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

Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Sheetal Kalyani
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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.
黄金比率搜索:基于深度学习的调制分类的低功耗对抗攻击
我们针对基于深度学习的自动调制分类(AMC)提出了一种最小功率的白盒对抗攻击。通过与现有的对抗性攻击方法进行比较,我们评估了所提方法的功效。此外,我们还针对各种最先进的架构(包括对抗训练、二值化和集合方法等防御机制)测试了所提攻击的鲁棒性。实验结果表明,所提出的攻击功能强大,耗电量极低,而且可以在更短的时间内生成,极大地挑战了当前 AMC 方法的复原能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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