Less is More: Modality-Decoupling for General AIGC Audio-Video Detection

• URL: http://arxiv.org/abs/2607.25543v1
• Date: Tue, 28 Jul 2026 10:23:36 GMT
• Title: Less is More: Modality-Decoupling for General AIGC Audio-Video Detection
• Abstract summary: Generative AI has rapidly expanded audio-visual forgery beyond human-centric deepfakes into general scenes.<n>Existing AIGC detection methods assume audio-visual content correspondence, identifying forgeries by spotting cross-modal inconsistencies.<n>We propose DAV-Det, a decoupled audio-visual AIGC detection system that independently models forensic evidence from each modality.
• Score: 32.46260205509194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI has rapidly expanded audio-visual forgery beyond human-centric deepfakes into general scenes. Existing AIGC detection methods assume audio-visual content correspondence, identifying forgeries by spotting cross-modal inconsistencies. However, we empirically find that this assumption does not consistently hold in general scenarios. We argue that, for general audio-visual AIGC detection, decision-level fusion is a more robust alternative to feature-level fusion. Therefore, we propose DAV-Det, a decoupled audio-visual AIGC detection system that independently models forensic evidence from each modality. The visual detector leverages multi-granularity representations at global, patch, and segment levels to capture spatial forgery cues, while the audio detector exploits both temporal and spectral irregularities via a gated temporal-spectral dual-branch architecture to model acoustic artifacts. Our method ranks 1st in the General AIGC Audio-Video Detection Challenge of the IJCAI-ECAI 2026 DDL 2.0 Workshop, with a final score of 0.8460. Code is available at https://github.com/tuffy-studio/DAV-Det.

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