图上节点注入攻击的对抗伪装

Shuchang Tao, Qi Cao, Huawei Shen, Yunfan Wu, Liang Hou, Xueqi Cheng
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引用次数: 7

摘要

近年来,针对图神经网络(GNN)的节点注入攻击以其较高的攻击成功率降低了GNN的性能,受到了越来越多的关注。然而,我们的研究表明,这些攻击在实际场景中经常失败,因为防御/检测方法可以很容易地识别和删除注入的节点。为了解决这个问题,我们致力于伪装节点注入攻击,使注入的节点看起来正常,防御/检测方法无法察觉。不幸的是,图数据的非欧几里得结构和缺乏直观先验给伪装的形式化、实现和评估带来了巨大的挑战。本文首先提出并定义伪装为注入节点与正常节点的自我网络之间的分布相似性。然后为了实现,我们提出了一个针对节点注入攻击的对抗性伪装框架,即CANA,以提高在实际场景中防御/检测方法下的攻击性能。在分布相似度的指导下,进一步设计了一种新的伪装度量。大量的实验表明,在具有更高伪装或不可感知性的防御/检测方法下,CANA可以显著提高攻击性能。这项工作促使我们提高对gnn在实际应用中的安全漏洞的认识。
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Adversarial Camouflage for Node Injection Attack on Graphs
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates. However, our study indicates that these attacks often fail in practical scenarios, since defense/detection methods can easily identify and remove the injected nodes. To address this, we devote to camouflage node injection attack, making injected nodes appear normal and imperceptible to defense/detection methods. Unfortunately, the non-Euclidean structure of graph data and the lack of intuitive prior present great challenges to the formalization, implementation, and evaluation of camouflage. In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes. Then for implementation, we propose an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve attack performance under defense/detection methods in practical scenarios. A novel camouflage metric is further designed under the guide of distribution similarity. Extensive experiments demonstrate that CANA can significantly improve the attack performance under defense/detection methods with higher camouflage or imperceptibility. This work urges us to raise awareness of the security vulnerabilities of GNNs in practical applications.
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