  Rapid advances in Generative Adversarial Networks (GANs) raise new challenges
for image attribution; detecting whether an image is synthetic and, if so,
determining which GAN architecture created it. Uniquely, we present a solution
to this task capable of 1) matching images invariant to their semantic content;
2) robust to benign transformations (changes in quality, resolution, shape,
etc.) commonly encountered as images are re-shared online. In order to
formalize our research, a challenging benchmark, Attribution88, is collected
for robust and practical image attribution. We then propose RepMix, our GAN
fingerprinting technique based on representation mixing and a novel loss. We
validate its capability of tracing the provenance of GAN-generated images
invariant to the semantic content of the image and also robust to
perturbations. We show our approach improves significantly from existing GAN
fingerprinting works on both semantic generalization and robustness. Data and
code are available at https://github.com/TuBui/image_attribution.
