  Convolutional neural network based face forgery detection methods have
achieved remarkable results during training, but struggled to maintain
comparable performance during testing. We observe that the detector is prone to
focus more on content information than artifact traces, suggesting that the
detector is sensitive to the intrinsic bias of the dataset, which leads to
severe overfitting. Motivated by this key observation, we design an easily
embeddable disentanglement framework for content information removal, and
further propose a Content Consistency Constraint (C2C) and a Global
Representation Contrastive Constraint (GRCC) to enhance the independence of
disentangled features. Furthermore, we cleverly construct two unbalanced
datasets to investigate the impact of the content bias. Extensive
visualizations and experiments demonstrate that our framework can not only
ignore the interference of content information, but also guide the detector to
mine suspicious artifact traces and achieve competitive performance.
