Label specificity attack: Change your label as I want

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Label specificity attack: Change your label as I want Huawei Wang, Huawei Wang orcid.org/0000-0002-6360-1116 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorYiwei Liu, Corresponding Author Yiwei Liu yiweiliu_disecc@163.com orcid.org/0000-0003-3400-3632 Defence Industry Secrecy Examination and Certification Center, Beijing, China Correspondence Yiwei Liu, Defence Industry Secrecy Examination and Certification Center, 100089 Beijing, China. Email: yiweiliu_disecc@163.comSearch for more papers by this authorPeng Yin, Peng Yin orcid.org/0000-0003-2883-0199 Defence Industry Secrecy Examination and Certification Center, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorHua Zhang, Hua Zhang orcid.org/0000-0002-0532-9783 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorXin Xu, Xin Xu Defence Industry Secrecy Examination and Certification Center, Beijing, ChinaSearch for more papers by this authorQiaoyan Wen, Qiaoyan Wen State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this author Huawei Wang, Huawei Wang orcid.org/0000-0002-6360-1116 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorYiwei Liu, Corresponding Author Yiwei Liu yiweiliu_disecc@163.com orcid.org/0000-0003-3400-3632 Defence Industry Secrecy Examination and Certification Center, Beijing, China Correspondence Yiwei Liu, Defence Industry Secrecy Examination and Certification Center, 100089 Beijing, China. Email: yiweiliu_disecc@163.comSearch for more papers by this authorPeng Yin, Peng Yin orcid.org/0000-0003-2883-0199 Defence Industry Secrecy Examination and Certification Center, Beijing, China School of Cyber Security, University of Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorHua Zhang, Hua Zhang orcid.org/0000-0002-0532-9783 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this authorXin Xu, Xin Xu Defence Industry Secrecy Examination and Certification Center, Beijing, ChinaSearch for more papers by this authorQiaoyan Wen, Qiaoyan Wen State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSearch for more papers by this author First published: 11 May 2022 https://doi.org/10.1002/int.22902Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Graph neural networks (GNN) have been widely used in many machine learning tasks, such as text classification, sequence labeling, protein interface prediction, and knowledge graph. With increasing security concerns, GNN have been proved to be vulnerable and unreliable. Recent years, inspired by adversarial model in computer vision, various attacks on graph data begin to emerge. However, the exist attacks mostly focus on security violation and attack specificity, and very few attacks concern error specificity. In this paper, we focus on dealing with this kind of attack on one node of the graph by slightly manipulating the graph structure. Our goal is to change the label of the node to what we want after attack. We formulate this case as label specificity attack problem. The biggest challenge in solving this problem is the lack of theoretical guidance to perform this attack. For this, we reinterpret structural entropy and define differential structural entropy (DS-entropy) to guide the manipulation. Based on DS-entropy, we propose the target-label principle and max-degree principle to execute our attack, and then design the corresponding algorithm DSEM. We compare our algorithm DSEM with two benchmarks on three classical graph data sets. Results show that our algorithm is effective in performing label specificity attacks. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation