A Robust Adversarial Network-Based End-to-End Communications System with Strong Generalization Ability Against Adversarial Attacks

Yudi Dong, Huaxia Wang, Yu Dong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

End-to-end learning of communications systems is a promising new paradigm for future communications, in which deep neural networks (DNNs) are implemented in the transmitter and receiver as an autoencoder architecture. However, due to DNN's natural vulnerability to adversarial perturbations, the end-to-end communications system exhibits security and robustness issues in terms of adversarial attacks over the air. The common defensive method, known as adversarial training, is to augment training data with adversarial perturbations, but it is hard to cover all possible perturbations and also hurt the system generalization. In this paper, we propose a novel and defensive mechanism based on a generative adversarial network (GAN) framework1 to achieve robust end-to-end learning of a communications system. 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. The results also show that our GAN-based system outperforms the conventional communications system and the autoencoder communications system with/without adversarial training.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
Pages4086-4091
Number of pages6
ISBN (Electronic)9781538683477
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
Volume2022-May
ISSN (Print)1550-3607

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period16/05/2220/05/22

Keywords

  • Adversarial attacks
  • Adversarial networks
  • Robust end-to-end learning
  • Wireless communications security

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