  Over the past years, deep generative models have achieved a new level of
performance. Generated data has become difficult, if not impossible, to be
distinguished from real data. While there are plenty of use cases that benefit
from this technology, there are also strong concerns on how this new technology
can be misused to generate deep fakes and enable misinformation at scale.
Unfortunately, current deep fake detection methods are not sustainable, as the
gap between real and fake continues to close. In contrast, our work enables a
responsible disclosure of such state-of-the-art generative models, that allows
model inventors to fingerprint their models, so that the generated samples
containing a fingerprint can be accurately detected and attributed to a source.
Our technique achieves this by an efficient and scalable ad-hoc generation of a
large population of models with distinct fingerprints. Our recommended
operation point uses a 128-bit fingerprint which in principle results in more
than $10^{38}$ identifiable models. Experiments show that our method fulfills
key properties of a fingerprinting mechanism and achieves effectiveness in deep
fake detection and attribution. Code and models are available at
https://github.com/ningyu1991/ScalableGANFingerprints .
