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.

中文翻译：

---

强大的基于对抗网络的端到端通信系统，具有强大的抵抗攻击的泛化能力

我们提出了一种基于生成对抗网络（GAN）框架的新型防御机制，以防御端到端通信系统中的对抗攻击。具体来说，我们利用生成网络来建模强大的对手，并使端到端通信系统能够通过minimax游戏与生成攻击网络进行对抗。我们表明，所提出的系统不仅可以很好地抵抗白盒和黑盒的对抗攻击，而且还具有出色的泛化能力，可以在没有攻击的情况下保持良好的性能。我们还表明，基于GAN的端到端系统在经过或不经过对抗训练的情况下，优于传统的通信系统和端到端通信系统。