  Improvements in Generative Adversarial Networks (GANs) have greatly reduced
the difficulty of producing new, photo-realistic images with unique semantic
meaning. With this rise in ability to generate fake images comes demand to
detect them. While numerous methods have been developed for this task, the
majority of them remain vulnerable to adversarial attacks. In this paper,
develop two novel adversarial attacks on co-occurrence based GAN detectors.
These are the first attacks to be presented against such a detector. We show
that our method can reduce accuracy from over 98% to less than 4%, with no
knowledge of the deep learning model or weights. Furthermore, accuracy can be
reduced to 0% with full knowledge of the deep learning model details.
