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GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning

2023-11-13 04:48:33
Qinlin He, Chunlei Peng, Dechuang Liu, Nannan Wang, Xinbo Gao

Abstract

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.

Abstract (translated)

深度伪造检测在个人隐私和公共安全方面具有关键作用。随着深度伪造技术的迭代进步,高品质伪造视频和图像变得越来越欺骗性。之前的研究表明,学者们试图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通常将生物特征与一般特征分离并冻结生物特征提取器。这些方法导致价值的一般特征被排除,可能導致性能下降,从而无法充分利用生物信息在协助深度伪造检测中的潜在能量。此外,在最近几年,关于深度伪造检测领域中眼神真实性的审查不足。在本文中,我们介绍了GazeForensics,一种创新的深度伪造检测方法,它利用从3D gaze估计模型获得的眼神表示来规范我们深度伪造检测模型中的相应表示,同时将一般特征集成到我们的模型中,以进一步增强模型的性能。实验结果表明,我们提出的GazeForensics超越了现有技术的水平。

URL

https://arxiv.org/abs/2311.07075

PDF

https://arxiv.org/pdf/2311.07075.pdf


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