机械故障诊断中的对抗攻击
Adversarial Attacks on Machinery Fault Diagnosis
摘要：
尽管基于神经网络的机械故障诊断方法取得了很大的进展，但其鲁棒性在很大程度上被忽视，因为它们很容易通过向输入添加不可感知的扰动而被欺骗。对于故障诊断问题，本文重新制定了各种对抗攻击，并在非目标和目标条件下深入研究它们。对6个典型的神经网络模型的实验结果表明，通过加入小扰动，模型的精度大大降低。我们进一步提出了一个简单，有效和通用的方案来保护受害者模型。这项工作提供了一个深入的研究对抗性的机械振动信号的例子，开发保护方法，防止对抗攻击和提高基于NN的模型的鲁棒性。
英文摘要：
Despite the great progress of neural network-based (NN-based) machinery fault diagnosis methods, their robustness has been largely neglected, for they can be easily fooled through adding imperceptible perturbation to the input. For fault diagnosis problems, in this paper, we reformulate various adversarial attacks and intensively investigate them under untargeted and targeted conditions. Experimental results on six typical NN-based models show that accuracies of the models are greatly reduced by adding small perturbations. We further propose a simple, efficient and universal scheme to protect the victim models. This work provides an in-depth look at adversarial examples of machinery vibration signals for developing protection methods against adversarial attack and improving the robustness of NN-based models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
