Adversarial camouflage for node injection attack on graphs

doi:10.1016/j.ins.2023.119611

	Adversarial camouflage for node injection attack on graphs
	Tao, Shuchang 1,3; Cao, Qi 1; Shen, Huawei 1,3; Wu, Yunfan 1,3; Hou, Liang 1,3; Sun, Fei 1; Cheng, Xueqi 2,3
	2023-11-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	649 页码:14
摘要	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.
关键词	Adversarial camouflage Node injection attack Adversarial attack Graph neural networks
DOI	10.1016/j.ins.2023.119611
收录类别	SCI
语种	英语
资助项目	National Key Ramp;D Program of China[2022YFB3103700] ; National Key Ramp;D Program of China[2022YFB3103701] ; National Natural Science Foundation of China[62102402] ; National Natural Science Foundation of China[62272125] ; National Natural Science Foundation of China[U21B2046] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001073549100001
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21156
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cao, Qi; Shen, Huawei
作者单位	1.Chinese Acad Sci, Data Intelligence Syst Res Ctr, Inst Comp Technol, Beijing, Peoples R China 2.Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Tao, Shuchang,Cao, Qi,Shen, Huawei,et al. Adversarial camouflage for node injection attack on graphs[J]. INFORMATION SCIENCES,2023,649:14.
APA	Tao, Shuchang.,Cao, Qi.,Shen, Huawei.,Wu, Yunfan.,Hou, Liang.,...&Cheng, Xueqi.(2023).Adversarial camouflage for node injection attack on graphs.INFORMATION SCIENCES,649,14.
MLA	Tao, Shuchang,et al."Adversarial camouflage for node injection attack on graphs".INFORMATION SCIENCES 649(2023):14.