  Photorealistic image generation has reached a new level of quality due to the
breakthroughs of generative adversarial networks (GANs). Yet, the dark side of
such deepfakes, the malicious use of generated media, raises concerns about
visual misinformation. While existing research work on deepfake detection
demonstrates high accuracy, it is subject to advances in generation techniques
and adversarial iterations on detection countermeasure techniques. Thus, we
seek a proactive and sustainable solution on deepfake detection, that is
agnostic to the evolution of generative models, by introducing artificial
fingerprints into the models.
  Our approach is simple and effective. We first embed artificial fingerprints
into training data, then validate a surprising discovery on the transferability
of such fingerprints from training data to generative models, which in turn
appears in the generated deepfakes. Experiments show that our fingerprinting
solution (1) holds for a variety of cutting-edge generative models, (2) leads
to a negligible side effect on generation quality, (3) stays robust against
image-level and model-level perturbations, (4) stays hard to be detected by
adversaries, and (5) converts deepfake detection and attribution into trivial
tasks and outperforms the recent state-of-the-art baselines. Our solution
closes the responsibility loop between publishing pre-trained generative model
inventions and their possible misuses, which makes it independent of the
current arms race. Code and models are available at
https://github.com/ningyu1991/ArtificialGANFingerprints .
