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IDEA: Invariant defense for graph adversarial robustness

文献类型：期刊论文

作者Tao, Shuchang1,2; Cao, Qi1; Shen, Huawei1,2; Wu, Yunfan1,2; Xu, Bingbing1; Cheng, Xueqi1,2
刊名INFORMATION SCIENCES
出版日期2024-10-01
卷号680页码:18
关键词Invariant defense Adversarial robustness Causal feature Graph neural networks
ISSN号0020-0255
DOI10.1016/j.ins.2024.121171
英文摘要Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suffer from severe performance decline under unseen attacks, due to either limited observed adversarial examples or pre-defined heuristics. To address these limitations, we analyze the causalities in graph adversarial attacks and conclude that causal features are key to achieve graph adversarial robustness, owing to their determinedness for labels and invariance across attacks. To learn these causal features, we innovatively propose an Invariant causal DE fense method against adversarial Attacks (IDEA). We derive node-based and structure-based invariance objectives from an information-theoretic perspective. IDEA ensures strong predictability for labels and invariant predictability across attacks, which is provably a causally invariant defense across various attacks. Extensive experiments demonstrate that IDEA attains state-of-the-art defense performance under all five attacks on all five datasets. The implementation of IDEA is available at https:// github .com /TaoShuchang /IDEA _repo.
资助项目National Key R&D Program of China[2022YFB3103700] ; National Key R&D Program of China[2022YFB3103701] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0680101] ; National Natural Science Foundation of China[62102402] ; National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62272125] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001302686700001
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/39615]  
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 AI Safety, Beijing, Peoples R China
2.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. IDEA: Invariant defense for graph adversarial robustness[J]. INFORMATION SCIENCES,2024,680:18.
APA Tao, Shuchang,Cao, Qi,Shen, Huawei,Wu, Yunfan,Xu, Bingbing,&Cheng, Xueqi.(2024).IDEA: Invariant defense for graph adversarial robustness.INFORMATION SCIENCES,680,18.
MLA Tao, Shuchang,et al."IDEA: Invariant defense for graph adversarial robustness".INFORMATION SCIENCES 680(2024):18.

入库方式： OAI收割

来源：计算技术研究所

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