Making GAN-Generated Images Difficult To Spot: A New Attack Against Synthetic Image Detectors

• URL: http://arxiv.org/abs/2104.12069v1
• Date: Sun, 25 Apr 2021 05:56:57 GMT
• Title: Making GAN-Generated Images Difficult To Spot: A New Attack Against Synthetic Image Detectors
• Authors: Xinwei Zhao, Matthew C. Stamm
• Abstract summary: We propose a new anti-forensic attack capable of fooling GAN-generated image detectors. Our attack uses an adversarially trained generator to synthesize traces that these detectors associate with real images. We show that our attack can fool eight state-of-the-art detection CNNs with synthetic images created using seven different GANs.
• Score: 24.809185168969066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visually realistic GAN-generated images have recently emerged as an important misinformation threat. Research has shown that these synthetic images contain forensic traces that are readily identifiable by forensic detectors. Unfortunately, these detectors are built upon neural networks, which are vulnerable to recently developed adversarial attacks. In this paper, we propose a new anti-forensic attack capable of fooling GAN-generated image detectors. Our attack uses an adversarially trained generator to synthesize traces that these detectors associate with real images. Furthermore, we propose a technique to train our attack so that it can achieve transferability, i.e. it can fool unknown CNNs that it was not explicitly trained against. We demonstrate the performance of our attack through an extensive set of experiments, where we show that our attack can fool eight state-of-the-art detection CNNs with synthetic images created using seven different GANs.

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