Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks

TL;DR

This paper introduces a novel GAN-based method with multiple generators to improve the resilience of deep learning-based modulation classification systems against adversarial attacks in wireless communications.

Contribution

It proposes a mixture GAN architecture tailored for radio signals, enhancing defense against adversarial perturbations unlike existing image-focused GAN defenses.

Findings

Defense GAN increases classification accuracy to approximately 81%.

Mixture of generators overcomes mode collapse in radio signal classification.

Proves effectiveness against FGSM adversarial attacks.

Abstract

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier. Specifically, we have shown the resiliency of our proposed defense GAN against the Fast-Gradient Sign method (FGSM) algorithm as one of the most potent kinds of attack algorithms to craft the perturbed signals. The existing defense-GAN has been designed for image classification and does not work in our case where the above-mentioned communication system is considered. Thus, our proposed countermeasure approach deploys GANs with a mixture of generators to overcome the mode collapsing problem in a typical GAN facing radio signal classification problem. Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.