An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines

Wen Su; Qingna Li

doi:10.1109/DSIT55514.2022.9943927

An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines

Wen Su, Qingna Li^*

^*此作品的通讯作者

数学学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.

源语言	英语
主期刊名	2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781665498685
DOI	https://doi.org/10.1109/DSIT55514.2022.9943927
出版状态	已出版 - 2022
活动	5th International Conference on Data Science and Information Technology, DSIT 2022 - Shanghai, 中国期限: 22 7月 2022 → 24 7月 2022

出版系列

姓名	2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

会议

会议	5th International Conference on Data Science and Information Technology, DSIT 2022
国家/地区	中国
市	Shanghai
时期	22/07/22 → 24/07/22

访问文件

10.1109/DSIT55514.2022.9943927

其它文件与链接

链接到 Scopus 的出版物

引用此

Su, W., & Li, Q. (2022). An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. 在 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSIT55514.2022.9943927

Su, Wen ; Li, Qingna. / An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings).

@inproceedings{e3abd83a53d14d229882df95abe1ab59,

title = "An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines",

abstract = "Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.",

keywords = "KKT system, adversarial perturbation, machine learning, nonlinear optimization, support vector machine",

author = "Wen Su and Qingna Li",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Data Science and Information Technology, DSIT 2022 ; Conference date: 22-07-2022 Through 24-07-2022",

year = "2022",

doi = "10.1109/DSIT55514.2022.9943927",

language = "English",

series = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",

address = "United States",

}

Su, W & Li, Q 2022, An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. 在 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 5th International Conference on Data Science and Information Technology, DSIT 2022, Shanghai, 中国, 22/07/22. https://doi.org/10.1109/DSIT55514.2022.9943927

An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. / Su, Wen; Li, Qingna.
2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines

AU - Su, Wen

AU - Li, Qingna

PY - 2022

Y1 - 2022

N2 - Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.

AB - Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.

KW - KKT system

KW - adversarial perturbation

KW - machine learning

KW - nonlinear optimization

KW - support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85143124752&partnerID=8YFLogxK

U2 - 10.1109/DSIT55514.2022.9943927

DO - 10.1109/DSIT55514.2022.9943927

M3 - Conference contribution

AN - SCOPUS:85143124752

T3 - 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

BT - 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Data Science and Information Technology, DSIT 2022

Y2 - 22 July 2022 through 24 July 2022

ER -

Su W, Li Q. An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines. 在 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings). doi: 10.1109/DSIT55514.2022.9943927

An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines

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