AIGC video detection based on the fusion of spatial-frequency-optical flow multimodal features

The rapid evolution of generative artificial intelligence (AI) (e.g., Sora, Hunyuan) makes it essential to develop effective detection strategies that can generalize across ever-evolving synthesis techniques. This study is motivated by the observation of a fundamental challenge in generative models: the inherent difficulty of maintaining cross-modal consistency between appearance and motion. To this end, we propose a multi-modal framework for AI generated content (AIGC) video forgery detection tasks, named cross-attention based video forgery detector (CrossAtt-VFD), based on joint multi-view analysis of content. Methodologically, we introduce a dual-branch architecture that simultaneously extracts…