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arXiv · 2605.01638

Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection

Tianxiao Li·Zhenglin Huang·Haiquan Wen·Yiwei He·Xinze Li·Bingyu Zhu·Wuhui Duan·Congang Chen·Zeyu Fu·Yi Dong·Baoyuan Wu·Jason Li·Guangliang Cheng
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Abstract

Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are constrained by their single-modality scope, simplified manipulations, or unrealistic distributions, which limit their ability to assess real-world robustness. To address these limitations, we present Omni-Fake, a unified omni-dataset for comprehensive multimodal deepfake detection in social-media settings. It comprises Omni-Fake-Set, a large-scale, high-quality dataset with 1M+ samples, and Omni-Fake-OOD, an out-of-distribution benchmark with 200k+ samples intentionally excluded from training to evaluate generalization. Omni-Fake spans four modalities (image, audio, video, and audio-video talking head) and supports a joint detection-localization-explanation protocol. On top of Omni-Fake, we further propose Omni-Fake-R1, a reinforcement-learning-driven multimodal detector that adaptively integrates visual and auditory cues and outputs structured decisions, localization, and natural-language explanations. Extensive experiments show significant gains in detection accuracy, cross-modal generalization, and explainability over state-of-the-art baselines. Project page: https://tianxiao1201.github.io/omni-fake-project-page/

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Tianxiao Li, Zhenglin Huang, Haiquan Wen, Yiwei He, Xinze Li, Bingyu Zhu, Wuhui Duan, Congang Chen, Zeyu Fu, Yi Dong, Baoyuan Wu, Jason Li, Guangliang Cheng. 2026-05-02. Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection. https://arxiv.org/abs/2605.01638

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