Improving The Generalization Ability Of Deepfake Detection Via Disentangled Representation Learning

Deepfake refers to a deep learning based technology which can synthesize visually realistic face images/videos. The misuse of this technology poses a great threat to the society. Although numerous approaches have been proposed to detect Deepfake forgeries, their generalization ability on unseen datasets is limited. In this paper, we propose a new approach that detects human face forgeries by automatically locating the forgery-related region to make the final decision. The proposed network contains two modules, including: the disentanglement module to extract forgery relevant information and the classification module to detect the manipulation artifacts from various regions at different scales. The experiment results on three widely used Deepfake datasets show that the proposed approach can achieve high detection accuracies and outperforms several state-of-the-arts methods especially when evaluated on the unseen datasets.