【论文阅读】A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization

摘要

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.
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性攻击。具体来说，我们利用生成网络来模拟一个强大的对手，并使端到端通信系统能够通过极大极小博弈来对抗生成攻击网络。研究表明，该系统不仅能很好地抵抗白盒和黑盒攻击，而且具有良好的泛化能力，可以在无攻击的情况下保持良好的性能。我们还表明，基于GAN的端到端系统优于传统通信系统和有/没有对抗性训练的端到端通信系统。

</