  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 unseen generated images outside of
the training settings. Such limitations occur due to data dependency arising
from the model's overfitting issue to the training data generated by specific
GANs. To overcome this issue, we adopt a self-supervised scheme to propose a
novel framework. Our proposed method is composed of the artificial fingerprint
generator reconstructing the high-quality artificial fingerprints of GAN images
for detailed analysis, and the GAN detector distinguishing GAN images by
learning the reconstructed artificial fingerprints. To improve the
generalization of the artificial fingerprint 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.
