Robust GANs against Dishonest Adversaries

Understand

Robustness of deep learning models is a property that has recently gained increasing attention.

• We explore a notion of robustness for generative adversarial models that is pertinent to their internal interactive structure, and show that, perhaps surprisingly, the GAN in its original form is not robust.
• Our notion of robustness relies on a perturbed discriminator, or noisy, adversarial interference with its feedback.
• We explore, theoretically and empirically, the effect of model and training properties on this robustness.