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arXiv · 2602.23393

Leveraging large multimodal models for audio-video deepfake detection: a pilot study

Songjun Cao·Yuqi Li·Yunpeng Luo·Jianjun Yin·Long Ma
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Abstract

Audio-visual deepfake detection (AVD) is increasingly important as modern generators can fabricate convincing speech and video. Most current multimodal detectors are small, task-specific models: they work well on curated tests but scale poorly and generalize weakly across domains. We introduce AV-LMMDetect, a supervised fine-tuned (SFT) large multimodal model that casts AVD as a prompted yes/no classification - "Is this video real or fake?". Built on Qwen 2.5 Omni, it jointly analyzes audio and visual streams for deepfake detection and is trained in two stages: lightweight LoRA alignment followed by audio-visual encoder full fine-tuning. On FakeAVCeleb and Mavos-DD, AV-LMMDetect matches or surpasses prior methods and sets a new state of the art on Mavos-DD datasets.

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Songjun Cao, Yuqi Li, Yunpeng Luo, Jianjun Yin, Long Ma. 2026-02-25. Leveraging large multimodal models for audio-video deepfake detection: a pilot study. https://arxiv.org/abs/2602.23393

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