What can Discriminator do? Towards Box-free Ownership Verification of
  Generative Adversarial Network
Ziheng Huang Boheng Li Yan Cai Run Wang Shangwei Guo Liming Fang Jing Chen Lina Wang
In recent decades, Generative Adversarial Network (GAN) and its variants have
achieved unprecedented success in image synthesis. However, well-trained GANs
are under the threat of illegal steal or leakage. The prior studies on remote
ownership verification assume a black-box setting where the defender can query
the suspicious model with specific inputs, which we identify is not enough for
generation tasks. To this end, in this paper, we propose a novel IP protection
scheme for GANs where ownership verification can be done by checking outputs
only, without choosing the inputs (i.e., box-free setting). Specifically, we
make use of the unexploited potential of the discriminator to learn a
hypersphere that captures the unique distribution learned by the paired
generator. Extensive evaluations on two popular GAN tasks and more than 10 GAN
architectures demonstrate our proposed scheme to effectively verify the
ownership. Our proposed scheme shown to be immune to popular input-based
removal attacks and robust against other existing attacks. The source code and
models are available at
https://github.com/AbstractTeen/gan_ownership_verification