DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by scholars to incorporate biometric features into the field of DeepFake detection. However, traditional biometric-based approaches tend to segregate biometric features from general ones and freeze the biometric feature extractor. These approaches resulted in the exclusion of valuable general features, potentially leading to a performance decline and, consequently, a failure to fully exploit the potential of biometric information in assisting DeepFake detection. Moreover, insufficient attention has been dedicated to scrutinizing gaze authenticity within the realm of DeepFake detection in recent years. In this paper, we introduce GazeForensics, an innovative DeepFake detection method that utilizes gaze representation obtained from a 3D gaze estimation model to regularize the corresponding representation within our DeepFake detection model, while concurrently integrating general features to further enhance the performance of our model. Experiment results reveal that our proposed GazeForensics outperforms the current state-of-the-art methods.

翻译：深度伪造检测对于个人隐私和公共安全至关重要。随着深度伪造技术的迭代发展，高质量伪造视频和图像正变得愈发具有欺骗性。先前研究中，众多学者尝试将生物特征引入深度伪造检测领域。然而，传统基于生物特征的方法倾向于将生物特征与通用特征分离，并冻结生物特征提取器。这些方法导致有价值的通用特征被排除，可能引发性能下降，进而未能充分挖掘生物信息在辅助深度伪造检测中的潜力。此外，近年来深度伪造检测领域对凝视真实性审查的关注不足。本文提出GazeForensics——一种创新性深度伪造检测方法，该方法通过从3D凝视估计模型中获取的凝视表征，对深度伪造检测模型中的对应表征进行正则化，同时整合通用特征以进一步提升模型性能。实验结果表明，我们提出的GazeForensics方法优于当前最先进的方法。