UNMASKING DEEPFAKES: A DUAL-STAGE TRANSFORMER MODEL FOR DETECTING AI-GENERATED FACIAL VIDEOS

As deepfakes pose increasing threats to media authenticity and public trust, accurate detection mechanisms are vital. This paper proposes a novel dual-stage transformer model for detecting deepfake facial videos. The model combines a spatial attention transformer to capture facial features across individual frames and a contextual consistency transformer to track identity coherence across time. Results from benchmark datasets (FaceForensics++, DFDC) show this architecture significantly outperforms conventional CNNs and RNNs in both accuracy and generalization. This research offers a pathway toward more robust and interpretable video forensics solutions.