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

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 this http URL, we introduce a dual-branch architecture that simultaneously extracts spatial-frequency and optical-flow this http URL approach enables the modeling of videos from complementary perceptual this http URL 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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.26274 [cs.CV]

(or arXiv:2609.26274v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.26274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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