{"title":"受自然树叶多角度启发的物理结构对抗补丁欺骗红外探测器","authors":"","doi":"10.1016/j.jksuci.2024.102122","DOIUrl":null,"url":null,"abstract":"<div><p>Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors. However, current research on infrared adversarial attacks mainly focuses on pedestrian detection tasks. Due to the complex shape and structure of vehicles and the changing working conditions, adversarial attack in infrared vehicle detection pose challenges like difficult multi-angle attack, poor physical transferability, and weak environmental adaptation. This paper proposed Leaf-like Mask Bar Code (LMBC), a novel adversarial attack method for multi-angle physical black-box attack on infrared detectors. Inspired by natural leaf structures, a mask was designed to restrict the adversarial patch contour. Then, adversarial parameters of the patches (angle, sparsity, and position) were optimized using the Genetic Algorithm with Multi-segment (GAM). Moreover, leaf-like structures in physical adversarial patches were constructed using suitable infrared coating materials. deploying them at multiple angles. Experimental results demonstrated LMBC’s efficacy, paralyzing the infrared vehicle detector with an Average Precision (AP) as low as 33.7% and an average Attack Success Rate (ASR) as high as 92.9% across a distance of 2.4m 4.2 m and angles of 0° 360°. Moreover, LMBC’s adversarial patches transferred to mainstream detectors (e.g., Faster RCNN, Yolov3, etc.) and pedestrian detection tasks.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002118/pdfft?md5=75ea3639728ca4afe725529410bfb979&pid=1-s2.0-S1319157824002118-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Physically structured adversarial patch inspired by natural leaves multiply angles deceives infrared detectors\",\"authors\":\"\",\"doi\":\"10.1016/j.jksuci.2024.102122\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors. However, current research on infrared adversarial attacks mainly focuses on pedestrian detection tasks. Due to the complex shape and structure of vehicles and the changing working conditions, adversarial attack in infrared vehicle detection pose challenges like difficult multi-angle attack, poor physical transferability, and weak environmental adaptation. This paper proposed Leaf-like Mask Bar Code (LMBC), a novel adversarial attack method for multi-angle physical black-box attack on infrared detectors. Inspired by natural leaf structures, a mask was designed to restrict the adversarial patch contour. Then, adversarial parameters of the patches (angle, sparsity, and position) were optimized using the Genetic Algorithm with Multi-segment (GAM). Moreover, leaf-like structures in physical adversarial patches were constructed using suitable infrared coating materials. deploying them at multiple angles. Experimental results demonstrated LMBC’s efficacy, paralyzing the infrared vehicle detector with an Average Precision (AP) as low as 33.7% and an average Attack Success Rate (ASR) as high as 92.9% across a distance of 2.4m 4.2 m and angles of 0° 360°. Moreover, LMBC’s adversarial patches transferred to mainstream detectors (e.g., Faster RCNN, Yolov3, etc.) and pedestrian detection tasks.</p></div>\",\"PeriodicalId\":48547,\"journal\":{\"name\":\"Journal of King Saud University-Computer and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1319157824002118/pdfft?md5=75ea3639728ca4afe725529410bfb979&pid=1-s2.0-S1319157824002118-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of King Saud University-Computer and Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1319157824002118\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1319157824002118","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors. However, current research on infrared adversarial attacks mainly focuses on pedestrian detection tasks. Due to the complex shape and structure of vehicles and the changing working conditions, adversarial attack in infrared vehicle detection pose challenges like difficult multi-angle attack, poor physical transferability, and weak environmental adaptation. This paper proposed Leaf-like Mask Bar Code (LMBC), a novel adversarial attack method for multi-angle physical black-box attack on infrared detectors. Inspired by natural leaf structures, a mask was designed to restrict the adversarial patch contour. Then, adversarial parameters of the patches (angle, sparsity, and position) were optimized using the Genetic Algorithm with Multi-segment (GAM). Moreover, leaf-like structures in physical adversarial patches were constructed using suitable infrared coating materials. deploying them at multiple angles. Experimental results demonstrated LMBC’s efficacy, paralyzing the infrared vehicle detector with an Average Precision (AP) as low as 33.7% and an average Attack Success Rate (ASR) as high as 92.9% across a distance of 2.4m 4.2 m and angles of 0° 360°. Moreover, LMBC’s adversarial patches transferred to mainstream detectors (e.g., Faster RCNN, Yolov3, etc.) and pedestrian detection tasks.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.