计算机科学 ›› 2024, Vol. 51 ›› Issue (1): 355-362.doi: 10.11896/jsjkx.230600127
郭宇星1, 姚凯旋1, 王智强1, 温亮亮1, 梁吉业1,2
GUO Yuxing1, YAO Kaixuan1, WANG Zhiqiang1, WEN Liangliang1, LIANG Jiye1,2
摘要: 在大数据时代,数据之间的紧密关联性是普遍存在的,图数据分析挖掘已经成为大数据技术的重要发展趋势。近几年,图神经网络作为一种新型的图表示学习工具引起了学术界和工业界的广泛关注。目前图神经网络已经在很多实际应用中取得了巨大的成功。最近人工智能的安全性和可信性成为了人们关注的重点,很多工作主要针对图像等规则数据的深度学习对抗攻击。文中主要聚焦于图数据这种典型非欧氏结构的黑盒对抗攻击问题,在图神经网络模型信息(结构、参数)未知的情况下,对图数据进行非随机微小扰动,从而实现对模型的对抗攻击,模型性能随之下降。基于节点选择的对抗攻击策略是一类重要的黑盒图对抗攻击方法,但现有方法在选择对抗攻击节点时主要依靠节点的拓扑结构信息(如度信息)而未充分考虑节点的特征信息,文中面向引文网络提出了一种基于特征拓扑融合的黑盒图对抗攻击方法。所提方法在选择重要性节点的过程中将图节点特征信息和拓扑结构信息进行融合,使得选出的节点在特征和拓扑两方面对于图数据都是重要的,攻击者对挑选出的重要节点施加不易察觉的扰动后对图数据产生了较大影响,进而实现对图神经网络模型的攻击。在3个基准数据集上进行实验,结果表明,所提出的攻击策略在模型参数未知的情况下能显著降低模型性能,且攻击效果优于现有的方法。
中图分类号:
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