Learning Forgery Region-aware and ID-independent Features for Face Manipulation Detection

Over the past several years, to solve the problem of malicious abuse of facial manipulation technology, face manipulation detection has obtained considerable attention. Although existing works achieved impressive performance on a hold-out test set, their methods suffered a significant performance drop on data from a different distribution than the training set used. In this paper, we conduct an in-depth analysis on existing typical models about poor generalization capability and propose a novel method for face manipulation detection, which can alleviate overfitting and improve the generalization ability by learning forgery region aware and ID-independent features. Specifically, a forgery region guided self-attention module (FR) is introduced to make the model focus on the forgery region and a landmark guided dropout module (LM) is designed to randomly remove features of structured informative regions for destroying identity features. These two regularization modules are then added to the basic classification network, e.g. Xception. With the help of the joint learning framework, both forgery region-aware and ID-independent features are learned for face manipulation detection with better generalization ability. Extensive experiments demonstrate that the proposed method can not only outperform competing state-of-the-art methods on the FaceForensics++ dataset but also achieve superior generalization performance on the Celeb-DF dataset.