用黄金分割法实现低功耗强攻击，挑战深度学习调制识别防御。
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification
- 基于黄金分割搜索优化，以极低功率生成强对抗样本。
- 攻击成功率高，生成时间短，功率消耗显著低于现有方法。
- 对多种防御机制有效，适合研究对抗攻击与系统安全者。
我们提出一种针对基于深度学习的自动调制分类（AMC）的极低功耗白盒对抗攻击。该攻击采用黄金分割搜索（GRS）方法，在最小功率下寻找高效攻击。通过与现有对抗攻击方法对比，验证了所提方法的有效性。此外，测试了该攻击在多种先进模型架构上的鲁棒性，包括对抗训练、二值化和集成方法等防御机制。实验结果表明，该攻击不仅威力强大，且所需功率极小，生成时间更短，显著挑战了当前AMC方法的抗干扰能力。
原文摘要 · Abstract (English)
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.
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