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.
@article{arxiv.1907.06515,
title = {Detecting and Simulating Artifacts in GAN Fake Images},
author = {Xu Zhang and Svebor Karaman and Shih-Fu Chang},
journal= {arXiv preprint arXiv:1907.06515},
year = {2019}
}
Comments
This is an extended version of our original AutoGAN paper which will be appeared in WIFS 2019