  We propose a novel defensive mechanism based on a generative adversarial
network (GAN) framework to defend against adversarial attacks in end-to-end
communications systems. Specifically, we utilize a generative network to model
a powerful adversary and enable the end-to-end communications system to combat
the generative attack network via a minimax game. We show that the proposed
system not only works well against white-box and black-box adversarial attacks
but also possesses excellent generalization capabilities to maintain good
performance under no attacks. We also show that our GAN-based end-to-end system
outperforms the conventional communications system and the end-to-end
communications system with/without adversarial training.
