  In this paper, we propose a novel CycleGAN without checkerboard artifacts for
counter-forensics of fake-image detection. Recent rapid advances in image
manipulation tools and deep image synthesis techniques, such as Generative
Adversarial Networks (GANs) have easily generated fake images, so detecting
manipulated images has become an urgent issue. Most state-of-the-art forgery
detection methods assume that images include checkerboard artifacts which are
generated by using DNNs. Accordingly, we propose a novel CycleGAN without any
checkerboard artifacts for counter-forensics of fake-mage detection methods for
the first time, as an example of GANs without checkerboard artifacts.
