  Deep neural networks can generate images that are astonishingly realistic, so
much so that it is often hard for humans to distinguish them from actual
photos. These achievements have been largely made possible by Generative
Adversarial Networks (GANs). While deep fake images have been thoroughly
investigated in the image domain - a classical approach from the area of image
forensics - an analysis in the frequency domain has been missing so far. In
this paper, we address this shortcoming and our results reveal that in
frequency space, GAN-generated images exhibit severe artifacts that can be
easily identified. We perform a comprehensive analysis, showing that these
artifacts are consistent across different neural network architectures, data
sets, and resolutions. In a further investigation, we demonstrate that these
artifacts are caused by upsampling operations found in all current GAN
architectures, indicating a structural and fundamental problem in the way
images are generated via GANs. Based on this analysis, we demonstrate how the
frequency representation can be used to identify deep fake images in an
automated way, surpassing state-of-the-art methods.
