On the Generalization of Face Forgery Detection with Domain Adversarial Learning

With the rapid development of generative adversarial networks (GAN), breakthrough progress has been made in fake face generation. In order to reduce the harmful effects of fake face generation technology to society, fake face identification has become a very important topic, which has attracted numerous research efforts. Although impressive progress has been made in fake face identification, there are still many problems to be solved. Among them, how to improve the generalization ability of the fake face detection model is a crucial issue, and it is also the key to deploy fake face detection techniques in real-world scenarios. This paper studies the fake face identification problem, aiming to improve the generalization ability of the fake face identification model. Motivated by the idea of unsupervised domain adaptation, this paper introduces the domain adversarial branch to weaken the extraction of non-robust features of specific generative models by the feature extractor, so that the model can extract features with stronger robustness and higher generalization ability, improving the identification performance on the fake face images generated by unknown GANs. Experimental results show that the method proposed in this paper can effectively improve the generalization ability of the identification model, and significantly improve the performance of the fake face identification model on the fake images generated by the unknown generation model.