CAM-VFD: Cross-Attention Multimodal Video Forgery Detection
It addresses the need for robust video forgery detection in multimedia forensics by leveraging cross-modal inconsistencies that single-modality detectors miss.
CAM-VFD introduces a cross-attention multimodal framework for video forgery detection that models cross-modal contradictions between appearance, motion, and depth, achieving 95.31% Top-1 accuracy on GenVidBench and 93.43% accuracy on GenVideo.
The rapid advancement of Deepfake technologies and video manipulation tools poses a critical challenge to multimedia forensics, judicial evidence integrity, and information authenticity. Current detectors rely on single-modality signals, treating appearance, geometry, and motion independently. However, advanced generators maintain within-modality consistency while producing cross-modal contradictions, which are forensically discriminative but invisible to any single-modal detector. We propose CAM-VFD, a Cross-Attention Multimodal Video Forgery Detection framework that models cross-modal contradiction as a directional forensic signal. The framework uses a cross-attention fusion mechanism in which CLIP-based appearance representations serve as queries against VideoMAE motion features and MiDaS depth features, enabling the identification of contradictions between visual, temporal, and geometric evidence. We examine this design through cross-modal attention discrepancy analysis, observing statistically separable real and fake distributions ($p<0.001$, Cohen's $d=0.68$). Experimental results on two generative video benchmarks indicate consistent performance, with 95.31\% Top-1 accuracy on GenVidBench and 93.43\% accuracy, 90.63\% F1-score, and 96.56\% AUROC on GenVideo. Moreover, CAM-VFD demonstrates stable performance under compression, noise, blur, and adversarial perturbations, suggesting that cross-modal reasoning may improve robustness in media forensics. The code is publicly available at \url{https://github.com/Hoda-Osama/CAM-VFD/tree/main}.