Duplicate from nebula/CDDB

Co-authored-by: binnng <nebula@users.noreply.huggingface.co>

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CDDB.tar

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README.md

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+---
+pretty_name: CDDB
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
+tags:
+- deepfake
+- continual-learning
+- computer-vision
+- image-forensics
+- wacv
+size_categories:
+- unknown
+annotations_creators:
+- no-annotation
+language:
+- en
+license: unknown
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: CDDB.tar
+dataset_info:
+  features: []
+---
+
+# Dataset Card for CDDB
+
+## Dataset Description
+
+CDDB is a benchmark dataset introduced in the WACV 2023 paper *A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials*.
+It is designed for continual deepfake detection, where manipulated images from different deepfake generation sources arrive sequentially instead of being observed all at once.
+
+The benchmark is intended to evaluate both:
+
+- binary deepfake detection (real vs. fake)
+- continual and incremental learning under distribution shifts across deepfake sources
+
+Compared with conventional static deepfake datasets, CDDB focuses on a more realistic setting in which new manipulation methods appear over time and a detector must adapt without catastrophically forgetting previously seen sources.
+
+## Supported Tasks
+
+- Binary image classification: real vs. fake
+- Multi-source deepfake classification
+- Continual learning / class-incremental learning
+- Domain generalization and robustness evaluation for deepfake detection
+
+## Dataset Sources
+
+- Paper: [A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials](https://openaccess.thecvf.com/content/WACV2023/html/Li_A_Continual_Deepfake_Detection_Benchmark_Dataset_Methods_and_Essentials_WACV_2023_paper.html)
+- Code repository: [Coral79/CDDB](https://github.com/Coral79/CDDB)
+
+## Paper Information
+
+**Title:** A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
+
+**Authors:** Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Luc Van Gool
+
+**Venue:** IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
+
+**Year:** 2023
+
+## Dataset Structure
+
+This repository currently hosts the dataset archive:
+
+- `CDDB.tar`
+
+After extraction, the dataset is expected to contain benchmark splits and source-specific subsets used for continual deepfake detection experiments. According to the original paper and project repository, CDDB is built from a collection of real and manipulated images aggregated from multiple existing deepfake datasets and generation pipelines.
+
+The benchmark includes deepfakes derived from multiple sources, including generative and manipulation pipelines such as:
+
+- ProGAN
+- StyleGAN
+- BigGAN
+- CycleGAN
+- GauGAN
+- CRN
+- IMLE
+- SAN
+- FaceForensics++
+- WhichFaceReal
+- GLOW
+- StarGAN
+- WildDeepfake
+
+The original benchmark is organized around different task sequences, including easy, hard, and long continual streams.
+
+## Dataset Creation
+
+CDDB was proposed to study continual deepfake detection in a more practical setting where deepfake generators evolve over time.
+Instead of treating detection as a stationary benchmark, the dataset groups data into sequential tasks so that models can be evaluated on adaptation, retention, and generalization.
+
+The benchmark is assembled from previously released open-source deepfake datasets and generation sources, rather than being collected from a single acquisition pipeline.
+
+## Intended Uses
+
+CDDB is intended for research use in:
+
+- deepfake detection
+- continual learning
+- incremental learning
+- robustness analysis under source shift
+- benchmarking anti-forgetting strategies
+
+It is particularly suitable for evaluating methods that must maintain performance on previously seen deepfake sources while adapting to newly introduced manipulations.
+
+## Out-of-Scope Uses
+
+This dataset is not intended to:
+
+- certify production-ready deepfake detectors
+- serve as a complete benchmark for all real-world manipulations
+- support identity, biometric, or surveillance decisions
+- be used in safety-critical or high-stakes automated decision systems without additional validation
+
+## Considerations and Limitations
+
+- CDDB is assembled from multiple existing datasets and generation methods, so its licensing and redistribution conditions may depend on the underlying sources.
+- The benchmark reflects the manipulation methods and dataset availability at the time of the original publication.
+- Performance on CDDB does not guarantee robustness to newer generative models or real-world post-processing pipelines.
+- Models trained on this dataset may learn source-specific artifacts instead of general manipulation cues.
+
+## Licensing Information
+
+The license for this redistributed archive is currently marked as `unknown`.
+Users should verify the licensing and redistribution terms of the original CDDB release and all upstream component datasets before commercial use or redistribution.
+
+## Citation
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@InProceedings{Li_2023_WACV,
+  author    = {Li, Chuqiao and Huang, Zhiwu and Paudel, Danda Pani and Wang, Yabin and Shahbazi, Mohamad and Hong, Xiaopeng and Van Gool, Luc},
+  title     = {A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials},
+  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+  month     = {January},
+  year      = {2023},
+  pages     = {1339--1349}
+}
+```
+
+## Acknowledgements
+
+This dataset card is based on the original WACV 2023 paper and the official project repository. Credit for the benchmark, data construction, and experimental protocol belongs to the original authors.