Self-supervised scheme for generalizing GAN image detection

Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news. To prevent such cases, vigorous research is conducted to distinguish the generated images from the real images, but challenges still remain to distinguish the generated images outside of the training settings. Such limitations occur due to data dependency arising from the model's overfitting issue to the specific Generative Adversarial Networks (GANs) and categories of the training data. To overcome this issue, we adopt a self-supervised scheme. Our method is composed of the artificial artifact generator reconstructing the high-quality artificial artifacts of GAN images, and the GAN detector distinguishing GAN images by learning the reconstructed artificial artifacts. To improve the generalization of the artificial artifact generator, we build multiple autoencoders with different numbers of upconvolution layers. With numerous ablation studies, the robust generalization of our method is validated by outperforming the generalization of the previous state-of-the-art algorithms, even without utilizing the GAN images of the training dataset.