  We study two important concepts in adversarial deep learning---adversarial
training and generative adversarial network (GAN). Adversarial training is the
technique used to improve the robustness of discriminator by combining
adversarial attacker and discriminator in the training phase. GAN is commonly
used for image generation by jointly optimizing discriminator and generator. We
show these two concepts are indeed closely related and can be used to
strengthen each other---adding a generator to the adversarial training
procedure can improve the robustness of discriminators, and adding an
adversarial attack to GAN training can improve the convergence speed and lead
to better generators. Combining these two insights, we develop a framework
called Rob-GAN to jointly optimize generator and discriminator in the presence
of adversarial attacks---the generator generates fake images to fool
discriminator; the adversarial attacker perturbs real images to fool the
discriminator, and the discriminator wants to minimize loss under fake and
adversarial images. Through this end-to-end training procedure, we are able to
simultaneously improve the convergence speed of GAN training, the quality of
synthetic images, and the robustness of discriminator under strong adversarial
attacks. Experimental results demonstrate that the obtained classifier is more
robust than the state-of-the-art adversarial training approach, and the
generator outperforms SN-GAN on ImageNet-143.
