  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.
