Audio-Visual Contrastive Pre-train for Face Forgery Detection

The highly realistic avatar in the metaverse may lead to severe leakage of facial privacy. Malicious users can more easily obtain the 3D structure of faces, thus using Deepfake technology to create counterfeit videos with higher realism. To automatically discern facial videos forged with the advancing generation techniques, deepfake detectors need to achieve stronger generalization abilities. Inspired by transfer learning, neural networks pre-trained on other large-scale face-related tasks would provide fundamental features for deepfake detection. We propose a video-level deepfake detection method based on a temporal transformer with a self-supervised audio-visual contrastive learning approach for pre-training the deepfake detector. The proposed method learns motion representations in the mouth region by encouraging the paired video and audio representations to be close while unpaired ones to be diverse. The deepfake detector adopts the pre-trained weights and partially fine-tunes on deepfake datasets. Extensive experiments show that our self-supervised pre-training method can effectively improve the accuracy and robustness of our deepfake detection model without extra human efforts. Compared with existing deepfake detection methods, our proposed method achieves better generalization ability in cross-dataset evaluations.