| --- |
| license: apache-2.0 |
| library_name: numpy |
| pipeline_tag: image-classification |
| tags: |
| - image-forensics |
| - source-attribution |
| - open-set-classification |
| --- |
| |
| # RAIW source-pipeline classifier |
|
|
| This is a compact, abstaining pixel-forensics classifier for three labels: |
| `openai`, `google`, and `unknown`. It is intended for original-export-looking |
| images whose metadata may have been removed without changing the pixels. |
|
|
| This model is **not a SynthID detector**. It does not decode or verify a |
| watermark payload. Its labels describe complete generation and export |
| pipelines, so renderer, geometry, encoding, post-processing, and watermark |
| state may be confounded. `unknown` does not mean that SynthID is absent, and a |
| provider label does not prove that SynthID is present. |
|
|
| ## Model |
|
|
| - Input: a 124-dimensional residual/spectral forensic feature vector plus a |
| 768-dimensional phase-free spectral vector. |
| - Head: 892 -> 128 -> 64 -> 3 MLP with GELU activations. |
| - Output: `openai`, `google`, or abstain as `unknown` using independent |
| provider margins. |
| - Artifact: `source-pipeline-mlp.npz` |
| - SHA-256: `6430b0738869ff07b6845b7c6149c2a83bc3f3785878082886c241c6f7ca0c4a` |
|
|
| The artifact contains only learned parameters, normalization constants, |
| labels, and thresholds. It does not contain training images, paths, image |
| hashes, embeddings, or a training catalog. |
|
|
| ## Evaluation |
|
|
| The operating point was selected on an internal calibration split. The locked |
| internal test and the public `bigdatamark/synthid-research` dataset were not |
| used for architecture, checkpoint, or threshold selection. |
|
|
| | Evaluation | Class | Recall | Precision | |
| |---|---|---:|---:| |
| | Locked internal test (5,369 images) | OpenAI | 60.4% | 84.9% | |
| | Locked internal test (5,369 images) | Google | 57.6% | 87.9% | |
| | Locked internal test (5,369 images) | Unknown | 98.6% | 93.7% | |
| | Public external originals (900 images) | OpenAI | 68.7% | 100.0% | |
| | Public external originals (900 images) | Google | 81.3% | 98.8% | |
| | Public external originals (900 images) | Unknown/Flux | 99.0% | 66.4% | |
|
|
| On the public external set, a 95% resize reduced Google recall to 1.0%, and a |
| JPEG quality-75 round trip caused the model to abstain on every image. These |
| are expected abstentions, not evidence that a watermark was removed. |
|
|
| ## Intended use |
|
|
| Use this model as one probabilistic signal in a broader forensic report or to |
| suggest an appropriate processing path. Do not use it to claim watermark |
| presence, authorship, policy compliance, fraud, or provenance. Applications |
| should expose the abstention and the model's narrow source-pipeline claim. |
|
|
| Feature extraction and inference are versioned in the |
| `remove-ai-watermarks` Python project rather than in this model repository. |
|
|
