Upload DeepSafe model weights backup (NPR, UniversalFakeDetect, CrossEfficientViT, meta-learner)

README.md

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+---
+license: mit
+tags:
+  - deepfake-detection
+  - image-forensics
+  - video-forensics
+  - ensemble
+  - pytorch
+---
+
+# DeepSafe Model Weights
+
+Backup model weights for the [DeepSafe](https://github.com/siddharthksah/DeepSafe) deepfake detection platform. These weights are mirrored here to ensure availability in case the original sources become unavailable.
+
+## Models Included
+
+### Image Detection Models
+
+| Model | File | Size | Original Source |
+|-------|------|------|----------------|
+| **NPR Deepfake Detection** | `npr_deepfakedetection/NPR.pth` | 5.6 MB | [chuangchuangtan/NPR-DeepfakeDetection](https://github.com/chuangchuangtan/NPR-DeepfakeDetection) |
+| **UniversalFakeDetect (FC)** | `universalfakedetect/fc_weights.pth` | 4 KB | [WisconsinAIVision/UniversalFakeDetect](https://github.com/WisconsinAIVision/UniversalFakeDetect) |
+| **CLIP ViT-L/14 Backbone** | `universalfakedetect/ViT-L-14.pt` | 890 MB | [OpenAI CLIP](https://github.com/openai/CLIP) |
+
+### Video Detection Models
+
+| Model | File | Size | Original Source |
+|-------|------|------|----------------|
+| **Cross-Efficient ViT** | `cross_efficient_vit/cross_efficient_vit.pth` | 388 MB | [davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection](https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection) |
+| **Efficient ViT** | `cross_efficient_vit/efficient_vit.pth` | 418 MB | Same as above |
+
+### Meta-Learner (Ensemble)
+
+| File | Size | Description |
+|------|------|-------------|
+| `meta_model_artifacts/deepsafe_meta_learner.joblib` | 569 KB | Trained stacking ensemble classifier |
+| `meta_model_artifacts/deepsafe_meta_scaler.joblib` | 767 B | Feature scaler |
+| `meta_model_artifacts/deepsafe_meta_imputer.joblib` | 975 B | Missing value imputer |
+| `meta_model_artifacts/deepsafe_meta_feature_columns.json` | 215 B | Feature column definitions |
+
+## Credits
+
+All model weights are the work of their respective original authors. DeepSafe mirrors them here strictly as a backup to prevent broken builds if upstream sources change. Full credit goes to:
+
+- **NPR Deepfake Detection**: Chuangchuang Tan et al. - [Paper](https://arxiv.org/abs/2310.14036) | [GitHub](https://github.com/chuangchuangtan/NPR-DeepfakeDetection)
+- **UniversalFakeDetect**: Utkarsh Ojha, Yuheng Li, Yong Jae Lee - [Paper](https://arxiv.org/abs/2302.10174) | [GitHub](https://github.com/WisconsinAIVision/UniversalFakeDetect)
+- **CLIP ViT-L/14**: Alec Radford et al. (OpenAI) - [Paper](https://arxiv.org/abs/2103.00020) | [GitHub](https://github.com/openai/CLIP)
+- **Cross-Efficient ViT**: Davide Coccomini et al. - [Paper](https://arxiv.org/abs/2107.02612) | [GitHub](https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection)
+
+## Usage
+
+These weights are used by DeepSafe's Docker-based microservices. See the [DeepSafe README](https://github.com/siddharthksah/DeepSafe) for setup instructions.
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download a specific weight file
+path = hf_hub_download(
+    repo_id="siddharthksah/DeepSafe-weights",
+    filename="npr_deepfakedetection/NPR.pth"
+)
+```
+
+## License
+
+MIT License (for the DeepSafe platform). Individual model weights retain their original licenses from their respective authors.

cross_efficient_vit/cross_efficient_vit.pth

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cross_efficient_vit/efficient_vit.pth

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meta_model_artifacts/deepsafe_meta_feature_columns.json

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+[
+  "npr_deepfakedetection_prob",
+  "spsl_deepfake_detection_prob",
+  "trufor_prob",
+  "ucf_deepfake_detection_prob",
+  "universalfakedetect_prob",
+  "wavelet_clip_detection_prob",
+  "yermandy_clip_detection_prob"
+]

meta_model_artifacts/deepsafe_meta_imputer.joblib

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meta_model_artifacts/deepsafe_meta_learner.joblib

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npr_deepfakedetection/NPR.pth

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universalfakedetect/ViT-L-14.pt

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universalfakedetect/fc_weights.pth

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