  Recent advances in deep generative models for photo-realistic images have led
to high quality visual results. Such models learn to generate data from a given
training distribution such that generated images can not be easily
distinguished from real images by the human eye. Yet, recent work on the
detection of such fake images pointed out that they are actually easily
distinguishable by artifacts in their frequency spectra. In this paper, we
propose to generate images according to the frequency distribution of the real
data by employing a spectral discriminator. The proposed discriminator is
lightweight, modular and works stably with different commonly used GAN losses.
We show that the resulting models can better generate images with realistic
frequency spectra, which are thus harder to detect by this cue.
