Datensatz

Zusammenfassung

Research in computer graphics has been in pursuit of realistic image
generation for a long time. Recent advances in machine learning with deep
generative models have shown increasing success of closing the realism gap by
using data-driven and learned components. There is an increasing concern that
real and fake images will become more and more difficult to tell apart. We take
a first step towards this larger research challenge by asking the question if
and to what extend a generated fake image can be attribute to a particular
Generative Adversarial Networks (GANs) of a certain architecture and trained
with particular data and random seed. Our analysis shows single samples from
GANs carry highly characteristic fingerprints which make attribution of images
to GANs possible. Surprisingly, this is even possible for GANs with same
architecture and same training that only differ by the training seed.