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Graph Adversarial Immunization for Certifiable Robustness

文献类型：期刊论文

作者Tao, Shuchang2,3; Cao, Qi2,3; Shen, Huawei2,3; Wu, Yunfan2,3; Hou, Liang2,3; Cheng, Xueqi1
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2024-04-01
卷号36期号:4页码:1597-1610
关键词Adversarial attack adversarial immunization certifiable robustness graph neural networks node classification
ISSN号1041-4347
DOI10.1109/TKDE.2023.3311105
英文摘要Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modification. In this paper, we propose and formulate graph adversarial immunization, i.e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack. We first propose edge-level immunization to vaccinate node pairs. Unfortunately, such edge-level immunization cannot defend against emerging node injection attacks, since it only immunizes existing node pairs. To this end, we further propose node-level immunization. To avoid computationally intensive combinatorial optimization associated with adversarial immunization, we develop AdvImmune-Edge and AdvImmune-Node algorithms to effectively obtain the immune node pairs or nodes. Extensive experiments demonstrate the superiority of AdvImmune methods. In particular, AdvImmune-Node remarkably improves the ratio of robust nodes by 79$\%$%, 294$\%$%, and 100$\%$%, after immunizing only 5$\%$% of nodes. Furthermore, AdvImmune methods show excellent defensive performance against various attacks, outperforming state-of-the-art defenses. To the best of our knowledge, this is the first attempt to improve certifiable robustness from graph data perspective without losing performance on clean graphs, providing new insights into graph adversarial learning.
资助项目National Key Ramp;D Program of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001181467200023
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38734]  
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, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Tao, Shuchang,Cao, Qi,Shen, Huawei,et al. Graph Adversarial Immunization for Certifiable Robustness[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(4):1597-1610.
APA Tao, Shuchang,Cao, Qi,Shen, Huawei,Wu, Yunfan,Hou, Liang,&Cheng, Xueqi.(2024).Graph Adversarial Immunization for Certifiable Robustness.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(4),1597-1610.
MLA Tao, Shuchang,et al."Graph Adversarial Immunization for Certifiable Robustness".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.4(2024):1597-1610.

入库方式： OAI收割

来源：计算技术研究所

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