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 spatial-frequency and optical-flow features. This approach enables the modeling of videos from complementary perceptual perspectives. The core of this process is a dedicated cross-attention mechanism, which governs the alignment of the two modalities and translates cross-modal inconsistencies into a potent diagnostic signal. This multi-modal strategy facilitates the detection of motion that is statistically inconsistent with the visual appearance of a scene. Comprehensive experimental results demonstrate that our model achieves an accuracy of 94.32%, a precision of 91.67%, and a recall of 96.25%, effectively verifying the advantages of the multi-modal fusion strategy.