Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes

• URL: http://arxiv.org/abs/2401.15668v1
• Date: Sun, 28 Jan 2024 14:22:11 GMT
• Title: Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes
• Authors: Weifeng Liu, Tianyi She, Jiawei Liu, Run Wang, Dongyu Yao, Ziyou Liang
• Abstract summary: Lip-forgery videos present a formidable challenge to existing DeepFake detection methods. We propose a novel approach dedicated to lip-forgery identification that exploits the inconsistency between lip movements and audio signals. Our approach gives an average accuracy of more than 95.3% in spotting lip-syncing videos.
• Score: 11.119780680692129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, whereas these methods also pose potential and severe security threats to humanity. DeepFake can be bifurcated into entertainment applications like face swapping and illicit uses such as lip-syncing fraud. However, lip-forgery videos, which neither change identity nor have discernible visual artifacts, present a formidable challenge to existing DeepFake detection methods. Our preliminary experiments have shown that the effectiveness of the existing methods often drastically decreases or even fails when tackling lip-syncing videos. In this paper, for the first time, we propose a novel approach dedicated to lip-forgery identification that exploits the inconsistency between lip movements and audio signals. We also mimic human natural cognition by capturing subtle biological links between lips and head regions to boost accuracy. To better illustrate the effectiveness and advances of our proposed method, we curate a high-quality LipSync dataset by employing the SOTA lip generator. We hope this high-quality and diverse dataset could be well served the further research on this challenging and interesting field. Experimental results show that our approach gives an average accuracy of more than 95.3% in spotting lip-syncing videos, significantly outperforming the baselines. Extensive experiments demonstrate the capability to tackle deepfakes and the robustness in surviving diverse input transformations. Our method achieves an accuracy of up to 90.2% in real-world scenarios (e.g., WeChat video call) and shows its powerful capabilities in real scenario deployment. To facilitate the progress of this research community, we release all resources at https://github.com/AaronComo/LipFD.

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