Deepfakes Video classification via CNN, LSTM, C3D and triplets [IWBF'20]
A state-of-the-art, open-source deepfake detection system built with PyTorch and EfficientNet-B0, featuring a user-friendly web interface for real-time image and video analysis.
Frequency-aware deepfake detection under lossy H.264 compression using XceptionNet, DCT features, learnable frequency masking, and cross-attention fusion.
An explainable deepfake detection framework leveraging Vision Transformers (ViT), temporal pooling, and Grad-CAM for interpretable image and video forgery detection across benchmark datasets.
Replication code for the paper 'Towards DeepFake video forensics based on facial textural disparities in multi-color channels'
Full-stack deepfake video detection system — EfficientNetV2-B3 + Temporal Attention BiLSTM | 99.54% AUC on Celeb-DF | React frontend + FastAPI backend
Detect deepfake images and videos with EfficientNet-B0, face extraction, Grad-CAM heatmaps, and a FastAPI REST API for clear analysis
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