  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.
