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A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks
arXiv - CS - Machine Learning Pub Date : 2021-03-03 , DOI: arxiv-2103.02654 Yudi Dong, Huaxia Wang, Yu-Dong Yao
arXiv - CS - Machine Learning Pub Date : 2021-03-03 , DOI: arxiv-2103.02654 Yudi Dong, Huaxia Wang, Yu-Dong Yao
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。
更新日期:2021-03-05
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。