🤖 AI Summary
This work addresses the limitations of existing audiovisual AIGC detection methods, which rely on the assumption of audio-visual consistency and often degrade in generalizable scenarios. To overcome this, we propose DAV-Det, the first modality-disentangled detection framework tailored for general-purpose AIGC content. Departing from conventional feature-level fusion, DAV-Det adopts decision-level fusion to independently model forgery traces in audio and video modalities. The visual branch leverages a three-level granularity representation—encompassing global, patch, and clip-level features—while the audio branch employs a gated dual-branch architecture operating in both time and frequency domains to capture anomalies. Evaluated on the IJCAI-ECAI 2026 DDL 2.0 Workshop challenge for general AIGC audiovisual detection, our method achieves state-of-the-art performance with a score of 0.8460, demonstrating superior robustness and generalization.
📝 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.