Camouflaged Adversarial Attack on Object Detector

Jeong-Soo Kim, Kyungmin Lee, Hyeongkeun Lee, Hunmin Yang, Se-Yoon Oh
{"title":"Camouflaged Adversarial Attack on Object Detector","authors":"Jeong-Soo Kim, Kyungmin Lee, Hyeongkeun Lee, Hunmin Yang, Se-Yoon Oh","doi":"10.23919/ICCAS52745.2021.9650004","DOIUrl":null,"url":null,"abstract":"The existence of physical-world adversarial examples such as adversarial patches proves the vulnerability of real-world deep learning systems. Therefore, it is essential to develop efficient adversarial attack algorithms to identify potential risks and build a robust system. The patch-based physical adversarial attack has shown its effectiveness in attacking neural network-based object detectors. However, the generated patches are quite perceptible for humans, violating the fundamental assumption of adversarial examples. In this work, we present task-specific loss functions that can generate imperceptible adversarial patches based on camouflaged patterns. First, we propose a constrained optimization method with two camouflage assessment metrics to quantify camouflage performance. Then, we show the regularization with those metrics can help generate the adversarial patches based on camouflage patterns. Furthermore, we validate our methods with various experiments and show that we can generate natural-style camouflaged adversarial patches with comparable attack performance.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICCAS52745.2021.9650004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The existence of physical-world adversarial examples such as adversarial patches proves the vulnerability of real-world deep learning systems. Therefore, it is essential to develop efficient adversarial attack algorithms to identify potential risks and build a robust system. The patch-based physical adversarial attack has shown its effectiveness in attacking neural network-based object detectors. However, the generated patches are quite perceptible for humans, violating the fundamental assumption of adversarial examples. In this work, we present task-specific loss functions that can generate imperceptible adversarial patches based on camouflaged patterns. First, we propose a constrained optimization method with two camouflage assessment metrics to quantify camouflage performance. Then, we show the regularization with those metrics can help generate the adversarial patches based on camouflage patterns. Furthermore, we validate our methods with various experiments and show that we can generate natural-style camouflaged adversarial patches with comparable attack performance.
对目标检测器的伪装对抗攻击
物理世界对抗性例子的存在,如对抗性补丁,证明了现实世界深度学习系统的脆弱性。因此,开发有效的对抗性攻击算法来识别潜在风险并构建健壮的系统至关重要。基于补丁的物理对抗性攻击在攻击基于神经网络的目标检测器方面已显示出其有效性。然而,生成的斑块对人类来说是相当可感知的,违反了对抗性示例的基本假设。在这项工作中,我们提出了特定于任务的损失函数,该函数可以基于伪装模式生成难以察觉的对抗补丁。首先,我们提出了一种包含两个伪装评估指标的约束优化方法来量化伪装性能。然后,我们展示了这些指标的正则化可以帮助生成基于伪装模式的对抗性补丁。此外,我们通过各种实验验证了我们的方法,并表明我们可以生成具有可比攻击性能的自然风格伪装对抗补丁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信