{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:45:54Z","timestamp":1767851154654,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation Singapore and DSO National Laboratories","award":["AISG2-GC-2023-006"],"award-info":[{"award-number":["AISG2-GC-2023-006"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3643832.3661854","type":"proceedings-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T17:14:23Z","timestamp":1717521263000},"page":"534-546","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7464-0823","authenticated-orcid":false,"given":"Dongfang","family":"Guo","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1510-6464","authenticated-orcid":false,"given":"Yuting","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1049-3987","authenticated-orcid":false,"given":"Yimin","family":"Dai","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1836-1122","authenticated-orcid":false,"given":"Pengfei","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-5666","authenticated-orcid":false,"given":"Xin","family":"Lou","sequence":"additional","affiliation":[{"name":"Singapore Institute of Technology, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-9973","authenticated-orcid":false,"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2012. When to use different shutter speeds (a complete list). https:\/\/expertphotography.com\/when-to-use-different-shutter-speeds\/"},{"key":"e_1_3_2_1_2_1","unstructured":"2018. What are the most common traffic signs? https:\/\/topdriver.com\/education-blog\/what-are-the-most-common-traffic-signs\/"},{"key":"e_1_3_2_1_3_1","unstructured":"2020. Teardown: Tesla\u015b hardware retrofits for model 3. https:\/\/www.eetasia.com\/teslas-hardware-retrofits-for-model-3\/"},{"key":"e_1_3_2_1_4_1","unstructured":"2021. The road to everywhere: are HD maps for autonomous driving sustainable? https:\/\/www.autonomousvehicleinternational.com\/features\/the-road-to-everywhere-are-hd-maps-for-autonomous-driving-sustainable.html"},{"key":"e_1_3_2_1_5_1","unstructured":"2022. Apollo traffic light perception. https:\/\/github.com\/ApolloAuto\/apollo\/blob\/master\/docs\/06_Perception\/traffic_light.md"},{"key":"e_1_3_2_1_6_1","unstructured":"2022. Manual on Uniform Traffic Control Devices for Streets and Highways. https:\/\/mutcd.fhwa.dot.gov\/"},{"key":"e_1_3_2_1_7_1","unstructured":"2023. Apollo hardware development platform. https:\/\/developer.apollo.auto\/platform\/hardware.html"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00076"},{"key":"e_1_3_2_1_9_1","unstructured":"Nicholas Carlini. 2023. A complete list of all (arXiv) adversarial example papers. https:\/\/nicholas.carlini.com\/writing\/2019\/all-adversarial-example-papers.html."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140448"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2012.6477759"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00444"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00175"},{"key":"e_1_3_2_1_14_1","unstructured":"GETCAMERAS. 2020. Rolling versus global shutter. https:\/\/www.get-cameras.com\/FAQ-ROLLING-VS-GLOBAL-SHUTTER"},{"key":"e_1_3_2_1_15_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPHOT.2010.5585094"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2716281.2836097"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500437"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00091"},{"key":"e_1_3_2_1_20_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Jing Pengfei","year":"2021","unstructured":"Pengfei Jing, Qiyi Tang, Yuefeng Du, Lei Xue, Xiapu Luo, Ting Wang, Sen Nie, and Shi Wu. 2021. Too good to be safe: Tricking lane detection in autonomous driving with crafted perturbations. In 30th USENIX Security Symposium (USENIX Security 21). 3237--3254."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485832.3488016"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639109"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2742647.2742651"},{"key":"e_1_3_2_1_24_1","volume-title":"Light can hack your face! black-box backdoor attack on face recognition systems. arXiv preprint arXiv:2009.06996","author":"Li Haoliang","year":"2020","unstructured":"Haoliang Li, Yufei Wang, Xiaofei Xie, Yang Liu, Shiqi Wang, Renjie Wan, Lap-Pui Chau, and Alex C Kot. 2020. Light can hack your face! black-box backdoor attack on face recognition systems. arXiv preprint arXiv:2009.06996 (2020)."},{"key":"e_1_3_2_1_25_1","volume-title":"Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770","author":"Liu Yanpei","year":"2016","unstructured":"Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2016. Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)."},{"key":"e_1_3_2_1_26_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Lovisotto Giulio","year":"2021","unstructured":"Giulio Lovisotto, Henry Turner, Ivo Sluganovic, Martin Strohmeier, and Ivan Martinovic. 2021. Slap: Improving physical adversarial examples with short-lived adversarial perturbations. In 30th USENIX Security Symposium (USENIX Security 21). 1865--1882."},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_28_1","volume-title":"23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID). 317--332","author":"Man Yanmao","year":"2020","unstructured":"Yanmao Man, Ming Li, and Ryan Gerdes. 2020. GhostImage: Remote perception attacks against camera-based image classification systems. In 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID). 317--332."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/medicina57101096"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2907630"},{"key":"e_1_3_2_1_31_1","volume-title":"System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31--September 4","author":"Mockus Jonas","year":"2005","unstructured":"Jonas Mockus. 2005. The Bayesian approach to global optimization. In System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31--September 4, 1981. Springer, 473--481."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3423359"},{"key":"e_1_3_2_1_33_1","unstructured":"Fernando Nogueira. 2014--. Bayesian Optimization: Open source constrained global optimization tool for Python. https:\/\/github.com\/fmfn\/BayesianOptimization"},{"key":"e_1_3_2_1_34_1","unstructured":"Jonathan Petit Bas Stottelaar Michael Feiri and Frank Kargl. 2015. Remote attacks on automated vehicles sensors: Experiments on camera and lidar. In BlackHat Europe 11."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2014.6846757"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2024.241053"},{"key":"e_1_3_2_1_38_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Sato Takami","year":"2021","unstructured":"Takami Sato, Junjie Shen, Ningfei Wang, Yunhan Jia, Xue Lin, and Qi Alfred Chen. 2021. Dirty road can attack: Security of deep learning based automated lane centering under physical-world attack. In 30th USENIX Security Symposium (USENIX Security 21). 3309--3326."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01443"},{"key":"e_1_3_2_1_40_1","volume-title":"The 2011 international joint conference on neural networks","author":"Stallkamp Johannes","unstructured":"Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. 2011. The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks. IEEE, 1453--1460."},{"key":"e_1_3_2_1_41_1","unstructured":"The Engineering ToolBox. 2004. Illuminance - recommended light levels. https:\/\/www.engineeringtoolbox.com\/light-level-rooms-d_708.html"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.444864"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484766"},{"key":"e_1_3_2_1_45_1","first-page":"109","article-title":"Can you trust autonomous vehicles: Contactless attacks against sensors of self-driving vehicle","volume":"24","author":"Yan Chen","year":"2016","unstructured":"Chen Yan, Wenyuan Xu, and Jianhao Liu. 2016. Can you trust autonomous vehicles: Contactless attacks against sensors of self-driving vehicle. Def Con 24, 8 (2016), 109.","journal-title":"Def Con"},{"key":"e_1_3_2_1_46_1","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Yan Chen","year":"2022","unstructured":"Chen Yan, Zhijian Xu, Zhanyuan Yin, Stefan Mangard, Xiaoyu Ji, Wenyuan Xu, Kaifa Zhao, Yajin Zhou, Ting Wang, Guofei Gu, et al. 2022. Rolling colors: Adversarial laser exploits against traffic light recognition. In 31st USENIX Security Symposium (USENIX Security 22). 1957--1974."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2017.2694834"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2482461"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2897101"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790106"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380422"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3117811.3117820"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.232"}],"event":{"name":"MOBISYS '24: 22nd Annual International Conference on Mobile Systems, Applications and Services","location":"Minato-ku, Tokyo Japan","acronym":"MOBISYS '24","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643832.3661854","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3643832.3661854","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:06Z","timestamp":1750291386000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3643832.3661854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":53,"alternative-id":["10.1145\/3643832.3661854","10.1145\/3643832"],"URL":"https:\/\/doi.org\/10.1145\/3643832.3661854","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}