Multimodal Deepfake Detection

Deepfakes generated from audio and video are becoming increasingly more realistic and accessible, posing risk to trust, security, and information integrity. Current detectors often struggle to generalize across manipulation methods, dataset and real-work conditions. Research on multimodal Deepfake detection, leverage complementary cues to improve robustness, generalization and interpretability across diverse forgery scenarios