Hybrid Domain Meta-Learning Network for Face Forgery Detection and Localization in Deepfakes

Existing face forgery detection methods often consider the manipulation detection problem as a binary classification problem, which are easily prone to overfitting. The detection performance degrades for datasets not appearing in the training stage. To solve this problem, we utilize the idea of domain generalization and design a hybrid domain meta-learning network (HDMNet) for face forgery detection and manipulation localization. The network allows the reveal of more essential face-swapping traces through multi-domain off-sets. Specifically, HDMNet consists of three modules, namely the feature extraction module (FEM), the face mask representation module (FMRM) and the meta-learning module (MLM). The FEM is composed of a comprehensive feature representation of image color features and high-frequency noise features. The FMRM utilizes graph convolution to predict face masks, providing supervision of domain knowledge. The MLM extracts domain-invariant features through a hybrid domain meta-learning strategy. Extensive experiments on several benchmark databases validate the effectiveness and good generalization ability of our method compared with several state-of-the-art methods.