README.md

metadata

title: Deepfake Detection API
emoji: 🔍
colorFrom: blue
colorTo: red
sdk: docker
app_port: 7860

Deepfake Detection API

Flask API for the deepfake detection frontend.

Health checks:

• /health
• /health/models

Detector configuration:

• IMAGE_DETECTOR_BACKEND=huggingface uses IMAGE_HF_MODEL_IDS.
• IMAGE_HF_MODEL_IDS=pretrained_model/huggingface/buildborderless__CommunityForensics-DeepfakeDet-ViT is the default local broad AI-image detector after running python download_hf_models.py.
• VIDEO_DETECTOR_BACKEND=huggingface uses VIDEO_HF_MODEL_ID.
• VIDEO_HF_MODEL_ID=pretrained_model/huggingface/Vansh180__VideoMae-ffc23-deepfake-detector is the default local temporal video detector after running python download_hf_models.py.
• DETECTOR_DEVICE=cpu can be changed to cuda on GPU hosts.
• ALLOW_LOCAL_MODEL_FALLBACK=true falls back to the bundled .pth checkpoints if Hugging Face models are unavailable.
• IMAGE_FAKE_THRESHOLD, VIDEO_FAKE_THRESHOLD, IMAGE_UNCERTAIN_MARGIN, and VIDEO_UNCERTAIN_MARGIN tune classification strictness.

Download the stronger Hugging Face model files into pretrained_model/huggingface/:

python download_hf_models.py

Required files in this Space:

• app.py
• detection.py
• video_detection.py
• model_loader.py
• detector_config.py
• hf_detectors.py
• cross_efficient_vit_model.py
• npr_model.py
• requirements.txt
• Dockerfile
• NPR.pth or pretrained_model/NPR.pth
• cross_efficient_vit.pth or pretrained_model/cross_efficient_vit.pth
• efficientnet.pth or pretrained_model/efficientnet.pth