论文标题
对抗性攻击和电力质量识别的防御方法
Adversarial Attacks and Defense Methods for Power Quality Recognition
论文作者
论文摘要
最近在文献中探讨了各种机器学习方法对对抗性示例的脆弱性。使用这些脆弱方法的电力系统对对抗性例子构成了巨大威胁。为此，我们首先提出了一种信号特异性方法和一种通用信号反应方法，用于使用生成的对抗示例来攻击电力系统。还提出并评估了基于可转移特征和上述两种方法的黑框攻击。然后，我们采用对抗性训练来捍卫系统免受对抗攻击。实验分析表明，与FGSM相比，我们的信号特异性攻击方法（快速梯度符号方法）提供的扰动较少，并且我们的信号敏捷攻击方法可以产生扰动，欺骗大多数自然信号的可能性很高。更重要的是，基于通用信号无关算法的攻击方法比基于信号特异性算法的攻击方法具有更高的黑框攻击传输速率。此外，结果表明，所提出的对抗训练可以提高功率系统对对抗性例子的鲁棒性。
Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples. Black-box attacks based on transferable characteristics and the above two methods are also proposed and evaluated. We then adopt adversarial training to defend systems against adversarial attacks. Experimental analyses demonstrate that our signal-specific attack method provides less perturbation compared to the FGSM (Fast Gradient Sign Method), and our signal-agnostic attack method can generate perturbations fooling most natural signals with high probability. What's more, the attack method based on the universal signal-agnostic algorithm has a higher transfer rate of black-box attacks than the attack method based on the signal-specific algorithm. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples.
