rachitss/Visual-Deepfake-Detection-Swin-LSTM-attention
A complete deepfake detection workflow built around a hybrid architecture that merges the Swin-Tiny transformer for spatial feature extraction with an LSTM + attention head for temporal sequences. The project trains on the full DeepFake Detection Challenge (DFDC) dataset, then serves the resulting model through a FastAPI+Streamlit web application.
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Jan 08, 2026
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