Attacks and Defenses for Free-Riders in Multi-Discriminator GAN. (arXiv:2201.09967v1 [cs.CV])

Web: http://arxiv.org/abs/2201.09967

Generative Adversarial Networks (GANs) are increasingly adopted by the
industry to synthesize realistic images. Due to data not being centrally
available, Multi-Discriminator (MD)-GANs training framework employs multiple
discriminators that have direct access to the real data. Distributedly training
a joint GAN model entails the risk of free-riders, i.e., participants that aim
to benefit from the common model while only pretending to participate in the
training process. In this paper, we conduct the first characterization study of
the impact of free-riders …

arxiv attacks cv gan