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.
@article{arxiv.2103.02654,
title = {A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks},
author = {Yudi Dong and Huaxia Wang and Yu-Dong Yao},
journal= {arXiv preprint arXiv:2103.02654},
year = {2022}
}