  Image forensics is an increasingly relevant problem, as it can potentially
address online disinformation campaigns and mitigate problematic aspects of
social media. Of particular interest, given its recent successes, is the
detection of imagery produced by Generative Adversarial Networks (GANs), e.g.
`deepfakes'. Leveraging large training sets and extensive computing resources,
recent work has shown that GANs can be trained to generate synthetic imagery
which is (in some ways) indistinguishable from real imagery. We analyze the
structure of the generating network of a popular GAN implementation, and show
that the network's treatment of color is markedly different from a real camera
in two ways. We further show that these two cues can be used to distinguish
GAN-generated imagery from camera imagery, demonstrating effective
discrimination between GAN imagery and real camera images used to train the
GAN.
