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

TL;DR

This paper introduces a GAN-based defense mechanism for end-to-end communication systems that effectively counters various adversarial attacks and generalizes well under different attack scenarios.

Contribution

It presents a novel GAN framework that models adversaries and enhances the robustness and generalization of communication systems against attacks.

Findings

Effective against white-box and black-box attacks

Outperforms conventional and adversarially trained systems

Maintains performance under no attack conditions

Abstract

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.