Attention‐guided black‐box adversarial attacks with large‐scale multiobjective evolutionary optimization

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Attention-guided black-box adversarial attacks with large-scale multiobjective evolutionary optimization Jie Wang, Jie Wang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, Anhui, ChinaSearch for more papers by this authorZhaoxia Yin, Corresponding Author Zhaoxia Yin zhaoxia.edu@gmail.com orcid.org/0000-0003-0387-4806 School of Communication and Electronic Engineering, East China Normal University, Shanghai, China Correspondence Zhaoxia Yin, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China. Email: zhaoxia.edu@gmail.comSearch for more papers by this authorJing Jiang, Jing Jiang School of Computer and Information, Anqing Normal University, Anqing, Anhui, ChinaSearch for more papers by this authorYang Du, Yang Du Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, Anhui, ChinaSearch for more papers by this author Jie Wang, Jie Wang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, Anhui, ChinaSearch for more papers by this authorZhaoxia Yin, Corresponding Author Zhaoxia Yin zhaoxia.edu@gmail.com orcid.org/0000-0003-0387-4806 School of Communication and Electronic Engineering, East China Normal University, Shanghai, China Correspondence Zhaoxia Yin, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China. Email: zhaoxia.edu@gmail.comSearch for more papers by this authorJing Jiang, Jing Jiang School of Computer and Information, Anqing Normal University, Anqing, Anhui, ChinaSearch for more papers by this authorYang Du, Yang Du Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, Anhui, ChinaSearch for more papers by this author First published: 27 April 2022 https://doi.org/10.1002/int.22892Read 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 Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Fooling deep neural networks (DNNs) with black-box optimization has become a popular adversarial attack fashion, as the prior structural knowledge of DNNs is always unknown. Nevertheless, recent black-box adversarial attacks may struggle to balance their attack ability and visual quality of the generated adversarial examples (AEs) in tackling high-resolution images. In this paper, we propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed LMOA. By considering the spatial semantic information of images, we first take advantage of the attention map to determine the perturbed pixels. Instead of attacking the entire image, reducing the perturbed pixels with the attention mechanism can help to avoid the notorious curse of dimensionality and thereby improve the performance of attacking. Second, a large-scale multiobjective evolutionary algorithm traverse the reduced pixels in the salient region. Benefiting from its characteristics, the generated AEs can fool target DNNs while being invisible by human vision. Extensive experimental results have verified the effectiveness of the proposed LMOA on the ImageNet data set. More importantly, it is more competitive to generate high-resolution AEs with the better visual quality than the existing black-box adversarial attacks. Open Research DATA AVAILABILITY STATEMENT Data sharing not applicable to this article as no data sets were generated or analyzed during the current study. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation