Detecting and Simulating Artifacts in GAN Fake Images

Xu Zhang; Svebor Karaman; Shih-Fu Chang

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arXiv · 1907.06515

Detecting and Simulating Artifacts in GAN Fake Images

Xu Zhang·Svebor Karaman·Shih-Fu Chang

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Abstract

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.

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BibTeX RIS

Xu Zhang, Svebor Karaman, Shih-Fu Chang. 2019-10-15. Detecting and Simulating Artifacts in GAN Fake Images. https://arxiv.org/abs/1907.06515

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