Camdar‐adv: Generating adversarial patches on 3D object

International Journal of Intelligent SystemsVolume 36, Issue 3 p. 1441-1453 RESEARCH ARTICLE Camdar-adv: Generating adversarial patches on 3D object Chang Chen, Chang Chen orcid.org/0000-0003-4165-3545 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Peng Cheng Laboratory, Shenzhen, ChinaSearch for more papers by this authorTeng Huang, Corresponding Author Teng Huang [email protected] orcid.org/0000-0001-7261-6398 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Peng Cheng Laboratory, Shenzhen, China Correspondence Teng Huang, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 511363, China. Email: [email protected]Search for more papers by this author Chang Chen, Chang Chen orcid.org/0000-0003-4165-3545 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Peng Cheng Laboratory, Shenzhen, ChinaSearch for more papers by this authorTeng Huang, Corresponding Author Teng Huang [email protected] orcid.org/0000-0001-7261-6398 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Peng Cheng Laboratory, Shenzhen, China Correspondence Teng Huang, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 511363, China. Email: [email protected]Search for more papers by this author First published: 22 December 2020 https://doi.org/10.1002/int.22349Citations: 34Read 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 Deep neural network model is the core technology for sensors of the autonomous driving platform to perceive the external environment. Recent research have shown that it has a certain vulnerability. The artificial designed adversarial examples can make the DNN model output the wrong results. These adversarial examples not only exist in the digital world, but also in the physical world. At present, research on autonomous driving platform mainly focus on attacking a single sensor. In this paper, we introduce Camdar-adv, a method for generating image adversarial examples on three-dimensional (3D) objects, which could potentially lunch a multisensor attack toward the autonomous driving platforms. Specifically, with objects that can attack LiDAR sensors, a geometric transformation can be used to project their shape onto the two-dimensional plane. Adversarial perturbations against optical image sensor could be added to the surface of the adversarial 3D objects precisely without changing its geometry. Test results on the open-source autonomous driving data set KITTI show that Camdar-adv can generate adversarial samples for the state of the art object detection model. From a fixed viewpoint, our method can achieve an attack success rate over 99%. Citing Literature Volume36, Issue3March 2021Pages 1441-1453 RelatedInformation