ELAA: An efficient local adversarial attack using model interpreters

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 10598-10620 RESEARCH ARTICLE ELAA: An efficient local adversarial attack using model interpreters Shangwei Guo, Shangwei Guo orcid.org/0000-0002-6443-5308 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorSiyuan Geng, Siyuan Geng orcid.org/0000-0002-4328-2701 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorTao Xiang, Corresponding Author Tao Xiang [email protected] orcid.org/0000-0002-9439-4623 College of Computer Science, Chongqing University, Chongqing, China Correspondence Tao Xiang, College of Computer Science, Chongqing University, Chongqing 400044, China. Email: [email protected]Search for more papers by this authorHangcheng Liu, Hangcheng Liu orcid.org/0000-0002-4392-2254 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorRuitao Hou, Ruitao Hou orcid.org/0000-0003-3734-7350 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author Shangwei Guo, Shangwei Guo orcid.org/0000-0002-6443-5308 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorSiyuan Geng, Siyuan Geng orcid.org/0000-0002-4328-2701 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorTao Xiang, Corresponding Author Tao Xiang [email protected] orcid.org/0000-0002-9439-4623 College of Computer Science, Chongqing University, Chongqing, China Correspondence Tao Xiang, College of Computer Science, Chongqing University, Chongqing 400044, China. Email: [email protected]Search for more papers by this authorHangcheng Liu, Hangcheng Liu orcid.org/0000-0002-4392-2254 College of Computer Science, Chongqing University, Chongqing, ChinaSearch for more papers by this authorRuitao Hou, Ruitao Hou orcid.org/0000-0003-3734-7350 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author First published: 19 September 2021 https://doi.org/10.1002/int.22680Citations: 6Read 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 Abstract Modern deep neural networks are highly vulnerable to adversarial examples, which attracts more and more researchers' attention to craft powerful adversarial examples. Most of these generation algorithms create global perturbations that would affect the visual quality of adversarial examples. To mitigate such drawbacks, some attacks attempt to generate local perturbations. However, existing local adversarial attacks are time-consuming and the generated adversarial examples are still distinguishable from clean images. In this paper, we propose a novel efficient local adversarial attack (ELAA) using model interpreters to generate severe local perturbations and improve the imperceptibly of the generated adversarial examples. Specifically, we take advantage of model interpretation methods to search the discriminative regions of clean images. Then, we generate local adversarial examples by adding masks to original clean images. We also propose a new optimization method to reduce the redundancy of local perturbations. Through extensive experiments, we show our ELAA can maintain a high attack ability while preserving the visual quality of clean images. Experimental results also demonstrate our local attack outperforms state-of-the-art local attack methods under various system settings. Citing Literature Volume37, Issue12December 2022Pages 10598-10620 RelatedInformation