Exposing unseen GAN-generated image using unsupervised domain adaptation

Abstract

Benefiting from the tremendous success of Generative Adversarial Networks (GANs), we are entering a world where it will be increasingly difficult for people to distinguish whether an image is synthetic, which puts new demands on digital image forensics. To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are generated by an unseen GAN model. In this work, an unsupervised domain adaptation strategy is introduced to achieve the detection of GAN-generated images in an unknown domain. A Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus obtaining a better generalization. Experimental results demonstrate that the proposed scheme obtains high detection accuracy on the generalization problem and is robust to common post-processing in reality, which shows that unsupervised methods can enhance the generalization ability of detection models to some extent.