New Omni-Fake dataset benchmarks multimodal deepfake detection on social media

Researchers have introduced Omni-Fake, a new benchmark dataset designed to improve the detection of multimodal deepfakes on social media. The dataset includes over 1 million samples across image, audio, video, and audio-video talking head modalities, along with an out-of-distribution benchmark to test generalization. Omni-Fake also supports a protocol for joint detection, localization, and explanation of deepfakes, and introduces a reinforcement-learning-based detector called Omni-Fake-R1 that integrates cross-modal cues for more accurate and explainable results. AI

影响 Enhances the ability to detect sophisticated multimodal deepfakes, crucial for maintaining information integrity on social media platforms.

排序理由 The cluster describes a new academic paper introducing a benchmark dataset and a novel detection method for multimodal deepfakes. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• arXiv
• audio-video talking head
• image
• Omni-Fake
• reinforcement learning
• social media
• video
• audio
• deepfake detection

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