计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 25-32.doi: 10.11896/jsjkx.210300299
所属专题： 人工智能安全
王超1, 魏祥麟2, 田青1, 焦翔1, 魏楠1, 段强2
WANG Chao1, WEI Xiang-lin2, TIAN Qing1, JIAO Xiang1, WEI Nan1, DUAN Qiang2
摘要： 基于深度神经网络(Deep Neural Network,DNN)的自动调制识别(Automatic Modulation Recognition,AMR)模型具有特征自提取、识别精度高、人工干预少的优势。但是,业界在设计面向AMR的DNN(AMR-oriented DNN,ADNN)模型时,往往仅关注识别精度,而忽视了对抗样本可能带来的安全威胁。为此,文中从人工智能安全的角度出发,探究了对抗样本对ADNN模型的安全威胁,并提出了一种新颖的基于特征梯度的对抗攻击方法。相比传统标签梯度的攻击方式,特征梯度攻击方法能够更有效地攻击ADNN提取的调制信号空时特征,且具有更好的迁移性。在公开数据集上的实验结果表明,无论白盒攻击还是黑盒攻击,所提出的基于特征梯度的对抗攻击方法的攻击效果和迁移性均优于当前的标签梯度攻击方法。
中图分类号:
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