  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 on MD-GAN. Based on two production prototypes of
MD-GAN, we find that free-riders drastically reduce the ability of MD-GANs to
produce images that are indistinguishable from real data, i.e., they increase
the FID score -- the standard measure to assess the quality of generated
images. To mitigate the model degradation, we propose a defense strategy
against free-riders in MD-GAN, termed DFG. DFG distinguishes free-riders and
benign participants through periodic probing and clustering of discriminators'
responses based on a reference response of free-riders, which then allows the
generator to exclude the detected free-riders from the training. Furthermore,
we extend our defense, termed DFG+, to enable discriminators to filter out
free-riders at the variant of MD-GAN that allows peer exchanges of
discriminators networks. Extensive evaluation on various scenarios of
free-riders, MD-GAN architecture, and three datasets show that our defenses
effectively detect free-riders. With 1 to 5 free-riders, DFG and DFG+ averagely
decreases FID by 5.22% to 11.53% for CIFAR10 and 5.79% to 13.22% for CIFAR100
in comparison to an attack without defense. In a shell, the proposed DFG(+) can
effectively defend against free-riders without affecting benign clients at a
negligible computation overhead.
