siddharthksah/deepsafe-weights

DeepSafe Model Weights

Backup model weights for the 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
UniversalFakeDetect (FC)	universalfakedetect/fc_weights.pth	4 KB	WisconsinAIVision/UniversalFakeDetect
CLIP ViT-L/14 Backbone	universalfakedetect/ViT-L-14.pt	890 MB	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
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 | GitHub
• UniversalFakeDetect: Utkarsh Ojha, Yuheng Li, Yong Jae Lee - Paper | GitHub
• CLIP ViT-L/14: Alec Radford et al. (OpenAI) - Paper | GitHub
• Cross-Efficient ViT: Davide Coccomini et al. - Paper | GitHub

Usage

These weights are used by DeepSafe's Docker-based microservices. See the DeepSafe README for setup instructions.

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.