Mining Generalized Multi-timescale Inconsistency for Detecting Deepfake Videos

Recent advancements in face forgery techniques have continuously evolved, leading to emergent security concerns in society. Existing detection methods have poor generalization ability due to the insufficient extraction of dynamic inconsistency cues on the one hand, and their inability to deal well with the gaps between forgery techniques on the other hand. To develop a new generalized framework that emphasizes extracting generalizable multi-timescale inconsistency cues. Firstly, we capture subtle dynamic inconsistency via magnifying the multipath dynamic inconsistency from the local-consecutive short-term temporal view. Secondly, the inter-group graph learning is conducted to establish the sufficient-interactive long-term temporal view for capturing dynamic inconsistency comprehensively. Finally, we design the domain alignment module to directly reduce the distribution gaps via simultaneously disarranging inter- and intra-domain feature distributions for obtaining a more generalized framework. Extensive experiments on six large-scale datasets and the designed generalization evaluation protocols show that our framework outperforms state-of-the-art deepfake video detection methods.