Multi-Definition Video Deepfake Detection via Semantics Reduction and Cross-Domain Training

The recent development of Deepfake videos directly threatens our information security and personal privacy. Although lots of previous works have made much progress on the Deepfake detection, we empirically find that the existing approaches do not perform well on the low definition (LD) and crossdefinition (high and low) videos. To address this problem, in this paper, we follow two motivations: (1) high-level semantics reduction and (2) cross-domain training. For (1), we propose the Facial Structure Destruction and Adversarial Jigsaw Loss to reduce our model to learn high-level semantics and focus on learning low-level discriminative information; For (2), we propose a domain generalization method based on adversarial learning. We conduct extensive experiments on the FaceForensics++ dataset. Results show the great effectiveness of our method and we also achieve very competitive performance against state-of-the-art methods.