervised Video Anomaly Detection", "abstract": "  Weakly supervised video anomaly detection aims to identify abnormal events in\nvideos using only video-level labels. Recently, two-stage self-training methods\nhave achieved significant improvements by self-generating pseudo labels and\nself-refining anomaly scores with these labels. As the pseudo labels play a\ncrucial role, we propose an enhancement framework by exploiting completeness\nand uncertainty properties for effective self-training. Specifically, we first\ndesign a multi-head classification module (each head serves as a classifier)\nwith a diversity loss to maximize the distribution differences of predicted\npseudo labels across heads. This encourages the generated pseudo labels to\ncover as many abnormal events as possible. We then devise an iterative\nuncertainty pseudo label refinement strategy, which improves not only the\ninitial pseudo labels but also the updated ones obtained by the desired\nclassifier in the second stage. Extensive experimental results demonstrate the\nproposed method performs favorably against state-of-the-art approaches on the\nUCF-Crime, TAD, and XD-Violence benchmark datasets.\n", "date": "2022-12-08", "categories": []}
{"id": "2212.02182", "title": "Anomaly Detection in Power Markets and Systems", "abstract": "  The widespread use of information and communication technology (ICT) over the\ncourse of the last decades has been a primary catalyst behind the\ndigitalization of power systems. Meanwhile, as the utilization rate of the\nInternet of Things (IoT) continues to rise along with recent advancements in\nICT, the need for secure and computationally efficient monitoring of critical\ninfrastructures like the electrical grid and the agents that participate in it\nis growing. A cyber-physical system, such as the electrical grid, may\nexperience anomalies for a number of different reasons. These may include\nphysical defects, mistakes in measurement and communication, cyberattacks, and\nother similar occurrences. The goal of this study is to emphasize what the most\ncommon incidents are with power systems and to give an overview and\nclassification of the most common ways to find problems, starting with the\nconsumer/prosumer end working up to the primary power producers. In addition,\nthis article aimed to discuss the methods and techniques, such as artificial\nintelligence (AI) that are used to identify anomalies in the power systems and\nmarkets.\n", "date": "2022-12-05", "categories": []}
{"id": "2212.02303", "title": "Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection", "abstract": "  A new Lossy Causal Temporal Convolutional Neural Network Autoencoder for\nanomaly detection is proposed in this work. Our framework uses a\nrate-distortion loss and an entropy bottleneck to learn a compressed latent\nrepresentation for the task. The main idea of using a rate-distortion loss is\nto introduce representation flexibility that ignores or becomes robust to\nunlikely events with distinctive patterns, such as anomalies. These anomalies\nmanifest as unique distortion features that can be accurately detected in\ntesting conditions. This new architecture allows us to train a fully\nunsupervised model that has high accuracy in detecting anomalies from a\ndistortion score despite being trained with some portion of unlabelled\nanomalous data. This setting is in stark contrast to many of the\nstate-of-the-art unsupervised methodologies that require the model to be only\ntrained on \"normal data\". We argue that this partially violates the concept of\nunsupervised training for anomaly detection as the model uses an informed\ndecision that selects what is normal from abnormal for training. Additionally,\nthere is evidence to suggest it also effects the models ability at\ngeneralisation. We demonstrate that models that succeed in the paradigm where\nthey are only trained on normal data fail to be robust when anomalous data is\ninjected into the training. In contrast, our compression-based approach\nconverges to a robust representation that tolerates some anomalous distortion.\nThe robust representation achieved by a model using a rate-distortion loss can\nbe used in a more realistic unsupervised anomaly detection scheme.\n", "date": "2022-12-05", "categories": []}
{"id": "2210.11068", "title": "Frequency of Interest-based Noise Attenuation Method to Improve Anomaly\n  Detection Performance", "abstract": "  Accurately extracting driving events is the way to maximize computational\nefficiency and anomaly detection performance in the tire frictional nose-based\nanomaly detection task. This study proposes a concise and highly useful method\nfor improving the precision of the event extraction that is hindered by extra\nnoise such as wind noise, which is difficult to characterize clearly due to its\nrandomness. The core of the proposed method is based on the identification of\nthe road friction sound corresponding to the frequency of interest and removing\nthe opposite characteristics with several frequency filters. Our method enables\nprecision maximization of driving event extraction while improving anomaly\ndetection performance by an average of 8.506%. Therefore, we conclude our\nmethod is a practical solution suitable for road surface anomaly detection\npurposes in outdoor edge computing environments.\n", "date": "2022-10-20", "categories": []}
{"id": "2212.00535", "title": "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks\n  with Augmented View", "abstract": "  Graph anomaly detection (GAD) is a vital task in graph-based machine learning\nand has been widely applied in many real-world applications. The primary goal\nof GAD is to capture anomalous nodes from graph datasets, which evidently\ndeviate from the majority of nodes. Recent methods have paid attention to\nvarious scales of contrastive strategies for GAD, i.e., node-subgraph and\nnode-node contrasts. However, they neglect the subgraph-subgraph comparison\ninformation which the normal and abnormal subgraph pairs behave differently in\nterms of embeddings and structures in GAD, resulting in sub-optimal task\nperformance. In this paper, we fulfill the above idea in the proposed\nmulti-view multi-scale contrastive learning framework with subgraph-subgraph\ncontrast for the first practice. To be specific, we regard the original input\ngraph as the first view and generate the second view by graph augmentation with\nedge modifications. With the guidance of maximizing the similarity of the\nsubgraph pairs, the proposed subgraph-subgraph contrast contributes to more\nrobust subgraph embeddings despite of the structure variation. Moreover, the\nintroduced subgraph-subgraph contrast cooperates well with the widely-adopted\nnode-subgraph and node-node contrastive counterparts for mutual GAD performance\npromotions. Besides, we also conduct sufficient experiments to investigate the\nimpact of different graph augmentation approaches on detection performance. The\ncomprehensive experimental results well demonstrate the superiority of our\nmethod compared with the state-of-the-art approaches and the effectiveness of\nthe multi-view subgraph pair contrastive strategy for the GAD task.\n", "date": "2022-12-01", "categories": []}
{"id": "2212.01096", "title": "Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive\n  Alignment", "abstract": "  Cross-domain graph anomaly detection (CD-GAD) describes the problem of\ndetecting anomalous nodes in an unlabelled target graph using auxiliary,\nrelated source graphs with labelled anomalous and normal nodes. Although it\npresents a promising approach to address the notoriously high false positive\nissue in anomaly detection, little work has been done in this line of research.\nThere are numerous domain adaptation methods in the literature, but it is\ndifficult to adapt them for GAD due to the unknown distributions of the\nanomalies and the complex node relations embedded in graph data. To this end,\nwe introduce a novel domain adaptation approach, namely Anomaly-aware\nContrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i)\nunsupervised contrastive learning of normal representations of nodes in the\ntarget graph, and (ii) anomaly-aware one-class alignment that aligns these\ncontrastive node representations and the representations of labelled normal\nnodes in the source graph, while enforcing significant deviation of the\nrepresentations of the normal nodes from the labelled anomalous nodes in the\nsource graph. In doing so, ACT effectively transfers anomaly-informed knowledge\nfrom the source graph to learn the complex node relations of the normal class\nfor GAD on the target graph without any specification of the anomaly\ndistributions. Extensive experiments on eight CD-GAD settings demonstrate that\nour approach ACT achieves substantially improved detection performance over 10\nstate-of-the-art GAD methods. Code is available at\nhttps://github.com/QZ-WANG/ACT.\n", "date": "2022-12-02", "categories": []}
{"id": "2212.00173", "title": "SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch", "abstract": "  Semi-supervised anomaly detection is a common problem, as often the datasets\ncontaining anomalies are partially labeled. We propose a canonical framework:\nSemi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that\nisn't limited by the assumption that labeled and unlabeled data come from the\nsame distribution. Indeed, the assumption is often violated in many\napplications - for example, the labeled data may contain only anomalies unlike\nunlabeled data, or unlabeled data may contain different types of anomalies, or\nlabeled data may contain only 'easy-to-label' samples. SPADE utilizes an\nensemble of one class classifiers as the pseudo-labeler to improve the\nrobustness of pseudo-labeling with distribution mismatch. Partial matching is\nproposed to automatically select the critical hyper-parameters for\npseudo-labeling without validation data, which is crucial with limited labeled\ndata. SPADE shows state-of-the-art semi-supervised anomaly detection\nperformance across a wide range of scenarios with distribution mismatch in both\ntabular and image domains. In some common real-world settings such as model\nfacing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art\nalternatives by 5% AUC in average.\n", "date": "2022-11-30", "categories": []}
{"id": "2212.00501", "title": "Crowd-level Abnormal Behavior Detection via Multi-scale Motion\n  Consistency Learning", "abstract": "  Detecting abnormal crowd motion emerging from complex interactions of\nindividuals is paramount to ensure the safety of crowds. Crowd-level abnormal\nbehaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the\ncrucial causes of many crowd disasters. In the recent decade, video anomaly\ndetection (VAD) techniques have achieved remarkable success in detecting\nindividual-level abnormal behaviors (e.g., sudden running, fighting and\nstealing), but research on VAD for CABs is rather limited. Unlike\nindividual-level anomaly, CABs usually do not exhibit salient difference from\nthe normal behaviors when observed locally, and the scale of CABs could vary\nfrom one scenario to another. In this paper, we present a systematic study to\ntackle the important problem of VAD for CABs with a novel crowd motion learning\nframework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first\ncaptures the spatial and temporal crowd motion consistency information in a\ngraph representation. Then, it simultaneously trains multiple feature graphs\nconstructed at different scales to capture rich crowd patterns. An attention\nnetwork is used to adaptively fuse the multi-scale features for better CAB\ndetection. For the empirical study, we consider three large-scale crowd event\ndatasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net\ncould substantially improve the state-of-the-art performance on all the\ndatasets.\n", "date": "2022-12-01", "categories": []}
{"id": "2211.15098", "title": "MGFN: Magnitude-Contrastive Glance-and-Focus Network for\n  Weakly-Supervised Video Anomaly Detection", "abstract": "  Weakly supervised detection of anomalies in surveillance videos is a\nchallenging task. Going beyond existing works that have deficient capabilities\nto localize anomalies in long videos, we propose a novel glance and focus\nnetwork to effectively integrate spatial-temporal information for accurate\nanomaly detection. In addition, we empirically found that existing approaches\nthat use feature magnitudes to represent the degree of anomalies typically\nignore the effects of scene variations, and hence result in sub-optimal\nperformance due to the inconsistency of feature magnitudes across scenes. To\naddress this issue, we propose the Feature Amplification Mechanism and a\nMagnitude Contrastive Loss to enhance the discriminativeness of feature\nmagnitudes for detecting anomalies. Experimental results on two large-scale\nbenchmarks UCF-Crime and XD-Violence manifest that our method outperforms\nstate-of-the-art approaches.\n", "date": "2022-11-28", "categories": []}
{"id": "2211.12311", "title": "Generalizable Industrial Visual Anomaly Detection with Self-Induction\n  Vision Transformer", "abstract": "  Industrial vision anomaly detection plays a critical role in the advanced\nintelligent manufacturing process, while some limitations still need to be\naddressed under such a context. First, existing reconstruction-based methods\nstruggle with the identity mapping of trivial shortcuts where the\nreconstruction error gap is legible between the normal and abnormal samples,\nleading to inferior detection capabilities. Then, the previous studies mainly\nconcentrated on the convolutional neural network (CNN) models that capture the\nlocal semantics of objects and neglect the global context, also resulting in\ninferior performance. Moreover, existing studies follow the individual learning\nfashion where the detection models are only capable of one category of the\nproduct while the generalizable detection for multiple categories has not been\nexplored. To tackle the above limitations, we proposed a self-induction vision\nTransformer(SIVT) for unsupervised generalizable multi-category industrial\nvisual anomaly detection and localization. The proposed SIVT first extracts\ndiscriminatory features from pre-trained CNN as property descriptors. Then, the\nself-induction vision Transformer is proposed to reconstruct the extracted\nfeatures in a self-supervisory fashion, where the auxiliary induction tokens\nare additionally introduced to induct the semantics of the original signal.\nFinally, the abnormal properties can be detected using the semantic feature\nresidual difference. We experimented with the SIVT on existing Mvtec AD\nbenchmarks, the results reveal that the proposed method can advance\nstate-of-the-art detection performance with an improvement of 2.8-6.3 in AUROC,\nand 3.3-7.6 in AP.\n", "date": "2022-11-22", "categories": []}
{"id": "2211.16023", "title": "Novelty Detection for Election Fraud: A Case Study with Agent-Based\n  Simulation Data", "abstract": "  In this paper, we propose a robust election simulation model and\nindependently developed election anomaly detection algorithm that demonstrates\nthe simulation's utility. The simulation generates artificial elections with\nsimilar properties and trends as elections from the real world, while giving\nusers control and knowledge over all the important components of the elections.\nWe generate a clean election results dataset without fraud as well as datasets\nwith varying degrees of fraud. We then measure how well the algorithm is able\nto successfully detect the level of fraud present. The algorithm determines how\nsimilar actual election results are as compared to the predicted results from\npolling and a regression model of other regions that have similar demographics.\nWe use k-means to partition electoral regions into clusters such that\ndemographic homogeneity is maximized among clusters. We then use a novelty\ndetection algorithm implemented as a one-class Support Vector Machine where the\nclean data is provided in the form of polling predictions and regression\npredictions. The regression predictions are built from the actual data in such\na way that the data supervises itself. We show both the effectiveness of the\nsimulation technique and the machine learning model in its success in\nidentifying fraudulent regions.\n", "date": "2022-11-29", "categories": []}
{"id": "2211.16092", "title": "Unsupervised Visual Defect Detection with Score-Based Generative Model", "abstract": "  Anomaly Detection (AD), as a critical problem, has been widely discussed. In\nthis paper, we specialize in one specific problem, Visual Defect Detection\n(VDD), in many industrial applications. And in practice, defect image samples\nare very rare and difficult to collect. Thus, we focus on the unsupervised\nvisual defect detection and localization tasks and propose a novel framework\nbased on the recent score-based generative models, which synthesize the real\nimage by iterative denoising through stochastic differential equations (SDEs).\nOur work is inspired by the fact that with noise injected into the original\nimage, the defects may be changed into normal cases in the denoising process\n(i.e., reconstruction). First, based on the assumption that the anomalous data\nlie in the low probability density region of the normal data distribution, we\nexplain a common phenomenon that occurs when reconstruction-based approaches\nare applied to VDD: normal pixels also change during the reconstruction\nprocess. Second, due to the differences in normal pixels between the\nreconstructed and original images, a time-dependent gradient value (i.e.,\nscore) of normal data distribution is utilized as a metric, rather than\nreconstruction loss, to gauge the defects. Third, a novel $T$ scales approach\nis developed to dramatically reduce the required number of iterations,\naccelerating the inference process. These practices allow our model to\ngeneralize VDD in an unsupervised manner while maintaining reasonably good\nperformance. We evaluate our method on several datasets to demonstrate its\neffectiveness.\n", "date": "2022-11-29", "categories": []}
{"id": "2211.16122", "title": "G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly\n  detection in sensor-based remote health monitoring", "abstract": "  Sensor-based remote health monitoring is used in industrial, urban and\nhealthcare settings to monitor ongoing operation of equipment and human health.\nAn important aim is to intervene early if anomalous events or adverse health is\ndetected. In the wild, these anomaly detection approaches are challenged by\nnoise, label scarcity, high dimensionality, explainability and wide variability\nin operating environments. The Contextual Matrix Profile (CMP) is a\nconfigurable 2-dimensional version of the Matrix Profile (MP) that uses the\ndistance matrix of all subsequences of a time series to discover patterns and\nanomalies. The CMP is shown to enhance the effectiveness of the MP and other\nSOTA methods at detecting, visualising and interpreting true anomalies in noisy\nreal world data from different domains. It excels at zooming out and\nidentifying temporal patterns at configurable time scales. However, the CMP\ndoes not address cross-sensor information, and cannot scale to high dimensional\ndata. We propose a novel, self-supervised graph-based approach for temporal\nanomaly detection that works on context graphs generated from the CMP distance\nmatrix. The learned graph embeddings encode the anomalous nature of a time\ncontext. In addition, we evaluate other graph outlier algorithms for the same\ntask. Given our pipeline is modular, graph construction, generation of graph\nembeddings, and pattern recognition logic can all be chosen based on the\nspecific pattern detection application. We verified the effectiveness of\ngraph-based anomaly detection and compared it with the CMP and 3 state-of-the\nart methods on two real-world healthcare datasets with different anomalies. Our\nproposed method demonstrated better recall, alert rate and generalisability.\n", "date": "2022-11-29", "categories": []}
{"id": "2210.17274", "title": "Anomaly Detection in Additive Manufacturing Processes using Supervised\n  Classification with Imbalanced Sensor Data based on Generative Adversarial\n  Network", "abstract": "  Supervised classification methods have been widely utilized for the quality\nassurance of the advanced manufacturing process, such as additive manufacturing\n(AM) for anomaly (defects) detection. However, since abnormal states (with\ndefects) occur much less frequently than normal ones (without defects) in a\nmanufacturing process, the number of sensor data samples collected from a\nnormal state is usually much more than that from an abnormal state. This issue\ncauses imbalanced training data for classification analysis, thus deteriorating\nthe performance of detecting abnormal states in the process. It is beneficial\nto generate effective artificial sample data for the abnormal states to make a\nmore balanced training set. To achieve this goal, this paper proposes a novel\ndata augmentation method based on a generative adversarial network (GAN) using\nadditive manufacturing process image sensor data. The novelty of our approach\nis that a standard GAN and classifier are jointly optimized with techniques to\nstabilize the learning process of standard GAN. The diverse and high-quality\ngenerated samples provide balanced training data to the classifier. The\niterative optimization between GAN and classifier provides the high-performance\nclassifier. The effectiveness of the proposed method is validated by both\nopen-source data and real-world case studies in polymer and metal AM processes.\n", "date": "2022-10-28", "categories": []}
{"id": "2211.10052", "title": "Pedestrian Spatio-Temporal Information Fusion For Video Anomaly\n  Detection", "abstract": "  Aiming at the problem that the current video anomaly detection cannot fully\nuse the temporal information and ignore the diversity of normal behavior, an\nanomaly detection method is proposed to integrate the spatiotemporal\ninformation of pedestrians. Based on the convolutional autoencoder, the input\nframe is compressed and restored through the encoder and decoder. Anomaly\ndetection is realized according to the difference between the output frame and\nthe true value. In order to strengthen the characteristic information\nconnection between continuous video frames, the residual temporal shift module\nand the residual channel attention module are introduced to improve the\nmodeling ability of the network on temporal information and channel\ninformation, respectively. Due to the excessive generalization of convolutional\nneural networks, in the memory enhancement modules, the hopping connections of\neach codec layer are added to limit autoencoders' ability to represent abnormal\nframes too vigorously and improve the anomaly detection accuracy of the\nnetwork. In addition, the objective function is modified by a feature\ndiscretization loss, which effectively distinguishes different normal behavior\npatterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets\nshow that the proposed method is superior to the current mainstream video\nanomaly detection methods while meeting the real-time requirements.\n", "date": "2022-11-18", "categories": []}
{"id": "2211.12141", "title": "MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate\n  Time Series", "abstract": "  Anomaly detection of time series, especially multivariate time series(time\nseries with multiple sensors), has been focused on for several years. Though\nexisting method has achieved great progress, there are several challenging\nproblems to be solved. Firstly, existing method including neural network only\nconcentrate on the relationship in terms of timestamp. To be exact, they only\nwant to know how does the data in the past influence which in the future.\nHowever, one sensor sometimes intervenes in other sensor such as the speed of\nwind may cause decrease of temperature. Secondly, there exist two categories of\nmodel for time series anomaly detection: prediction model and reconstruction\nmodel. Prediction model is adept at learning timely representation while short\nof capability when faced with sparse anomaly. Conversely, reconstruction model\nis opposite. Therefore, how can we efficiently get the relationship both in\nterms of both timestamp and sensors becomes our main topic. Our approach uses\nGAT, which is originated from graph neural network, to obtain connection\nbetween sensors. And LSTM is used to obtain relationships timely. Our approach\nis also designed to be double headed to calculate both prediction loss and\nreconstruction loss via VAE(Variational Auto-Encoder). In order to take\nadvantage of two sorts of model, multi-task optimization algorithm is used in\nthis model.\n", "date": "2022-11-22", "categories": []}
{"id": "2211.13968", "title": "MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly\n  Detection", "abstract": "  Visual anomaly detection plays a crucial role in not only manufacturing\ninspection to find defects of products during manufacturing processes, but also\nmaintenance inspection to keep equipment in optimum working condition\nparticularly outdoors. Due to the scarcity of the defective samples,\nunsupervised anomaly detection has attracted great attention in recent years.\nHowever, existing datasets for unsupervised anomaly detection are biased\ntowards manufacturing inspection, not considering maintenance inspection which\nis usually conducted under outdoor uncontrolled environment such as varying\ncamera viewpoints, messy background and degradation of object surface after\nlong-term working. We focus on outdoor maintenance inspection and contribute a\ncomprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which\ncontains more than 100K high-resolution color images in various outdoor\nindustrial scenarios. This dataset is generated by a 3D graphics software and\ncovers both surface and logical anomalies with pixel-precise ground truth.\nExtensive evaluations of representative algorithms for unsupervised anomaly\ndetection are conducted, and we expect MIAD and corresponding experimental\nresults can inspire research community in outdoor unsupervised anomaly\ndetection tasks. Worthwhile and related future work can be spawned from our new\ndataset.\n", "date": "2022-11-25", "categories": []}
{"id": "2211.14938", "title": "An Anomaly Detection Method for Satellites Using Monte Carlo Dropout", "abstract": "  Recently, there has been a significant amount of interest in satellite\ntelemetry anomaly detection (AD) using neural networks (NN). For AD purposes,\nthe current approaches focus on either forecasting or reconstruction of the\ntime series, and they cannot measure the level of reliability or the\nprobability of correct detection. Although the Bayesian neural network\n(BNN)-based approaches are well known for time series uncertainty estimation,\nthey are computationally intractable. In this paper, we present a tractable\napproximation for BNN based on the Monte Carlo (MC) dropout method for\ncapturing the uncertainty in the satellite telemetry time series, without\nsacrificing accuracy. For time series forecasting, we employ an NN, which\nconsists of several Long Short-Term Memory (LSTM) layers followed by various\ndense layers. We employ the MC dropout inside each LSTM layer and before the\ndense layers for uncertainty estimation. With the proposed uncertainty region\nand by utilizing a post-processing filter, we can effectively capture the\nanomaly points. Numerical results show that our proposed time series AD\napproach outperforms the existing methods from both prediction accuracy and AD\nperspectives.\n", "date": "2022-11-27", "categories": []}
{"id": "2211.15527", "title": "A Study of Representational Properties of Unsupervised Anomaly Detection\n  in Brain MRI", "abstract": "  Anomaly detection in MRI is of high clinical value in imaging and diagnosis.\nUnsupervised methods for anomaly detection provide interesting formulations\nbased on reconstruction or latent embedding, offering a way to observe\nproperties related to factorization. We study four existing modeling methods,\nand report our empirical observations using simple data science tools, to seek\noutcomes from the perspective of factorization as it would be most relevant to\nthe task of unsupervised anomaly detection, considering the case of brain\nstructural MRI. Our study indicates that anomaly detection algorithms that\nexhibit factorization related properties are well capacitated with delineatory\ncapabilities to distinguish between normal and anomaly data. We have validated\nour observations in multiple anomaly and normal datasets.\n", "date": "2022-11-28", "categories": []}
{"id": "2208.01521", "title": "DSR -- A dual subspace re-projection network for surface anomaly\n  detection", "abstract": "  The state-of-the-art in discriminative unsupervised surface anomaly detection\nrelies on external datasets for synthesizing anomaly-augmented training images.\nSuch approaches are prone to failure on near-in-distribution anomalies since\nthese are difficult to be synthesized realistically due to their similarity to\nanomaly-free regions. We propose an architecture based on quantized feature\nspace representation with dual decoders, DSR, that avoids the image-level\nanomaly synthesis requirement. Without making any assumptions about the visual\nproperties of anomalies, DSR generates the anomalies at the feature level by\nsampling the learned quantized feature space, which allows a controlled\ngeneration of near-in-distribution anomalies. DSR achieves state-of-the-art\nresults on the KSDD2 and MVTec anomaly detection datasets. The experiments on\nthe challenging real-world KSDD2 dataset show that DSR significantly\noutperforms other unsupervised surface anomaly detection methods, improving the\nprevious top-performing methods by 10% AP in anomaly detection and 35% AP in\nanomaly localization.\n", "date": "2022-08-02", "categories": []}
{"id": "2211.07747", "title": "Detection of fraudulent financial papers by picking a collection of\n  characteristics using optimization algorithms and classification techniques\n  based on squirrels", "abstract": "  To produce important investment decisions, investors require financial\nrecords and economic information. However, most companies manipulate investors\nand financial institutions by inflating their financial statements. Fraudulent\nFinancial Activities exist in any monetary or financial transaction scenario,\nwhether physical or electronic. A challenging problem that arises in this\ndomain is the issue that affects and troubles individuals and institutions.\nThis problem has attracted more attention in the field in part owing to the\nprevalence of financial fraud and the paucity of previous research. For this\npurpose, in this study, the main approach to solve this problem, an anomaly\ndetection-based approach based on a combination of feature selection based on\nsquirrel optimization pattern and classification methods have been used. The\naim is to develop this method to provide a model for detecting anomalies in\nfinancial statements using a combination of selected features with the nearest\nneighbor classifications, neural networks, support vector machine, and\nBayesian. Anomaly samples are then analyzed and compared to recommended\ntechniques using assessment criteria. Squirrel optimization's meta-exploratory\ncapability, along with the approach's ability to identify abnormalities in\nfinancial data, has been shown to be effective in implementing the suggested\nstrategy. They discovered fake financial statements because of their expertise.\n", "date": "2022-10-12", "categories": []}
{"id": "2211.13681", "title": "Meta-Learning for Automated Selection of Anomaly Detectors for\n  Semi-Supervised Datasets", "abstract": "  In anomaly detection, a prominent task is to induce a model to identify\nanomalies learned solely based on normal data. Generally, one is interested in\nfinding an anomaly detector that correctly identifies anomalies, i.e., data\npoints that do not belong to the normal class, without raising too many false\nalarms. Which anomaly detector is best suited depends on the dataset at hand\nand thus needs to be tailored. The quality of an anomaly detector may be\nassessed via confusion-based metrics such as the Matthews correlation\ncoefficient (MCC). However, since during training only normal data is available\nin a semi-supervised setting, such metrics are not accessible. To facilitate\nautomated machine learning for anomaly detectors, we propose to employ\nmeta-learning to predict MCC scores based on metrics that can be computed with\nnormal data only. First promising results can be obtained considering the\nhypervolume and the false positive rate as meta-features.\n", "date": "2022-11-24", "categories": []}
{"id": "2211.13808", "title": "Detecting Anomalies using Generative Adversarial Networks on Images", "abstract": "  Automatic detection of anomalies such as weapons or threat objects in baggage\nsecurity, or detecting impaired items in industrial production is an important\ncomputer vision task demanding high efficiency and accuracy. Most of the\navailable data in the anomaly detection task is imbalanced as the number of\npositive/anomalous instances is sparse. Inadequate availability of the data\nmakes training of a deep neural network architecture for anomaly detection\nchallenging. This paper proposes a novel Generative Adversarial Network (GAN)\nbased model for anomaly detection. It uses normal (non-anomalous) images to\nlearn about the normality based on which it detects if an input image contains\nan anomalous/threat object. The proposed model uses a generator with an\nencoder-decoder network having dense convolutional skip connections for\nenhanced reconstruction and to capture the data distribution. A self-attention\naugmented discriminator is used having the ability to check the consistency of\ndetailed features even in distant portions. We use spectral normalisation to\nfacilitate stable and improved training of the GAN. Experiments are performed\non three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) and\nSIXray (for X-ray baggage security). On the MVTec AD and SIXray datasets, our\nmodel achieves an improvement of upto 21% and 4.6%, respectively\n", "date": "2022-11-24", "categories": []}
{"id": "2211.13900", "title": "A Deep Learning Anomaly Detection Method in Textual Data", "abstract": "  In this article, we propose using deep learning and transformer architectures\ncombined with classical machine learning algorithms to detect and identify text\nanomalies in texts. Deep learning model provides a very crucial context\ninformation about the textual data which all textual context are converted to a\nnumerical representation. We used multiple machine learning methods such as\nSentence Transformers, Auto Encoders, Logistic Regression and Distance\ncalculation methods to predict anomalies. The method are tested on the texts\ndata and we used syntactic data from different source injected into the\noriginal text as anomalies or use them as target. Different methods and\nalgorithm are explained in the field of outlier detection and the results of\nthe best technique is presented. These results suggest that our algorithm could\npotentially reduce false positive rates compared with other anomaly detection\nmethods that we are testing.\n", "date": "2022-11-25", "categories": []}
{"id": "2208.03288", "title": "Convolutional Ensembling based Few-Shot Defect Detection Technique", "abstract": "  Over the past few years, there has been a significant improvement in the\ndomain of few-shot learning. This learning paradigm has shown promising results\nfor the challenging problem of anomaly detection, where the general task is to\ndeal with heavy class imbalance. Our paper presents a new approach to few-shot\nclassification, where we employ the knowledge-base of multiple pre-trained\nconvolutional models that act as the backbone for our proposed few-shot\nframework. Our framework uses a novel ensembling technique for boosting the\naccuracy while drastically decreasing the total parameter count, thus paving\nthe way for real-time implementation. We perform an extensive hyperparameter\nsearch using a power-line defect detection dataset and obtain an accuracy of\n92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model\non competing standards with the existing state-of-the-art methods and\noutperform them.\n", "date": "2022-08-05", "categories": []}
{"id": "2208.11290", "title": "ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels", "abstract": "  Existing works on anomaly detection (AD) rely on clean labels from human\nannotators that are expensive to acquire in practice. In this work, we propose\na method to leverage weak/noisy labels (e.g., risk scores generated by machine\nrules for detecting malware) that are cheaper to obtain for anomaly detection.\nSpecifically, we propose ADMoE, the first framework for anomaly detection\nalgorithms to learn from noisy labels. In a nutshell, ADMoE leverages\nmixture-of-experts (MoE) architecture to encourage specialized and scalable\nlearning from multiple noisy sources. It captures the similarities among noisy\nlabels by sharing most model parameters, while encouraging specialization by\nbuilding \"expert\" sub-networks. To further juice out the signals from noisy\nlabels, ADMoE uses them as input features to facilitate expert learning.\nExtensive results on eight datasets (including a proprietary enterprise\nsecurity dataset) demonstrate the effectiveness of ADMoE, where it brings up to\n34% performance improvement over not using it. Also, it outperforms a total of\n13 leading baselines with equivalent network parameters and FLOPS. Notably,\nADMoE is model-agnostic to enable any neural network-based detection methods to\nhandle noisy labels, where we showcase its results on both multiple-layer\nperceptron (MLP) and the leading AD method DeepSAD.\n", "date": "2022-08-24", "categories": []}
{"id": "2211.12565", "title": "A Novel Center-based Deep Contrastive Metric Learning Method for the\n  Detection of Polymicrogyria in Pediatric Brain MRI", "abstract": "  Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in\nchildren, which can be associated with seizures, developmental delay and motor\nweakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but\nsome cases can be challenging to detect even for experienced radiologists. In\nthis study, we create an open pediatric MRI dataset (PPMR) with PMG and\ncontrols from the Children's Hospital of Eastern Ontario (CHEO), Ottawa,\nCanada. The differences between PMG MRIs and control MRIs are subtle and the\ntrue distribution of the features of the disease is unknown. This makes\nautomatic detection of cases of potential PMG in MRI difficult. We propose an\nanomaly detection method based on a novel center-based deep contrastive metric\nlearning loss function (cDCM) which enables the automatic detection of cases of\npotential PMG. Additionally, based on our proposed loss function, we customize\na deep learning model structure that integrates dilated convolution,\nsqueeze-and-excitation blocks and feature fusion for our PPMR dataset. Despite\nworking with a small and imbalanced dataset our method achieves 92.01% recall\nat 55.04% precision. This will facilitate a computer aided tool for\nradiologists to select potential PMG MRIs. To the best of our knowledge, this\nresearch is the first to apply machine learning techniques to identify PMG from\nMRI only.\n", "date": "2022-11-22", "categories": []}
{"id": "2211.12650", "title": "FRE: A Fast Method For Anomaly Detection And Segmentation", "abstract": "  This paper presents a fast and principled approach for solving the visual\nanomaly detection and segmentation problem. In this setup, we have access to\nonly anomaly-free training data and want to detect and identify anomalies of an\narbitrary nature on test data. We propose the application of linear statistical\ndimensionality reduction techniques on the intermediate features produced by a\npretrained DNN on the training data, in order to capture the low-dimensional\nsubspace truly spanned by said features. We show that the \\emph{feature\nreconstruction error} (FRE), which is the $\\ell_2$-norm of the difference\nbetween the original feature in the high-dimensional space and the pre-image of\nits low-dimensional reduced embedding, is extremely effective for anomaly\ndetection. Further, using the same feature reconstruction error concept on\nintermediate convolutional layers, we derive FRE maps that provide pixel-level\nspatial localization of the anomalies in the image (i.e. segmentation).\nExperiments using standard anomaly detection datasets and DNN architectures\ndemonstrate that our method matches or exceeds best-in-class quality\nperformance, but at a fraction of the computational and memory cost required by\nthe state of the art. It can be trained and run very efficiently, even on a\ntraditional CPU.\n", "date": "2022-11-23", "categories": []}
{"id": "2206.05876", "title": "Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised\n  Anomalous Sound Detection for Machine Condition Monitoring Applying Domain\n  Generalization Techniques", "abstract": "  We present the task description and discussion on the results of the DCASE\n2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for\nmachine condition monitoring applying domain generalization techniques''.\nDomain shifts are a critical problem for the application of ASD systems.\nBecause domain shifts can change the acoustic characteristics of data, a model\ntrained in a source domain performs poorly for a target domain. In DCASE 2021\nChallenge Task 2, we organized an ASD task for handling domain shifts. In this\ntask, it was assumed that the occurrences of domain shifts are known. However,\nin practice, the domain of each sample may not be given, and the domain shifts\ncan occur implicitly. In 2022 Task 2, we focus on domain generalization\ntechniques that detects anomalies regardless of the domain shifts.\nSpecifically, the domain of each sample is not given in the test data and only\none threshold is allowed for all domains. Analysis of 81 submissions from 31\nteams revealed two remarkable types of domain generalization techniques: 1)\ndomain-mixing-based approach that obtains generalized representations and 2)\ndomain-classification-based approach that explicitly or implicitly classifies\ndifferent domains to improve detection performance for each domain.\n", "date": "2022-06-13", "categories": []}
{"id": "2210.09572", "title": "Spatio-Temporal-based Context Fusion for Video Anomaly Detection", "abstract": "  Video anomaly detection aims to discover abnormal events in videos, and the\nprincipal objects are target objects such as people and vehicles. Each target\nin the video data has rich spatio-temporal context information. Most existing\nmethods only focus on the temporal context, ignoring the role of the spatial\ncontext in anomaly detection. The spatial context information represents the\nrelationship between the detection target and surrounding targets. Anomaly\ndetection makes a lot of sense. To this end, a video anomaly detection\nalgorithm based on target spatio-temporal context fusion is proposed. Firstly,\nthe target in the video frame is extracted through the target detection network\nto reduce background interference. Then the optical flow map of two adjacent\nframes is calculated. Motion features are used multiple targets in the video\nframe to construct spatial context simultaneously, re-encoding the target\nappearance and motion features, and finally reconstructing the above features\nthrough the spatio-temporal dual-stream network, and using the reconstruction\nerror to represent the abnormal score. The algorithm achieves frame-level AUCs\nof 98.5% and 86.3% on the UCSDped2 and Avenue datasets, respectively. On the\nUCSDped2 dataset, the spatio-temporal dual-stream network improves frames by\n5.1% and 0.3%, respectively, compared to the temporal and spatial stream\nnetworks. After using spatial context encoding, the frame-level AUC is enhanced\nby 1%, which verifies the method's effectiveness.\n", "date": "2022-10-18", "categories": []}
{"id": "2210.11833", "title": "Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with\n  Synthetic Data", "abstract": "  Ground Penetrating Radar (GPR) has been widely used to estimate the healthy\noperation of some urban roads and underground facilities. When identifying\nsubsurface anomalies by GPR in an area, the obtained data could be unbalanced,\nand the numbers and types of possible underground anomalies could not be\nacknowledged in advance. In this paper, a novel method is proposed to improve\nthe subsurface anomaly detection from GPR B-scan images. A normal (i.e. without\nsubsurface objects) GPR image section is firstly collected in the detected\narea. Concerning that the GPR image is essentially the representation of\nelectromagnetic (EM) wave and propagation time, and to preserve both the\nsubsurface background and objects' details, the normal GPR image is segmented\nand then fused with simulated GPR images that contain different kinds of\nobjects to generate the synthetic data for the detection area based on the\nwavelet decompositions. Pre-trained CNNs could then be fine-tuned with the\nsynthetic data, and utilized to extract features of segmented GPR images\nsubsequently obtained in the detection area. The extracted features could be\nclassified by the one-class learning algorithm in the feature space without\npre-set anomaly types or numbers. The conducted experiments demonstrate that\nfine-tuning the pre-trained CNN with the proposed synthetic data could\neffectively improve the feature extraction of the network for the objects in\nthe detection area. Besides, the proposed method requires only a section of\nnormal data that could be easily obtained in the detection area, and could also\nmeet the timeliness requirements in practical applications.\n", "date": "2022-10-21", "categories": []}
{"id": "2211.11013", "title": "Machine Learning Methods for Anomaly Detection in Nuclear Power Plant\n  Power Transformers", "abstract": "  Power transformers are an important component of a nuclear power plant (NPP).\nCurrently, the NPP operates a lot of power transformers with extended service\nlife, which exceeds the designated 25 years. Due to the extension of the\nservice life, the task of monitoring the technical condition of power\ntransformers becomes urgent. An important method for monitoring power\ntransformers is Chromatographic Analysis of Dissolved Gas. It is based on the\nprinciple of controlling the concentration of gases dissolved in transformer\noil. The appearance of almost any type of defect in equipment is accompanied by\nthe formation of gases that dissolve in oil, and specific types of defects\ngenerate their gases in different quantities. At present, at NPPs, the\nmonitoring systems for transformer equipment use predefined control limits for\nthe concentration of dissolved gases in the oil. This study describes the\nstages of developing an algorithm to detect defects and faults in transformers\nautomatically using machine learning and data analysis methods. Among machine\nlearning models, we trained Logistic Regression, Decision Trees, Random Forest,\nGradient Boosting, Neural Networks. The best of them were then combined into an\nensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To\ndevelop mathematical models, we used data on the state of transformers,\ncontaining time series with values of gas concentrations (H2, CO, C2H4, C2H2).\nThe datasets were labeled and contained four operating modes: normal mode,\npartial discharge, low energy discharge, low-temperature overheating.\n", "date": "2022-11-20", "categories": []}
{"id": "2211.10129", "title": "Rare Yet Popular: Evidence and Implications from Labeled Datasets for\n  Network Anomaly Detection", "abstract": "  Anomaly detection research works generally propose algorithms or end-to-end\nsystems that are designed to automatically discover outliers in a dataset or a\nstream. While literature abounds concerning algorithms or the definition of\nmetrics for better evaluation, the quality of the ground truth against which\nthey are evaluated is seldom questioned. In this paper, we present a systematic\nanalysis of available public (and additionally our private) ground truth for\nanomaly detection in the context of network environments, where data is\nintrinsically temporal, multivariate and, in particular, exhibits spatial\nproperties, which, to the best of our knowledge, we are the first to explore.\nOur analysis reveals that, while anomalies are, by definition, temporally rare\nevents, their spatial characterization clearly shows some type of anomalies are\nsignificantly more popular than others. We find that simple clustering can\nreduce the need for human labeling by a factor of 2x-10x, that we are first to\nquantitatively analyze in the wild.\n", "date": "2022-11-18", "categories": []}
{"id": "2211.08525", "title": "LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection", "abstract": "  This paper presents an anomaly detection model that combines the strong\nstatistical foundation of density-estimation-based anomaly detection methods\nwith the representation-learning ability of deep-learning models. The method\ncombines an autoencoder, for learning a low-dimensional representation of the\ndata, with a density-estimation model based on random Fourier features and\ndensity matrices in an end-to-end architecture that can be trained using\ngradient-based optimization techniques. The method predicts a degree of\nnormality for new samples based on the estimated density. A systematic\nexperimental evaluation was performed on different benchmark datasets. The\nexperimental results show that the method performs on par with or outperforms\nother state-of-the-art methods.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.08910", "title": "On the Connection of Generative Models and Discriminative Models for\n  Anomaly Detection", "abstract": "  Anomaly detection (AD) has attracted considerable attention in both academia\nand industry. Due to the lack of anomalous data in many practical cases, AD is\nusually solved by first modeling the normal data pattern and then determining\nif data fit this model. Generative models (GMs) seem a natural tool to achieve\nthis purpose, which learn the normal data distribution and estimate it using a\nprobability density function (PDF). However, some works have observed the ideal\nperformance of such GM-based AD methods. In this paper, we propose a new\nperspective on the ideal performance of GM-based AD methods. We state that in\nthese methods, the implicit assumption that connects GMs'results to AD's goal\nis usually implausible due to normal data's multi-peaked distribution\ncharacteristic, which is quite common in practical cases. We first\nqualitatively formulate this perspective, and then focus on the Gaussian\nmixture model (GMM) to intuitively illustrate the perspective, which is a\ntypical GM and has the natural property to approximate multi-peaked\ndistributions. Based on the proposed perspective, in order to bypass the\nimplicit assumption in the GMM-based AD method, we suggest integrating the\nDiscriminative idea to orient GMM to AD tasks (DiGMM). With DiGMM, we establish\na connection of generative and discriminative models, which are two key\nparadigms for AD and are usually treated separately before. This connection\nprovides a possible direction for future works to jointly consider the two\nparadigms and incorporate their complementary characteristics for AD.\n", "date": "2022-11-16", "categories": []}
{"id": "2211.08378", "title": "Anomaly Detection in Multiplex Dynamic Networks: from Blockchain\n  Security to Brain Disease Prediction", "abstract": "  The problem of identifying anomalies in dynamic networks is a fundamental\ntask with a wide range of applications. However, it raises critical challenges\ndue to the complex nature of anomalies, lack of ground truth knowledge, and\ncomplex and dynamic interactions in the network. Most existing approaches\nusually study networks with a single type of connection between vertices, while\nin many applications interactions between objects vary, yielding multiplex\nnetworks. We propose ANOMULY, a general, unsupervised edge anomaly detection\nframework for multiplex dynamic networks. In each relation type, ANOMULY sees\nnode embeddings at different GNN layers as hierarchical node states and employs\na GRU cell to capture temporal properties of the network and update node\nembeddings over time. We then add an attention mechanism that incorporates\ninformation across different types of relations. Our case study on brain\nnetworks shows how this approach could be employed as a new tool to understand\nabnormal brain activity that might reveal a brain disease or disorder.\nExtensive experiments on nine real-world datasets demonstrate that ANOMULY\nachieves state-of-the-art performance.\n", "date": "2022-11-15", "categories": []}
{"id": "2211.08415", "title": "Online Anomalous Subtrajectory Detection on Road Networks with Deep\n  Reinforcement Learning", "abstract": "  Detecting anomalous trajectories has become an important task in many\nlocation-based applications. While many approaches have been proposed for this\ntask, they suffer from various issues including (1) incapability of detecting\nanomalous subtrajectories, which are finer-grained anomalies in trajectory\ndata, and/or (2) non-data driven, and/or (3) requirement of sufficient\nsupervision labels which are costly to collect. In this paper, we propose a\nnovel reinforcement learning based solution called RL4OASD, which avoids all\naforementioned issues of existing approaches. RL4OASD involves two networks,\none responsible for learning features of road networks and trajectories and the\nother responsible for detecting anomalous subtrajectories based on the learned\nfeatures, and the two networks can be trained iteratively without labeled data.\nExtensive experiments are conducted on two real datasets, and the results show\nthat our solution can significantly outperform the state-of-the-art methods\n(with 20-30% improvement) and is efficient for online detection (it takes less\nthan 0.1ms to process each newly generated data point).\n", "date": "2022-11-12", "categories": []}
{"id": "2211.04550", "title": "OutlierDetection.jl: A modular outlier detection ecosystem for the Julia\n  programming language", "abstract": "  OutlierDetection.jl is an open-source ecosystem for outlier detection in\nJulia. It provides a range of high-performance outlier detection algorithms\nimplemented directly in Julia. In contrast to previous packages, our ecosystem\nenables the development highly-scalable outlier detection algorithms using a\nhigh-level programming language. Additionally, it provides a standardized, yet\nflexible, interface for future outlier detection algorithms and allows for\nmodel composition unseen in previous packages. Best practices such as unit\ntesting, continuous integration, and code coverage reporting are enforced\nacross the ecosystem. The most recent version of OutlierDetection.jl is\navailable at https://github.com/OutlierDetectionJL/OutlierDetection.jl.\n", "date": "2022-11-08", "categories": []}
{"id": "2211.04293", "title": "Evaluation of Color Anomaly Detection in Multispectral Images For\n  Synthetic Aperture Sensing", "abstract": "  In this article, we evaluate unsupervised anomaly detection methods in\nmultispectral images obtained with a wavelength-independent synthetic aperture\nsensing technique, called Airborne Optical Sectioning (AOS). With a focus on\nsearch and rescue missions that apply drones to locate missing or injured\npersons in dense forest and require real-time operation, we evaluate runtime\nvs. quality of these methods. Furthermore, we show that color anomaly detection\nmethods that normally operate in the visual range always benefit from an\nadditional far infrared (thermal) channel. We also show that, even without\nadditional thermal bands, the choice of color space in the visual range already\nhas an impact on the detection results. Color spaces like HSV and HLS have the\npotential to outperform the widely used RGB color space, especially when color\nanomaly detection is used for forest-like environments.\n", "date": "2022-11-08", "categories": []}
{"id": "2211.01834", "title": "Toward Unsupervised Outlier Model Selection", "abstract": "  Today there exists no shortage of outlier detection algorithms in the\nliterature, yet the complementary and critical problem of unsupervised outlier\nmodel selection (UOMS) is vastly understudied. In this work we propose ELECT, a\nnew approach to select an effective candidate model, i.e. an outlier detection\nalgorithm and its hyperparameter(s), to employ on a new dataset without any\nlabels. At its core, ELECT is based on meta-learning; transferring prior\nknowledge (e.g. model performance) on historical datasets that are similar to\nthe new one to facilitate UOMS. Uniquely, it employs a dataset similarity\nmeasure that is performance-based, which is more direct and goal-driven than\nother measures used in the past. ELECT adaptively searches for similar\nhistorical datasets, as such, it can serve an output on-demand, being able to\naccommodate varying time budgets. Extensive experiments show that ELECT\nsignificantly outperforms a wide range of basic UOMS baselines, including no\nmodel selection (always using the same popular model such as iForest) as well\nas more recent selection strategies based on meta-features.\n", "date": "2022-11-03", "categories": []}
{"id": "2211.00277", "title": "HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly\n  Detection", "abstract": "  Network or physical attacks on industrial equipment or computer systems may\ncause massive losses. Therefore, a quick and accurate anomaly detection (AD)\nbased on monitoring data, especially the multivariate time-series (MTS) data,\nis of great significance. As the key step of anomaly detection for MTS data,\nlearning the relations among different variables has been explored by many\napproaches. However, most of the existing approaches do not consider the\nheterogeneity between variables, that is, different types of variables\n(continuous numerical variables, discrete categorical variables or hybrid\nvariables) may have different and distinctive edge distributions. In this\npaper, we propose a novel semi-supervised anomaly detection framework based on\na heterogeneous feature network (HFN) for MTS, learning heterogeneous structure\ninformation from a mass of unlabeled time-series data to improve the accuracy\nof anomaly detection, and using attention coefficient to provide an explanation\nfor the detected anomalies. Specifically, we first combine the embedding\nsimilarity subgraph generated by sensor embedding and feature value similarity\nsubgraph generated by sensor values to construct a time-series heterogeneous\ngraph, which fully utilizes the rich heterogeneous mutual information among\nvariables. Then, a prediction model containing nodes and channel attentions is\njointly optimized to obtain better time-series representations. This approach\nfuses the state-of-the-art technologies of heterogeneous graph structure\nlearning (HGSL) and representation learning. The experiments on four sensor\ndatasets from real-world applications demonstrate that our approach detects the\nanomalies more accurately than those baseline approaches, thus providing a\nbasis for the rapid positioning of anomalies.\n", "date": "2022-11-01", "categories": []}
{"id": "2211.00829", "title": "Exploiting Spatial-temporal Correlations for Video Anomaly Detection", "abstract": "  Video anomaly detection (VAD) remains a challenging task in the pattern\nrecognition community due to the ambiguity and diversity of abnormal events.\nExisting deep learning-based VAD methods usually leverage proxy tasks to learn\nthe normal patterns and discriminate the instances that deviate from such\npatterns as abnormal. However, most of them do not take full advantage of\nspatial-temporal correlations among video frames, which is critical for\nunderstanding normal patterns. In this paper, we address unsupervised VAD by\nlearning the evolution regularity of appearance and motion in the long and\nshort-term and exploit the spatial-temporal correlations among consecutive\nframes in normal videos more adequately. Specifically, we proposed to utilize\nthe spatiotemporal long short-term memory (ST-LSTM) to extract and memorize\nspatial appearances and temporal variations in a unified memory cell. In\naddition, inspired by the generative adversarial network, we introduce a\ndiscriminator to perform adversarial learning with the ST-LSTM to enhance the\nlearning capability. Experimental results on standard benchmarks demonstrate\nthe effectiveness of spatial-temporal correlations for unsupervised VAD. Our\nmethod achieves competitive performance compared to the state-of-the-art\nmethods with AUCs of 96.7%, 87.8%, and 73.1% on the UCSD Ped2, CUHK Avenue, and\nShanghaiTech, respectively.\n", "date": "2022-11-02", "categories": []}
{"id": "2211.00882", "title": "DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection\n  Network", "abstract": "  Unsupervised approaches for video anomaly detection may not perform as good\nas supervised approaches. However, learning unknown types of anomalies using an\nunsupervised approach is more practical than a supervised approach as\nannotation is an extra burden. In this paper, we use isolation tree-based\nunsupervised clustering to partition the deep feature space of the video\nsegments. The RGB- stream generates a pseudo anomaly score and the flow stream\ngenerates a pseudo dynamicity score of a video segment. These scores are then\nfused using a majority voting scheme to generate preliminary bags of positive\nand negative segments. However, these bags may not be accurate as the scores\nare generated only using the current segment which does not represent the\nglobal behavior of a typical anomalous event. We then use a refinement strategy\nbased on a cross-branch feed-forward network designed using a popular I3D\nnetwork to refine both scores. The bags are then refined through a segment\nre-mapping strategy. The intuition of adding the dynamicity score of a segment\nwith the anomaly score is to enhance the quality of the evidence. The method\nhas been evaluated on three popular video anomaly datasets, i.e., UCF-Crime,\nCCTV-Fights, and UBI-Fights. Experimental results reveal that the proposed\nframework achieves competitive accuracy as compared to the state-of-the-art\nvideo anomaly detection methods.\n", "date": "2022-11-02", "categories": []}
{"id": "2210.12767", "title": "Falsehoods that ML researchers believe about OOD detection", "abstract": "  An intuitive way to detect out-of-distribution (OOD) data is via the density\nfunction of a fitted probabilistic generative model: points with low density\nmay be classed as OOD. But this approach has been found to fail, in deep\nlearning settings. In this paper, we list some falsehoods that machine learning\nresearchers believe about density-based OOD detection. Many recent works have\nproposed likelihood-ratio-based methods to `fix' the problem. We propose a\nframework, the OOD proxy framework, to unify these methods, and we argue that\nlikelihood ratio is a principled method for OOD detection and not a mere `fix'.\nFinally, we discuss the relationship between domain discrimination and\nsemantics.\n", "date": "2022-10-23", "categories": []}
{"id": "2211.00349", "title": "Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual\n  Anomaly Detector", "abstract": "  Unsupervised visual anomaly detection conveys practical significance in many\nscenarios and is a challenging task due to the unbounded definition of\nanomalies. Moreover, most previous methods are application-specific, and\nestablishing a unified model for anomalies across application scenarios remains\nunsolved. This paper proposes a novel hybrid framework termed Siamese\nTransition Masked Autoencoders(ST-MAE) to handle various visual anomaly\ndetection tasks uniformly via deep feature transition. Concretely, the proposed\nmethod first extracts hierarchical semantics features from a pre-trained deep\nconvolutional neural network and then develops a feature decoupling strategy to\nsplit the deep features into two disjoint feature patch subsets. Leveraging the\ndecoupled features, the ST-MAE is developed with the Siamese encoders that\noperate on each subset of feature patches and perform the latent\nrepresentations transition of two subsets, along with a lightweight decoder\nthat reconstructs the original feature from the transitioned latent\nrepresentation. Finally, the anomalous attributes can be detected using the\nsemantic deep feature residual. Our deep feature transition scheme yields a\nnontrivial and semantic self-supervisory task to extract prototypical normal\npatterns, which allows for learning uniform models that generalize well for\ndifferent visual anomaly detection tasks. The extensive experiments conducted\ndemonstrate that the proposed ST-MAE method can advance state-of-the-art\nperformance on multiple benchmarks across application scenarios with a superior\ninference efficiency, which exhibits great potential to be the uniform model\nfor unsupervised visual anomaly detection.\n", "date": "2022-11-01", "categories": []}
{"id": "2211.00441", "title": "Zero Day Threat Detection Using Metric Learning Autoencoders", "abstract": "  The proliferation of zero-day threats (ZDTs) to companies' networks has been\nimmensely costly and requires novel methods to scan traffic for malicious\nbehavior at massive scale. The diverse nature of normal behavior along with the\nhuge landscape of attack types makes deep learning methods an attractive option\nfor their ability to capture highly-nonlinear behavior patterns. In this paper,\nthe authors demonstrate an improvement upon a previously introduced\nmethodology, which used a dual-autoencoder approach to identify ZDTs in network\nflow telemetry. In addition to the previously-introduced asset-level graph\nfeatures, which help abstractly represent the role of a host in its network,\nthis new model uses metric learning to train the second autoencoder on labeled\nattack data. This not only produces stronger performance, but it has the added\nadvantage of improving the interpretability of the model by allowing for\nmulticlass classification in the latent space. This can potentially save human\nthreat hunters time when they investigate predicted ZDTs by showing them which\nknown attack classes were nearby in the latent space. The models presented here\nare also trained and evaluated with two more datasets, and continue to show\npromising results even when generalizing to new network topologies.\n", "date": "2022-11-01", "categories": []}
{"id": "2208.09711", "title": "Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection\n  with Birch Clustering on CICIDS-2017 Dataset", "abstract": "  Machine learning algorithms have been widely used in intrusion detection\nsystems, including Multi-layer Perceptron (MLP). In this study, we proposed a\ntwo-stage model that combines the Birch clustering algorithm and MLP classifier\nto improve the performance of network anomaly multi-classification. In our\nproposed method, we first apply Birch or Kmeans as an unsupervised clustering\nalgorithm to the CICIDS-2017 dataset to pre-group the data. The generated\npseudo-label is then added as an additional feature to the training of the\nMLP-based classifier. The experimental results show that using Birch and\nK-Means clustering for data pre-grouping can improve intrusion detection system\nperformance. Our method can achieve 99.73% accuracy in multi-classification\nusing Birch clustering, which is better than similar researches using a\nstand-alone MLP model.\n", "date": "2022-08-20", "categories": []}
{"id": "2210.15030", "title": "A Hierarchical Approach to Conditional Random Fields for System Anomaly\n  Detection", "abstract": "  Anomaly detection to recognize unusual events in large scale systems in a\ntime sensitive manner is critical in many industries, eg. bank fraud,\nenterprise systems, medical alerts, etc. Large-scale systems often grow in size\nand complexity over time, and anomaly detection algorithms need to adapt to\nchanging structures. A hierarchical approach takes advantage of the implicit\nrelationships in complex systems and localized context. The features in complex\nsystems may vary drastically in data distribution, capturing different aspects\nfrom multiple data sources, and when put together provide a more complete view\nof the system. In this paper, two datasets are considered, the 1st comprising\nof system metrics from machines running on a cloud service, and the 2nd of\napplication metrics from a large-scale distributed software system with\ninherent hierarchies and interconnections amongst its system nodes. Comparing\nalgorithms, across the changepoint based PELT algorithm, cognitive\nlearning-based Hierarchical Temporal Memory algorithms, Support Vector Machines\nand Conditional Random Fields provides a basis for proposing a Hierarchical\nGlobal-Local Conditional Random Field approach to accurately capture anomalies\nin complex systems across various features. Hierarchical algorithms can learn\nboth the intricacies of specific features, and utilize these in a global\nabstracted representation to detect anomalous patterns robustly across\nmulti-source feature data and distributed systems. A graphical network analysis\non complex systems can further fine-tune datasets to mine relationships based\non available features, which can benefit hierarchical models. Furthermore,\nhierarchical solutions can adapt well to changes at a localized level, learning\non new data and changing environments when parts of a system are over-hauled,\nand translate these learnings to a global view of the system over time.\n", "date": "2022-10-26", "categories": []}
{"id": "2210.16038", "title": "Deep Learning-Based Anomaly Detection in Synthetic Aperture Radar\n  Imaging", "abstract": "  In this paper, we proposed to investigate unsupervised anomaly detection in\nSynthetic Aperture Radar (SAR) images. Our approach considers anomalies as\nabnormal patterns that deviate from their surroundings but without any prior\nknowledge of their characteristics. In the literature, most model-based\nalgorithms face three main issues. First, the speckle noise corrupts the image\nand potentially leads to numerous false detections. Second, statistical\napproaches may exhibit deficiencies in modeling spatial correlation in SAR\nimages. Finally, neural networks based on supervised learning approaches are\nnot recommended due to the lack of annotated SAR data, notably for the class of\nabnormal patterns. Our proposed method aims to address these issues through a\nself-supervised algorithm. The speckle is first removed through the deep\nlearning SAR2SAR algorithm. Then, an adversarial autoencoder is trained to\nreconstruct an anomaly-free SAR image. Finally, a change detection processing\nstep is applied between the input and the output to detect anomalies.\nExperiments are performed to show the advantages of our method compared to the\nconventional Reed-Xiaoli algorithm, highlighting the importance of an efficient\ndespeckling pre-processing step.\n", "date": "2022-10-28", "categories": []}
{"id": "2210.14913", "title": "AltUB: Alternating Training Method to Update Base Distribution of\n  Normalizing Flow for Anomaly Detection", "abstract": "  Unsupervised anomaly detection is coming into the spotlight these days in\nvarious practical domains due to the limited amount of anomaly data. One of the\nmajor approaches for it is a normalizing flow which pursues the invertible\ntransformation of a complex distribution as images into an easy distribution as\nN(0, I). In fact, algorithms based on normalizing flow like FastFlow and\nCFLOW-AD establish state-of-the-art performance on unsupervised anomaly\ndetection tasks. Nevertheless, we investigate these algorithms convert normal\nimages into not N(0, I) as their destination, but an arbitrary normal\ndistribution. Moreover, their performances are often unstable, which is highly\ncritical for unsupervised tasks because data for validation are not provided.\nTo break through these observations, we propose a simple solution AltUB which\nintroduces alternating training to update the base distribution of normalizing\nflow for anomaly detection. AltUB effectively improves the stability of\nperformance of normalizing flow. Furthermore, our method achieves the new\nstate-of-the-art performance of the anomaly segmentation task on the MVTec AD\ndataset with 98.8% AUROC.\n", "date": "2022-10-26", "categories": []}
{"id": "2210.15540", "title": "Masked Transformer for image Anomaly Localization", "abstract": "  Image anomaly detection consists in detecting images or image portions that\nare visually different from the majority of the samples in a dataset. The task\nis of practical importance for various real-life applications like biomedical\nimage analysis, visual inspection in industrial production, banking, traffic\nmanagement, etc. Most of the current deep learning approaches rely on image\nreconstruction: the input image is projected in some latent space and then\nreconstructed, assuming that the network (mostly trained on normal data) will\nnot be able to reconstruct the anomalous portions. However, this assumption\ndoes not always hold. We thus propose a new model based on the Vision\nTransformer architecture with patch masking: the input image is split in\nseveral patches, and each patch is reconstructed only from the surrounding\ndata, thus ignoring the potentially anomalous information contained in the\npatch itself. We then show that multi-resolution patches and their collective\nembeddings provide a large improvement in the model's performance compared to\nthe exclusive use of the traditional square patches. The proposed model has\nbeen tested on popular anomaly detection datasets such as MVTec and head CT and\nachieved good results when compared to other state-of-the-art approaches.\n", "date": "2022-10-27", "categories": []}
{"id": "2208.14834", "title": "Deep Anomaly Detection and Search via Reinforcement Learning", "abstract": "  Semi-supervised Anomaly Detection (AD) is a kind of data mining task which\naims at learning features from partially-labeled datasets to help detect\noutliers. In this paper, we classify existing semi-supervised AD methods into\ntwo categories: unsupervised-based and supervised-based, and point out that\nmost of them suffer from insufficient exploitation of labeled data and\nunder-exploration of unlabeled data. To tackle these problems, we propose Deep\nAnomaly Detection and Search (DADS), which applies Reinforcement Learning (RL)\nto balance exploitation and exploration. During the training process, the agent\nsearches for possible anomalies with hierarchically-structured datasets and\nuses the searched anomalies to enhance performance, which in essence draws\nlessons from the idea of ensemble learning. Experimentally, we compare DADS\nwith several state-of-the-art methods in the settings of leveraging labeled\nknown anomalies to detect both other known anomalies and unknown anomalies.\nResults show that DADS can efficiently and precisely search anomalies from\nunlabeled data and learn from them, thus achieving good performance.\n", "date": "2022-08-31", "categories": []}
{"id": "2210.14056", "title": "Unsupervised Anomaly Detection for Auditing Data and Impact of\n  Categorical Encodings", "abstract": "  In this paper, we introduce the Vehicle Claims dataset, consisting of\nfraudulent insurance claims for automotive repairs. The data belongs to the\nmore broad category of Auditing data, which includes also Journals and Network\nIntrusion data. Insurance claim data are distinctively different from other\nauditing data (such as network intrusion data) in their high number of\ncategorical attributes. We tackle the common problem of missing benchmark\ndatasets for anomaly detection: datasets are mostly confidential, and the\npublic tabular datasets do not contain relevant and sufficient categorical\nattributes. Therefore, a large-sized dataset is created for this purpose and\nreferred to as Vehicle Claims (VC) dataset. The dataset is evaluated on shallow\nand deep learning methods. Due to the introduction of categorical attributes,\nwe encounter the challenge of encoding them for the large dataset. As One Hot\nencoding of high cardinal dataset invokes the \"curse of dimensionality\", we\nexperiment with GEL encoding and embedding layer for representing categorical\nattributes. Our work compares competitive learning, reconstruction-error,\ndensity estimation and contrastive learning approaches for Label, One Hot, GEL\nencoding and embedding layer to handle categorical values.\n", "date": "2022-10-25", "categories": []}
{"id": "2210.14796", "title": "AD-DMKDE: Anomaly Detection through Density Matrices and Fourier\n  Features", "abstract": "  This paper presents a novel density estimation method for anomaly detection\nusing density matrices (a powerful mathematical formalism from quantum\nmechanics) and Fourier features. The method can be seen as an efficient\napproximation of Kernel Density Estimation (KDE). A systematic comparison of\nthe proposed method with eleven state-of-the-art anomaly detection methods on\nvarious data sets is presented, showing competitive performance on different\nbenchmark data sets. The method is trained efficiently and it uses optimization\nto find the parameters of data embedding. The prediction phase complexity of\nthe proposed algorithm is constant relative to the training data size, and it\nperforms well in data sets with different anomaly rates. Its architecture\nallows vectorization and can be implemented on GPU/TPU hardware.\n", "date": "2022-10-26", "categories": []}
{"id": "2206.03687", "title": "A Unified Model for Multi-class Anomaly Detection", "abstract": "  Despite the rapid advance of unsupervised anomaly detection, existing methods\nrequire to train separate models for different objects. In this work, we\npresent UniAD that accomplishes anomaly detection for multiple classes with a\nunified framework. Under such a challenging setting, popular reconstruction\nnetworks may fall into an \"identical shortcut\", where both normal and anomalous\nsamples can be well recovered, and hence fail to spot outliers. To tackle this\nobstacle, we make three improvements. First, we revisit the formulations of\nfully-connected layer, convolutional layer, as well as attention layer, and\nconfirm the important role of query embedding (i.e., within attention layer) in\npreventing the network from learning the shortcut. We therefore come up with a\nlayer-wise query decoder to help model the multi-class distribution. Second, we\nemploy a neighbor masked attention module to further avoid the information leak\nfrom the input feature to the reconstructed output feature. Third, we propose a\nfeature jittering strategy that urges the model to recover the correct message\neven with noisy inputs. We evaluate our algorithm on MVTec-AD and CIFAR-10\ndatasets, where we surpass the state-of-the-art alternatives by a sufficiently\nlarge margin. For example, when learning a unified model for 15 categories in\nMVTec-AD, we surpass the second competitor on the tasks of both anomaly\ndetection (from 88.1% to 96.5%) and anomaly localization (from 89.5% to 96.8%).\nCode is available at https://github.com/zhiyuanyou/UniAD.\n", "date": "2022-06-08", "categories": []}
{"id": "2210.13927", "title": "Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future\n  Research Directions", "abstract": "  Crowd anomaly detection is one of the most popular topics in computer vision\nin the context of smart cities. A plethora of deep learning methods have been\nproposed that generally outperform other machine learning solutions. Our review\nprimarily discusses algorithms that were published in mainstream conferences\nand journals between 2020 and 2022. We present datasets that are typically used\nfor benchmarking, produce a taxonomy of the developed algorithms, and discuss\nand compare their performances. Our main findings are that the heterogeneities\nof pre-trained convolutional models have a negligible impact on crowd video\nanomaly detection performance. We conclude our discussion with fruitful\ndirections for future research.\n", "date": "2022-10-25", "categories": []}
{"id": "2210.13358", "title": "Novelty Detection in Time Series via Weak Innovations Representation: A\n  Deep Learning Approach", "abstract": "  We consider novelty detection in time series with unknown and nonparametric\nprobability structures. A deep learning approach is proposed to causally\nextract an innovations sequence consisting of novelty samples statistically\nindependent of all past samples of the time series. A novelty detection\nalgorithm is developed for the online detection of novel changes in the\nprobability structure in the innovations sequence. A minimax optimality under a\nBayes risk measure is established for the proposed novelty detection method,\nand its robustness and efficacy are demonstrated in experiments using real and\nsynthetic datasets.\n", "date": "2022-10-24", "categories": []}
{"id": "2210.09535", "title": "Graph Anomaly Detection with Unsupervised GNNs", "abstract": "  Graph-based anomaly detection finds numerous applications in the real-world.\nThus, there exists extensive literature on the topic that has recently shifted\ntoward deep detection models due to advances in deep learning and graph neural\nnetworks (GNNs). A vast majority of prior work focuses on detecting\nnode/edge/subgraph anomalies within a single graph, with much less work on\ngraph-level anomaly detection in a graph database. This work aims to fill two\ngaps in the literature: We (1) design GLAM, an end-to-end graph-level anomaly\ndetection model based on GNNs, and (2) focus on unsupervised model selection,\nwhich is notoriously hard due to lack of any labels, yet especially critical\nfor deep NN based models with a long list of hyper-parameters. Further, we\npropose a new pooling strategy for graph-level embedding, called MMD-pooling,\nthat is geared toward detecting distribution anomalies which has not been\nconsidered before. Through extensive experiments on 15 real-world datasets, we\nshow that (i) GLAM outperforms node-level and two-stage (i.e. not end-to-end)\nbaselines, and (ii) model selection picks a significantly more effective model\nthan expectation (i.e. average) -- without using any labels -- among candidates\nwith otherwise large variation in performance.\n", "date": "2022-10-18", "categories": []}
{"id": "2210.11158", "title": "VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe\n  Inspection", "abstract": "  Video understanding is an important problem in computer vision. Currently,\nthe well-studied task in this research is human action recognition, where the\nclips are manually trimmed from the long videos, and a single class of human\naction is assumed for each clip. However, we may face more complicated\nscenarios in the industrial applications. For example, in the real-world urban\npipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant.\nTo recognize them correctly, we need to understand the detailed video content.\nFor this reason, we propose to advance research areas of video understanding,\nwith a shift from traditional action recognition to industrial anomaly\nanalysis. In particular, we introduce two high-quality video benchmarks, namely\nQV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe\nsystems. Based on these new datasets, we will host two competitions including\n(1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization\non CCTV-Pipe. In this report, we describe the details of these benchmarks, the\nproblem definitions of competition tracks, the evaluation metric, and the\nresult summary. We expect that, this competition would bring new opportunities\nand challenges for video understanding in smart city and beyond. The details of\nour VideoPipe challenge can be found in https://videopipe.github.io.\n", "date": "2022-10-20", "categories": []}
{"id": "2206.07647", "title": "Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a\n  Scalable Hyper-Ensemble Solution", "abstract": "  Outlier detection (OD) literature exhibits numerous algorithms as it applies\nto diverse domains. However, given a new detection task, it is unclear how to\nchoose an algorithm to use, nor how to set its hyperparameter(s) (HPs) in\nunsupervised settings. HP tuning is an ever-growing problem with the arrival of\nmany new detectors based on deep learning, which usually come with a long list\nof HPs. Surprisingly, the issue of model selection in the outlier mining\nliterature has been \"the elephant in the room\"; a significant factor in\nunlocking the utmost potential of deep methods, yet little said or done to\nsystematically tackle the issue. In the first part of this paper, we conduct\nthe first large-scale analysis on the HP sensitivity of deep OD methods, and\nthrough more than 35,000 trained models, quantitatively demonstrate that model\nselection is inevitable. Next, we design a HP-robust and scalable deep\nhyper-ensemble model called ROBOD that assembles models with varying HP\nconfigurations, bypassing the choice paralysis. Importantly, we introduce novel\nstrategies to speed up ensemble training, such as parameter sharing,\nbatch/simultaneous training, and data subsampling, that allow us to train fewer\nmodels with fewer parameters. Extensive experiments on both image and tabular\ndatasets show that ROBOD achieves and retains robust, state-of-the-art\ndetection performance as compared to its modern counterparts, while taking only\n$2$-$10$\\% of the time by the naive hyper-ensemble with independent training.\n", "date": "2022-06-15", "categories": []}
{"id": "2210.10773", "title": "Anomaly Detection Requires Better Representations", "abstract": "  Anomaly detection seeks to identify unusual phenomena, a central task in\nscience and industry. The task is inherently unsupervised as anomalies are\nunexpected and unknown during training. Recent advances in self-supervised\nrepresentation learning have directly driven improvements in anomaly detection.\nIn this position paper, we first explain how self-supervised representations\ncan be easily used to achieve state-of-the-art performance in commonly reported\nanomaly detection benchmarks. We then argue that tackling the next generation\nof anomaly detection tasks requires new technical and conceptual improvements\nin representation learning.\n", "date": "2022-10-19", "categories": []}
{"id": "2210.07829", "title": "Asymmetric Student-Teacher Networks for Industrial Anomaly Detection", "abstract": "  Industrial defect detection is commonly addressed with anomaly detection (AD)\nmethods where no or only incomplete data of potentially occurring defects is\navailable. This work discovers previously unknown problems of student-teacher\napproaches for AD and proposes a solution, where two neural networks are\ntrained to produce the same output for the defect-free training examples. The\ncore assumption of student-teacher networks is that the distance between the\noutputs of both networks is larger for anomalies since they are absent in\ntraining. However, previous methods suffer from the similarity of student and\nteacher architecture, such that the distance is undesirably small for\nanomalies. For this reason, we propose asymmetric student-teacher networks\n(AST). We train a normalizing flow for density estimation as a teacher and a\nconventional feed-forward network as a student to trigger large distances for\nanomalies: The bijectivity of the normalizing flow enforces a divergence of\nteacher outputs for anomalies compared to normal data. Outside the training\ndistribution the student cannot imitate this divergence due to its\nfundamentally different architecture. Our AST network compensates for wrongly\nestimated likelihoods by a normalizing flow, which was alternatively used for\nanomaly detection in previous work. We show that our method produces\nstate-of-the-art results on the two currently most relevant defect detection\ndatasets MVTec AD and MVTec 3D-AD regarding image-level anomaly detection on\nRGB and 3D data.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.09766", "title": "DAGAD: Data Augmentation for Graph Anomaly Detection", "abstract": "  Graph anomaly detection in this paper aims to distinguish abnormal nodes that\nbehave differently from the benign ones accounting for the majority of\ngraph-structured instances. Receiving increasing attention from both academia\nand industry, yet existing research on this task still suffers from two\ncritical issues when learning informative anomalous behavior from graph data.\nFor one thing, anomalies are usually hard to capture because of their subtle\nabnormal behavior and the shortage of background knowledge about them, which\ncauses severe anomalous sample scarcity. Meanwhile, the overwhelming majority\nof objects in real-world graphs are normal, bringing the class imbalance\nproblem as well. To bridge the gaps, this paper devises a novel Data\nAugmentation-based Graph Anomaly Detection (DAGAD) framework for attributed\ngraphs, equipped with three specially designed modules: 1) an information\nfusion module employing graph neural network encoders to learn representations,\n2) a graph data augmentation module that fertilizes the training set with\ngenerated samples, and 3) an imbalance-tailored learning module to discriminate\nthe distributions of the minority (anomalous) and majority (normal) classes. A\nseries of experiments on three datasets prove that DAGAD outperforms ten\nstate-of-the-art baseline detectors concerning various mostly-used metrics,\ntogether with an extensive ablation study validating the strength of our\nproposed modules.\n", "date": "2022-10-18", "categories": []}
{"id": "2206.10071", "title": "BOND: Benchmarking Unsupervised Outlier Node Detection on Static\n  Attributed Graphs", "abstract": "  Detecting which nodes in graphs are outliers is a relatively new machine\nlearning task with numerous applications. Despite the proliferation of\nalgorithms developed in recent years for this task, there has been no standard\ncomprehensive setting for performance evaluation. Consequently, it has been\ndifficult to understand which methods work well and when under a broad range of\nsettings. To bridge this gap, we present--to the best of our knowledge--the\nfirst comprehensive benchmark for unsupervised outlier node detection on static\nattributed graphs called BOND, with the following highlights. (1) We benchmark\nthe outlier detection performance of 14 methods ranging from classical matrix\nfactorization to the latest graph neural networks. (2) Using nine real\ndatasets, our benchmark assesses how the different detection methods respond to\ntwo major types of synthetic outliers and separately to \"organic\" (real\nnon-synthetic) outliers. (3) Using an existing random graph generation\ntechnique, we produce a family of synthetically generated datasets of different\ngraph sizes that enable us to compare the running time and memory usage of the\ndifferent outlier detection algorithms. Based on our experimental results, we\ndiscuss the pros and cons of existing graph outlier detection algorithms, and\nwe highlight opportunities for future research. Importantly, our code is freely\navailable and meant to be easily extendable:\nhttps://github.com/pygod-team/pygod/tree/main/benchmark\n", "date": "2022-06-21", "categories": []}
{"id": "2210.02841", "title": "Detecting Irregular Network Activity with Adversarial Learning and\n  Expert Feedback", "abstract": "  Anomaly detection is a ubiquitous and challenging task relevant across many\ndisciplines. With the vital role communication networks play in our daily\nlives, the security of these networks is imperative for smooth functioning of\nsociety. To this end, we propose a novel self-supervised deep learning\nframework CAAD for anomaly detection in wireless communication systems.\nSpecifically, CAAD employs contrastive learning in an adversarial setup to\nlearn effective representations of normal and anomalous behavior in wireless\nnetworks. We conduct rigorous performance comparisons of CAAD with several\nstate-of-the-art anomaly detection techniques and verify that CAAD yields a\nmean performance improvement of 92.84%. Additionally, we also augment CAAD\nenabling it to systematically incorporate expert feedback through a novel\ncontrastive learning feedback loop to improve the learned representations and\nthereby reduce prediction uncertainty (CAAD-EF). We view CAAD-EF as a novel,\nholistic and widely applicable solution to anomaly detection.\n", "date": "2022-10-01", "categories": []}
{"id": "2210.08011", "title": "Autoencoder based Anomaly Detection and Explained Fault Localization in\n  Industrial Cooling Systems", "abstract": "  Anomaly detection in large industrial cooling systems is very challenging due\nto the high data dimensionality, inconsistent sensor recordings, and lack of\nlabels. The state of the art for automated anomaly detection in these systems\ntypically relies on expert knowledge and thresholds. However, data is viewed\nisolated and complex, multivariate relationships are neglected. In this work,\nwe present an autoencoder based end-to-end workflow for anomaly detection\nsuitable for multivariate time series data in large industrial cooling systems,\nincluding explained fault localization and root cause analysis based on expert\nknowledge. We identify system failures using a threshold on the total\nreconstruction error (autoencoder reconstruction error including all sensor\nsignals). For fault localization, we compute the individual reconstruction\nerror (autoencoder reconstruction error for each sensor signal) allowing us to\nidentify the signals that contribute most to the total reconstruction error.\nExpert knowledge is provided via look-up table enabling root-cause analysis and\nassignment to the affected subsystem. We demonstrated our findings in a cooling\nsystem unit including 34 sensors over a 8-months time period using 4-fold cross\nvalidation approaches and automatically created labels based on thresholds\nprovided by domain experts. Using 4-fold cross validation, we reached a\nF1-score of 0.56, whereas the autoencoder results showed a higher consistency\nscore (CS of 0.92) compared to the automatically created labels (CS of 0.62) --\nindicating that the anomaly is recognized in a very stable manner. The main\nanomaly was found by the autoencoder and automatically created labels and was\nalso recorded in the log files. Further, the explained fault localization\nhighlighted the most affected component for the main anomaly in a very\nconsistent manner.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.08132", "title": "VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for\n  Ubiquitous IoT", "abstract": "  Vertical heterogenous networks (VHetNets) and artificial intelligence (AI)\nplay critical roles in 6G and beyond networks. This article presents an\nAI-native VHetNets architecture to enable the synergy of VHetNets and AI,\nthereby supporting varieties of AI services while facilitating automatic and\nintelligent network management. Anomaly detection in Internet of Things (IoT)\nis a major AI service required by many fields, including intrusion detection,\nstate monitoring, device-activity analysis, security supervision and so on.\nConventional anomaly detection technologies mainly consider the anomaly\ndetection as a standalone service that is independent of any other network\nmanagement functionalities, which cannot be used directly in ubiquitous IoT due\nto the resource constrained end nodes and decentralized data distribution. In\nthis article, we develop an AI-native VHetNets-enabled framework to provide the\nanomaly detection service for ubiquitous IoT, whose implementation is assisted\nby intelligent network management functionalities. We first discuss the\npossibilities of VHetNets used for distributed AI model training to provide\nanomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. After\nthat, we study the application of AI approaches in helping provide automatic\nand intelligent network management functionalities for VHetNets, i.e., AI for\nVHetNets, whose aim is to facilitate the efficient implementation of anomaly\ndetection service. Finally, a case study is presented to demonstrate the\nefficiency and effectiveness of the proposed AI-native VHetNets-enabled anomaly\ndetection framework.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.08212", "title": "D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments", "abstract": "  How can we detect outliers, both scattered and clustered, and also explicitly\nassign them to respective micro-clusters, without knowing apriori how many\nmicro-clusters exist? How can we perform both tasks in-house, i.e., without any\npost-hoc processing, so that both detection and assignment can benefit\nsimultaneously from each other? Presenting outliers in separate micro-clusters\nis informative to analysts in many real-world applications. However, a na\\\"ive\nsolution based on post-hoc clustering of the outliers detected by any existing\nmethod suffers from two main drawbacks: (a) appropriate hyperparameter values\nare commonly unknown for clustering, and most algorithms struggle with clusters\nof varying shapes and densities; (b) detection and assignment cannot benefit\nfrom one another. In this paper, we propose D.MCA to $\\underline{D}$etect\noutliers with explicit $\\underline{M}$icro-$\\underline{C}$luster\n$\\underline{A}$ssignment. Our method performs both detection and assignment\niteratively, and in-house, by using a novel strategy that prunes entire\nmicro-clusters out of the training set to improve the performance of the\ndetection. It also benefits from a novel strategy that avoids clustered\noutliers to mask each other, which is a well-known problem in the literature.\nAlso, D.MCA is designed to be robust to a critical hyperparameter by employing\na hyperensemble \"warm up\" phase. Experiments performed on 16 real-world and\nsynthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art\ncompetitors, especially on the explicit outlier micro-cluster assignment task.\n", "date": "2022-10-15", "categories": []}
{"id": "2210.08768", "title": "N-pad : Neighboring Pixel-based Industrial Anomaly Detection", "abstract": "  Identifying defects in the images of industrial products has been an\nimportant task to enhance quality control and reduce maintenance costs. In\nrecent studies, industrial anomaly detection models were developed using\npre-trained networks to learn nominal representations. To employ the relative\npositional information of each pixel, we present \\textit{\\textbf{N-pad}}, a\nnovel method for anomaly detection and segmentation in a one-class learning\nsetting that includes the neighborhood of the target pixel for model training\nand evaluation. Within the model architecture, pixel-wise nominal distributions\nare estimated by using the features of neighboring pixels with the target pixel\nto allow possible marginal misalignment. Moreover, the centroids from clusters\nof nominal features are identified as a representative nominal set.\nAccordingly, anomaly scores are inferred based on the Mahalanobis distances and\nEuclidean distances between the target pixel and the estimated distributions or\nthe centroid set, respectively. Thus, we have achieved state-of-the-art\nperformance in MVTec-AD with AUROC of 99.37 for anomaly detection and 98.75 for\nanomaly segmentation, reducing the error by 34\\% compared to the next best\nperforming model. Experiments in various settings further validate our model.\n", "date": "2022-10-17", "categories": []}
{"id": "2210.08847", "title": "tegdet: An extensible Python Library for Anomaly Detection using\n  Time-Evolving Graphs", "abstract": "  This paper presents a new Python library for anomaly detection in\nunsupervised learning approaches. The input for the library is a univariate\ntime series representing observations of a given phenomenon. Then, it can\nidentify anomalous epochs, i.e., time intervals where the observations are\nabove a given percentile of a baseline distribution, defined by a dissimilarity\nmetric. Using time-evolving graphs for the anomaly detection, the library\nleverages valuable information given by the inter-dependencies among data.\nCurrently, the library implements 28 different dissimilarity metrics, and it\nhas been designed to be easily extended with new ones. Through an API, the\nlibrary exposes a complete functionality to carry out the anomaly detection.\nSummarizing, to the best of our knowledge, this library is the only one\npublicly available, that based on dynamic graphs, can be extended with other\nstate-of-the-art anomaly detection techniques. Our experimentation shows\npromising results regarding the execution times of the algorithms and the\naccuracy of the implemented techniques. Additionally, the paper provides\nguidelines for setting the parameters of the detectors to improve their\nperformance and prediction accuracy.\n", "date": "2022-10-17", "categories": []}
{"id": "2210.09132", "title": "Pseudo-OOD training for robust language models", "abstract": "  While pre-trained large-scale deep models have garnered attention as an\nimportant topic for many downstream natural language processing (NLP) tasks,\nsuch models often make unreliable predictions on out-of-distribution (OOD)\ninputs. As such, OOD detection is a key component of a reliable\nmachine-learning model for any industry-scale application. Common approaches\noften assume access to additional OOD samples during the training stage,\nhowever, outlier distribution is often unknown in advance. Instead, we propose\na post hoc framework called POORE - POsthoc pseudo-Ood REgularization, that\ngenerates pseudo-OOD samples using in-distribution (IND) data. The model is\nfine-tuned by introducing a new regularization loss that separates the\nembeddings of IND and OOD data, which leads to significant gains on the OOD\nprediction task during testing. We extensively evaluate our framework on three\nreal-world dialogue systems, achieving new state-of-the-art in OOD detection.\n", "date": "2022-10-17", "categories": []}
{"id": "2210.07548", "title": "Reconstructed Student-Teacher and Discriminative Networks for Anomaly\n  Detection", "abstract": "  Anomaly detection is an important problem in computer vision; however, the\nscarcity of anomalous samples makes this task difficult. Thus, recent anomaly\ndetection methods have used only normal images with no abnormal areas for\ntraining. In this work, a powerful anomaly detection method is proposed based\non student-teacher feature pyramid matching (STPM), which consists of a student\nand teacher network. Generative models are another approach to anomaly\ndetection. They reconstruct normal images from an input and compute the\ndifference between the predicted normal and the input. Unfortunately, STPM does\nnot have the ability to generate normal images. To improve the accuracy of\nSTPM, this work uses a student network, as in generative models, to reconstruct\nnormal features. This improves the accuracy; however, the anomaly maps for\nnormal images are not clean because STPM does not use anomaly images for\ntraining, which decreases the accuracy of the image-level anomaly detection. To\nfurther improve accuracy, a discriminative network trained with\npseudo-anomalies from anomaly maps is used in our method, which consists of two\npairs of student-teacher networks and a discriminative network. The method\ndisplayed high accuracy on the MVTec anomaly detection dataset.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.07812", "title": "Surface abnormality detection in medical and inspection systems using\n  energy variations in co-occurrence matrixes", "abstract": "  Detection of surface defects is one of the most important issues in the field\nof image processing and machine vision. In this article, a method for detecting\nsurface defects based on energy changes in co-occurrence matrices is presented.\nThe presented method consists of two stages of training and testing. In the\ntraining phase, the co-occurrence matrix operator is first applied on healthy\nimages and then the amount of output energy is calculated. In the following,\naccording to the changes in the amount of energy, a suitable feature vector is\ndefined, and with the help of it, a suitable threshold for the health of the\nimages is obtained. Then, in the test phase, with the help of the calculated\nquorum, the defective parts are distinguished from the healthy ones. In the\nresults section, the mentioned method has been applied on stone and ceramic\nimages and its detection accuracy has been calculated and compared with some\nprevious methods. Among the advantages of the presented method, we can mention\nhigh accuracy, low calculations and compatibility with all types of levels due\nto the use of the training stage. The proposed approach can be used in medical\napplications to detect abnormalities such as diseases. So, the performance is\nevaluated on 2d-hela dataset to classify cell phenotypes. The proposed approach\nprovides about 89.56 percent accuracy on 2d-hela.\n", "date": "2022-10-14", "categories": []}
{"id": "2210.06614", "title": "Anomaly Detection via Federated Learning", "abstract": "  Machine learning has helped advance the field of anomaly detection by\nincorporating classifiers and autoencoders to decipher between normal and\nanomalous behavior. Additionally, federated learning has provided a way for a\nglobal model to be trained with multiple clients' data without requiring the\nclient to directly share their data. This paper proposes a novel anomaly\ndetector via federated learning to detect malicious network activity on a\nclient's server. In our experiments, we use an autoencoder with a classifier in\na federated learning framework to determine if the network activity is benign\nor malicious. By using our novel min-max scalar and sampling technique, called\nFedSam, we determined federated learning allows the global model to learn from\neach client's data and, in turn, provide a means for each client to improve\ntheir intrusion detection system's defense against cyber-attacks.\n", "date": "2022-10-12", "categories": []}
{"id": "2206.13167", "title": "Local Evaluation of Time Series Anomaly Detection Algorithms", "abstract": "  In recent years, specific evaluation metrics for time series anomaly\ndetection algorithms have been developed to handle the limitations of the\nclassical precision and recall. However, such metrics are heuristically built\nas an aggregate of multiple desirable aspects, introduce parameters and wipe\nout the interpretability of the output. In this article, we first highlight the\nlimitations of the classical precision/recall, as well as the main issues of\nthe recent event-based metrics -- for instance, we show that an adversary\nalgorithm can reach high precision and recall on almost any dataset under weak\nassumption. To cope with the above problems, we propose a theoretically\ngrounded, robust, parameter-free and interpretable extension to\nprecision/recall metrics, based on the concept of ``affiliation'' between the\nground truth and the prediction sets. Our metrics leverage measures of duration\nbetween ground truth and predictions, and have thus an intuitive\ninterpretation. By further comparison against random sampling, we obtain a\nnormalized precision/recall, quantifying how much a given set of results is\nbetter than a random baseline prediction. By construction, our approach keeps\nthe evaluation local regarding ground truth events, enabling fine-grained\nvisualization and interpretation of algorithmic results. We compare our\nproposal against various public time series anomaly detection datasets,\nalgorithms and metrics. We further derive theoretical properties of the\naffiliation metrics that give explicit expectations about their behavior and\nensure robustness against adversary strategies.\n", "date": "2022-06-27", "categories": []}
{"id": "2210.05061", "title": "InQMAD: Incremental Quantum Measurement Anomaly Detection", "abstract": "  Streaming anomaly detection refers to the problem of detecting anomalous data\nsamples in streams of data. This problem poses challenges that classical and\ndeep anomaly detection methods are not designed to cope with, such as\nconceptual drift and continuous learning. State-of-the-art flow anomaly\ndetection methods rely on fixed memory using hash functions or nearest\nneighbors that may not be able to constrain high-frequency values as in a\nmoving average or remove seamless outliers and cannot be trained in an\nend-to-end deep learning architecture. We present a new incremental anomaly\ndetection method that performs continuous density estimation based on random\nFourier features and the mechanism of quantum measurements and density matrices\nthat can be viewed as an exponential moving average density. It can process\npotentially endless data and its update complexity is constant $O(1)$. A\nsystematic evaluation against 12 state-of-the-art streaming anomaly detection\nalgorithms using 12 streaming datasets is presented.\n", "date": "2022-10-11", "categories": []}
{"id": "2208.11727", "title": "Hyperparameter Optimization for Unsupervised Outlier Detection", "abstract": "  Given an unsupervised outlier detection (OD) algorithm, how can we optimize\nits hyperparameter(s) (HP) on a new dataset, without any labels? In this work,\nwe address this challenging hyperparameter optimization for unsupervised OD\nproblem, and propose the first systematic approach called HPOD that is based on\nmeta-learning. HPOD capitalizes on the prior performance of a large collection\nof HPs on existing OD benchmark datasets, and transfers this information to\nenable HP evaluation on a new dataset without labels. Moreover, HPOD adapts a\nprominent sampling paradigm to identify promising HPs efficiently. Extensive\nexperiments show that HPOD works with both deep (e.g., Robust AutoEncoder) and\nshallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (iForest)) OD\nalgorithms on discrete and continuous HP spaces, and outperforms a wide range\nof baselines with on average 58% and 66% performance improvement over the\ndefault HPs of LOF and iForest.\n", "date": "2022-08-24", "categories": []}
{"id": "2209.04561", "title": "Deep Baseline Network for Time Series Modeling and Anomaly Detection", "abstract": "  Deep learning has seen increasing applications in time series in recent\nyears. For time series anomaly detection scenarios, such as in finance,\nInternet of Things, data center operations, etc., time series usually show very\nflexible baselines depending on various external factors. Anomalies unveil\nthemselves by lying far away from the baseline. However, the detection is not\nalways easy due to some challenges including baseline shifting, lacking of\nlabels, noise interference, real time detection in streaming data, result\ninterpretability, etc. In this paper, we develop a novel deep architecture to\nproperly extract the baseline from time series, namely Deep Baseline Network\n(DBLN). By using this deep network, we can easily locate the baseline position\nand then provide reliable and interpretable anomaly detection result. Empirical\nevaluation on both synthetic and public real-world datasets shows that our\npurely unsupervised algorithm achieves superior performance compared with\nstate-of-art methods and has good practical applications.\n", "date": "2022-09-10", "categories": []}
{"id": "2210.04145", "title": "Fine-grained Anomaly Detection in Sequential Data via Counterfactual\n  Explanations", "abstract": "  Anomaly detection in sequential data has been studied for a long time because\nof its potential in various applications, such as detecting abnormal system\nbehaviors from log data. Although many approaches can achieve good performance\non anomalous sequence detection, how to identify the anomalous entries in\nsequences is still challenging due to a lack of information at the entry-level.\nIn this work, we propose a novel framework called CFDet for fine-grained\nanomalous entry detection. CFDet leverages the idea of interpretable machine\nlearning. Given a sequence that is detected as anomalous, we can consider\nanomalous entry detection as an interpretable machine learning task because\nidentifying anomalous entries in the sequence is to provide an interpretation\nto the detection result. We make use of the deep support vector data\ndescription (Deep SVDD) approach to detect anomalous sequences and propose a\nnovel counterfactual interpretation-based approach to identify anomalous\nentries in the sequences. Experimental results on three datasets show that\nCFDet can correctly detect anomalous entries.\n", "date": "2022-10-09", "categories": []}
{"id": "2210.04570", "title": "The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and\n  Localization", "abstract": "  We present Eyecandies, a novel synthetic dataset for unsupervised anomaly\ndetection and localization. Photo-realistic images of procedurally generated\ncandies are rendered in a controlled environment under multiple lightning\nconditions, also providing depth and normal maps in an industrial conveyor\nscenario. We make available anomaly-free samples for model training and\nvalidation, while anomalous instances with precise ground-truth annotations are\nprovided only in the test set. The dataset comprises ten classes of candies,\neach showing different challenges, such as complex textures, self-occlusions\nand specularities. Furthermore, we achieve large intra-class variation by\nrandomly drawing key parameters of a procedural rendering pipeline, which\nenables the creation of an arbitrary number of instances with photo-realistic\nappearance. Likewise, anomalies are injected into the rendering graph and\npixel-wise annotations are automatically generated, overcoming human-biases and\npossible inconsistencies.\n  We believe this dataset may encourage the exploration of original approaches\nto solve the anomaly detection task, e.g. by combining color, depth and normal\nmaps, as they are not provided by most of the existing datasets. Indeed, in\norder to demonstrate how exploiting additional information may actually lead to\nhigher detection performance, we show the results obtained by training a deep\nconvolutional autoencoder to reconstruct different combinations of inputs.\n", "date": "2022-10-10", "categories": []}
{"id": "2210.00100", "title": "Image-Based Detection of Modifications in Gas Pump PCBs with Deep\n  Convolutional Autoencoders", "abstract": "  In this paper, we introduce an approach for detecting modifications in\nassembled printed circuit boards based on photographs taken without tight\ncontrol over perspective and illumination conditions. One instance of this\nproblem is the visual inspection of gas pumps PCBs, which can be modified by\nfraudsters wishing to deceive costumers or evade taxes. Given the uncontrolled\nenvironment and the huge number of possible modifications, we address the\nproblem as a case of anomaly detection, proposing an approach that is directed\ntowards the characteristics of that scenario, while being well-suited for other\nsimilar applications. The proposed approach employs a deep convolutional\nautoencoder trained to reconstruct images of an unmodified board, but which\nremains unable to do the same for images showing modifications. By comparing\nthe input image with its reconstruction, it is possible to segment anomalies\nand modifications in a pixel-wise manner. Experiments performed on a dataset\nbuilt to represent real-world situations (and which we will make publicly\navailable) show that our approach outperforms other state-of-the-art approaches\nfor anomaly segmentation in the considered scenario, while producing comparable\nresults on the popular MVTec-AD dataset for a more general object anomaly\ndetection task.\n", "date": "2022-09-30", "categories": []}
{"id": "2210.03558", "title": "A deep learning approach for detection and localization of leaf\n  anomalies", "abstract": "  The detection and localization of possible diseases in crops are usually\nautomated by resorting to supervised deep learning approaches. In this work, we\ntackle these goals with unsupervised models, by applying three different types\nof autoencoders to a specific open-source dataset of healthy and unhealthy\npepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed\nto screen unlabeled images of such a dataset, and compared in terms of image\nreconstruction, anomaly removal, detection and localization. The\nvector-quantized variational architecture turns out to be the best performing\none with respect to all these targets.\n", "date": "2022-10-07", "categories": []}
{"id": "2210.02449", "title": "DEGAN: Time Series Anomaly Detection using Generative Adversarial\n  Network Discriminators and Density Estimation", "abstract": "  Developing efficient time series anomaly detection techniques is important to\nmaintain service quality and provide early alarms. Generative neural network\nmethods are one class of the unsupervised approaches that are achieving\nincreasing attention in recent years. In this paper, we have proposed an\nunsupervised Generative Adversarial Network (GAN)-based anomaly detection\nframework, DEGAN. It relies solely on normal time series data as input to train\na well-configured discriminator (D) into a standalone anomaly predictor. In\nthis framework, time series data is processed by the sliding window method.\nExpected normal patterns in data are leveraged to develop a generator (G)\ncapable of generating normal data patterns. Normal data is also utilized in\nhyperparameter tuning and D model selection steps. Validated D models are then\nextracted and applied to evaluate unseen (testing) time series and identify\npatterns that have anomalous characteristics. Kernel density estimation (KDE)\nis applied to data points that are likely to be anomalous to generate\nprobability density functions on the testing time series. The segments with the\nhighest relative probabilities are detected as anomalies. To evaluate the\nperformance, we tested on univariate acceleration time series for five miles of\na Class I railroad track. We implemented the framework to detect the real\nanomalous observations identified by operators. The results show that\nleveraging the framework with a CNN D architecture results in average best\nrecall and precision of 80% and 86%, respectively, which demonstrates that a\nwell-trained standalone D model has the potential to be a reliable anomaly\ndetector. Moreover, the influence of GAN hyperparameters, GAN architectures,\nsliding window sizes, clustering of time series, and model validation with\nlabeled/unlabeled data were also investigated.\n", "date": "2022-10-05", "categories": []}
{"id": "2210.02558", "title": "Improved Anomaly Detection by Using the Attention-Based Isolation Forest", "abstract": "  A new modification of Isolation Forest called Attention-Based Isolation\nForest (ABIForest) for solving the anomaly detection problem is proposed. It\nincorporates the attention mechanism in the form of the Nadaraya-Watson\nregression into the Isolation Forest for improving solution of the anomaly\ndetection problem. The main idea underlying the modification is to assign\nattention weights to each path of trees with learnable parameters depending on\ninstances and trees themselves. The Huber's contamination model is proposed to\nbe used for defining the attention weights and their parameters. As a result,\nthe attention weights are linearly depend on the learnable attention parameters\nwhich are trained by solving the standard linear or quadratic optimization\nproblem. ABIForest can be viewed as the first modification of Isolation Forest,\nwhich incorporates the attention mechanism in a simple way without applying\ngradient-based algorithms. Numerical experiments with synthetic and real\ndatasets illustrate outperforming results of ABIForest. The code of proposed\nalgorithms is available.\n", "date": "2022-10-05", "categories": []}
{"id": "2210.01840", "title": "Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet\n  of Things", "abstract": "  Detecting anomalies at the time of happening is vital in environments like\nbuildings and homes to identify potential cyber-attacks. This paper discussed\nthe various mechanisms to detect anomalies as soon as they occur. We shed light\non crucial considerations when building machine learning models. We constructed\nand gathered data from multiple self-build (DIY) IoT devices with different\nin-situ sensors and found effective ways to find the point, contextual and\ncombine anomalies. We also discussed several challenges and potential solutions\nwhen dealing with sensing devices that produce data at different sampling rates\nand how we need to pre-process them in machine learning models. This paper also\nlooks at the pros and cons of extracting sub-datasets based on environmental\nconditions.\n", "date": "2022-10-04", "categories": []}
{"id": "2209.13363", "title": "Anomaly Detection in Aerial Videos with Transformers", "abstract": "  Unmanned aerial vehicles (UAVs) are widely applied for purposes of\ninspection, search, and rescue operations by the virtue of low-cost,\nlarge-coverage, real-time, and high-resolution data acquisition capacities.\nMassive volumes of aerial videos are produced in these processes, in which\nnormal events often account for an overwhelming proportion. It is extremely\ndifficult to localize and extract abnormal events containing potentially\nvaluable information from long video streams manually. Therefore, we are\ndedicated to developing anomaly detection methods to solve this issue. In this\npaper, we create a new dataset, named DroneAnomaly, for anomaly detection in\naerial videos. This dataset provides 37 training video sequences and 22 testing\nvideo sequences from 7 different realistic scenes with various anomalous\nevents. There are 87,488 color video frames (51,635 for training and 35,853 for\ntesting) with the size of $640 \\times 640$ at 30 frames per second. Based on\nthis dataset, we evaluate existing methods and offer a benchmark for this task.\nFurthermore, we present a new baseline model, ANomaly Detection with\nTransformers (ANDT), which treats consecutive video frames as a sequence of\ntubelets, utilizes a Transformer encoder to learn feature representations from\nthe sequence, and leverages a decoder to predict the next frame. Our network\nmodels normality in the training phase and identifies an event with\nunpredictable temporal dynamics as an anomaly in the test phase. Moreover, To\ncomprehensively evaluate the performance of our proposed method, we use not\nonly our Drone-Anomaly dataset but also another dataset. We will make our\ndataset and code publicly available. A demo video is available at\nhttps://youtu.be/ancczYryOBY. We make our dataset and code publicly available .\n", "date": "2022-09-25", "categories": []}
{"id": "2209.14930", "title": "Graph Anomaly Detection with Graph Neural Networks: Current Status and\n  Challenges", "abstract": "  Graphs are used widely to model complex systems, and detecting anomalies in a\ngraph is an important task in the analysis of complex systems. Graph anomalies\nare patterns in a graph that do not conform to normal patterns expected of the\nattributes and/or structures of the graph. In recent years, graph neural\nnetworks (GNNs) have been studied extensively and have successfully performed\ndifficult machine learning tasks in node classification, link prediction, and\ngraph classification thanks to the highly expressive capability via message\npassing in effectively learning graph representations. To solve the graph\nanomaly detection problem, GNN-based methods leverage information about the\ngraph attributes (or features) and/or structures to learn to score anomalies\nappropriately. In this survey, we review the recent advances made in detecting\ngraph anomalies using GNN models. Specifically, we summarize GNN-based methods\naccording to the graph type (i.e., static and dynamic), the anomaly type (i.e.,\nnode, edge, subgraph, and whole graph), and the network architecture (e.g.,\ngraph autoencoder, graph convolutional network). To the best of our knowledge,\nthis survey is the first comprehensive review of graph anomaly detection\nmethods based on GNNs.\n", "date": "2022-09-29", "categories": []}
{"id": "2210.01707", "title": "Multiple Instance Learning for Detecting Anomalies over Sequential\n  Real-World Datasets", "abstract": "  Detecting anomalies over real-world datasets remains a challenging task. Data\nannotation is an intensive human labor problem, particularly in sequential\ndatasets, where the start and end time of anomalies are not known. As a result,\ndata collected from sequential real-world processes can be largely unlabeled or\ncontain inaccurate labels. These characteristics challenge the application of\nanomaly detection techniques based on supervised learning. In contrast,\nMultiple Instance Learning (MIL) has been shown effective on problems with\nincomplete knowledge of labels in the training dataset, mainly due to the\nnotion of bags. While largely under-leveraged for anomaly detection, MIL\nprovides an appealing formulation for anomaly detection over real-world\ndatasets, and it is the primary contribution of this paper. In this paper, we\npropose an MIL-based formulation and various algorithmic instantiations of this\nframework based on different design decisions for key components of the\nframework. We evaluate the resulting algorithms over four datasets that capture\ndifferent physical processes along different modalities. The experimental\nevaluation draws out several observations. The MIL-based formulation performs\nno worse than single instance learning on easy to moderate datasets and\noutperforms single-instance learning on more challenging datasets. Altogether,\nthe results show that the framework generalizes well over diverse datasets\nresulting from different real-world application domains.\n", "date": "2022-10-04", "categories": []}
{"id": "2208.05994", "title": "Anomaly segmentation model for defects detection in electroluminescence\n  images of heterojunction solar cells", "abstract": "  Efficient defect detection in solar cell manufacturing is crucial for stable\ngreen energy technology manufacturing. This paper presents a\ndeep-learning-based automatic detection model SeMaCNN for classification and\nsemantic segmentation of electroluminescent images for solar cell quality\nevaluation and anomalies detection. The core of the model is an anomaly\ndetection algorithm based on Mahalanobis distance that can be trained in a\nsemi-supervised manner on imbalanced data with small number of digital\nelectroluminescence images with relevant defects. This is particularly valuable\nfor prompt model integration into the industrial landscape. The model has been\ntrained with the on-plant collected dataset consisting of 68 748\nelectroluminescent images of heterojunction solar cells with a busbar grid. Our\nmodel achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and\nprecision 96.9% within the validation subset consisting of 1049 manually\nannotated images. The model was also tested on the open ELPV dataset and\ndemonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The\nSeMaCNN model demonstrates a good balance between its performance and\ncomputational costs, which make it applicable for integrating into quality\ncontrol systems of solar cell manufacturing.\n", "date": "2022-08-11", "categories": []}
{"id": "2209.10658", "title": "Explaining Anomalies using Denoising Autoencoders for Financial Tabular\n  Data", "abstract": "  Recent advances in Explainable AI (XAI) increased the demand for deployment\nof safe and interpretable AI models in various industry sectors. Despite the\nlatest success of deep neural networks in a variety of domains, understanding\nthe decision-making process of such complex models still remains a challenging\ntask for domain experts. Especially in the financial domain, merely pointing to\nan anomaly composed of often hundreds of mixed type columns, has limited value\nfor experts. Hence, in this paper, we propose a framework for explaining\nanomalies using denoising autoencoders designed for mixed type tabular data. We\nspecifically focus our technique on anomalies that are erroneous observations.\nThis is achieved by localizing individual sample columns (cells) with potential\nerrors and assigning corresponding confidence scores. In addition, the model\nprovides the expected cell value estimates to fix the errors. We evaluate our\napproach based on three standard public tabular datasets (Credit Default,\nAdult, IEEE Fraud) and one proprietary dataset (Holdings). We find that\ndenoising autoencoders applied to this task already outperform other approaches\nin the cell error detection rates as well as in the expected value rates.\nAdditionally, we analyze how a specialized loss designed for cell error\ndetection can further improve these metrics. Our framework is designed for a\ndomain expert to understand abnormal characteristics of an anomaly, as well as\nto improve in-house data quality management processes.\n", "date": "2022-09-21", "categories": []}
{"id": "2206.15033", "title": "A Causal Approach to Detecting Multivariate Time-series Anomalies and\n  Root Causes", "abstract": "  Detecting anomalies and the corresponding root causes in multivariate time\nseries plays an important role in monitoring the behaviors of various\nreal-world systems, e.g., IT system operations or manufacturing industry.\nPrevious anomaly detection approaches model the joint distribution without\nconsidering the underlying mechanism of multivariate time series, making them\ncomputationally hungry and hard to identify root causes. In this paper, we\nformulate the anomaly detection problem from a causal perspective and view\nanomalies as instances that do not follow the regular causal mechanism to\ngenerate the multivariate data. We then propose a causality-based framework for\ndetecting anomalies and root causes. It first learns the causal structure from\ndata and then infers whether an instance is an anomaly relative to the local\ncausal mechanism whose conditional distribution can be directly estimated from\ndata. In light of the modularity property of causal systems (the causal\nprocesses to generate different variables are irrelevant modules), the original\nproblem is divided into a series of separate, simpler, and low-dimensional\nanomaly detection problems so that where an anomaly happens (root causes) can\nbe directly identified. We evaluate our approach with both simulated and public\ndatasets as well as a case study on real-world AIOps applications, showing its\nefficacy, robustness, and practical feasibility.\n", "date": "2022-06-30", "categories": []}
{"id": "2209.13965", "title": "Anomaly detection optimization using big data and deep learning to\n  reduce false-positive", "abstract": "  Anomaly-based Intrusion Detection System (IDS) has been a hot research topic\nbecause of its ability to detect new threats rather than only memorized\nsignatures threats of signature-based IDS. Especially after the availability of\nadvanced technologies that increase the number of hacking tools and increase\nthe risk impact of an attack. The problem of any anomaly-based model is its\nhigh false-positive rate. The high false-positive rate is the reason why\nanomaly IDS is not commonly applied in practice. Because anomaly-based models\nclassify an unseen pattern as a threat where it may be normal but not included\nin the training dataset. This type of problem is called overfitting where the\nmodel is not able to generalize. Optimizing Anomaly-based models by having a\nbig training dataset that includes all possible normal cases may be an optimal\nsolution but could not be applied in practice. Although we can increase the\nnumber of training samples to include much more normal cases, still we need a\nmodel that has more ability to generalize. In this research paper, we propose\napplying deep model instead of traditional models because it has more ability\nto generalize. Thus, we will obtain less false-positive by using big data and\ndeep model. We made a comparison between machine learning and deep learning\nalgorithms in the optimization of anomaly-based IDS by decreasing the\nfalse-positive rate. We did an experiment on the NSL-KDD benchmark and compared\nour results with one of the best used classifiers in traditional learning in\nIDS optimization. The experiment shows 10% lower false-positive by using deep\nlearning instead of traditional learning.\n", "date": "2022-09-28", "categories": []}
{"id": "2209.13116", "title": "Spatio-Temporal Relation Learning for Video Anomaly Detection", "abstract": "  Anomaly identification is highly dependent on the relationship between the\nobject and the scene, as different/same object actions in same/different scenes\nmay lead to various degrees of normality and anomaly. Therefore, object-scene\nrelation actually plays a crucial role in anomaly detection but is inadequately\nexplored in previous works. In this paper, we propose a Spatial-Temporal\nRelation Learning (STRL) framework to tackle the video anomaly detection task.\nFirst, considering dynamic characteristics of the objects as well as scene\nareas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit\nspatial and temporal evolution patterns for representation learning. For better\npattern extraction, two decoding branches are designed in the STAE module, i.e.\nan appearance branch capturing spatial cues by directly predicting the next\nframe, and a motion branch focusing on modeling the dynamics via optical flow\nprediction. Then, to well concretize the object-scene relation, a Relation\nLearning (RL) module is devised to analyze and summarize the normal relations\nby introducing the Knowledge Graph Embedding methodology. Specifically in this\nprocess, the plausibility of object-scene relation is measured by jointly\nmodeling object/scene features and optimizable object-scene relation maps.\nExtensive experiments are conducted on three public datasets, and the superior\nperformance over the state-of-the-art methods demonstrates the effectiveness of\nour method.\n", "date": "2022-09-27", "categories": []}
{"id": "2209.11763", "title": "Enhancing Claim Classification with Feature Extraction from\n  Anomaly-Detection-Derived Routine and Peculiarity Profiles", "abstract": "  Usage-based insurance is becoming the new standard in vehicle insurance; it\nis therefore relevant to find efficient ways of using insureds' driving data.\nApplying anomaly detection to vehicles' trip summaries, we develop a method\nallowing to derive a \"routine\" and a \"peculiarity\" anomaly profile for each\nvehicle. To this end, anomaly detection algorithms are used to compute a\nroutine and a peculiarity anomaly score for each trip a vehicle makes. The\nformer measures the anomaly degree of the trip compared to the other trips made\nby the concerned vehicle, while the latter measures its anomaly degree compared\nto trips made by any vehicle. The resulting anomaly scores vectors are used as\nroutine and peculiarity profiles. Features are then extracted from these\nprofiles, for which we investigate the predictive power in the claim\nclassification framework. Using real data, we find that features extracted from\nthe vehicles' peculiarity profile improve classification.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.12398", "title": "Real-time Anomaly Detection for Multivariate Data Streams", "abstract": "  We present a real-time multivariate anomaly detection algorithm for data\nstreams based on the Probabilistic Exponentially Weighted Moving Average\n(PEWMA). Our formulation is resilient to (abrupt transient, abrupt\ndistributional, and gradual distributional) shifts in the data. The novel\nanomaly detection routines utilize an incremental online algorithm to handle\nstreams. Furthermore, our proposed anomaly detection algorithm works in an\nunsupervised manner eliminating the need for labeled examples. Our algorithm\nperforms well and is resilient in the face of concept drifts.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.12440", "title": "Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly\n  Detection", "abstract": "  Unsupervised anomaly detection is a challenging task in industrial\napplications since it is impracticable to collect sufficient anomalous samples.\nIn this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is\nproposed by jointly exploring effective generation method of forged anomalous\nsamples and the normal sample features as the guidance information of\nsegmentation for anomaly detection. Specifically, to ensure that the generated\nforged anomaly samples are conducive to model training, the Saliency\nAugmentation Module (SAM) is proposed. SAM introduces a saliency map to\ngenerate saliency Perlin noise map, and develops an adaptive segmentation\nstrategy to generate irregular masks in the saliency region. Then, the masks\nare utilized to generate forged anomalous samples as negative samples for\ntraining. Unfortunately, the distribution gap between forged and real anomaly\nsamples makes it difficult for models trained based on forged samples to\neffectively locate real anomalies. Towards this end, the Self-supervised\nGuidance Network (SGN) is proposed. It leverages the self-supervised module to\nextract features that are noise-free and contain normal semantic information as\nthe prior knowledge of the segmentation module. The segmentation module with\nthe knowledge of normal patterns segments out the abnormal regions that are\ndifferent from the guidance features. To evaluate the effectiveness of SGSF for\nanomaly detection, extensive experiments are conducted on three anomaly\ndetection datasets. The experimental results show that SGSF achieves\nstate-of-the-art anomaly detection results.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.12441", "title": "Visual Anomaly Detection Via Partition Memory Bank Module and Error\n  Estimation", "abstract": "  Reconstruction method based on the memory module for visual anomaly detection\nattempts to narrow the reconstruction error for normal samples while enlarging\nit for anomalous samples. Unfortunately, the existing memory module is not\nfully applicable to the anomaly detection task, and the reconstruction error of\nthe anomaly samples remains small. Towards this end, this work proposes a new\nunsupervised visual anomaly detection method to jointly learn effective normal\nfeatures and eliminate unfavorable reconstruction errors. Specifically, a novel\nPartition Memory Bank (PMB) module is proposed to effectively learn and store\ndetailed features with semantic integrity of normal samples. It develops a new\npartition mechanism and a unique query generation method to preserve the\ncontext information and then improves the learning ability of the memory\nmodule. The proposed PMB and the skip connection are alternatively explored to\nmake the reconstruction of abnormal samples worse. To obtain more precise\nanomaly localization results and solve the problem of cumulative reconstruction\nerror, a novel Histogram Error Estimation module is proposed to adaptively\neliminate the unfavorable errors by the histogram of the difference image. It\nimproves the anomaly localization performance without increasing the cost. To\nevaluate the effectiveness of the proposed method for anomaly detection and\nlocalization, extensive experiments are conducted on three widely-used anomaly\ndetection datasets. The encouraging performance of the proposed method compared\nto the recent approaches based on the memory module demonstrates its\nsuperiority.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.12665", "title": "Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit\n  Data", "abstract": "  Over the last few decades, extensive use of information and communication\ntechnologies has been the main driver of the digitalization of power systems.\nProper and secure monitoring of the critical grid infrastructure became an\nintegral part of the modern power system. Using phasor measurement units (PMUs)\nto surveil the power system is one of the technologies that have a promising\nfuture. Increased frequency of measurements and smarter methods for data\nhandling can improve the ability to reliably operate power grids. The increased\ncyber-physical interaction offers both benefits and drawbacks, where one of the\ndrawbacks comes in the form of anomalies in the measurement data. The anomalies\ncan be caused by both physical faults on the power grid, as well as\ndisturbances, errors, and cyber attacks in the cyber layer. This paper aims to\ndevelop a hybrid AI-based model that is based on various methods such as Long\nShort Term Memory (LSTM), Convolutional Neural Network (CNN) and other relevant\nhybrid algorithms for anomaly detection in phasor measurement unit data. The\ndataset used within this research was acquired by the University of Texas,\nwhich consists of real data from grid measurements. In addition to the real\ndata, false data that has been injected to produce anomalies has been analyzed.\nThe impacts and mitigating methods to prevent such kind of anomalies are\ndiscussed.\n", "date": "2022-09-21", "categories": []}
{"id": "2208.14319", "title": "Representation Learning based and Interpretable Reactor System Diagnosis\n  Using Denoising Padded Autoencoder", "abstract": "  With the mass construction of Gen III nuclear reactors, it is a popular trend\nto use deep learning (DL) techniques for fast and effective diagnosis of\npossible accidents. To overcome the common problems of previous work in\ndiagnosing reactor accidents using deep learning theory, this paper proposes a\ndiagnostic process that ensures robustness to noisy and crippled data and is\ninterpretable. First, a novel Denoising Padded Autoencoder (DPAE) is proposed\nfor representation extraction of monitoring data, with representation extractor\nstill effective on disturbed data with signal-to-noise ratios up to 25.0 and\nmonitoring data missing up to 40.0%. Secondly, a diagnostic framework using\nDPAE encoder for extraction of representations followed by shallow statistical\nlearning algorithms is proposed, and such stepwise diagnostic approach is\ntested on disturbed datasets with 41.8% and 80.8% higher classification and\nregression task evaluation metrics, in comparison with the end-to-end\ndiagnostic approaches. Finally, a hierarchical interpretation algorithm using\nSHAP and feature ablation is presented to analyze the importance of the input\nmonitoring parameters and validate the effectiveness of the high importance\nparameters. The outcomes of this study provide a referential method for\nbuilding robust and interpretable intelligent reactor anomaly diagnosis systems\nin scenarios with high safety requirements.\n", "date": "2022-08-30", "categories": []}
{"id": "2209.10178", "title": "Deep Learning based pipeline for anomaly detection and quality\n  enhancement in industrial binder jetting processes", "abstract": "  Anomaly detection describes methods of finding abnormal states, instances or\ndata points that differ from a normal value space. Industrial processes are a\ndomain where predicitve models are needed for finding anomalous data instances\nfor quality enhancement. A main challenge, however, is absence of labels in\nthis environment. This paper contributes to a data-centric way of approaching\nartificial intelligence in industrial production. With a use case from additive\nmanufacturing for automotive components we present a deep-learning-based image\nprocessing pipeline. Additionally, we integrate the concept of domain\nrandomisation and synthetic data in the loop that shows promising results for\nbridging advances in deep learning and its application to real-world,\nindustrial production processes.\n", "date": "2022-09-21", "categories": []}
{"id": "2209.11427", "title": "A Robust and Explainable Data-Driven Anomaly Detection Approach For\n  Power Electronics", "abstract": "  Timely and accurate detection of anomalies in power electronics is becoming\nincreasingly critical for maintaining complex production systems. Robust and\nexplainable strategies help decrease system downtime and preempt or mitigate\ninfrastructure cyberattacks. This work begins by explaining the types of\nuncertainty present in current datasets and machine learning algorithm outputs.\nThree techniques for combating these uncertainties are then introduced and\nanalyzed. We further present two anomaly detection and classification\napproaches, namely the Matrix Profile algorithm and anomaly transformer, which\nare applied in the context of a power electronic converter dataset.\nSpecifically, the Matrix Profile algorithm is shown to be well suited as a\ngeneralizable approach for detecting real-time anomalies in streaming\ntime-series data. The STUMPY python library implementation of the iterative\nMatrix Profile is used for the creation of the detector. A series of custom\nfilters is created and added to the detector to tune its sensitivity, recall,\nand detection accuracy. Our numerical results show that, with simple parameter\ntuning, the detector provides high accuracy and performance in a variety of\nfault scenarios.\n", "date": "2022-09-23", "categories": []}
{"id": "2209.10995", "title": "Challenges in Visual Anomaly Detection for Mobile Robots", "abstract": "  We consider the task of detecting anomalies for autonomous mobile robots\nbased on vision. We categorize relevant types of visual anomalies and discuss\nhow they can be detected by unsupervised deep learning methods. We propose a\nnovel dataset built specifically for this task, on which we test a\nstate-of-the-art approach; we finally discuss deployment in a real scenario.\n", "date": "2022-09-22", "categories": []}
{"id": "2209.09923", "title": "Collaborative Anomaly Detection", "abstract": "  In recommendation systems, items are likely to be exposed to various users\nand we would like to learn about the familiarity of a new user with an existing\nitem. This can be formulated as an anomaly detection (AD) problem\ndistinguishing between \"common users\" (nominal) and \"fresh users\" (anomalous).\nConsidering the sheer volume of items and the sparsity of user-item paired\ndata, independently applying conventional single-task detection methods on each\nitem quickly becomes difficult, while correlations between items are ignored.\nTo address this multi-task anomaly detection problem, we propose collaborative\nanomaly detection (CAD) to jointly learn all tasks with an embedding encoding\ncorrelations among tasks. We explore CAD with conditional density estimation\nand conditional likelihood ratio estimation. We found that: $i$) estimating a\nlikelihood ratio enjoys more efficient learning and yields better results than\ndensity estimation. $ii$) It is beneficial to select a small number of tasks in\nadvance to learn a task embedding model, and then use it to warm-start all task\nembeddings. Consequently, these embeddings can capture correlations between\ntasks and generalize to new correlated tasks.\n", "date": "2022-09-20", "categories": []}
{"id": "2209.10121", "title": "Leak Detection in Natural Gas Pipeline Using Machine Learning Models", "abstract": "  Leak detection in gas pipelines is an important and persistent problem in the\nOil and Gas industry. This is particularly important as pipelines are the most\ncommon way of transporting natural gas. This research aims to study the ability\nof data-driven intelligent models to detect small leaks for a natural gas\npipeline using basic operational parameters and then compare the intelligent\nmodels among themselves using existing performance metrics. This project\napplies the observer design technique to detect leaks in natural gas pipelines\nusing a regressoclassification hierarchical model where an intelligent model\nacts as a regressor and a modified logistic regression model acts as a\nclassifier. Five intelligent models (gradient boosting, decision trees, random\nforest, support vector machine and artificial neural network) are studied in\nthis project using a pipeline data stream of four weeks. The results shows that\nwhile support vector machine and artificial neural networks are better\nregressors than the others, they do not provide the best results in leak\ndetection due to their internal complexities and the volume of data used. The\nrandom forest and decision tree models are the most sensitive as they can\ndetect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent\nmodels had high reliability with zero false alarm rate in testing phase. The\naverage time to leak detection for all the intelligent models was compared to a\nreal time transient model in literature. The results show that intelligent\nmodels perform relatively well in the problem of leak detection. This result\nsuggests that intelligent models could be used alongside a real time transient\nmodel to significantly improve leak detection results.\n", "date": "2022-09-21", "categories": []}
{"id": "2209.09786", "title": "An Outlier Exposure Approach to Improve Visual Anomaly Detection\n  Performance for Mobile Robots", "abstract": "  We consider the problem of building visual anomaly detection systems for\nmobile robots. Standard anomaly detection models are trained using large\ndatasets composed only of non-anomalous data. However, in robotics\napplications, it is often the case that (potentially very few) examples of\nanomalies are available. We tackle the problem of exploiting these data to\nimprove the performance of a Real-NVP anomaly detection model, by minimizing,\njointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We\nperform quantitative experiments on a novel dataset (which we publish as\nsupplementary material) designed for anomaly detection in an indoor patrolling\nscenario. On a disjoint test set, our approach outperforms alternatives and\nshows that exposing even a small number of anomalous frames yields significant\nperformance improvements.\n", "date": "2022-09-20", "categories": []}
{"id": "2206.09426", "title": "ADBench: Anomaly Detection Benchmark", "abstract": "  Given a long list of anomaly detection algorithms developed in the last few\ndecades, how do they perform with regard to (i) varying levels of supervision,\n(ii) different types of anomalies, and (iii) noisy and corrupted data? In this\nwork, we answer these key questions by conducting (to our best knowledge) the\nmost comprehensive anomaly detection benchmark with 30 algorithms on 57\nbenchmark datasets, named ADBench. Our extensive experiments (98,436 in total)\nidentify meaningful insights into the role of supervision and anomaly types,\nand unlock future directions for researchers in algorithm selection and design.\nWith ADBench, researchers can easily conduct comprehensive and fair evaluations\nfor newly proposed methods on the datasets (including our contributed ones from\nnatural language and computer vision domains) against the existing baselines.\nTo foster accessibility and reproducibility, we fully open-source ADBench and\nthe corresponding results.\n", "date": "2022-06-19", "categories": []}
{"id": "2209.07005", "title": "Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing\n  Flow and Dictionary Learning", "abstract": "  A common study area in anomaly identification is industrial images anomaly\ndetection based on texture background. The interference of texture images and\nthe minuteness of texture anomalies are the main reasons why many existing\nmodels fail to detect anomalies. We propose a strategy for anomaly detection\nthat combines dictionary learning and normalizing flow based on the\naforementioned questions. The two-stage anomaly detection approach already in\nuse is enhanced by our method. In order to improve baseline method, this\nresearch add normalizing flow in representation learning and combines deep\nlearning and dictionary learning. Improved algorithms have exceeded 95$\\%$\ndetection accuracy on all MVTec AD texture type data after experimental\nvalidation. It shows strong robustness. The baseline method's detection\naccuracy for the Carpet data was 67.9%. The article was upgraded, raising the\ndetection accuracy to 99.7%.\n", "date": "2022-09-15", "categories": []}
{"id": "2209.08618", "title": "Koopman-theoretic Approach for Identification of Exogenous Anomalies in\n  Nonstationary Time-series Data", "abstract": "  In many scenarios, it is necessary to monitor a complex system via a\ntime-series of observations and determine when anomalous exogenous events have\noccurred so that relevant actions can be taken. Determining whether current\nobservations are abnormal is challenging. It requires learning an extrapolative\nprobabilistic model of the dynamics from historical data, and using a limited\nnumber of current observations to make a classification. We leverage recent\nadvances in long-term probabilistic forecasting, namely {\\em Deep Probabilistic\nKoopman}, to build a general method for classifying anomalies in\nmulti-dimensional time-series data. We also show how to utilize models with\ndomain knowledge of the dynamics to reduce type I and type II error. We\ndemonstrate our proposed method on the important real-world task of global\natmospheric pollution monitoring, integrating it with NASA's Global Earth\nSystem Model. The system successfully detects localized anomalies in air\nquality due to events such as COVID-19 lockdowns and wildfires.\n", "date": "2022-09-18", "categories": []}
{"id": "2209.07869", "title": "LogGD:Detecting Anomalies from System Logs by Graph Neural Networks", "abstract": "  Log analysis is one of the main techniques engineers use to troubleshoot\nfaults of large-scale software systems. During the past decades, many log\nanalysis approaches have been proposed to detect system anomalies reflected by\nlogs. They usually take log event counts or sequential log events as inputs and\nutilize machine learning algorithms including deep learning models to detect\nsystem anomalies. These anomalies are often identified as violations of\nquantitative relational patterns or sequential patterns of log events in log\nsequences. However, existing methods fail to leverage the spatial structural\nrelationships among log events, resulting in potential false alarms and\nunstable performance. In this study, we propose a novel graph-based log anomaly\ndetection method, LogGD, to effectively address the issue by transforming log\nsequences into graphs. We exploit the powerful capability of Graph Transformer\nNeural Network, which combines graph structure and node semantics for log-based\nanomaly detection. We evaluate the proposed method on four widely-used public\nlog datasets. Experimental results show that LogGD can outperform\nstate-of-the-art quantitative-based and sequence-based methods and achieve\nstable performance under different window size settings. The results confirm\nthat LogGD is effective in log-based anomaly detection.\n", "date": "2022-09-16", "categories": []}
{"id": "2209.06435", "title": "Real-world Video Anomaly Detection by Extracting Salient Features in\n  Videos", "abstract": "  We propose a lightweight and accurate method for detecting anomalies in\nvideos. Existing methods used multiple-instance learning (MIL) to determine the\nnormal/abnormal status of each segment of the video. Recent successful\nresearches argue that it is important to learn the temporal relationships among\nsegments to achieve high accuracy, instead of focusing on only a single\nsegment. Therefore we analyzed the existing methods that have been successful\nin recent years, and found that while it is indeed important to learn all\nsegments together, the temporal orders among them are irrelevant to achieving\nhigh accuracy. Based on this finding, we do not use the MIL framework, but\ninstead propose a lightweight model with a self-attention mechanism to\nautomatically extract features that are important for determining\nnormal/abnormal from all input segments. As a result, our neural network model\nhas 1.3\\% of the number of parameters of the existing method. We evaluated the\nframe-level detection accuracy of our method on three benchmark datasets\n(UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can\nachieve the comparable or better accuracy than state-of-the-art methods.\n", "date": "2022-09-14", "categories": []}
{"id": "2209.04635", "title": "A Comparative Study on Unsupervised Anomaly Detection for Time Series:\n  Experiments and Analysis", "abstract": "  The continued digitization of societal processes translates into a\nproliferation of time series data that cover applications such as fraud\ndetection, intrusion detection, and energy management, where anomaly detection\nis often essential to enable reliability and safety. Many recent studies target\nanomaly detection for time series data. Indeed, area of time series anomaly\ndetection is characterized by diverse data, methods, and evaluation strategies,\nand comparisons in existing studies consider only part of this diversity, which\nmakes it difficult to select the best method for a particular problem setting.\nTo address this shortcoming, we introduce taxonomies for data, methods, and\nevaluation strategies, provide a comprehensive overview of unsupervised time\nseries anomaly detection using the taxonomies, and systematically evaluate and\ncompare state-of-the-art traditional as well as deep learning techniques. In\nthe empirical study using nine publicly available datasets, we apply the most\ncommonly-used performance evaluation metrics to typical methods under a fair\nimplementation standard. Based on the structuring offered by the taxonomies, we\nreport on empirical studies and provide guidelines, in the form of comparative\ntables, for choosing the methods most suitable for particular application\nsettings. Finally, we propose research directions for this dynamic field.\n", "date": "2022-09-10", "categories": []}
{"id": "2209.05049", "title": "Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly\n  Detection", "abstract": "  Anomaly detection on the attributed network has recently received increasing\nattention in many research fields, such as cybernetic anomaly detection and\nfinancial fraud detection. With the wide application of deep learning on graph\nrepresentations, existing approaches choose to apply euclidean graph encoders\nas their backbone, which may lose important hierarchical information,\nespecially in complex networks. To tackle this problem, we propose an efficient\nanomaly detection framework using hyperbolic self-supervised contrastive\nlearning. Specifically, we first conduct the data augmentation by performing\nsubgraph sampling. Then we utilize the hierarchical information in hyperbolic\nspace through exponential mapping and logarithmic mapping and obtain the\nanomaly score by subtracting scores of the positive pairs from the negative\npairs via a discriminating process. Finally, extensive experiments on four\nreal-world datasets demonstrate that our approach performs superior over\nrepresentative baseline approaches.\n", "date": "2022-09-12", "categories": []}
{"id": "2209.04173", "title": "Explanation Method for Anomaly Detection on Mixed Numerical and\n  Categorical Spaces", "abstract": "  Most proposals in the anomaly detection field focus exclusively on the\ndetection stage, specially in the recent deep learning approaches. While\nproviding highly accurate predictions, these models often lack transparency,\nacting as \"black boxes\". This criticism has grown to the point that explanation\nis now considered very relevant in terms of acceptability and reliability. In\nthis paper, we addressed this issue by inspecting the ADMNC (Anomaly Detection\non Mixed Numerical and Categorical Spaces) model, an existing very accurate\nalthough opaque anomaly detector capable to operate with both numerical and\ncategorical inputs. This work presents the extension EADMNC (Explainable\nAnomaly Detection on Mixed Numerical and Categorical spaces), which adds\nexplainability to the predictions obtained with the original model. We\npreserved the scalability of the original method thanks to the Apache Spark\nframework. EADMNC leverages the formulation of the previous ADMNC model to\noffer pre hoc and post hoc explainability, while maintaining the accuracy of\nthe original architecture. We present a pre hoc model that globally explains\nthe outputs by segmenting input data into homogeneous groups, described with\nonly a few variables. We designed a graphical representation based on\nregression trees, which supervisors can inspect to understand the differences\nbetween normal and anomalous data. Our post hoc explanations consist of a\ntext-based template method that locally provides textual arguments supporting\neach detection. We report experimental results on extensive real-world data,\nparticularly in the domain of network intrusion detection. The usefulness of\nthe explanations is assessed by theory analysis using expert knowledge in the\nnetwork intrusion domain.\n", "date": "2022-09-09", "categories": []}
{"id": "2209.04184", "title": "Anomaly Detection through Unsupervised Federated Learning", "abstract": "  Federated learning (FL) is proving to be one of the most promising paradigms\nfor leveraging distributed resources, enabling a set of clients to\ncollaboratively train a machine learning model while keeping the data\ndecentralized. The explosive growth of interest in the topic has led to rapid\nadvancements in several core aspects like communication efficiency, handling\nnon-IID data, privacy, and security capabilities. However, the majority of FL\nworks only deal with supervised tasks, assuming that clients' training sets are\nlabeled. To leverage the enormous unlabeled data on distributed edge devices,\nin this paper, we aim to extend the FL paradigm to unsupervised tasks by\naddressing the problem of anomaly detection in decentralized settings. In\nparticular, we propose a novel method in which, through a preprocessing phase,\nclients are grouped into communities, each having similar majority (i.e.,\ninlier) patterns. Subsequently, each community of clients trains the same\nanomaly detection model (i.e., autoencoders) in a federated fashion. The\nresulting model is then shared and used to detect anomalies within the clients\nof the same community that joined the corresponding federated process.\nExperiments show that our method is robust, and it can detect communities\nconsistent with the ideal partitioning in which groups of clients having the\nsame inlier patterns are known. Furthermore, the performance is significantly\nbetter than those in which clients train models exclusively on local data and\ncomparable with federated models of ideal communities' partition.\n", "date": "2022-09-09", "categories": []}
{"id": "2209.03576", "title": "Suspicious and Anomaly Detection", "abstract": "  In this project we propose a CNN architecture to detect anomaly and\nsuspicious activities; the activities chosen for the project are running,\njumping and kicking in public places and carrying gun, bat and knife in public\nplaces. With the trained model we compare it with the pre-existing models like\nYolo, vgg16, vgg19. The trained Model is then implemented for real time\ndetection and also used the. tflite format of the trained .h5 model to build an\nandroid classification.\n", "date": "2022-09-08", "categories": []}
{"id": "2209.02899", "title": "Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework\n  for Video Anomaly Detection", "abstract": "  Video anomaly detection aims to find the events in a video that do not\nconform to the expected behavior. The prevalent methods mainly detect anomalies\nby snippet reconstruction or future frame prediction error. However, the error\nis highly dependent on the local context of the current snippet and lacks the\nunderstanding of normality. To address this issue, we propose to detect\nanomalous events not only by the local context, but also according to the\nconsistency between the testing event and the knowledge about normality from\nthe training data. Concretely, we propose a novel two-stream framework based on\ncontext recovery and knowledge retrieval, where the two streams can complement\neach other. For the context recovery stream, we propose a spatiotemporal U-Net\nwhich can fully utilize the motion information to predict the future frame.\nFurthermore, we propose a maximum local error mechanism to alleviate the\nproblem of large recovery errors caused by complex foreground objects. For the\nknowledge retrieval stream, we propose an improved learnable locality-sensitive\nhashing, which optimizes hash functions via a Siamese network and a mutual\ndifference loss. The knowledge about normality is encoded and stored in hash\ntables, and the distance between the testing event and the knowledge\nrepresentation is used to reveal the probability of anomaly. Finally, we fuse\nthe anomaly scores from the two streams to detect anomalies. Extensive\nexperiments demonstrate the effectiveness and complementarity of the two\nstreams, whereby the proposed two-stream framework achieves state-of-the-art\nperformance on four datasets.\n", "date": "2022-09-07", "categories": []}
{"id": "2209.01822", "title": "HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies\n  Associated with Human Disease", "abstract": "  Automated anomaly detection from medical images, such as MRIs and X-rays, can\nsignificantly reduce human effort in disease diagnosis. Owing to the complexity\nof modeling anomalies and the high cost of manual annotation by domain experts\n(e.g., radiologists), a typical technique in the current medical imaging\nliterature has focused on deriving diagnostic models from healthy subjects\nonly, assuming the model will detect the images from patients as outliers.\nHowever, in many real-world scenarios, unannotated datasets with a mix of both\nhealthy and diseased individuals are abundant. Therefore, this paper poses the\nresearch question of how to improve unsupervised anomaly detection by utilizing\n(1) an unannotated set of mixed images, in addition to (2) the set of healthy\nimages as being used in the literature. To answer the question, we propose\nHealthyGAN, a novel one-directional image-to-image translation method, which\nlearns to translate the images from the mixed dataset to only healthy images.\nBeing one-directional, HealthyGAN relaxes the requirement of cycle consistency\nof existing unpaired image-to-image translation methods, which is unattainable\nwith mixed unannotated data. Once the translation is learned, we generate a\ndifference map for any given image by subtracting its translated output.\nRegions of significant responses in the difference map correspond to potential\nanomalies (if any). Our HealthyGAN outperforms the conventional\nstate-of-the-art methods by significant margins on two publicly available\ndatasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset\ncollected from Mayo Clinic. The implementation is publicly available at\nhttps://github.com/mahfuzmohammad/HealthyGAN.\n", "date": "2022-09-05", "categories": []}
{"id": "2209.02379", "title": "Self-Calibrating Anomaly and Change Detection for Autonomous Inspection\n  Robots", "abstract": "  Automatic detection of visual anomalies and changes in the environment has\nbeen a topic of recurrent attention in the fields of machine learning and\ncomputer vision over the past decades. A visual anomaly or change detection\nalgorithm identifies regions of an image that differ from a reference image or\ndataset. The majority of existing approaches focus on anomaly or fault\ndetection in a specific class of images or environments, while general purpose\nvisual anomaly detection algorithms are more scarce in the literature. In this\npaper, we propose a comprehensive deep learning framework for detecting\nanomalies and changes in a priori unknown environments after a reference\ndataset is gathered, and without need for retraining the model. We use the\nSuperPoint and SuperGlue feature extraction and matching methods to detect\nanomalies based on reference images taken from a similar location and with\npartial overlapping of the field of view. We also introduce a self-calibrating\nmethod for the proposed model in order to address the problem of sensitivity to\nfeature matching thresholds and environmental conditions. To evaluate the\nproposed framework, we have used a ground robot system for the purpose of\nreference and query data collection. We show that high accuracy can be obtained\nusing the proposed method. We also show that the calibration process enhances\nchanges and foreign object detection performance\n", "date": "2022-08-26", "categories": []}
{"id": "2209.01124", "title": "nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation\n  Methods", "abstract": "  The wide variety of in-distribution and out-of-distribution data in medical\nimaging makes universal anomaly detection a challenging task. Recently a number\nof self-supervised methods have been developed that train end-to-end models on\nhealthy data augmented with synthetic anomalies. However, it is difficult to\ncompare these methods as it is not clear whether gains in performance are from\nthe task itself or the training pipeline around it. It is also difficult to\nassess whether a task generalises well for universal anomaly detection, as they\nare often only tested on a limited range of anomalies. To assist with this we\nhave developed nnOOD, a framework that adapts nnU-Net to allow for comparison\nof self-supervised anomaly localisation methods. By isolating the synthetic,\nself-supervised task from the rest of the training process we perform a more\nfaithful comparison of the tasks, whilst also making the workflow for\nevaluating over a given dataset quick and easy. Using this we have implemented\nthe current state-of-the-art tasks and evaluated them on a challenging X-ray\ndataset.\n", "date": "2022-09-02", "categories": []}
{"id": "2208.14045", "title": "Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM", "abstract": "  Detecting anomalous regions in images is a frequently encountered problem in\nindustrial monitoring. A relevant example is the analysis of tissues and other\nproducts that in normal conditions conform to a specific texture, while defects\nintroduce changes in the normal pattern. We address the anomaly detection\nproblem by training a deep autoencoder, and we show that adopting a loss\nfunction based on Complex Wavelet Structural Similarity (CW-SSIM) yields\nsuperior detection performance on this type of images compared to traditional\nautoencoder loss functions. Our experiments on well-known anomaly detection\nbenchmarks show that a simple model trained with this loss function can achieve\ncomparable or superior performance to state-of-the-art methods leveraging\ndeeper, larger and more computationally demanding neural networks.\n", "date": "2022-08-30", "categories": []}
{"id": "2208.14337", "title": "Denoising Architecture for Unsupervised Anomaly Detection in Time-Series", "abstract": "  Anomalies in time-series provide insights of critical scenarios across a\nrange of industries, from banking and aerospace to information technology,\nsecurity, and medicine. However, identifying anomalies in time-series data is\nparticularly challenging due to the imprecise definition of anomalies, the\nfrequent absence of labels, and the enormously complex temporal correlations\npresent in such data. The LSTM Autoencoder is an Encoder-Decoder scheme for\nAnomaly Detection based on Long Short Term Memory Networks that learns to\nreconstruct time-series behavior and then uses reconstruction error to identify\nabnormalities. We introduce the Denoising Architecture as a complement to this\nLSTM Encoder-Decoder model and investigate its effect on real-world as well as\nartificially generated datasets. We demonstrate that the proposed architecture\nincreases both the accuracy and the training speed, thereby, making the LSTM\nAutoencoder more efficient for unsupervised anomaly detection tasks.\n", "date": "2022-08-30", "categories": []}
{"id": "2207.02059", "title": "Transformer based Models for Unsupervised Anomaly Segmentation in Brain\n  MR Images", "abstract": "  The quality of patient care associated with diagnostic radiology is\nproportionate to a physician workload. Segmentation is a fundamental limiting\nprecursor to both diagnostic and therapeutic procedures. Advances in machine\nlearning (ML) aim to increase diagnostic efficiency by replacing a single\napplication with generalized algorithms. The goal of unsupervised anomaly\ndetection (UAD) is to identify potential anomalous regions unseen during\ntraining, where convolutional neural network (CNN) based autoencoders (AEs) and\nvariational autoencoders (VAEs) are considered a de facto approach for\nreconstruction based-anomaly segmentation. The restricted receptive field in\nCNNs limits the CNN to model the global context. Hence, if the anomalous\nregions cover large parts of the image, the CNN-based AEs are not capable of\nbringing a semantic understanding of the image. Meanwhile, vision transformers\n(ViTs) have emerged as a competitive alternative to CNNs. It relies on the\nself-attention mechanism that can relate image patches to each other. We\ninvestigate in this paper Transformer capabilities in building AEs for the\nreconstruction-based UAD task to reconstruct a coherent and more realistic\nimage. We focus on anomaly segmentation for brain magnetic resonance imaging\n(MRI) and present five Transformer-based models while enabling segmentation\nperformance comparable to or superior to state-of-the-art (SOTA) models. The\nsource code is made publicly available on GitHub:\nhttps://github.com/ahmedgh970/Transformers_Unsupervised_Anomaly_Segmentation.git.\n", "date": "2022-07-05", "categories": []}
{"id": "2208.13169", "title": "RUAD: unsupervised anomaly detection in HPC systems", "abstract": "  The increasing complexity of modern high-performance computing (HPC) systems\nnecessitates the introduction of automated and data-driven methodologies to\nsupport system administrators' effort toward increasing the system's\navailability. Anomaly detection is an integral part of improving the\navailability as it eases the system administrator's burden and reduces the time\nbetween an anomaly and its resolution. However, current state-of-the-art (SoA)\napproaches to anomaly detection are supervised and semi-supervised, so they\nrequire a human-labelled dataset with anomalies - this is often impractical to\ncollect in production HPC systems. Unsupervised anomaly detection approaches\nbased on clustering, aimed at alleviating the need for accurate anomaly data,\nhave so far shown poor performance.\n  In this work, we overcome these limitations by proposing RUAD, a novel\nRecurrent Unsupervised Anomaly Detection model. RUAD achieves better results\nthan the current semi-supervised and unsupervised SoA approaches. This is\nachieved by considering temporal dependencies in the data and including\nlong-short term memory cells in the model architecture. The proposed approach\nis assessed on a complete ten-month history of a Tier-0 system (Marconi100 from\nCINECA with 980 nodes). RUAD achieves an area under the curve (AUC) of 0.763 in\nsemi-supervised training and an AUC of 0.767 in unsupervised training, which\nimproves upon the SoA approach that achieves an AUC of 0.747 in semi-supervised\ntraining and an AUC of 0.734 in unsupervised training. It also vastly\noutperforms the current SoA unsupervised anomaly detection approach based on\nclustering, achieving the AUC of 0.548.\n", "date": "2022-08-28", "categories": []}
{"id": "2208.13433", "title": "Towards In-distribution Compatibility in Out-of-distribution Detection", "abstract": "  Deep neural network, despite its remarkable capability of discriminating\ntargeted in-distribution samples, shows poor performance on detecting anomalous\nout-of-distribution data. To address this defect, state-of-the-art solutions\nchoose to train deep networks on an auxiliary dataset of outliers. Various\ntraining criteria for these auxiliary outliers are proposed based on heuristic\nintuitions. However, we find that these intuitively designed outlier training\ncriteria can hurt in-distribution learning and eventually lead to inferior\nperformance. To this end, we identify three causes of the in-distribution\nincompatibility: contradictory gradient, false likelihood, and distribution\nshift. Based on our new understandings, we propose a new out-of-distribution\ndetection method by adapting both the top-design of deep models and the loss\nfunction. Our method achieves in-distribution compatibility by pursuing less\ninterference with the probabilistic characteristic of in-distribution features.\nOn several benchmarks, our method not only achieves the state-of-the-art\nout-of-distribution detection performance but also improves the in-distribution\naccuracy.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.13515", "title": "Detecting Surprising Situations in Event Data", "abstract": "  Process mining is a set of techniques that are used by organizations to\nunderstand and improve their operational processes. The first essential step in\ndesigning any process reengineering procedure is to find process improvement\nopportunities. In existing work, it is usually assumed that the set of\nproblematic process instances in which an undesirable outcome occurs is known\nprior or is easily detectable. So the process enhancement procedure involves\nfinding the root causes and the treatments for the problem in those process\ninstances. For example, the set of problematic instances is considered as those\nwith outlier values or with values smaller/bigger than a given threshold in one\nof the process features. However, on various occasions, using this approach,\nmany process enhancement opportunities, not captured by these problematic\nprocess instances, are missed. To overcome this issue, we formulate finding the\nprocess enhancement areas as a context-sensitive anomaly/outlier detection\nproblem. We define a process enhancement area as a set of situations (process\ninstances or prefixes of process instances) where the process performance is\nsurprising. We aim to characterize those situations where process\nperformance/outcome is significantly different from what was expected\nconsidering its performance/outcome in similar situations. To evaluate the\nvalidity and relevance of the proposed approach, we have implemented and\nevaluated it on several real-life event logs.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.11264", "title": "Towards an Awareness of Time Series Anomaly Detection Models'\n  Adversarial Vulnerability", "abstract": "  Time series anomaly detection is extensively studied in statistics,\neconomics, and computer science. Over the years, numerous methods have been\nproposed for time series anomaly detection using deep learning-based methods.\nMany of these methods demonstrate state-of-the-art performance on benchmark\ndatasets, giving the false impression that these systems are robust and\ndeployable in many practical and industrial real-world scenarios. In this\npaper, we demonstrate that the performance of state-of-the-art anomaly\ndetection methods is degraded substantially by adding only small adversarial\nperturbations to the sensor data. We use different scoring metrics such as\nprediction errors, anomaly, and classification scores over several public and\nprivate datasets ranging from aerospace applications, server machines, to\ncyber-physical systems in power plants. Under well-known adversarial attacks\nfrom Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD)\nmethods, we demonstrate that state-of-the-art deep neural networks (DNNs) and\ngraph neural networks (GNNs) methods, which claim to be robust against\nanomalies and have been possibly integrated in real-life systems, have their\nperformance drop to as low as 0%. To the best of our understanding, we\ndemonstrate, for the first time, the vulnerabilities of anomaly detection\nsystems against adversarial attacks. The overarching goal of this research is\nto raise awareness towards the adversarial vulnerabilities of time series\nanomaly detectors.\n", "date": "2022-08-24", "categories": []}
{"id": "2208.09240", "title": "An Unsupervised Short- and Long-Term Mask Representation for\n  Multivariate Time Series Anomaly Detection", "abstract": "  Anomaly detection of multivariate time series is meaningful for system\nbehavior monitoring. This paper proposes an anomaly detection method based on\nunsupervised Short- and Long-term Mask Representation learning (SLMR). The main\nidea is to extract short-term local dependency patterns and long-term global\ntrend patterns of the multivariate time series by using multi-scale residual\ndilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore,\nour approach can comprehend temporal contexts and feature correlations by\ncombining spatial-temporal masked self-supervised representation learning and\nsequence split. It considers the importance of features is different, and we\nintroduce the attention mechanism to adjust the contribution of each feature.\nFinally, a forecasting-based model and a reconstruction-based model are\nintegrated to focus on single timestamp prediction and latent representation of\ntime series. Experiments show that the performance of our method outperforms\nother state-of-the-art models on three real-world datasets. Further analysis\nshows that our method is good at interpretability.\n", "date": "2022-08-19", "categories": []}
{"id": "2208.11113", "title": "Towards Open Set Video Anomaly Detection", "abstract": "  Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events\nfrom video data where both known anomalies and novel ones exist in testing.\nUnsupervised models learned solely from normal videos are applicable to any\ntesting anomalies but suffer from a high false positive rate. In contrast,\nweakly supervised methods are effective in detecting known anomalies but could\nfail in an open world. We develop a novel weakly supervised method for the\nOpenVAD problem by integrating evidential deep learning (EDL) and normalizing\nflows (NFs) into a multiple instance learning (MIL) framework. Specifically, we\npropose to use graph neural networks and triplet loss to learn discriminative\nfeatures for training the EDL classifier, where the EDL is capable of\nidentifying the unknown anomalies by quantifying the uncertainty. Moreover, we\ndevelop an uncertainty-aware selection strategy to obtain clean anomaly\ninstances and a NFs module to generate the pseudo anomalies. Our method is\nsuperior to existing approaches by inheriting the advantages of both the\nunsupervised NFs and the weakly-supervised MIL framework. Experimental results\non multiple real-world video datasets show the effectiveness of our method.\n", "date": "2022-08-23", "categories": []}
{"id": "2208.10231", "title": "An anomaly detection approach for backdoored neural networks: face\n  recognition as a case study", "abstract": "  Backdoor attacks allow an attacker to embed functionality jeopardizing proper\nbehavior of any algorithm, machine learning or not. This hidden functionality\ncan remain inactive for normal use of the algorithm until activated by the\nattacker. Given how stealthy backdoor attacks are, consequences of these\nbackdoors could be disastrous if such networks were to be deployed for\napplications as critical as border or access control. In this paper, we propose\na novel backdoored network detection method based on the principle of anomaly\ndetection, involving access to the clean part of the training data and the\ntrained network. We highlight its promising potential when considering various\ntriggers, locations and identity pairs, without the need to make any\nassumptions on the nature of the backdoor and its setup. We test our method on\na novel dataset of backdoored networks and report detectability results with\nperfect scores.\n", "date": "2022-08-22", "categories": []}
{"id": "2208.08834", "title": "Outlier Detection using Self-Organizing Maps for Automated Blood Cell\n  Analysis", "abstract": "  The quality of datasets plays a crucial role in the successful training and\ndeployment of deep learning models. Especially in the medical field, where\nsystem performance may impact the health of patients, clean datasets are a\nsafety requirement for reliable predictions. Therefore, outlier detection is an\nessential process when building autonomous clinical decision systems. In this\nwork, we assess the suitability of Self-Organizing Maps for outlier detection\nspecifically on a medical dataset containing quantitative phase images of white\nblood cells. We detect and evaluate outliers based on quantization errors and\ndistance maps. Our findings confirm the suitability of Self-Organizing Maps for\nunsupervised Out-Of-Distribution detection on the dataset at hand.\nSelf-Organizing Maps perform on par with a manually specified filter based on\nexpert domain knowledge. Additionally, they show promise as a tool in the\nexploration and cleaning of medical datasets. As a direction for future\nresearch, we suggest a combination of Self-Organizing Maps and feature\nextraction based on deep learning.\n", "date": "2022-08-18", "categories": []}
{"id": "2208.08200", "title": "AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection\n  Approach", "abstract": "  Graph anomaly detection on attributed networks has become a prevalent\nresearch topic due to its broad applications in many influential domains. In\nreal-world scenarios, nodes and edges in attributed networks usually display\ndistinct heterogeneity, i.e. attributes of different types of nodes show great\nvariety, different types of relations represent diverse meanings. Anomalies\nusually perform differently from the majority in various perspectives of\nheterogeneity in these networks. However, existing graph anomaly detection\napproaches do not leverage heterogeneity in attributed networks, which is\nhighly related to anomaly detection. In light of this problem, we propose\nAHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach\nbased on the encoder-decoder framework. Specifically, for the encoder, we\ndesign three levels of attention, i.e. attribute level, node type level, and\nedge level attentions to capture the heterogeneity of network structure, node\nproperties and information of a single node, respectively. In the decoder, we\nexploit structure, attribute, and node type reconstruction terms to obtain an\nanomaly score for each node. Extensive experiments show the superiority of\nAHEAD on several real-world heterogeneous information networks compared with\nthe state-of-arts in the unsupervised setting. Further experiments verify the\neffectiveness and robustness of our triple attention, model backbone, and\ndecoder in general.\n", "date": "2022-08-17", "categories": []}
{"id": "2208.08265", "title": "Semi-Supervised Anomaly Detection Based on Quadratic Multiform\n  Separation", "abstract": "  In this paper we propose a novel method for semi-supervised anomaly detection\n(SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the\nframework of quadratic multiform separation (QMS), a recently introduced\nclassification model. QMS22 tackles SSAD by solving a multi-class\nclassification problem involving both the training set and the test set of the\noriginal problem. The classification problem intentionally includes classes\nwith overlapping samples. One of the classes contains mixture of normal samples\nand outliers, and all other classes contain only normal samples. An outlier\nscore is then calculated for every sample in the test set using the outcome of\nthe classification problem. We also include performance evaluation of QMS22\nagainst top performing classifiers using ninety-five benchmark imbalanced\ndatasets from the KEEL repository. These classifiers are BRM (Bagging-Random\nMiner), OCKRA (One-Class K-means with Randomly-projected features Algorithm),\nISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is\nshown by using the area under the curve of the receiver operating\ncharacteristic curve as the performance measure, QMS22 significantly\noutperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal\nthat there is no statistically significant difference when testing QMS22\nagainst BRM nor QMS22 against OCKRA.\n", "date": "2022-08-17", "categories": []}
{"id": "2208.07675", "title": "Enhancement to Training of Bidirectional GAN : An Approach to Demystify\n  Tax Fraud", "abstract": "  Outlier detection is a challenging activity. Several machine learning\ntechniques are proposed in the literature for outlier detection. In this\narticle, we propose a new training approach for bidirectional GAN (BiGAN) to\ndetect outliers. To validate the proposed approach, we train a BiGAN with the\nproposed training approach to detect taxpayers, who are manipulating their tax\nreturns. For each taxpayer, we derive six correlation parameters and three\nratio parameters from tax returns submitted by him/her. We train a BiGAN with\nthe proposed training approach on this nine-dimensional derived ground-truth\ndata set. Next, we generate the latent representation of this data set using\nthe $encoder$ (encode this data set using the $encoder$) and regenerate this\ndata set using the $generator$ (decode back using the $generator$) by giving\nthis latent representation as the input. For each taxpayer, compute the cosine\nsimilarity between his/her ground-truth data and regenerated data. Taxpayers\nwith lower cosine similarity measures are potential return manipulators. We\napplied our method to analyze the iron and steel taxpayers data set provided by\nthe Commercial Taxes Department, Government of Telangana, India.\n", "date": "2022-08-16", "categories": []}
{"id": "2208.05636", "title": "Locality-aware Attention Network with Discriminative Dynamics Learning\n  for Weakly Supervised Anomaly Detection", "abstract": "  Video anomaly detection is recently formulated as a multiple instance\nlearning task under weak supervision, in which each video is treated as a bag\nof snippets to be determined whether contains anomalies. Previous efforts\nmainly focus on the discrimination of the snippet itself without modeling the\ntemporal dynamics, which refers to the variation of adjacent snippets.\nTherefore, we propose a Discriminative Dynamics Learning (DDL) method with two\nobjective functions, i.e., dynamics ranking loss and dynamics alignment loss.\nThe former aims to enlarge the score dynamics gap between positive and negative\nbags while the latter performs temporal alignment of the feature dynamics and\nscore dynamics within the bag. Moreover, a Locality-aware Attention Network\n(LA-Net) is constructed to capture global correlations and re-calibrate the\nlocation preference across snippets, followed by a multilayer perceptron with\ncausal convolution to obtain anomaly scores. Experimental results show that our\nmethod achieves significant improvements on two challenging benchmarks, i.e.,\nUCF-Crime and XD-Violence.\n", "date": "2022-08-11", "categories": []}
{"id": "2208.05251", "title": "Consistency-based Self-supervised Learning for Temporal Anomaly\n  Localization", "abstract": "  This work tackles Weakly Supervised Anomaly detection, in which a predictor\nis allowed to learn not only from normal examples but also from a few labeled\nanomalies made available during training. In particular, we deal with the\nlocalization of anomalous activities within the video stream: this is a very\nchallenging scenario, as training examples come only with video-level\nannotations (and not frame-level). Several recent works have proposed various\nregularization terms to address it i.e. by enforcing sparsity and smoothness\nconstraints over the weakly-learned frame-level anomaly scores. In this work,\nwe get inspired by recent advances within the field of self-supervised learning\nand ask the model to yield the same scores for different augmentations of the\nsame video sequence. We show that enforcing such an alignment improves the\nperformance of the model on XD-Violence.\n", "date": "2022-08-10", "categories": []}
{"id": "2207.10298", "title": "Deep Learning for Unsupervised Anomaly Localization in Industrial\n  Images: A Survey", "abstract": "  Currently, deep learning-based visual inspection has been highly successful\nwith the help of supervised learning methods. However, in real industrial\nscenarios, the scarcity of defect samples, the cost of annotation, and the lack\nof a priori knowledge of defects may render supervised-based methods\nineffective. In recent years, unsupervised anomaly localization algorithms have\nbecome more widely used in industrial inspection tasks. This paper aims to help\nresearchers in this field by comprehensively surveying recent achievements in\nunsupervised anomaly localization in industrial images using deep learning. The\nsurvey reviews more than 120 significant publications covering different\naspects of anomaly localization, mainly covering various concepts, challenges,\ntaxonomies, benchmark datasets, and quantitative performance comparisons of the\nmethods reviewed. In reviewing the achievements to date, this paper provides\ndetailed predictions and analysis of several future research directions. This\nreview provides detailed technical information for researchers interested in\nindustrial anomaly localization and who wish to apply it to the localization of\nanomalies in other fields.\n", "date": "2022-07-21", "categories": []}
{"id": "2208.01841", "title": "Robust Learning of Deep Time Series Anomaly Detection Models with\n  Contaminated Training Data", "abstract": "  Time series anomaly detection (TSAD) is an important data mining task with\nnumerous applications in the IoT era. In recent years, a large number of deep\nneural network-based methods have been proposed, demonstrating significantly\nbetter performance than conventional methods on addressing challenging TSAD\nproblems in a variety of areas. Nevertheless, these deep TSAD methods typically\nrely on a clean training dataset that is not polluted by anomalies to learn the\n\"normal profile\" of the underlying dynamics. This requirement is nontrivial\nsince a clean dataset can hardly be provided in practice. Moreover, without the\nawareness of their robustness, blindly applying deep TSAD methods with\npotentially contaminated training data can possibly incur significant\nperformance degradation in the detection phase. In this work, to tackle this\nimportant challenge, we firstly investigate the robustness of commonly used\ndeep TSAD methods with contaminated training data which provides a guideline\nfor applying these methods when the provided training data are not guaranteed\nto be anomaly-free. Furthermore, we propose a model-agnostic method which can\neffectively improve the robustness of learning mainstream deep TSAD models with\npotentially contaminated data. Experiment results show that our method can\nconsistently prevent or mitigate performance degradation of mainstream deep\nTSAD models on widely used benchmark datasets.\n", "date": "2022-08-03", "categories": []}
{"id": "2208.01998", "title": "Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time\n  Series", "abstract": "  We propose a robust principal component analysis (RPCA) framework to recover\nlow-rank and sparse matrices from temporal observations. We develop an online\nversion of the batch temporal algorithm in order to process larger datasets or\nstreaming data. We empirically compare the proposed approaches with different\nRPCA frameworks and show their effectiveness in practical situations.\n", "date": "2022-08-03", "categories": []}
{"id": "2208.01188", "title": "Curved Geometric Networks for Visual Anomaly Recognition", "abstract": "  Learning a latent embedding to understand the underlying nature of data\ndistribution is often formulated in Euclidean spaces with zero curvature.\nHowever, the success of the geometry constraints, posed in the embedding space,\nindicates that curved spaces might encode more structural information, leading\nto better discriminative power and hence richer representations. In this work,\nwe investigate benefits of the curved space for analyzing anomalies or\nout-of-distribution objects in data. This is achieved by considering embeddings\nvia three geometry constraints, namely, spherical geometry (with positive\ncurvature), hyperbolic geometry (with negative curvature) or mixed geometry\n(with both positive and negative curvatures). Three geometric constraints can\nbe chosen interchangeably in a unified design given the task at hand. Tailored\nfor the embeddings in the curved space, we also formulate functions to compute\nthe anomaly score. Two types of geometric modules (i.e., Geometric-in-One and\nGeometric-in-Two models) are proposed to plug in the original Euclidean\nclassifier, and anomaly scores are computed from the curved embeddings. We\nevaluate the resulting designs under a diverse set of visual recognition\nscenarios, including image detection (multi-class OOD detection and one-class\nanomaly detection) and segmentation (multi-class anomaly segmentation and\none-class anomaly segmentation). The empirical results show the effectiveness\nof our proposal through the consistent improvement over various scenarios.\n", "date": "2022-08-02", "categories": []}
{"id": "2208.00493", "title": "Scrutinizing Shipment Records To Thwart Illegal Timber Trade", "abstract": "  Timber and forest products made from wood, like furniture, are valuable\ncommodities, and like the global trade of many highly-valued natural resources,\nface challenges of corruption, fraud, and illegal harvesting. These grey and\nblack market activities in the wood and forest products sector are not limited\nto the countries where the wood was harvested, but extend throughout the global\nsupply chain and have been tied to illicit financial flows, like trade-based\nmoney laundering, document fraud, species mislabeling, and other illegal\nactivities. The task of finding such fraudulent activities using trade data, in\nthe absence of ground truth, can be modelled as an unsupervised anomaly\ndetection problem. However existing approaches suffer from certain shortcomings\nin their applicability towards large scale trade data. Trade data is\nheterogeneous, with both categorical and numerical attributes in a tabular\nformat. The overall challenge lies in the complexity, volume and velocity of\ndata, with large number of entities and lack of ground truth labels. To\nmitigate these, we propose a novel unsupervised anomaly detection --\nContrastive Learning based Heterogeneous Anomaly Detection (CHAD) that is\ngenerally applicable for large-scale heterogeneous tabular data. We demonstrate\nour model CHAD performs favorably against multiple comparable baselines for\npublic benchmark datasets, and outperforms them in the case of trade data. More\nimportantly we demonstrate our approach reduces assumptions and efforts\nrequired hyperparameter tuning, which is a key challenging aspect in an\nunsupervised training paradigm. Specifically, our overarching objective\npertains to detecting suspicious timber shipments and patterns using Bill of\nLading trade record data. Detecting anomalous transactions in shipment records\ncan enable further investigation by government agencies and supply chain\nconstituents.\n", "date": "2022-07-31", "categories": []}
{"id": "2207.00367", "title": "A geometric framework for outlier detection in high-dimensional data", "abstract": "  Outlier or anomaly detection is an important task in data analysis. We\ndiscuss the problem from a geometrical perspective and provide a framework that\nexploits the metric structure of a data set. Our approach rests on the manifold\nassumption, i.e., that the observed, nominally high-dimensional data lie on a\nmuch lower dimensional manifold and that this intrinsic structure can be\ninferred with manifold learning methods. We show that exploiting this structure\nsignificantly improves the detection of outlying observations in\nhigh-dimensional data. We also suggest a novel, mathematically precise, and\nwidely applicable distinction between distributional and structural outliers\nbased on the geometry and topology of the data manifold that clarifies\nconceptual ambiguities prevalent throughout the literature. Our experiments\nfocus on functional data as one class of structured high-dimensional data, but\nthe framework we propose is completely general and we include image and graph\ndata applications. Our results show that the outlier structure of\nhigh-dimensional and non-tabular data can be detected and visualized using\nmanifold learning methods and quantified using standard outlier scoring methods\napplied to the manifold embedding vectors.\n", "date": "2022-07-01", "categories": []}
{"id": "2207.14315", "title": "SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection\n  and Segmentation", "abstract": "  Visual anomaly detection is commonly used in industrial quality inspection.\nIn this paper, we present a new dataset as well as a new self-supervised\nlearning method for ImageNet pre-training to improve anomaly detection and\nsegmentation in 1-class and 2-class 5/10/high-shot training setups. We release\nthe Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color\nimages (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3\ndomains, making it the largest industrial anomaly detection dataset to date.\nBoth image and pixel-level labels are provided. We also propose a new\nself-supervised framework - SPot-the-difference (SPD) - which can regularize\ncontrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to\nbe more suitable for anomaly detection tasks. Our experiments on VisA and\nMVTec-AD dataset show that SPD consistently improves these contrastive\npre-training baselines and even the supervised pre-training. For example, SPD\nimproves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation\nby 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the\n2-class high-shot regime. We open-source the project at\nhttp://github.com/amazon-research/spot-diff .\n", "date": "2022-07-28", "categories": []}
{"id": "2207.13361", "title": "Learning Appearance-motion Normality for Video Anomaly Detection", "abstract": "  Video anomaly detection is a challenging task in the computer vision\ncommunity. Most single task-based methods do not consider the independence of\nunique spatial and temporal patterns, while two-stream structures lack the\nexploration of the correlations. In this paper, we propose spatial-temporal\nmemories augmented two-stream auto-encoder framework, which learns the\nappearance normality and motion normality independently and explores the\ncorrelations via adversarial learning. Specifically, we first design two proxy\ntasks to train the two-stream structure to extract appearance and motion\nfeatures in isolation. Then, the prototypical features are recorded in the\ncorresponding spatial and temporal memory pools. Finally, the encoding-decoding\nnetwork performs adversarial learning with the discriminator to explore the\ncorrelations between spatial and temporal patterns. Experimental results show\nthat our framework outperforms the state-of-the-art methods, achieving AUCs of\n98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.\n", "date": "2022-07-27", "categories": []}
{"id": "2207.13430", "title": "Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study\n  with Facial Datasets", "abstract": "  Anomaly detection in multimedia datasets is a widely studied area. Yet, the\nconcept drift challenge in data has been ignored or poorly handled by the\nmajority of the anomaly detection frameworks. The state-of-the-art approaches\nassume that the data distribution at training and deployment time will be the\nsame. However, due to various real-life environmental factors, the data may\nencounter drift in its distribution or can drift from one class to another in\nthe late future. Thus, a one-time trained model might not perform adequately.\nIn this paper, we systematically investigate the effect of concept drift on\nvarious detection models and propose a modified Adaptive Gaussian Mixture Model\n(AGMM) based framework for anomaly detection in multimedia data. In contrast to\nthe baseline AGMM, the proposed extension of AGMM remembers the past for a\nlonger period in order to handle the drift better. Extensive experimental\nanalysis shows that the proposed model better handles the drift in data as\ncompared with the baseline AGMM. Further, to facilitate research and comparison\nwith the proposed framework, we contribute three multimedia datasets\nconstituting faces as samples. The face samples of individuals correspond to\nthe age difference of more than ten years to incorporate a longer temporal\ncontext.\n", "date": "2022-07-27", "categories": []}
{"id": "2207.11466", "title": "Anomaly Detection for Fraud in Cryptocurrency Time Series", "abstract": "  Since the inception of Bitcoin in 2009, the market of cryptocurrencies has\ngrown beyond initial expectations as daily trades exceed $10 billion. As\nindustries become automated, the need for an automated fraud detector becomes\nvery apparent. Detecting anomalies in real time prevents potential accidents\nand economic losses. Anomaly detection in multivariate time series data poses a\nparticular challenge because it requires simultaneous consideration of temporal\ndependencies and relationships between variables. Identifying an anomaly in\nreal time is not an easy task specifically because of the exact anomalistic\nbehavior they observe. Some points may present pointwise global or local\nanomalistic behavior, while others may be anomalistic due to their frequency or\nseasonal behavior or due to a change in the trend. In this paper we suggested\nworking on real time series of trades of Ethereum from specific accounts and\nsurveyed a large variety of different algorithms traditional and new. We\ncategorized them according to the strategy and the anomalistic behavior which\nthey search and showed that when bundling them together to different groups,\nthey can prove to be a good real-time detector with an alarm time of no longer\nthan a few seconds and with very high confidence.\n", "date": "2022-07-23", "categories": []}
{"id": "2207.11541", "title": "FastATDC: Fast Anomalous Trajectory Detection and Classification", "abstract": "  Automated detection of anomalous trajectories is an important problem with\nconsiderable applications in intelligent transportation systems. Many existing\nstudies have focused on distinguishing anomalous trajectories from normal\ntrajectories, ignoring the large differences between anomalous trajectories. A\nrecent study has made great progress in identifying abnormal trajectory\npatterns and proposed a two-stage algorithm for anomalous trajectory detection\nand classification (ATDC). This algorithm has excellent performance but suffers\nfrom a few limitations, such as high time complexity and poor interpretation.\nHere, we present a careful theoretical and empirical analysis of the ATDC\nalgorithm, showing that the calculation of anomaly scores in both stages can be\nsimplified, and that the second stage of the algorithm is much more important\nthan the first stage. Hence, we develop a FastATDC algorithm that introduces a\nrandom sampling strategy in both stages. Experimental results show that\nFastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover,\nFastATDC outperforms the baseline algorithms and is comparable to the ATDC\nalgorithm.\n", "date": "2022-07-23", "categories": []}
{"id": "2207.11564", "title": "A general-purpose method for applying Explainable AI for Anomaly\n  Detection", "abstract": "  The need for explainable AI (XAI) is well established but relatively little\nhas been published outside of the supervised learning paradigm. This paper\nfocuses on a principled approach to applying explainability and\ninterpretability to the task of unsupervised anomaly detection. We argue that\nexplainability is principally an algorithmic task and interpretability is\nprincipally a cognitive task, and draw on insights from the cognitive sciences\nto propose a general-purpose method for practical diagnosis using explained\nanomalies. We define Attribution Error, and demonstrate, using real-world\nlabeled datasets, that our method based on Integrated Gradients (IG) yields\nsignificantly lower attribution errors than alternative methods.\n", "date": "2022-07-23", "categories": []}
{"id": "2207.11789", "title": "Hierarchical Semi-Supervised Contrastive Learning for\n  Contamination-Resistant Anomaly Detection", "abstract": "  Anomaly detection aims at identifying deviant samples from the normal data\ndistribution. Contrastive learning has provided a successful way to sample\nrepresentation that enables effective discrimination on anomalies. However,\nwhen contaminated with unlabeled abnormal samples in training set under\nsemi-supervised settings, current contrastive-based methods generally 1) ignore\nthe comprehensive relation between training data, leading to suboptimal\nperformance, and 2) require fine-tuning, resulting in low efficiency. To\naddress the above two issues, in this paper, we propose a novel hierarchical\nsemi-supervised contrastive learning (HSCL) framework, for\ncontamination-resistant anomaly detection. Specifically, HSCL hierarchically\nregulates three complementary relations: sample-to-sample, sample-to-prototype,\nand normal-to-abnormal relations, enlarging the discrimination between normal\nand abnormal samples with a comprehensive exploration of the contaminated data.\nBesides, HSCL is an end-to-end learning approach that can efficiently learn\ndiscriminative representations without fine-tuning. HSCL achieves\nstate-of-the-art performance in multiple scenarios, such as one-class\nclassification and cross-dataset detection. Extensive ablation studies further\nverify the effectiveness of each considered relation. The code is available at\nhttps://github.com/GaoangW/HSCL.\n", "date": "2022-07-24", "categories": []}
{"id": "2207.11931", "title": "Hybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors\n  Detection, Tracking, and Recognition in Massive Hajj Crowds", "abstract": "  Individual abnormal behaviors vary depending on crowd sizes, contexts, and\nscenes. Challenges such as partial occlusions, blurring, large-number abnormal\nbehavior, and camera viewing occur in large-scale crowds when detecting,\ntracking, and recognizing individuals with abnormal behaviors. In this paper,\nour contribution is twofold. First, we introduce an annotated and labeled\nlarge-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose\ntwo methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests\n(RFs) to detect and recognize Spatio-temporal abnormal behaviors in small and\nlarge-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained\nCNN model is fine-tuned to verify whether every frame is normal or abnormal in\nthe spatial domain. If anomalous behaviors are observed, a motion-based\nindividuals detection method based on the magnitudes and orientations of\nHorn-Schunck optical flow is used to locate and track individuals with abnormal\nbehaviors. A Kalman filter is employed in large-scale crowd videos to predict\nand track the detected individuals in the subsequent frames. Then, means,\nvariances, and standard deviations statistical features are computed and fed to\nthe RF to classify individuals with abnormal behaviors in the temporal domain.\nIn large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object\ndetection technique to detect individuals with abnormal behaviors in the\nspatial domain.\n", "date": "2022-07-25", "categories": []}
{"id": "2207.12087", "title": "Learning State Machines to Monitor and Detect Anomalies on a Kubernetes\n  Cluster", "abstract": "  These days more companies are shifting towards using cloud environments to\nprovide their services to their client. While it is easy to set up a cloud\nenvironment, it is equally important to monitor the system's runtime behaviour\nand identify anomalous behaviours that occur during its operation. In recent\nyears, the utilisation of \\ac{rnn} and \\ac{dnn} to detect anomalies that might\noccur during runtime has been a trending approach. However, it is unclear how\nto explain the decisions made by these networks and how these networks should\nbe interpreted to understand the runtime behaviour that they model. On the\ncontrary, state machine models provide an easier manner to interpret and\nunderstand the behaviour that they model. In this work, we propose an approach\nthat learns state machine models to model the runtime behaviour of a cloud\nenvironment that runs multiple microservice applications. To the best of our\nknowledge, this is the first work that tries to apply state machine models to\nmicroservice architectures. The state machine model is used to detect the\ndifferent types of attacks that we launch on the cloud environment. From our\nexperiment results, our approach can detect the attacks very well, achieving a\nbalanced accuracy of 99.2% and an F1 score of 0.982.\n", "date": "2022-06-28", "categories": []}
{"id": "2207.12208", "title": "Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series", "abstract": "  Subsequence anomaly detection in long sequences is an important problem with\napplications in a wide range of domains. However, the approaches proposed so\nfar in the literature have severe limitations: they either require prior domain\nknowledge used to design the anomaly discovery algorithms, or become cumbersome\nand expensive to use in situations with recurrent anomalies of the same type.\nIn this work, we address these problems, and propose an unsupervised method\nsuitable for domain agnostic subsequence anomaly detection. Our method,\nSeries2Graph, is based on a graph representation of a novel low-dimensionality\nembedding of subsequences. Series2Graph needs neither labeled instances (like\nsupervised techniques) nor anomaly-free data (like zero-positive learning\ntechniques), and identifies anomalies of varying lengths. The experimental\nresults, on the largest set of synthetic and real datasets used to date,\ndemonstrate that the proposed approach correctly identifies single and\nrecurrent anomalies without any prior knowledge of their characteristics,\noutperforming by a large margin several competing approaches in accuracy, while\nbeing up to orders of magnitude faster. This paper has appeared in VLDB 2020.\n", "date": "2022-07-25", "categories": []}
{"id": "2207.10172", "title": "Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw\n  Puzzles", "abstract": "  Video Anomaly Detection (VAD) is an important topic in computer vision.\nMotivated by the recent advances in self-supervised learning, this paper\naddresses VAD by solving an intuitive yet challenging pretext task, i.e.,\nspatio-temporal jigsaw puzzles, which is cast as a multi-label fine-grained\nclassification problem. Our method exhibits several advantages over existing\nworks: 1) the spatio-temporal jigsaw puzzles are decoupled in terms of spatial\nand temporal dimensions, responsible for capturing highly discriminative\nappearance and motion features, respectively; 2) full permutations are used to\nprovide abundant jigsaw puzzles covering various difficulty levels, allowing\nthe network to distinguish subtle spatio-temporal differences between normal\nand abnormal events; and 3) the pretext task is tackled in an end-to-end manner\nwithout relying on any pre-trained models. Our method outperforms\nstate-of-the-art counterparts on three public benchmarks. Especially on\nShanghaiTech Campus, the result is superior to reconstruction and\nprediction-based methods by a large margin.\n", "date": "2022-07-20", "categories": []}
{"id": "2207.10948", "title": "Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly\n  Detection", "abstract": "  Existing methods for anomaly detection based on memory-augmented autoencoder\n(AE) have the following drawbacks: (1) Establishing a memory bank requires\nadditional memory space. (2) The fixed number of prototypes from subjective\nassumptions ignores the data feature differences and diversity. To overcome\nthese drawbacks, we introduce DLAN-AC, a Dynamic Local Aggregation Network with\nAdaptive Clusterer, for anomaly detection. First, The proposed DLAN can\nautomatically learn and aggregate high-level features from the AE to obtain\nmore representative prototypes, while freeing up extra memory space. Second,\nThe proposed AC can adaptively cluster video data to derive initial prototypes\nwith prior information. In addition, we also propose a dynamic redundant\nclustering strategy (DRCS) to enable DLAN for automatically eliminating feature\nclusters that do not contribute to the construction of prototypes. Extensive\nexperiments on benchmarks demonstrate that DLAN-AC outperforms most existing\nmethods, validating the effectiveness of our method. Our code is publicly\navailable at https://github.com/Beyond-Zw/DLAN-AC.\n", "date": "2022-07-22", "categories": []}
{"id": "2207.07361", "title": "Registration based Few-Shot Anomaly Detection", "abstract": "  This paper considers few-shot anomaly detection (FSAD), a practical yet\nunder-studied setting for anomaly detection (AD), where only a limited number\nof normal images are provided for each category at training. So far, existing\nFSAD studies follow the one-model-per-category learning paradigm used for\nstandard AD, and the inter-category commonality has not been explored. Inspired\nby how humans detect anomalies, i.e., comparing an image in question to normal\nimages, we here leverage registration, an image alignment task that is\ninherently generalizable across categories, as the proxy task, to train a\ncategory-agnostic anomaly detection model. During testing, the anomalies are\nidentified by comparing the registered features of the test image and its\ncorresponding support (normal) images. As far as we know, this is the first\nFSAD method that trains a single generalizable model and requires no\nre-training or parameter fine-tuning for new categories. Experimental results\nhave shown that the proposed method outperforms the state-of-the-art FSAD\nmethods by 3%-8% in AUC on the MVTec and MPDD benchmarks.\n", "date": "2022-07-15", "categories": []}
{"id": "2207.03688", "title": "GCN-based Multi-task Representation Learning for Anomaly Detection in\n  Attributed Networks", "abstract": "  Anomaly detection in attributed networks has received a considerable\nattention in recent years due to its applications in a wide range of domains\nsuch as finance, network security, and medicine. Traditional approaches cannot\nbe adopted on attributed networks' settings to solve the problem of anomaly\ndetection. The main limitation of such approaches is that they inherently\nignore the relational information between data features. With a rapid explosion\nin deep learning- and graph neural networks-based techniques, spotting rare\nobjects on attributed networks has significantly stepped forward owing to the\npotentials of deep techniques in extracting complex relationships. In this\npaper, we propose a new architecture on anomaly detection. The main goal of\ndesigning such an architecture is to utilize multi-task learning which would\nenhance the detection performance. Multi-task learning-based anomaly detection\nis still in its infancy and only a few studies in the existing literature have\ncatered to the same. We incorporate both community detection and multi-view\nrepresentation learning techniques for extracting distinct and complementary\ninformation from attributed networks and subsequently fuse the captured\ninformation for achieving a better detection result. The mutual collaboration\nbetween two main components employed in this architecture, i.e.,\ncommunity-specific learning and multi-view representation learning, exhibits a\npromising solution to reach more effective results.\n", "date": "2022-07-08", "categories": []}
{"id": "2207.03934", "title": "Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly\n  Detection in Decision Support Systems", "abstract": "  The detection of anomalous behaviours is an emerging need in many\napplications, particularly in contexts where security and reliability are\ncritical aspects. While the definition of anomaly strictly depends on the\ndomain framework, it is often impractical or too time consuming to obtain a\nfully labelled dataset. The use of unsupervised models to overcome the lack of\nlabels often fails to catch domain specific anomalies as they rely on general\ndefinitions of outlier. This paper suggests a new active learning based\napproach, ALIF, to solve this problem by reducing the number of required labels\nand tuning the detector towards the definition of anomaly provided by the user.\nThe proposed approach is particularly appealing in the presence of a Decision\nSupport System (DSS), a case that is increasingly popular in real-world\nscenarios. While it is common that DSS embedded with anomaly detection\ncapabilities rely on unsupervised models, they don't have a way to improve\ntheir performance: ALIF is able to enhance the capabilities of DSS by\nexploiting the user feedback during common operations. ALIF is a lightweight\nmodification of the popular Isolation Forest that proved superior performances\nwith respect to other state-of-art algorithms in a multitude of real anomaly\ndetection datasets.\n", "date": "2022-07-08", "categories": []}
{"id": "2207.03206", "title": "Leveraging Log Instructions in Log-based Anomaly Detection", "abstract": "  Artificial Intelligence for IT Operations (AIOps) describes the process of\nmaintaining and operating large IT systems using diverse AI-enabled methods and\ntools for, e.g., anomaly detection and root cause analysis, to support the\nremediation, optimization, and automatic initiation of self-stabilizing IT\nactivities. The core step of any AIOps workflow is anomaly detection, typically\nperformed on high-volume heterogeneous data such as log messages (logs),\nmetrics (e.g., CPU utilization), and distributed traces. In this paper, we\npropose a method for reliable and practical anomaly detection from system logs.\nIt overcomes the common disadvantage of related works, i.e., the need for a\nlarge amount of manually labeled training data, by building an anomaly\ndetection model with log instructions from the source code of 1000+ GitHub\nprojects. The instructions from diverse systems contain rich and heterogenous\ninformation about many different normal and abnormal IT events and serve as a\nfoundation for anomaly detection. The proposed method, named ADLILog, combines\nthe log instructions and the data from the system of interest (target system)\nto learn a deep neural network model through a two-phase learning procedure.\nThe experimental results show that ADLILog outperforms the related approaches\nby up to 60% on the F1 score while satisfying core non-functional requirements\nfor industrial deployments such as unsupervised design, efficient model\nupdates, and small model sizes.\n", "date": "2022-07-07", "categories": []}
{"id": "2207.03478", "title": "Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors", "abstract": "  Anomaly detection methods strive to discover patterns that differ from the\nnorm in a semantic way. This goal is ambiguous as a data point differing from\nthe norm by an attribute e.g., age, race or gender, may be considered anomalous\nby some operators while others may consider this attribute irrelevant. Breaking\nfrom previous research, we present a new anomaly detection method that allows\noperators to exclude an attribute from being considered as relevant for anomaly\ndetection. Our approach then learns representations which do not contain\ninformation over the nuisance attributes. Anomaly scoring is performed using a\ndensity-based approach. Importantly, our approach does not require specifying\nthe attributes that are relevant for detecting anomalies, which is typically\nimpossible in anomaly detection, but only attributes to ignore. An empirical\ninvestigation is presented verifying the effectiveness of our approach.\n", "date": "2022-07-07", "categories": []}
{"id": "2207.02279", "title": "Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection", "abstract": "  Video anomaly detection is a core problem in vision. Correctly detecting and\nidentifying anomalous behaviors in pedestrians from video data will enable\nsafety-critical applications such as surveillance, activity monitoring, and\nhuman-robot interaction. In this paper, we propose to leverage trajectory\nlocalization and prediction for unsupervised pedestrian anomaly event\ndetection. Different than previous reconstruction-based approaches, our\nproposed framework rely on the prediction errors of normal and abnormal\npedestrian trajectories to detect anomalies spatially and temporally. We\npresent experimental results on real-world benchmark datasets on varying\ntimescales and show that our proposed trajectory-predictor-based anomaly\ndetection pipeline is effective and efficient at identifying anomalous\nactivities of pedestrians in videos. Code will be made available at\nhttps://github.com/akanuasiegbu/Leveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection.\n", "date": "2022-07-05", "categories": []}
{"id": "2207.02281", "title": "BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for\n  Pedestrian Anomaly Detection", "abstract": "  We present BiPOCO, a Bi-directional trajectory predictor with POse\nCOnstraints, for detecting anomalous activities of pedestrians in videos. In\ncontrast to prior work based on feature reconstruction, our work identifies\npedestrian anomalous events by forecasting their future trajectories and\ncomparing the predictions with their expectations. We introduce a set of novel\ncompositional pose-based losses with our predictor and leverage prediction\nerrors of each body joint for pedestrian anomaly detection. Experimental\nresults show that our BiPOCO approach can detect pedestrian anomalous\nactivities with a high detection rate (up to 87.0%) and incorporating pose\nconstraints helps distinguish normal and anomalous poses in prediction. This\nwork extends current literature of using prediction-based methods for anomaly\ndetection and can benefit safety-critical applications such as autonomous\ndriving and surveillance. Code is available at\nhttps://github.com/akanuasiegbu/BiPOCO.\n", "date": "2022-07-05", "categories": []}
{"id": "2207.02606", "title": "DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition", "abstract": "  Anomaly detection can be conceived either through generative modelling of\nregular training data or by discriminating with respect to negative training\ndata. These two approaches exhibit different failure modes. Consequently,\nhybrid algorithms present an attractive research goal. Unfortunately, dense\nanomaly detection requires translational equivariance and very large input\nresolutions. These requirements disqualify all previous hybrid approaches to\nthe best of our knowledge. We therefore design a novel hybrid algorithm based\non reinterpreting discriminative logits as a logarithm of the unnormalized\njoint distribution $\\hat{p}(\\mathbf{x}, \\mathbf{y})$. Our model builds on a\nshared convolutional representation from which we recover three dense\npredictions: i) the closed-set class posterior $P(\\mathbf{y}|\\mathbf{x})$, ii)\nthe dataset posterior $P(d_{in}|\\mathbf{x})$, iii) unnormalized data likelihood\n$\\hat{p}(\\mathbf{x})$. The latter two predictions are trained both on the\nstandard training data and on a generic negative dataset. We blend these two\npredictions into a hybrid anomaly score which allows dense open-set recognition\non large natural images. We carefully design a custom loss for the data\nlikelihood in order to avoid backpropagation through the untractable\nnormalizing constant $Z(\\theta)$. Experiments evaluate our contributions on\nstandard dense anomaly detection benchmarks as well as in terms of open-mIoU -\na novel metric for dense open-set performance. Our submissions achieve\nstate-of-the-art performance despite neglectable computational overhead over\nthe standard semantic segmentation baseline.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.00705", "title": "Multivariate Time Series Anomaly Detection with Few Positive Samples", "abstract": "  Given the scarcity of anomalies in real-world applications, the majority of\nliterature has been focusing on modeling normality. The learned representations\nenable anomaly detection as the normality model is trained to capture certain\nkey underlying data regularities under normal circumstances. In practical\nsettings, particularly industrial time series anomaly detection, we often\nencounter situations where a large amount of normal operation data is available\nalong with a small number of anomaly events collected over time. This practical\nsituation calls for methodologies to leverage these small number of anomaly\nevents to create a better anomaly detector. In this paper, we introduce two\nmethodologies to address the needs of this practical situation and compared\nthem with recently developed state of the art techniques. Our proposed methods\nanchor on representative learning of normal operation with autoregressive (AR)\nmodel along with loss components to encourage representations that separate\nnormal versus few positive examples. We applied the proposed methods to two\nindustrial anomaly detection datasets and demonstrated effective performance in\ncomparison with approaches from literature. Our study also points out\nadditional challenges with adopting such methods in practical applications.\n", "date": "2022-07-02", "categories": []}
{"id": "2207.01106", "title": "Anomaly Detection with Adversarially Learned Perturbations of Latent\n  Space", "abstract": "  Anomaly detection is to identify samples that do not conform to the\ndistribution of the normal data. Due to the unavailability of anomalous data,\ntraining a supervised deep neural network is a cumbersome task. As such,\nunsupervised methods are preferred as a common approach to solve this task.\nDeep autoencoders have been broadly adopted as a base of many unsupervised\nanomaly detection methods. However, a notable shortcoming of deep autoencoders\nis that they provide insufficient representations for anomaly detection by\ngeneralizing to reconstruct outliers. In this work, we have designed an\nadversarial framework consisting of two competing components, an Adversarial\nDistorter, and an Autoencoder. The Adversarial Distorter is a convolutional\nencoder that learns to produce effective perturbations and the autoencoder is a\ndeep convolutional neural network that aims to reconstruct the images from the\nperturbed latent feature space. The networks are trained with opposing goals in\nwhich the Adversarial Distorter produces perturbations that are applied to the\nencoder's latent feature space to maximize the reconstruction error and the\nautoencoder tries to neutralize the effect of these perturbations to minimize\nit. When applied to anomaly detection, the proposed method learns semantically\nricher representations due to applying perturbations to the feature space. The\nproposed method outperforms the existing state-of-the-art methods in anomaly\ndetection on image and video datasets.\n", "date": "2022-07-03", "categories": []}
{"id": "2207.01112", "title": "Augment to Detect Anomalies with Continuous Labelling", "abstract": "  Anomaly detection is to recognize samples that differ in some respect from\nthe training observations. These samples which do not conform to the\ndistribution of normal data are called outliers or anomalies. In real-world\nanomaly detection problems, the outliers are absent, not well defined, or have\na very limited number of instances. Recent state-of-the-art deep learning-based\nanomaly detection methods suffer from high computational cost, complexity,\nunstable training procedures, and non-trivial implementation, making them\ndifficult to deploy in real-world applications. To combat this problem, we\nleverage a simple learning procedure that trains a lightweight convolutional\nneural network, reaching state-of-the-art performance in anomaly detection. In\nthis paper, we propose to solve anomaly detection as a supervised regression\nproblem. We label normal and anomalous data using two separable distributions\nof continuous values. To compensate for the unavailability of anomalous samples\nduring training time, we utilize straightforward image augmentation techniques\nto create a distinct set of samples as anomalies. The distribution of the\naugmented set is similar but slightly deviated from the normal data, whereas\nreal anomalies are expected to have an even further distribution. Therefore,\ntraining a regressor on these augmented samples will result in more separable\ndistributions of labels for normal and real anomalous data points. Anomaly\ndetection experiments on image and video datasets show the superiority of the\nproposed method over the state-of-the-art approaches.\n", "date": "2022-07-03", "categories": []}
{"id": "2207.01391", "title": "Task-oriented Self-supervised Learning for Anomaly Detection in\n  Electroencephalography", "abstract": "  Accurate automated analysis of electroencephalography (EEG) would largely\nhelp clinicians effectively monitor and diagnose patients with various brain\ndiseases. Compared to supervised learning with labelled disease EEG data which\ncan train a model to analyze specific diseases but would fail to monitor\npreviously unseen statuses, anomaly detection based on only normal EEGs can\ndetect any potential anomaly in new EEGs. Different from existing anomaly\ndetection strategies which do not consider any property of unavailable abnormal\ndata during model development, a task-oriented self-supervised learning\napproach is proposed here which makes use of available normal EEGs and expert\nknowledge about abnormal EEGs to train a more effective feature extractor for\nthe subsequent development of anomaly detector. In addition, a specific two\nbranch convolutional neural network with larger kernels is designed as the\nfeature extractor such that it can more easily extract both larger scale and\nsmall-scale features which often appear in unavailable abnormal EEGs. The\neffectively designed and trained feature extractor has shown to be able to\nextract better feature representations from EEGs for development of anomaly\ndetector based on normal data and future anomaly detection for new EEGs, as\ndemonstrated on three EEG datasets. The code is available at\nhttps://github.com/ironing/EEG-AD.\n", "date": "2022-07-04", "categories": []}
{"id": "2206.15352", "title": "Learning Citywide Patterns of Life from Trajectory Monitoring", "abstract": "  The recent proliferation of real-world human mobility datasets has catalyzed\ngeospatial and transportation research in trajectory prediction, demand\nforecasting, travel time estimation, and anomaly detection. However, these\ndatasets also enable, more broadly, a descriptive analysis of intricate systems\nof human mobility. We formally define patterns of life analysis as a natural,\nexplainable extension of online unsupervised anomaly detection, where we not\nonly monitor a data stream for anomalies but also explicitly extract normal\npatterns over time. To learn patterns of life, we adapt Grow When Required\n(GWR) episodic memory from research in computational biology and neurorobotics\nto a new domain of geospatial analysis. This biologically-inspired neural\nnetwork, related to self-organizing maps (SOM), constructs a set of \"memories\"\nor prototype traffic patterns incrementally as it iterates over the GPS stream.\nIt then compares each new observation to its prior experiences, inducing an\nonline, unsupervised clustering and anomaly detection on the data. We mine\npatterns-of-interest from the Porto taxi dataset, including both major public\nholidays and newly-discovered transportation anomalies, such as festivals and\nconcerts which, to our knowledge, have not been previously acknowledged or\nreported in prior work. We anticipate that the capability to incrementally\nlearn normal and abnormal road transportation behavior will be useful in many\ndomains, including smart cities, autonomous vehicles, and urban planning and\nmanagement.\n", "date": "2022-06-30", "categories": []}
{"id": "2206.03935", "title": "Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays", "abstract": "  Chest X-ray (CXR) is the most typical radiological exam for diagnosis of\nvarious diseases. Due to the expensive and time-consuming annotations,\ndetecting anomalies in CXRs in an unsupervised fashion is very promising.\nHowever, almost all of the existing methods consider anomaly detection as a\none-class classification (OCC) problem. They model the distribution of only\nknown normal images during training and identify the samples not conforming to\nnormal profile as anomalies in the testing phase. A large number of unlabeled\nimages containing anomalies are thus ignored in the training phase, although\nthey are easy to obtain in clinical practice. In this paper, we propose a novel\nstrategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing\nboth known normal images and unlabeled images. The proposed method consists of\ntwo modules. During training, one module takes both known normal and unlabeled\nimages as inputs, capturing anomalous features from unlabeled images in some\nway, while the other one models the distribution of only known normal images.\nSubsequently, inter-discrepancy between the two modules, and intra-discrepancy\ninside the module that is trained on only normal images are designed as anomaly\nscores to indicate anomalies. Experiments on three CXR datasets demonstrate\nthat the proposed DDAD achieves consistent, significant gains and outperforms\nstate-of-the-art methods. Code is available at\nhttps://github.com/caiyu6666/DDAD.\n", "date": "2022-06-08", "categories": []}
{"id": "2206.14265", "title": "Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT\n  devices", "abstract": "  The growing number of IoT devices and their use to monitor the operation of\nmachines and equipment increases interest in anomaly detection algorithms\nrunning on devices. However, the difficulty is the limitations of the available\ncomputational and memory resources on the devices. In the case of\nmicrocontrollers (MCUs), these are single megabytes of program and several\nhundred kilobytes of working memory. Consequently, algorithms must be\nappropriately matched to the capabilities of the devices. In the paper, we\nanalyse the processing pipeline for anomaly detection and implementation of the\nLocal Outliner Factor (LOF) algorithm on a MCU. We also show that it is\npossible to train such an algorithm directly on the device, which gives great\npotential to use the solution in real devices.\n", "date": "2022-06-28", "categories": []}
{"id": "2206.14342", "title": "Intrinsic Anomaly Detection for Multi-Variate Time Series", "abstract": "  We introduce a novel, practically relevant variation of the anomaly detection\nproblem in multi-variate time series: intrinsic anomaly detection. It appears\nin diverse practical scenarios ranging from DevOps to IoT, where we want to\nrecognize failures of a system that operates under the influence of a\nsurrounding environment. Intrinsic anomalies are changes in the functional\ndependency structure between time series that represent an environment and time\nseries that represent the internal state of a system that is placed in said\nenvironment. We formalize this problem, provide under-studied public and new\npurpose-built data sets for it, and present methods that handle intrinsic\nanomaly detection. These address the short-coming of existing anomaly detection\nmethods that cannot differentiate between expected changes in the system's\nstate and unexpected ones, i.e., changes in the system that deviate from the\nenvironment's influence. Our most promising approach is fully unsupervised and\ncombines adversarial learning and time series representation learning, thereby\naddressing problems such as label sparsity and subjectivity, while allowing to\nnavigate and improve notoriously problematic anomaly detection data sets.\n", "date": "2022-06-29", "categories": []}
{"id": "2206.14597", "title": "Generative Anomaly Detection for Time Series Datasets", "abstract": "  Traffic congestion anomaly detection is of paramount importance in\nintelligent traffic systems. The goals of transportation agencies are two-fold:\nto monitor the general traffic conditions in the area of interest and to locate\nroad segments under abnormal congestion states. Modeling congestion patterns\ncan achieve these goals for citywide roadways, which amounts to learning the\ndistribution of multivariate time series (MTS). However, existing works are\neither not scalable or unable to capture the spatial-temporal information in\nMTS simultaneously. To this end, we propose a principled and comprehensive\nframework consisting of a data-driven generative approach that can perform\ntractable density estimation for detecting traffic anomalies. Our approach\nfirst clusters segments in the feature space and then uses conditional\nnormalizing flow to identify anomalous temporal snapshots at the cluster level\nin an unsupervised setting. Then, we identify anomalies at the segment level by\nusing a kernel density estimator on the anomalous cluster. Extensive\nexperiments on synthetic datasets show that our approach significantly\noutperforms several state-of-the-art congestion anomaly detection and diagnosis\nmethods in terms of Recall and F1-Score. We also use the generative model to\nsample labeled data, which can train classifiers in a supervised setting,\nalleviating the lack of labeled data for anomaly detection in sparse settings.\n", "date": "2022-06-28", "categories": []}
{"id": "2206.12786", "title": "Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection\n  in Graphs", "abstract": "  We propose a new approach, the calibrated nonparametric scan statistic\n(CNSS), for more accurate detection of anomalous patterns in large-scale,\nreal-world graphs. Scan statistics identify connected subgraphs that are\ninteresting or unexpected through maximization of a likelihood ratio statistic;\nin particular, nonparametric scan statistics (NPSSs) identify subgraphs with a\nhigher than expected proportion of individually significant nodes. However, we\nshow that recently proposed NPSS methods are miscalibrated, failing to account\nfor the maximization of the statistic over the multiplicity of subgraphs. This\nresults in both reduced detection power for subtle signals, and low precision\nof the detected subgraph even for stronger signals. Thus we develop a new\nstatistical approach to recalibrate NPSSs, correctly adjusting for multiple\nhypothesis testing and taking the underlying graph structure into account.\nWhile the recalibration, based on randomization testing, is computationally\nexpensive, we propose both an efficient (approximate) algorithm and new,\nclosed-form lower bounds (on the expected maximum proportion of significant\nnodes for subgraphs of a given size, under the null hypothesis of no anomalous\npatterns). These advances, along with the integration of recent core-tree\ndecomposition methods, enable CNSS to scale to large real-world graphs, with\nsubstantial improvement in the accuracy of detected subgraphs. Extensive\nexperiments on both semi-synthetic and real-world datasets are demonstrated to\nvalidate the effectiveness of our proposed methods, in comparison with\nstate-of-the-art counterparts.\n", "date": "2022-06-26", "categories": []}
{"id": "2206.12914", "title": "Video Anomaly Detection via Prediction Network with Enhanced\n  Spatio-Temporal Memory Exchange", "abstract": "  Video anomaly detection is a challenging task because most anomalies are\nscarce and non-deterministic. Many approaches investigate the reconstruction\ndifference between normal and abnormal patterns, but neglect that anomalies do\nnot necessarily correspond to large reconstruction errors. To address this\nissue, we design a Convolutional LSTM Auto-Encoder prediction framework with\nenhanced spatio-temporal memory exchange using bi-directionalilty and a\nhigher-order mechanism. The bi-directional structure promotes learning the\ntemporal regularity through forward and backward predictions. The unique\nhigher-order mechanism further strengthens spatial information interaction\nbetween the encoder and the decoder. Considering the limited receptive fields\nin Convolutional LSTMs, we also introduce an attention module to highlight\ninformative features for prediction. Anomalies are eventually identified by\ncomparing the frames with their corresponding predictions. Evaluations on three\npopular benchmarks show that our framework outperforms most existing\nprediction-based anomaly detection methods.\n", "date": "2022-06-26", "categories": []}
{"id": "2206.13046", "title": "DPOAD: Differentially Private Outsourcing of Anomaly Detection through\n  Iterative Sensitivity Learning", "abstract": "  Outsourcing anomaly detection to third-parties can allow data owners to\novercome resource constraints (e.g., in lightweight IoT devices), facilitate\ncollaborative analysis (e.g., under distributed or multi-party scenarios), and\nbenefit from lower costs and specialized expertise (e.g., of Managed Security\nService Providers). Despite such benefits, a data owner may feel reluctant to\noutsource anomaly detection without sufficient privacy protection. To that end,\nmost existing privacy solutions would face a novel challenge, i.e., preserving\nprivacy usually requires the difference between data entries to be eliminated\nor reduced, whereas anomaly detection critically depends on that difference.\nSuch a conflict is recently resolved under a local analysis setting with\ntrusted analysts (where no outsourcing is involved) through moving the focus of\ndifferential privacy (DP) guarantee from \"all\" to only \"benign\" entries. In\nthis paper, we observe that such an approach is not directly applicable to the\noutsourcing setting, because data owners do not know which entries are \"benign\"\nprior to outsourcing, and hence cannot selectively apply DP on data entries.\nTherefore, we propose a novel iterative solution for the data owner to\ngradually \"disentangle\" the anomalous entries from the benign ones such that\nthe third-party analyst can produce accurate anomaly results with sufficient DP\nguarantee. We design and implement our Differentially Private Outsourcing of\nAnomaly Detection (DPOAD) framework, and demonstrate its benefits over baseline\nLaplace and PainFree mechanisms through experiments with real data from\ndifferent application domains.\n", "date": "2022-06-27", "categories": []}
{"id": "2206.13385", "title": "3D unsupervised anomaly detection and localization through virtual\n  multi-view projection and reconstruction: Clinical validation on low-dose\n  chest computed tomography", "abstract": "  Computer-aided diagnosis for low-dose computed tomography (CT) based on deep\nlearning has recently attracted attention as a first-line automatic testing\ntool because of its high accuracy and low radiation exposure. However, existing\nmethods rely on supervised learning, imposing an additional burden to doctors\nfor collecting disease data or annotating spatial labels for network training,\nconsequently hindering their implementation. We propose a method based on a\ndeep neural network for computer-aided diagnosis called virtual multi-view\nprojection and reconstruction for unsupervised anomaly detection. Presumably,\nthis is the first method that only requires data from healthy patients for\ntraining to identify three-dimensional (3D) regions containing any anomalies.\nThe method has three key components. Unlike existing computer-aided diagnosis\ntools that use conventional CT slices as the network input, our method 1)\nimproves the recognition of 3D lung structures by virtually projecting an\nextracted 3D lung region to obtain two-dimensional (2D) images from diverse\nviews to serve as network inputs, 2) accommodates the input diversity gain for\naccurate anomaly detection, and 3) achieves 3D anomaly/disease localization\nthrough a novel 3D map restoration method using multiple 2D anomaly maps. The\nproposed method based on unsupervised learning improves the patient-level\nanomaly detection by 10% (area under the curve, 0.959) compared with a gold\nstandard based on supervised learning (area under the curve, 0.848), and it\nlocalizes the anomaly region with 93% accuracy, demonstrating its high\nperformance.\n", "date": "2022-06-18", "categories": []}
{"id": "2206.10830", "title": "A Feature Memory Rearrangement Network for Visual Inspection of Textured\n  Surface Defects Toward Edge Intelligent Manufacturing", "abstract": "  Recent advances in the industrial inspection of textured surfaces-in the form\nof visual inspection-have made such inspections possible for efficient,\nflexible manufacturing systems. We propose an unsupervised feature memory\nrearrangement network (FMR-Net) to accurately detect various textural defects\nsimultaneously. Consistent with mainstream methods, we adopt the idea of\nbackground reconstruction; however, we innovatively utilize artificial\nsynthetic defects to enable the model to recognize anomalies, while traditional\nwisdom relies only on defect-free samples. First, we employ an encoding module\nto obtain multiscale features of the textured surface. Subsequently, a\ncontrastive-learning-based memory feature module (CMFM) is proposed to obtain\ndiscriminative representations and construct a normal feature memory bank in\nthe latent space, which can be employed as a substitute for defects and fast\nanomaly scores at the patch level. Next, a novel global feature rearrangement\nmodule (GFRM) is proposed to further suppress the reconstruction of residual\ndefects. Finally, a decoding module utilizes the restored features to\nreconstruct the normal texture background. In addition, to improve inspection\nperformance, a two-phase training strategy is utilized for accurate defect\nrestoration refinement, and we exploit a multimodal inspection method to\nachieve noise-robust defect localization. We verify our method through\nextensive experiments and test its practical deployment in collaborative\nedge--cloud intelligent manufacturing scenarios by means of a multilevel\ndetection method, demonstrating that FMR-Net exhibits state-of-the-art\ninspection accuracy and shows great potential for use in edge-computing-enabled\nsmart industries.\n", "date": "2022-06-22", "categories": []}
{"id": "2206.09247", "title": "Reduced Robust Random Cut Forest for Out-Of-Distribution detection in\n  machine learning models", "abstract": "  Most machine learning-based regressors extract information from data\ncollected via past observations of limited length to make predictions in the\nfuture. Consequently, when input to these trained models is data with\nsignificantly different statistical properties from data used for training,\nthere is no guarantee of accurate prediction. Consequently, using these models\non out-of-distribution input data may result in a completely different\npredicted outcome from the desired one, which is not only erroneous but can\nalso be hazardous in some cases. Successful deployment of these machine\nlearning models in any system requires a detection system, which should be able\nto distinguish between out-of-distribution and in-distribution data (i.e.\nsimilar to training data). In this paper, we introduce a novel approach for\nthis detection process using a Reduced Robust Random Cut Forest (RRRCF) data\nstructure, which can be used on both small and large data sets. Similar to the\nRobust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced\nrepresentation of the training data sub-space in form of cut trees. Empirical\nresults of this method on both low and high-dimensional data showed that\ninference about data being in/out of training distribution can be made\nefficiently and the model is easy to train with no difficult hyper-parameter\ntuning. The paper discusses two different use-cases for testing and validating\nresults.\n", "date": "2022-06-18", "categories": []}
{"id": "2206.10400", "title": "Using EBGAN for Anomaly Intrusion Detection", "abstract": "  As an active network security protection scheme, intrusion detection system\n(IDS) undertakes the important responsibility of detecting network attacks in\nthe form of malicious network traffic. Intrusion detection technology is an\nimportant part of IDS. At present, many scholars have carried out extensive\nresearch on intrusion detection technology. However, developing an efficient\nintrusion detection method for massive network traffic data is still difficult.\nSince Generative Adversarial Networks (GANs) have powerful modeling\ncapabilities for complex high-dimensional data, they provide new ideas for\naddressing this problem. In this paper, we put forward an EBGAN-based intrusion\ndetection method, IDS-EBGAN, that classifies network records as normal traffic\nor malicious traffic. The generator in IDS-EBGAN is responsible for converting\nthe original malicious network traffic in the training set into adversarial\nmalicious examples. This is because we want to use adversarial learning to\nimprove the ability of discriminator to detect malicious traffic. At the same\ntime, the discriminator adopts Autoencoder model. During testing, IDS-EBGAN\nuses reconstruction error of discriminator to classify traffic records.\n", "date": "2022-06-21", "categories": []}
{"id": "2206.08568", "title": "Multi-Contextual Predictions with Vision Transformer for Video Anomaly\n  Detection", "abstract": "  Video Anomaly Detection(VAD) has been traditionally tackled in two main\nmethodologies: the reconstruction-based approach and the prediction-based one.\nAs the reconstruction-based methods learn to generalize the input image, the\nmodel merely learns an identity function and strongly causes the problem called\ngeneralizing issue. On the other hand, since the prediction-based ones learn to\npredict a future frame given several previous frames, they are less sensitive\nto the generalizing issue. However, it is still uncertain if the model can\nlearn the spatio-temporal context of a video. Our intuition is that the\nunderstanding of the spatio-temporal context of a video plays a vital role in\nVAD as it provides precise information on how the appearance of an event in a\nvideo clip changes. Hence, to fully exploit the context information for anomaly\ndetection in video circumstances, we designed the transformer model with three\ndifferent contextual prediction streams: masked, whole and partial. By learning\nto predict the missing frames of consecutive normal frames, our model can\neffectively learn various normality patterns in the video, which leads to a\nhigh reconstruction error at the abnormal cases that are unsuitable to the\nlearned context. To verify the effectiveness of our approach, we assess our\nmodel on the public benchmark datasets: USCD Pedestrian 2, CUHK Avenue and\nShanghaiTech and evaluate the performance with the anomaly score metric of\nreconstruction error. The results demonstrate that our proposed approach\nachieves a competitive performance compared to the existing video anomaly\ndetection methods.\n", "date": "2022-06-17", "categories": []}
{"id": "2206.08093", "title": "Applications of Machine Learning to the Identification of Anomalous ER\n  Claims", "abstract": "  Improper health insurance payments resulting from fraud and upcoding result\nin tens of billions of dollars in excess health care costs annually in the\nUnited States, motivating machine learning researchers to build anomaly\ndetection models for health insurance claims. This article describes two such\nstrategies specifically for ER claims. The first is an upcoding model based on\nseverity code distributions, stratified by hierarchical diagnosis code\nclusters. A statistically significant difference in mean upcoding anomaly\nscores is observed between free-standing ERs and acute care hospitals, with\nfree-standing ERs being more anomalous. The second model is a random forest\nthat minimizes improper payments by optimally sorting ER claims within review\nqueues. Depending on the percentage of claims reviewed, the random forest saved\n12% to 40% above a baseline approach that prioritized claims by billed amount.\n", "date": "2022-06-16", "categories": []}
{"id": "2206.07519", "title": "Smart Meter Data Anomaly Detection using Variational Recurrent\n  Autoencoders with Attention", "abstract": "  In the digitization of energy systems, sensors and smart meters are\nincreasingly being used to monitor production, operation and demand. Detection\nof anomalies based on smart meter data is crucial to identify potential risks\nand unusual events at an early stage, which can serve as a reference for timely\ninitiation of appropriate actions and improving management. However, smart\nmeter data from energy systems often lack labels and contain noise and various\npatterns without distinctively cyclical. Meanwhile, the vague definition of\nanomalies in different energy scenarios and highly complex temporal\ncorrelations pose a great challenge for anomaly detection. Many traditional\nunsupervised anomaly detection algorithms such as cluster-based or\ndistance-based models are not robust to noise and not fully exploit the\ntemporal dependency in a time series as well as other dependencies amongst\nmultiple variables (sensors). This paper proposes an unsupervised anomaly\ndetection method based on a Variational Recurrent Autoencoder with attention\nmechanism. with \"dirty\" data from smart meters, our method pre-detects missing\nvalues and global anomalies to shrink their contribution while training. This\npaper makes a quantitative comparison with the VAE-based baseline approach and\nfour other unsupervised learning methods, demonstrating its effectiveness and\nsuperiority. This paper further validates the proposed method by a real case\nstudy of detecting the anomalies of water supply temperature from an industrial\nheating plant.\n", "date": "2022-06-08", "categories": []}
{"id": "2206.07604", "title": "ARES: Locally Adaptive Reconstruction-based Anomaly Scoring", "abstract": "  How can we detect anomalies: that is, samples that significantly differ from\na given set of high-dimensional data, such as images or sensor data? This is a\npractical problem with numerous applications and is also relevant to the goal\nof making learning algorithms more robust to unexpected inputs. Autoencoders\nare a popular approach, partly due to their simplicity and their ability to\nperform dimension reduction. However, the anomaly scoring function is not\nadaptive to the natural variation in reconstruction error across the range of\nnormal samples, which hinders their ability to detect real anomalies. In this\npaper, we empirically demonstrate the importance of local adaptivity for\nanomaly scoring in experiments with real data. We then propose our novel\nAdaptive Reconstruction Error-based Scoring approach, which adapts its scoring\nbased on the local behaviour of reconstruction error over the latent space. We\nshow that this improves anomaly detection performance over relevant baselines\nin a wide variety of benchmark datasets.\n", "date": "2022-06-15", "categories": []}
{"id": "2206.05460", "title": "Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly\n  Detection", "abstract": "  This paper aims to develop an acoustic signal-based unsupervised anomaly\ndetection method for automatic machine monitoring. Existing approaches such as\ndeep autoencoder (DAE), variational autoencoder (VAE), conditional variational\nautoencoder (CVAE) etc. have limited representation capabilities in the latent\nspace and, hence, poor anomaly detection performance. Different models have to\nbe trained for each different kind of machines to accurately perform the\nanomaly detection task. To solve this issue, we propose a new method named as\nhierarchical conditional variational autoencoder (HCVAE). This method utilizes\navailable taxonomic hierarchical knowledge about industrial facility to refine\nthe latent space representation. This knowledge helps model to improve the\nanomaly detection performance as well. We demonstrated the generalization\ncapability of a single HCVAE model for different types of machines by using\nappropriate conditions. Additionally, to show the practicability of the\nproposed approach, (i) we evaluated HCVAE model on different domain and (ii) we\nchecked the effect of partial hierarchical knowledge. Our results show that\nHCVAE method validates both of these points, and it outperforms the baseline\nsystem on anomaly detection task by utmost 15 % on the AUC score metric.\n", "date": "2022-06-11", "categories": []}
{"id": "2206.06355", "title": "Anomaly Detection and Inter-Sensor Transfer Learning on Smart\n  Manufacturing Datasets", "abstract": "  Smart manufacturing systems are being deployed at a growing rate because of\ntheir ability to interpret a wide variety of sensed information and act on the\nknowledge gleaned from system observations. In many cases, the principal goal\nof the smart manufacturing system is to rapidly detect (or anticipate) failures\nto reduce operational cost and eliminate downtime. This often boils down to\ndetecting anomalies within the sensor date acquired from the system. The smart\nmanufacturing application domain poses certain salient technical challenges. In\nparticular, there are often multiple types of sensors with varying capabilities\nand costs. The sensor data characteristics change with the operating point of\nthe environment or machines, such as, the RPM of the motor. The anomaly\ndetection process therefore has to be calibrated near an operating point. In\nthis paper, we analyze four datasets from sensors deployed from manufacturing\ntestbeds. We evaluate the performance of several traditional and ML-based\nforecasting models for predicting the time series of sensor data. Then,\nconsidering the sparse data from one kind of sensor, we perform transfer\nlearning from a high data rate sensor to perform defect type classification.\nTaken together, we show that predictive failure classification can be achieved,\nthus paving the way for predictive maintenance.\n", "date": "2022-06-13", "categories": []}
{"id": "2206.04792", "title": "Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex\n  Evolving Data Stream", "abstract": "  Online anomaly detection from a data stream is critical for the safety and\nsecurity of many applications but is facing severe challenges due to complex\nand evolving data streams from IoT devices and cloud-based infrastructures.\nUnfortunately, existing approaches fall too short for these challenges; online\nanomaly detection methods bear the burden of handling the complexity while\noffline deep anomaly detection methods suffer from the evolving data\ndistribution. This paper presents a framework for online deep anomaly\ndetection, ARCUS, which can be instantiated with any autoencoder-based deep\nanomaly detection methods. It handles the complex and evolving data streams\nusing an adaptive model pooling approach with two novel techniques:\nconcept-driven inference and drift-aware model pool update; the former detects\nanomalies with a combination of models most appropriate for the complexity, and\nthe latter adapts the model pool dynamically to fit the evolving data streams.\nIn comprehensive experiments with ten data sets which are both high-dimensional\nand concept-drifted, ARCUS improved the anomaly detection accuracy of the\nstreaming variants of state-of-the-art autoencoder-based methods and that of\nthe state-of-the-art streaming anomaly detection methods by up to 22% and 37%,\nrespectively.\n", "date": "2022-06-09", "categories": []}
{"id": "2206.04325", "title": "CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented\n  Anomaly Localization", "abstract": "  For a long time, anomaly localization has been widely used in industries.\nPrevious studies focused on approximating the distribution of normal features\nwithout adaptation to a target dataset. However, since anomaly localization\nshould precisely discriminate normal and abnormal features, the absence of\nadaptation may make the normality of abnormal features overestimated. Thus, we\npropose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes\nsophisticated anomaly localization using features adapted to the target\ndataset. CFA consists of (1) a learnable patch descriptor that learns and\nembeds target-oriented features and (2) scalable memory bank independent of the\nsize of the target dataset. And, CFA adopts transfer learning to increase the\nnormal feature density so that abnormal features can be clearly distinguished\nby applying patch descriptor and memory bank to a pre-trained CNN. The proposed\nmethod outperforms the previous methods quantitatively and qualitatively. For\nexample, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in\nanomaly localization of MVTec AD benchmark. In addition, this paper points out\nthe negative effects of biased features of pre-trained CNNs and emphasizes the\nimportance of the adaptation to the target dataset. The code is publicly\navailable at https://github.com/sungwool/CFA_for_anomaly_localization.\n", "date": "2022-06-09", "categories": []}
{"id": "2206.03876", "title": "Progressive GANomaly: Anomaly detection with progressively growing GANs", "abstract": "  In medical imaging, obtaining large amounts of labeled data is often a\nhurdle, because annotations and pathologies are scarce. Anomaly detection is a\nmethod that is capable of detecting unseen abnormal data while only being\ntrained on normal (unannotated) data. Several algorithms based on generative\nadversarial networks (GANs) exist to perform this task, yet certain limitations\nare in place because of the instability of GANs. This paper proposes a new\nmethod by combining an existing method, GANomaly, with progressively growing\nGANs. The latter is known to be more stable, considering its ability to\ngenerate high-resolution images. The method is tested using Fashion MNIST,\nMedical Out-of-Distribution Analysis Challenge (MOOD), and in-house brain MRI;\nusing patches of sizes 16x16 and 32x32. Progressive GANomaly outperforms a\none-class SVM or regular GANomaly on Fashion MNIST. Artificial anomalies are\ncreated in MOOD images with varying intensities and diameters. Progressive\nGANomaly detected the most anomalies with varying intensity and size.\nAdditionally, the intermittent reconstructions are proven to be better from\nprogressive GANomaly. On the in-house brain MRI dataset, regular GANomaly\noutperformed the other methods.\n", "date": "2022-06-08", "categories": []}
{"id": "2206.03321", "title": "Early Abnormal Detection of Sewage Pipe Network: Bagging of Various\n  Abnormal Detection Algorithms", "abstract": "  Abnormalities of the sewage pipe network will affect the normal operation of\nthe whole city. Therefore, it is important to detect the abnormalities early.\nThis paper propose an early abnormal-detection method. The abnormalities are\ndetected by using the conventional algorithms, such as isolation forest\nalgorithm, two innovations are given: (1) The current and historical data\nmeasured by the sensors placed in the sewage pipe network (such as ultrasonic\nDoppler flowmeter) are taken as the overall dataset, and then the general\ndataset is detected by using the conventional anomaly detection method to\ndiagnose the anomaly of the data. The anomaly refers to the sample different\nfrom the others samples in the whole dataset. Because the definition of anomaly\nis not through the algorithm, but the whole dataset, the construction of the\nwhole dataset is the key to propose the early abnormal-detection algorithms.\n(2) A bagging strategy for a variety of conventional anomaly detection\nalgorithms is proposed to achieve the early detection of anomalies with the\nhigh precision and recall. The results show that this method can achieve the\nearly anomaly detection with the highest precision of 98.21%, the recall rate\n63.58% and F1-score of 0.774.\n", "date": "2022-06-06", "categories": []}
{"id": "2206.03461", "title": "Fast Unsupervised Brain Anomaly Detection and Segmentation with\n  Diffusion Models", "abstract": "  Deep generative models have emerged as promising tools for detecting\narbitrary anomalies in data, dispensing with the necessity for manual\nlabelling. Recently, autoregressive transformers have achieved state-of-the-art\nperformance for anomaly detection in medical imaging. Nonetheless, these models\nstill have some intrinsic weaknesses, such as requiring images to be modelled\nas 1D sequences, the accumulation of errors during the sampling process, and\nthe significant inference times associated with transformers. Denoising\ndiffusion probabilistic models are a class of non-autoregressive generative\nmodels recently shown to produce excellent samples in computer vision\n(surpassing Generative Adversarial Networks), and to achieve log-likelihoods\nthat are competitive with transformers while having fast inference times.\nDiffusion models can be applied to the latent representations learnt by\nautoencoders, making them easily scalable and great candidates for application\nto high dimensional data, such as medical images. Here, we propose a method\nbased on diffusion models to detect and segment anomalies in brain imaging. By\ntraining the models on healthy data and then exploring its diffusion and\nreverse steps across its Markov chain, we can identify anomalous areas in the\nlatent space and hence identify anomalies in the pixel space. Our diffusion\nmodels achieve competitive performance compared with autoregressive approaches\nacross a series of experiments with 2D CT and MRI data involving synthetic and\nreal pathological lesions with much reduced inference times, making their usage\nclinically viable.\n", "date": "2022-06-07", "categories": []}
{"id": "2206.01524", "title": "Anomaly detection in surveillance videos using transformer based\n  attention model", "abstract": "  Surveillance footage can catch a wide range of realistic anomalies. This\nresearch suggests using a weakly supervised strategy to avoid annotating\nanomalous segments in training videos, which is time consuming. In this\napproach only video level labels are used to obtain frame level anomaly scores.\nWeakly supervised video anomaly detection (WSVAD) suffers from the wrong\nidentification of abnormal and normal instances during the training process.\nTherefore it is important to extract better quality features from the available\nvideos. WIth this motivation, the present paper uses better quality\ntransformer-based features named Videoswin Features followed by the attention\nlayer based on dilated convolution and self attention to capture long and short\nrange dependencies in temporal domain. This gives us a better understanding of\navailable videos. The proposed framework is validated on real-world dataset\ni.e. ShanghaiTech Campus dataset which results in competitive performance than\ncurrent state-of-the-art methods. The model and the code are available at\nhttps://github.com/kapildeshpande/Anomaly-Detection-in-Surveillance-Videos\n", "date": "2022-06-03", "categories": []}
{"id": "2206.01851", "title": "Out-of-Distribution Detection using BiGAN and MDL", "abstract": "  We consider the following problem: we have a large dataset of normal data\navailable. We are now given a new, possibly quite small, set of data, and we\nare to decide if these are normal data, or if they are indicating a new\nphenomenon. This is a novelty detection or out-of-distribution detection\nproblem. An example is in medicine, where the normal data is for people with no\nknown disease, and the new dataset people with symptoms. Other examples could\nbe in security. We solve this problem by training a bidirectional generative\nadversarial network (BiGAN) on the normal data and using a Gaussian graphical\nmodel to model the output. We then use universal source coding, or minimum\ndescription length (MDL) on the output to decide if it is a new distribution,\nin an implementation of Kolmogorov and Martin-L\\\"{o}f randomness. We apply the\nmethodology to both MNIST data and a real-world electrocardiogram (ECG) dataset\nof healthy and patients with Kawasaki disease, and show better performance in\nterms of the ROC curve than similar methods.\n", "date": "2022-06-03", "categories": []}
{"id": "2206.02345", "title": "Anomaly Detection with Test Time Augmentation and Consistency Evaluation", "abstract": "  Deep neural networks are known to be vulnerable to unseen data: they may\nwrongly assign high confidence stcores to out-distribuion samples. Recent works\ntry to solve the problem using representation learning methods and specific\nmetrics. In this paper, we propose a simple, yet effective post-hoc anomaly\ndetection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD),\ninspired by a novel observation. Specifically, we observe that in-distribution\ndata enjoy more consistent predictions for its original and augmented versions\non a trained network than out-distribution data, which separates\nin-distribution and out-distribution samples. Experiments on various\nhigh-resolution image benchmark datasets demonstrate that TTA-AD achieves\ncomparable or better detection performance under dataset-vs-dataset anomaly\ndetection settings with a 60%~90\\% running time reduction of existing\nclassifier-based algorithms. We provide empirical verification that the key to\nTTA-AD lies in the remaining classes between augmented features, which has long\nbeen partially ignored by previous works. Additionally, we use RUNS as a\nsurrogate to analyze our algorithm theoretically.\n", "date": "2022-06-06", "categories": []}
{"id": "2206.00489", "title": "Attack-Agnostic Adversarial Detection", "abstract": "  The growing number of adversarial attacks in recent years gives attackers an\nadvantage over defenders, as defenders must train detectors after knowing the\ntypes of attacks, and many models need to be maintained to ensure good\nperformance in detecting any upcoming attacks. We propose a way to end the\ntug-of-war between attackers and defenders by treating adversarial attack\ndetection as an anomaly detection problem so that the detector is agnostic to\nthe attack. We quantify the statistical deviation caused by adversarial\nperturbations in two aspects. The Least Significant Component Feature (LSCF)\nquantifies the deviation of adversarial examples from the statistics of benign\nsamples and Hessian Feature (HF) reflects how adversarial examples distort the\nlandscape of the model's optima by measuring the local loss curvature.\nEmpirical results show that our method can achieve an overall ROC AUC of 94.9%,\n89.7%, and 94.6% on CIFAR10, CIFAR100, and SVHN, respectively, and has\ncomparable performance to adversarial detectors trained with adversarial\nexamples on most of the attacks.\n", "date": "2022-06-01", "categories": []}
{"id": "2206.00506", "title": "Proximally Sensitive Error for Anomaly Detection and Feature Learning", "abstract": "  Mean squared error (MSE) is one of the most widely used metrics to expression\ndifferences between multi-dimensional entities, including images. However, MSE\nis not locally sensitive as it does not take into account the spatial\narrangement of the (pixel) differences, which matters for structured data types\nlike images. Such spatial arrangements carry information about the source of\nthe differences; therefore, an error function that also incorporates the\nlocation of errors can lead to a more meaningful distance measure. We introduce\nProximally Sensitive Error (PSE), through which we suggest that a regional\nemphasis in the error measure can 'highlight' semantic differences between\nimages over syntactic/random deviations. We demonstrate that this emphasis can\nbe leveraged upon for the task of anomaly/occlusion detection. We further\nexplore its utility as a loss function to help a model focus on learning\nrepresentations of semantic objects instead of minimizing syntactic\nreconstruction noise.\n", "date": "2022-06-01", "categories": []}
{"id": "2606.21322", "title": "Distinguishing indistinguishable attractors: Unsupervised anomaly detection with reservoir computers", "abstract": "Detecting when a nonlinear dynamical system departs from its normal regime is a recurring problem across the sciences, from cardiology to climate and energy systems. We show that a very simple Kolmogorov--Smirnov test on the output weights of a reservoir computer is highly sensitive to regime changes in nonlinear dynamical systems, including those invisible to both classical nonlinear measures and modern deep-learning detectors. The core idea of our algorithm is to treat the readout layer of a reservoir computer as a representation of the input dynamics. Since the input mapping and the reservoir itself are random and fixed, the trained output weights are the only object encoding the system at hand. We summarize this fingerprint by the empirical cumulative distribution function of the readout weights and compare it to a reference band built from the training data. This unsupervised, online detector distinguishes two visually indistinguishable butterfly-shaped attractors, resolves parameter drifts seven times smaller than the strongest deep-learning baseline, flags noise four orders of magnitude below the signal, and identifies ventricular flutter in a clinical ECG recording. More broadly, we aim to establish a perspective on reservoir computers in which the trained output weights are treated as a representation of the learned system in their own right, rather than merely as a means to forecasting.", "date": "2026-06-19", "categories": []}
{"id": "2606.21326", "title": "Sea-Scan: High-Accuracy, ML-based Dark Vessel Detection and Localisation via Weakly Supervised DAS Monitoring", "abstract": "We present an ML-based vessel detection and localization system, trained with weak supervision from imperfect AIS labels, that achieves a 97.8% detection rate at 1.98% false-trigger rate, successfully identifies dark-vessel events from unlabeled data.", "date": "2026-06-19", "categories": []}
{"id": "2606.22261", "title": "Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection", "abstract": "Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event has departed from normal behavior. We study a normal-world modeling formulation for this setting. Instead of learning a large and incomplete space of abnormal classes, the model learns the normal world from abundant normal events and uses a few abnormal examples only to calibrate the boundary of normality. We instantiate this idea as a Hypergraph Entropic Normal-World Model. The model represents multivariate sensor windows as context-conditioned hypergraphs, where hyperedges capture high-order relations among groups of variables. It then defines abnormality by an entropy-aware normal-world energy that combines temporal prediction surprise, hypergraph consistency surprise, and latent normal-manifold departure. On the NASA C-MAPSS turbofan degradation benchmark, the proposed full energy achieves strong zero-shot and few-shot performance across all four subsets and reaches AUROC 0.9983 on FD004, the most complex setting with multiple operating conditions and fault modes. Beyond standard detection metrics, we introduce mechanistic validation tests to probe whether the energy encodes normal-world structure rather than a superficial input-output mapping. The learned energy accepts unseen healthy engines, increases along degradation trajectories, and sharply penalizes context-mismatched cross-variable coupling breaks. These results suggest that normal-world energy can serve as an anomaly score, a graded risk measure, and a testable representation of normal system behavior under severe abnormal-label scarcity.", "date": "2026-06-20", "categories": []}
{"id": "2606.22556", "title": "HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection", "abstract": "Anomaly detection is essential for medical image analysis, where pathological regions often appear as rare deviations from normal anatomical structures. While training-based methods have achieved promising performance, they require task-specific optimization and extensive normal data, which limits scalability across modalities and institutions. Training-free approaches offer greater flexibility by leveraging pretrained visual representations, yet existing methods typically rely on simple nearest-neighbor retrieval and naive aggregation strategies, which may fail to capture hierarchical semantics and ignore the reliability of multiple anomaly responses. In this work, we propose HiMatch-AD, a DINOv3-driven hierarchical matching framework for training-free medical anomaly detection. Our method first retrieves semantically relevant normal references via dual-branch matching that jointly considers global CLS-token similarity and patch-level representations. Hierarchical anomaly maps are then generated across multiple transformer stages by comparing clustered normal features with query representations. To robustly aggregate anomaly responses, we introduce a unified uncertainty-based fusion mechanism that adaptively weights maps according to their reliability. The entire framework operates without any task-specific training. Extensive experiments on the BMAD benchmark, including brain MRI, liver CT, and retinal OCT datasets, demonstrate that HiMatch-AD consistently outperforms both training-based and DINO-based state-of-the-art methods, which highlights the effectiveness of multi-level matching and uncertainty-aware fusion for scalable medical anomaly detection.", "date": "2026-06-21", "categories": []}
{"id": "2606.22581", "title": "From CVE to CWE: Syscall-Based HIDS Generalisation", "abstract": "Host intrusion detection systems (HIDS) based on system-call traces are typically trained and evaluated against individual Common Vulnerabilities and Exposures (CVE) instances. In operational settings, however, defenders need to recognise new exploits of an already known type of weakness. We empirically examine whether a one-class anomaly detector trained on the normal behaviour of a set of CVEs that share a Common Weakness Enumeration (CWE) class generalises to a different, unseen CVE inside the same class. Using six scenarios drawn from LID-DS-2021 and grouped into three CWE families (CWE-307 broken authentication, CWE-89 SQL injection, CWE-434 unrestricted file upload), we extract a 66-dimensional Peng-Guo-style feature vector per sliding window and train Isolation Forest and SGD One-Class SVM detectors with normal-only thresholds calibrated to fixed target false positive rates. We define and answer four research questions covering self-detection, asymmetric cross-CVE transfer, the value of a combined CWE-level normal profile, and the effect of feature filtering on transferability. The combined CWE-307 detector reaches F1 = 0.6976 at calibration target FPR = 0.05 (precision = 0.8994, recall = 0.5698), whereas CWE-89 and CWE-434 collapse to F1 <= 0.21 under the same protocol. Cross-CVE transfer turns out to be strongly direction-dependent and dominated by the breadth of the source normal profile rather than by the CWE label. We conclude that CWE-level generalisation in HIDS is empirically attainable for some but not all weakness families with current syscall features, and we argue that calibrated FPR is a methodological prerequisite for honest reporting in this setting.", "date": "2026-06-21", "categories": []}
{"id": "2606.23126", "title": "MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection", "abstract": "While recent advancements in anomaly detection have demonstrated the efficacy of CNN- and Transformer-based approaches, these architectures face inherent limitations: CNNs struggle to capture long-range dependencies, whereas Transformers suffer from quadratic computational complexity. Consequently, Mamba-based architectures have attracted considerable attention, as they successfully combine superior long-range dependency modeling with linear computational complexity. By critically rethinking the structural evolution across the Mamba lineage 1-3 series, this paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection. MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales. The proposed DSS module effectively models both global dependencies and local representations by integrating parallel-cascaded Hybrid State Space (HSS) blocks and frequency-enhanced convolution operations. The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling, leveraging the dual computational paths of linear recurrence and parallel matrix formulation to model local continuity and global contextual comparison, thereby better serving the core anomaly detection objective of precisely reconstructing normal representations while magnifying anomalous deviations. Additionally, we propose a semantics-adaptive progressive scanning strategy that decays scanning complexity along the feature pyramid.", "date": "2026-06-22", "categories": []}
{"id": "2606.24375", "title": "MATCH: Flow Matching for Multi-View Anomaly Detection", "abstract": "Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of several view points, which has led to the field of multi-view anomaly detection. We present MATCH, the first multi-view anomaly detection method based on Flow Matching (FM). With the ODE formulation of Flow Matching, we can estimate likelihoods and thereby derive an anomaly score to detect anomalies in multi-view image data at object, image, and pixel-level. The architectural flexibility of FM models allows us to efficiently transform features of different spatial sizes to the normal distribution. We evaluate thoroughly on the already established Real-IAD data set and are also the first to provide a comprehensive evaluation of popular anomaly detection methods for the MANTA-Tiny data set. MATCH achieves state-of-the-art performance in both anomaly detection and segmentation, all while running on consumer-level hardware. By omitting the costly divergence term needed for likelihood estimation, we ensure that MATCH is usable in real-time production scenarios. Lastly, several ablation studies are conducted to validate the methodological choices.", "date": "2026-06-23", "categories": []}
{"id": "2606.26151", "title": "Unsupervised Memory-Enhanced Video Transformers: Obstacle Detection for Autonomous Agricultural Rover", "abstract": "While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge. Conventional safety sensors, such as LiDAR, fail to detect obstacles positioned below the plant canopy, posing a significant risk. While camera-based supervised learning methods can detect common objects, they perform poorly when faced with obstacles that were not present in their training data. Actual unsupervised anomaly detection offers a solution by learning the normal visual patterns of an environment, but often fails for the dynamic scenes captured by a moving rover.\\\\ This paper introduces Video Memory Transformers for Anomaly Detection (VMTAD), a fully unsupervised method designed for real-time obstacle detection in dynamic agricultural scenes. VMTAD utilizes a transformer-driven architecture augmented with a dedicated memory module. This memory module leverages temporal context by processing encoded representations of preceding frames. This approach enables the system to effectively address the dynamic context caused by the robot's movement. The model is trained using only images that represent normal operation, requiring no data labels.\\\\ VMTAD was rigorously evaluated on the 'Grillion' agricultural rover. On a challenging rapeseed dataset, VMTAD achieved state-of-the-art performance, reaching a 0.973 detection and 0.997 segmentation Area Under the Receiver Operating Characteristic curve. A lightweight variant provides an optimal balance of high accuracy and real-time inference (14 ms), which is critical for safety, as confirmed by our analysis of the rover's total stopping distance.", "date": "2026-06-23", "categories": []}
{"id": "2606.25273", "title": "CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection", "abstract": "Zero-shot 3D anomaly detection is essential for industrial quality inspection, where labeled anomaly samples are scarce. Meanwhile, existing methods lack an effective mechanism to fuse complementary 2D color images with 3D geometric structures, limiting their ability to detect both surface and structural defects in a unified framework. To address these issues, we propose CoGeoAD, a unified CLIP-based framework that fuses color and geometric features by constructing pixel-aligned paired multi-view images. The framework introduces a Data-Driven Multi-View Attention (MVA) mechanism to adaptively aggregate 3D features and a Multi-Stage Color-Geometric Fusion (MS-CGF) module to hierarchically integrate multi-level features from both modalities. Extensive experiments on the MVTec3D-AD and Eyecandies benchmarks demonstrate that CoGeoAD achieves state-of-the-art performance, effectively capturing both structural and textural anomalies in complex industrial scenarios. our source code is available at https://github.com/kingdomShu/CoGeoAD.", "date": "2026-06-24", "categories": []}
{"id": "2606.25368", "title": "Hypergraph Normal World Models for Logical Visual Anomaly Detection", "abstract": "Visual anomaly detection is often deployed with only normal training images. Most one-class detectors map test patches or features to a normal reference distribution. This works well for local structural defects. Logical anomalies are different. Each visible part may look normal, while the whole image violates a normal count, co-occurrence, or spatial relation. This paper studies whether a model can learn such a category-specific normal world from nominal images alone. We propose the Hypergraph Normal World Model, a normal-only detector that distills frozen DINOv2 patch tokens into patch, relation, and hypergraph statistics. It builds spatial hyperedges over token groups. It then scores each test image with an information quotient that separates local, relational, hyperedge, and hyperedge-relation evidence. On the available MVTec LOCO breakfast-box validation data, the full hypergraph model improves logical anomaly AUROC from 0.8434 for DINOv2 patch-kNN to 0.9279. It also improves over the non-hypergraph variant, from 0.9013 to 0.9279. Few-shot experiments show that the model remains effective with very limited normal images. We also test whether the score reflects normal-world knowledge rather than a shallow mapping. t-SNE separates logical anomalies in the learned energy space. Relation counterfactuals increase the information quotient by 83.13 on average. Random hypergraphs reduce logical AUROC, and hyperedge attribution is much larger on logical anomalies. Qualitative examples show that high scores are driven by relation-bearing terms. These results suggest that logical visual anomaly detection should model normal relations, not only normal local patches.", "date": "2026-06-24", "categories": []}
{"id": "2606.25740", "title": "Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection", "abstract": "3D anomaly detection in point clouds is critical for high-precision industrial manufacturing. Reconstruction-based methods have laid a strong foundation by detecting 3D anomalies through comparisons between defective inputs and their reconstructed normal counterparts. However, existing methods still suffer from two challenges: 1) the foreground weak defective regions such as scratches are hard to reconstruct and detect, where the anomaly deviations in normalized point clouds can be as small as $10^{-3}$; 2) the background non-defective regions are prone to get positional bias in reconstruction, which leads to false positives. To address these challenges, we propose \\textbf{PCDiff}, a point cloud diffusion framework for instance-level 3D anomaly generation and detection. In the generation phase, an instance-level multi-modal attention is embedded into the generation framework, where anomalies are conditioned with texture gradient, image patch, text and mask. The instance-level condition enables the high-quality generation of weak-defective anomalies. In the detection phase, a joint local-global reconstruction algorithm is introduced to ensure local anomaly restoration and global geometric consistency, which preserves background normal structure while restoring the foreground defect. Extensive experiments demonstrate that the proposed PCDiff significantly outperforms state-of-the-art methods in both 3D anomaly generation fidelity and reconstruction quality, leading to substantial improvements in anomaly detection accuracy.", "date": "2026-06-24", "categories": []}
{"id": "2606.26687", "title": "DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection", "abstract": "In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing Flows (NFs) provide exact density estimation but suffer from catastrophic forgetting as parameter updates across tasks distort the density manifold. While parameter isolation can prevent interference, it must preserve the strict invertibility and Jacobian validity of NFs. To satisfy these requirements, we exploit the inherent property that affine coupling layers maintain transformation validity regardless of subnet parameterization. Based on this, we propose DeCoFlow, which decomposes subnets into a frozen universal base and task-specific low-rank adapters to isolate updates. We further introduce Task-Specific Alignment, Auxiliary Coupling Layers, and Tail-Aware Loss to compensate for frozen-base rigidity. DeCoFlow achieves state-of-the-art image-level AUROCs of 98.40% on MVTec-AD and 93.00% on VisA, while maintaining parameter-level zero forgetting (0.00% FM under correct routing) with only 2.27M parameters per task.", "date": "2026-06-25", "categories": []}
{"id": "2606.27411", "title": "Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder", "abstract": "We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder-decoder architecture trained to discard information via auxiliary trash qubits. Anomaly scores reflect the degree to which inputs resist compression relative to normal data, with higher scores corresponding to deviations from the learned normal manifold. Evaluated on publicly available brain MRI DICOM datasets, the method achieves a slice-level ROC-AUC of approximately 0.95 and a patch-level ROC-AUC of approximately 0.813, outperforming classical autoencoder and PCA baselines. Analysis of the learned parameters reveals a pronounced encoder-decoder asymmetry, where effective anomaly detection arises from structured information compression within the encoder rather than increased parameter magnitude or decoder expressivity. This results in a controlled compression-reconstruction trade-off with a clear operating regime that supports principled threshold selection. Qualitative evaluation further shows that the QAE produces spatially localized anomaly heatmaps aligned with tumorous regions. The results, supported by promising baseline performances, demonstrate that quantum autoencoders provide an interpretable and controllable mechanism for anomaly detection based on incompressibility with respect to a learned latent representation. This work highlights the potential of quantum autoencoders as a principled tool for studying compression dynamics in quantum machine learning, with promising implications for decision support in medical imaging workflows.", "date": "2026-06-25", "categories": []}
{"id": "2606.28268", "title": "Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation", "abstract": "Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly segmentation remain limited by their reliance on pixel-level heuristics, such as confidence thresholding or entropy minimisation, which fail to preserve structural consistency under noise and texture variation. Moreover, they typically treat anomaly maps as flat intensity fields, ignoring the higher-order spatial relationships that characterise complex defect geometries. We introduce TopoTTA (Topological Test-Time Adaptation), a novel framework that integrates persistent homology, a tool from topological data analysis, into the TTA pipeline to enforce geometric and structural coherence during adaptation. By applying multi-level cubical complex filtration to anomaly score maps, TopoTTA derives robust topological pseudo-labels that guide a lightweight test-time classifier, enhancing segmentation quality without retraining the backbone model. The approach avoids reliance on method-specific raw-score thresholding for mask binarisation, preserves connectivity, and generalises across both 2D and 3D modalities. Extensive experiments across six standard benchmarks (MVTec AD, VisA, Real-IAD, MVTec 3D-AD, AnomalyShapeNet, and MVTec LOCO) demonstrate an average 15% F1 improvement over state-of-the-art unsupervised anomaly detection and segmentation methods, with the largest gains on anomalies exhibiting complex geometric or structural variations. These findings suggest that integrating topological reasoning into test-time adaptation provides a principled route to structure-aware generalisation, bridging the gap between geometric learning and robust adaptation.", "date": "2026-06-26", "categories": []}
{"id": "2606.28467", "title": "An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations", "abstract": "Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use. This paper proposes an end-to-end agentic pipeline that combines deep time-series forecasting, variational anomaly detection, and LLM-based reasoning to generate prioritized, actionable maintenance recommendations. The system tracks seven office appliances using a hybrid Singular Spectrum Analysis (SSA) and Long Short-Term Memory (LSTM) forecasting model, and applies a per-appliance LSTM Variational Autoencoder (VAE) with attention to flag abnormal daily consumption episodes. A three-stage LangChain pipeline begins with a Context Agent that always retrieves three core RAG sources (model reliability, hourly baseline, and expert knowledge) and conditionally adds up to three more (forecast context, anomaly history, global baseline) based on event characteristics, capped at eight reasoning steps. A Diagnosis Agent converts the evidence into a structured JSON diagnosis, and a Report Agent renders a human-readable narrative. A reflective memory layer incorporates operator feedback. The dashboard shows real-time 30-minute forecasts, intraday consumption, the previous day anomaly report, and a feedback form. We evaluate the forecasting model, anomaly detector with appliance-specific thresholds, and LLM reasoning on a 16-scenario benchmark including sustained and transient spikes, unexpected shutdowns, and systemic events, comparing five LLM backends under static vs. dynamic retrieval. Dynamic retrieval matches full static retrieval across all backends while cutting average context from six to three-six sources per event. The best backend scores 90.4/100 with a 100% pass rate at a 70-point threshold, and a fully local 7B-parameter model passes all 16 scenarios.", "date": "2026-06-26", "categories": []}
{"id": "2606.28688", "title": "LogiCo: A Unified Framework for Logical and Structural Anomaly Detection", "abstract": "Current anomaly detection methods primarily focus on structural anomalies, while paying insufficient attention to anomalies that violate logical constraints. Conversely, top-performing logical anomaly detection approaches address this by modeling global semantic consistency, but perform poorly on subtle structural anomalies due to inadequate detection granularity. In this paper, we propose LogiCo, a unified framework for Logical and structural anomaly detection via Component-level feature reconstruction. Unlike existing methods that rely on explicit global semantic modeling, LogiCo employs a novel component-level feature reconstruction technique to capture inter-component logical constraints. Specifically, LogiCo maps pre-trained image features into a discrete component-level feature space and performs collaborative feature reconstruction at both component and patch levels, enabling it to effectively detect both logical and structural anomalies. Furthermore, to address the specific challenge of count-related logical anomalies, we integrate a segmentation-map discriminator that extends the model's capability to identify quantitative inconsistencies. LogiCo achieves state-of-the-art performance on both logical and structural anomaly detection across four benchmarks, including MVTec-LOCO, MVTec-AD, VisA, and Real-IAD, demonstrating its superiority and practical feasibility. The code is available at https://github.com/cnulab/LogiCo.", "date": "2026-06-27", "categories": []}
{"id": "2606.28923", "title": "Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks", "abstract": "Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure. Logging of all cloud computing events enables investigation into any incidents after they are detected. Automated detection of threats using the logs based on heuristics or anomaly detection could result in a high false positive rate due to its relatively static nature. In this article, we present an industrial case study of a self-supervised learning method using graph neural networks applied to AWS CloudTrail logs to surface suspicious events for analyst review. The model produces an anomaly score for each event and dynamically adapts to changes in the organization without requiring periodic retraining. Based on our experiments across five organizations, the proposed model produced substantially fewer alerts than a domain expert rule-based baseline in almost all cases, reducing alert volumes to approximately 1 per hour from thousands generated by traditional methods. We note that this evaluation covers only flagged events, and false negatives cannot be estimated from the current data; findings should therefore be interpreted as a practical deployment study offering insights into real-world constraints rather than a fully validated detection system. We discuss these limitations and the requirements for extending the approach to other cloud environments as future work.", "date": "2026-06-27", "categories": []}
{"id": "2606.28970", "title": "RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection", "abstract": "Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are often efficient, but they usually rely on a fixed data view and a single notion of abnormality. Deep anomaly detectors can learn more flexible scoring functions, but they are substantially slower and difficult to tune in unsupervised settings due to the lack of a reliable supervisory signal. We propose RGLD, a randomized global-local density estimator for efficient unsupervised tabular anomaly detection. RGLD combines a global random-feature density branch, which identifies samples in broadly low-density regions, with a local neighbor branch, which detects samples that are weakly supported by nearby observations. Both branches operate over feature-bagged randomized views, allowing RGLD to expose anomaly evidence that may be hidden in any single representation. We conduct experiments on 47 tabular datasets against 23 statistical and deep anomaly detection baselines under fully unsupervised setting. RGLD achieves the strongest dataset-level AUROC performance, ranking 1st in dataset wins, and ranks 2nd in AUPRC wins. RGLD is also faster than all evaluated deep detectors, achieving 50x-580x speedups, and remains competitive with statistical methods in runtime, yielding a favorable accuracy-efficiency tradeoff.", "date": "2026-06-27", "categories": []}
{"id": "2606.29181", "title": "Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection", "abstract": "Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies. AF3AD uses a center-conditioned parametric deformation model defined in local PCA frames, with kernel-controlled spatial falloff, anisotropy, directional gating, and normal/tangential displacement fields, enabling a broad set of geometric defect presets. We demonstrate its ease-of-use and effectiveness by integrating AF3AD with an offset-prediction detector and a reconstruction-based anomaly detection method, showing that AF3AD transfers across detection paradigms. Experiments on AnomalyShapeNet and Real3D-AD show consistent improvements in object- and point-level detection and localization, supported by ablations on preset groups and robustness under noise. AF3AD is designed as a standalone synthesis tool to facilitate adoption across different 3D anomaly detection paradigms. Code is available at github.com/vpc-ccg/AF3AD.", "date": "2026-06-28", "categories": []}
{"id": "2606.29428", "title": "Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors", "abstract": "Zero-shot anomaly detection aims to identify defects in arbitrary novel domains; however, existing models assume that the auxiliary data contains a rich diversity of anomalies, neglecting the far more complex and unpredictable variations in real-world target domains. This study introduces DIVE, the first approach to investigate the scenario of limited auxiliary anomaly priors and resolve the resulting substantial performance degradation. Through a shallow-and-deep text embedding injection strategy during visual encoding, DIVE learns to abstract generic anomaly concepts shared across the auxiliary training domain and diverse target domains. Moreover, we propose a disentanglement mechanism to tackle the suboptimal alignment between visual embeddings entangled with object semantics and object-agnostic textual prompts. Experiments demonstrate that, under the setting of limited anomaly patterns in auxiliary data, DIVE outperforms SOTA baselines by up to 16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and 47.0% on three segmentation metrics, in terms of average performance across twelve datasets. Furthermore, it maintains highly competitive performance when auxiliary data exhibits sufficient anomaly diversity.", "date": "2026-06-28", "categories": []}
{"id": "2606.29498", "title": "Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection", "abstract": "Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neglecting their spatial extent within frames. Yet, spatial localization is essential for interpretability and practical deployment in real-world settings. We introduce a patch-based spatiotemporal framework for weakly supervised anomaly localization that jointly models where and when anomalies occur. Our approach operates on grid-level patch features and learns region-level anomaly scores under a multiple instance learning paradigm. We further propose a Proximity-Aware Top-k spatiotemporal selection strategy that enables the model to generate fine-grained spatial anomaly maps without requiring bounding-box supervision during training. Our method surpasses existing state-of-the-art approaches across multiple benchmarks, yielding substantial gains in spatiotemporal localization accuracy. In addition, we release frame-level bounding-box annotations for the test sets of two widely used datasets, along with our code and pretrained models, providing new resources to facilitate future research in spatially grounded WSVAD.", "date": "2026-06-28", "categories": []}
{"id": "2606.29506", "title": "Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection", "abstract": "Automated \"suspicious behavior\" flagging is a headline promise of AI surveillance, and the field reports high frame-level ROC-AUC on standard video anomaly detection benchmarks. Those numbers are measured by training and testing on the same camera and scene. We audit what happens when that assumption is dropped. We build an unsupervised normality model from the all-normal training frames of one dataset, using frozen off-the-shelf embeddings (CLIP, DINOv2, ResNet-50, EfficientNet-B0) and a nearest-neighbour distance, and score the test frames of the same and of other datasets. Across 4 real datasets (UCSD Ped1, UCSD Ped2, CUHK Avenue, ShanghaiTech) and 4 backbones, same-dataset AUC averages 0.704 but cross-dataset AUC averages 0.499, which is chance: a detector calibrated on one scene is no better than a coin flip on another, and in several pairs it is below chance. The strongest backbone makes this worse, not better: DINOv2 has the best same-dataset AUC (up to 0.901 on Ped2) and the largest cross-dataset drop. The collapse is not an artefact of the scoring rule: replacing the nearest-neighbour detector with a PaDiM-style Mahalanobis detector reproduces it almost exactly (cross-dataset gap 0.202 versus 0.208). Even at a favourable operating point the false-alarm rate is on the order of 31,931 per hour. We conclude that the benchmark numbers quoted for surveillance anomaly detection describe a calibrated laboratory setting and overstate deployable reliability by a wide margin, and we release the code that reproduces every number.", "date": "2026-06-28", "categories": []}
{"id": "2606.29714", "title": "UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction", "abstract": "Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set. Existing approaches mainly infer this boundary from normal-side evidence and provide limited abnormal-side evidence for deployment-specific tolerance. Within the normal side, they often struggle to jointly capture local correspondences and global support-query relations, making their boundaries less reliable for unseen anomalies. To address these issues, we propose UniVAD v2, a two-sided support-conditioned boundary construction framework for unified visual anomaly detection. Built on the component-patch divide-and-conquer framework of UniVAD, UniVAD v2 strengthens the normal side with an Optimal Transport-based Relational Modeling module (OTRM), which complements retrieval with support-query matching through transport-style allocation, and an Adaptive Coordination mechanism for Retrieval and Relational Modeling (ACRRM), which estimates episode-conditioned reliabilities to fuse the two sources of evidence. On the abnormal side, a Few-Shot Abnormal Reference module (FAR) converts optional abnormal references into rejection-side evidence for boundary adjustment. Experiments on six datasets spanning industrial, logical, and medical anomaly detection demonstrate strong cross-domain generalization. Under the 1N-shot protocol, UniVAD v2 improves the mean image-level AUC over UniVAD from 83.0\\% to 84.5\\%, and further reaches 85.7\\% in the 1N+1A-shot setting. On the MVTec-AD Severity Split (MVTec-AD-SS), UniVAD v2 achieves 96.2\\% image-level AUC and 96.9\\% pixel-level AUC, showing that abnormal references enable controllable boundary customization without retraining.", "date": "2026-06-29", "categories": []}
{"id": "2606.29721", "title": "Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies", "abstract": "Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source. Despite its importance, most publicly available AIS datasets lack predefined anomaly labels, forcing prior studies to rely on either distribution-based rarity or domain rule/expert-assisted labeling. These approaches, however, face fundamental limitations: statistical rarity often fails to reflect practically critical events, while expert-based labeling is costly, subjective, and difficult to scale. Moreover, both paradigms tend to overlook interaction-driven hazards such as near-miss approaches between vessels. To address these challenges, we propose an equation-grounded anomaly taxonomy that is implementable under a limited AIS observation schema and extensible to other AIS datasets. Specifically, the taxonomy defines three anomaly types: unexpected AIS activity (A1), route deviation (A2), and close approach (A3), covering both single-vessel and inter-vessel anomalies. Building on this taxonomy, we introduce a unified score-synthesize-label pipeline that produces LLM-guided plausibility scores, uses them to synthesize anomalies, and assigns timestamp-level labels. To rigorously assess detection performance, we further design benchmark evaluation settings that account for variations in temporal-window length and anomaly-type composition, and evaluate a broad range of time-series models and anomaly detection models. Together, these contributions provide a systematic basis for evaluating maritime anomaly detection methods across different anomaly types. Our code is available at https://github.com/snudial/open-maritime-anomaly-detection.", "date": "2026-06-29", "categories": []}
{"id": "2606.29791", "title": "What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics", "abstract": "Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised settings where no information about anomalies is available during training. Recent advances have leveraged the inlier-memorization (IM) effect, a phenomenon in which deep models memorize inlier patterns earlier than those of outliers, as a powerful signal for distinguishing outliers. However, despite its empirical success, the theoretical understanding of the IM effect remains limited. In this work, we present a theoretical study of the IM effect. Focusing on a simple autoencoder, we show that, under mild assumptions, the model can successfully memorize inliers while failing to memorize outliers during certain stages of early training. In particular, we characterize not only the emergence of the IM effect, but also its strength and persistence, and analyze how these properties depend on the data distribution and parameter initialization. In addition, building on these insights, we derive simple yet practical guidelines for enhancing the IM effect, including data preprocessing and parameter initialization schemes, achieving state-of-the-art performance on the ADBench datasets. Our findings provide a theoretical foundation for the IM effect and offer actionable directions for improving IM-based outlier detection methods.", "date": "2026-06-29", "categories": []}
{"id": "2606.30009", "title": "Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection", "abstract": "Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally fall into two paradigms. GNN-based methods effectively capture structural patterns but struggle to capture fine-grained textual semantics. Methods integrating LLMs with graphs improve semantic understanding yet fail to fully comprehend topological relationships among neighboring nodes. Moreover, both paradigms overlook the correspondence between textual semantics and graph topological relationships, limiting their ability to identify nodes whose semantics are inconsistent with their neighborhoods. In this paper, we formalize TAG anomaly detection as a node-to-neighborhood semantic consistency problem, where anomalies may arise from either textual semantic mismatch or topological deviation between a node and its neighbors. We propose N2NSC (Node-to-Neighborhood Semantic Consistency), a framework that captures the correspondence between graph topology and textual semantics through two complementary fusion paths. The two pathways work synergistically, enabling the LLM to fully leverage both textual and structural neighborhood information for anomaly detection. Extensive experiments across eight datasets demonstrate that N2NSC consistently outperforms current state-of-the-art methods.", "date": "2026-06-29", "categories": []}
{"id": "2606.30196", "title": "Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector", "abstract": "This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstrate that certain embedding dimensions are sensitive to perturbations and can serve as indicators of decoding anomalies. By leveraging the consistency between successive encoding and decoding, we successfully build an accurate detector. Additionally, we explore modifying specific dimensions of interest to attempt to correct them. This work underscores the importance of understanding and analyzing the embeddings themselves to enhance the reliability of multimodal representations.", "date": "2026-06-29", "categories": []}
{"id": "2606.31875", "title": "SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Driving", "abstract": "Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent relationships rather than movement statistics alone. A child running away from a guardian, a person being carried by another, or a pursuer chasing a pedestrian across a sidewalk are all anomalous in social context, yet none produces an obvious motion signal that current anomaly detectors are equipped to flag. We introduce SENSE-VAD, the first synthetic video anomaly detection benchmark for autonomous driving explicitly designed around socially complex anomalies. Using the CARLA simulator and Unreal Engine (UE), we generate distinct anomaly scenarios across multiple categories: individual behaviors, group behaviors, person--object interactions, cyclist interactions, vehicle & agent, each annotated with per-frame binary labels. A key design principle is the separation of social anomaly from motion-based or appearance-based anomaly: many scenarios involve motion of objects that appears unremarkable in isolation but is anomalous in relational context. We additionally provide real-world normal and anomalous videos as a sim-to-real transfer probe. We evaluate state-of-the-art video anomaly detection baselines and demonstrate that socially complex anomalies constitute a distinct and currently unsolved challenge. Our dataset, annotations, and generation code are publicly available.", "date": "2026-06-30", "categories": []}