To detect GAN generated images, conventional supervised machine learning algorithms require collecting a large number of real images as well as fake images generated by 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 upsampling component included in the common GAN pipelines. 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-theart performances on detecting fake images generated by popular GAN models such as CycleGAN.
Finally, in REF , it is recognized that GAN artifacts are caused by the up-sampling components included in the common GAN pipeline, which is identified using a classifier working on spectrum data rather than image pixels.
Detecting and Simulating Artifacts in GAN Fake Images
2019 IEEE International Workshop on Information Forensics and Security (WIFS)
2019 IEEE International Workshop on Information Forensics and Security (WIFS)
Computer Science Engineering