Towards generalized face forgery detection with domain-robust representation learning

Face forgery detection is crucial for the security of digital identities. However, existing methods often struggle to generalize effectively to unseen domains due to the domain shift between training and testing data. We propose a Domain-robust Representation Learning (DRRL) method for generalized face forgery detection. Specifically, we observe that domain shifts in face forgery detection tasks are often caused by forgery differences and content differences between domain data, while the limitations of training data lead the model to overfit to these feature expressions in the seen domain. Therefore, DRRL enhances the model's generalization to unseen domains by first adding representative data representations to mitigate overfitting to seen data and then removing the features of expressed domain information to learn a robust, discriminative representation of domain variation. Data augmentation is achieved by stylizing sample representations and exploring representative new styles to generate rich data variants, with the Content-style Augmentation (CSA) module and Forgery-style Augmentation (FSA) module implemented for content and forgery expression, respectively. Based on this, the Content Decorrelation (CTD) module and Sensitive Channels Drop (SCD) module are used to remove content features irrelevant to forgery and domain-sensitive forgery features, encouraging the model to focus on clean and robust forgery features, thereby achieving the goal of learning domain-robust representations. Extensive experiments on five large-scale datasets demonstrate that our method exhibits advanced and stable generalization performance in practical scenarios.