  To detect GAN generated images, conventional supervised machine learning
algorithms require collection of a number of real and fake images from the
targeted GAN model. However, the specific model used by the attacker is often
unavailable. To address this, we propose a GAN simulator, AutoGAN, which can
simulate the artifacts produced by the common pipeline shared by several
popular GAN models. Additionally, we identify a unique artifact caused by the
up-sampling component included in the common GAN pipeline. We show
theoretically such artifacts are manifested as replications of spectra in the
frequency domain and thus propose a classifier model based on the spectrum
input, rather than the pixel input. By using the simulated images to train a
spectrum based classifier, even without seeing the fake images produced by the
targeted GAN model during training, our approach achieves state-of-the-art
performances on detecting fake images generated by popular GAN models such as
CycleGAN.
