  With advances in Generative Adversarial Networks (GANs) leading to
dramatically-improved synthetic images and video, there is an increased need
for algorithms which extend traditional forensics to this new category of
imagery. While GANs have been shown to be helpful in a number of computer
vision applications, there are other problematic uses such as `deep fakes'
which necessitate such forensics. Source camera attribution algorithms using
various cues have addressed this need for imagery captured by a camera, but
there are fewer options for synthetic imagery. We address the problem of
attributing a synthetic image to a specific generator in a white box setting,
by inverting the process of generation. This enables us to simultaneously
determine whether the generator produced the image and recover an input which
produces a close match to the synthetic image.
