Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification

TL;DR

This paper introduces a low-power, white-box adversarial attack using Golden Ratio Search to effectively compromise deep learning-based modulation classifiers, highlighting vulnerabilities and challenging current defenses.

Contribution

It presents a novel minimal-power attack method for AMC that outperforms existing approaches in efficiency and effectiveness, even against robust architectures.

Findings

The attack requires minimal power to succeed.

It is faster to generate than existing methods.

It effectively bypasses various defense mechanisms.

Abstract

We propose a minimal power white box adversarial attack for Deep Learning based Automatic Modulation Classification (AMC). The proposed attack uses the Golden Ratio Search (GRS) method to find powerful attacks with minimal power. We evaluate the efficacy of the proposed method by comparing it with existing adversarial attack approaches. Additionally, we test the robustness of the proposed attack against various state-of-the-art architectures, including defense mechanisms such as adversarial training, binarization, and ensemble methods. Experimental results demonstrate that the proposed attack is powerful, requires minimal power, and can be generated in less time, significantly challenging the resilience of current AMC methods.