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arXiv · 2609.26274

AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features

S. Hong·X. Q. Wang·C. Zhang·J. C. Wang·P. X. Duan·Y. W. Wang
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Abstract

The rapid evolution of generative 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 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 demonstrated that our model achieves an accuracy of 94.22%, a precision of 91.67 %,and a recall of 96.25 %, effectively verifying the advantages of the multi-modal fusion strategy.

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S. Hong, X. Q. Wang, C. Zhang, J. C. Wang, P. X. Duan, Y. W. Wang. 2026-08-17. AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features. https://arxiv.org/abs/2609.26274

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