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AUCPro: AUC-Oriented Provable Robustness Learning

文献类型：期刊论文

作者Bao, Shilong4; Xu, Qianqian3; Yang, Zhiyong4; He, Yuan1; Cao, Xiaochun2; Huang, Qingming3,4
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2025-06-01
卷号47期号:6页码:4579-4596
关键词Robustness Training Perturbation methods Machine learning Heavily-tailed distribution Smoothing methods Gaussian noise Data mining Standards Protocols AUC-oriented learning adversarial robustness machine learning
ISSN号0162-8828
DOI10.1109/TPAMI.2025.3545639
英文摘要The current studies of provable robustness for deep neural networks (DNNs) usually assume that the class distribution is overall balanced. However, in real-world applications especially for safety-sensitive systems, the class distribution often exhibits a long-tailed property. It is well-known that the Area Under the ROC Curve (AUC) is a more proper metric for long-tailed learning problems. Motivated by this fact, an AUC-oriented provable robustness learning framework (named AUCPro) is first proposed in this paper. The key is to construct a proxy model smoothed by the isotropic Gaussian noise and then consider optimizing the proxy model from the AUC-oriented learning point of view. Theoretically, we provide a certified safety region for AUCPro within which the model would be free from the & ell;(2 )adversarial attacks. Most importantly, we propose a novel standard to theoretically study the robustness generalization toward unseen data for provable robustness learning approaches. To the best of our knowledge, such a problem remains barely considered in the machine learning community. To be specific, under a general principle for performance-robustness trade-off, we prove that the generalization ability of the resulting model could be equivalently expressed as the expected adversarial risk of AUC under & ell;(2) perturbation. On top of this, we present two practical settings to explore the excess risk formed by the difference between the empirical risk of AUCPro and the derived generalization performance. Finally, comprehensive experiments speak to the efficacy of our proposed algorithm.
资助项目National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation of China[62441232] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[62025604] ; National Natural Science Foundation of China[62411540034] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[U23B2051] ; National Natural Science Foundation of China[62122075] ; National Natural Science Foundation of China[62206264] ; National Natural Science Foundation of China[92370102] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0680201] ; Postdoctoral Fellowship Program of CPSF[GZB20240729]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001484716600038
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/42377]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Qianqian; Huang, Qingming
作者单位1.Alibaba Grp, Secur Dept, Hangzhou 311121, Peoples R China
2.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Bao, Shilong,Xu, Qianqian,Yang, Zhiyong,et al. AUCPro: AUC-Oriented Provable Robustness Learning[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2025,47(6):4579-4596.
APA Bao, Shilong,Xu, Qianqian,Yang, Zhiyong,He, Yuan,Cao, Xiaochun,&Huang, Qingming.(2025).AUCPro: AUC-Oriented Provable Robustness Learning.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,47(6),4579-4596.
MLA Bao, Shilong,et al."AUCPro: AUC-Oriented Provable Robustness Learning".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47.6(2025):4579-4596.

入库方式： OAI收割

来源：计算技术研究所

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