Learning domain-invariant representation for generalizing face forgery detection

With the improvement of face forgery techniques, people can easily generate various forgery face images, which brings serious challenges to public confidence. Numerous face forgery detection methods have been proposed, few of them can gain satisfying performance when training and testing forgery face images are generated by different forgery methods. To address this problem, we propose a domain-invariant representation learning (DIRL) method, which include feature distribution discrepancy minimization (FDDM) and optimal classier distance minimization (OCDM). FDDM adopts the idea of joint disentanglement and generative adversarial training to separate the irrelevant information and constrains the generator to reduce the discrepancy in feature distribution between source and target domains to obtain domain-shared features. OCDM uses the meta-learning method obtain a domain-invariant representation by minimizing the optimal classier distance between the domain-shared features of source and target domains. With FDDM and OCDM, we can learn an effective domain-invariant representation space in which there exists an ideal classifier equivalent to the optimal source domain classifier and the optimal target domain classifier, and the minimum classifier error can be achieved in both the source and target domains. Ablation experiments on the FaceForensics++ dataset show that compared to the baseline, our method can bring a decrease of 3.56% to 8.56% on Half Total Error Rate (HTER) when the test and training sets are generated by different face forgery methods. And the performance comparison experiments with the state-of-the-art methods also show that our method outperforms other methods.