演示:基于多传感器融合的AD感知在物理世界攻击下的安全性

Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security Pub Date : 1900-01-01 DOI:10.14722/autosec.2022.23038

Yulong Cao, Ningfei Wang, Chaowei Xiao, Dawei Yang, Jin Fang, Ruigang Yang, Qi Alfred Chen, Mingyan Liu, Bo Li

{"title":"演示:基于多传感器融合的AD感知在物理世界攻击下的安全性","authors":"Yulong Cao, Ningfei Wang, Chaowei Xiao, Dawei Yang, Jin Fang, Ruigang Yang, Qi Alfred Chen, Mingyan Liu, Bo Li","doi":"10.14722/autosec.2022.23038","DOIUrl":null,"url":null,"abstract":"—In autonomous driving (AD) vehicles, Multi-Sensor Fusion (MSF) is used to combine perception results from multiple sensors such as LiDARs (Light Detection And Ranging) and cam- eras for both accuracy and robustness. In this work, we design the first attack that fundamentally defeats MSF-based AD perception by generating 3D adversarial objects. This demonstration will include video and figure demonstrations for the generated 3D adversarial objects and the end-to-end consequences.","PeriodicalId":399600,"journal":{"name":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demo: Security of Multi-Sensor Fusion based Perception in AD under Physical-World Attacks\",\"authors\":\"Yulong Cao, Ningfei Wang, Chaowei Xiao, Dawei Yang, Jin Fang, Ruigang Yang, Qi Alfred Chen, Mingyan Liu, Bo Li\",\"doi\":\"10.14722/autosec.2022.23038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"—In autonomous driving (AD) vehicles, Multi-Sensor Fusion (MSF) is used to combine perception results from multiple sensors such as LiDARs (Light Detection And Ranging) and cam- eras for both accuracy and robustness. In this work, we design the first attack that fundamentally defeats MSF-based AD perception by generating 3D adversarial objects. This demonstration will include video and figure demonstrations for the generated 3D adversarial objects and the end-to-end consequences.\",\"PeriodicalId\":399600,\"journal\":{\"name\":\"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14722/autosec.2022.23038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14722/autosec.2022.23038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

在自动驾驶(AD)车辆中，多传感器融合(MSF)用于将来自多个传感器(如激光雷达(光探测和测距)和摄像头)的感知结果结合起来，以获得准确性和鲁棒性。在这项工作中，我们设计了第一个通过生成3D对抗对象从根本上击败基于msf的AD感知的攻击。此演示将包括视频和图形演示生成的3D对抗对象和端到端后果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Demo: Security of Multi-Sensor Fusion based Perception in AD under Physical-World Attacks

—In autonomous driving (AD) vehicles, Multi-Sensor Fusion (MSF) is used to combine perception results from multiple sensors such as LiDARs (Light Detection And Ranging) and cam- eras for both accuracy and robustness. In this work, we design the first attack that fundamentally defeats MSF-based AD perception by generating 3D adversarial objects. This demonstration will include video and figure demonstrations for the generated 3D adversarial objects and the end-to-end consequences.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security

自引率

0.00%

发文量