{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:52:44Z","timestamp":1776181964013,"version":"3.50.1"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1109\/percom45495.2020.9127389","type":"proceedings-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T22:43:00Z","timestamp":1593470580000},"page":"1-10","source":"Crossref","is-referenced-by-count":132,"title":["An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models"],"prefix":"10.1109","author":[{"given":"Yao","family":"Deng","sequence":"first","affiliation":[]},{"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Tianyi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guannan","family":"Lou","sequence":"additional","affiliation":[]},{"given":"Miryung","family":"Kim","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","first-page":"37","article-title":"Autoencoders, unsupervised learning, and deep architectures","volume-title":"Proc. of ICML","author":"Baldi"},{"key":"ref2","article-title":"End to end learning for self-driving cars","volume":"abs\/1604.07316","author":"Bojarski","year":"2016","journal-title":"CoRR"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref4","volume-title":"chrisgundling. cg23","year":"2017"},{"key":"ref5","article-title":"Waymo launches its first commercial self-driving car service","author":"Fingas"},{"key":"ref6","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. of NeurIPS","author":"Goodfellow"},{"key":"ref7","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. of ICLR. OpenReview.net","author":"Goodfellow"},{"key":"ref8","article-title":"Countering adversarial images using input transformations","volume-title":"Proc. of ICLR. OpenReview.net","author":"Guo"},{"key":"ref9","article-title":"Convergence of edge computing and deep learning: A comprehensive survey","author":"Han","year":"2019","journal-title":"arXiv preprint arXiv:1907.08349"},{"key":"ref10","article-title":"Adversarial example defense: Ensembles of weak defenses are not strong","volume-title":"Proc. of USENIX workshop","author":"He"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_1"},{"key":"ref13","first-page":"3842","article-title":"Apegan: Adversarial perturbation elimination with gan","volume-title":"Proc. of ICASSP","author":"Jin"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"ref15","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. of ICLR. OpenReview.net","author":"Kingma"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"ref17","article-title":"Adversarial machine learning at scale","volume-title":"Proc. of ICLR. OpenReview.net","author":"Kurakin"},{"key":"ref18","article-title":"Tencent keen security lab: Experimental security research of tesla autopilot"},{"key":"ref19","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Proc. of NeurIPS","author":"Lee"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700202"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2200\/S00787ED1V01Y201707CSL009"},{"key":"ref22","article-title":"Delving into transferable adversarial examples and black-box attacks","author":"Liu","year":"2017","journal-title":"Open-Review.net"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_7"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-2468-4_10"},{"key":"ref27","article-title":"cleverhans v2. 0.0: an adversarial machine learning library","volume":"10","author":"Papernot","year":"2016","journal-title":"arXiv preprint arXiv:1610.00768"},{"key":"ref28","article-title":"Transferability in machine learning: from phenomena to black-box attacks using adversarial samples","volume":"abs\/1605.07277","author":"Papernot","year":"2016","journal-title":"CoRR"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/machines5010006"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00465"},{"key":"ref33","article-title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models","author":"Rauber","year":"2017","journal-title":"arXiv preprint arXiv:1707.04131"},{"key":"ref34","article-title":"Learning a driving simulator","volume":"abs\/1608.01230","author":"Santana","year":"2016","journal-title":"CoRR"},{"key":"ref35","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. of ICLR. Open-Review.net","author":"Simonyan"},{"key":"ref36","article-title":"Intriguing properties of neural networks","volume-title":"Proc. of ICLR. OpenReview.net","author":"Szegedy"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"ref38","first-page":"601","article-title":"Stealing machine learning models via prediction apis","volume-title":"Proc. of USENIX","author":"Tram\u00e8r"},{"key":"ref39","article-title":"Udacity challenge 2: Steering angle prediction","year":"2017"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3199856"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2019.8767397"},{"key":"ref42","first-page":"5283","article-title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","volume-title":"Proc. of ICML","author":"Wong"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/543"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"ref45","first-page":"417","article-title":"Deep defense: Training dnns with improved adversarial robustness","volume-title":"Proc. of NeurIPS","author":"Yan"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886017"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238187"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2016.2614599"},{"key":"ref49","first-page":"7924","article-title":"Robust detection of adversarial attacks by modeling the intrinsic properties of deep neural networks","volume-title":"Proc. of NeurIPS 31","author":"Zheng"}],"event":{"name":"2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)","location":"Austin, TX, USA","start":{"date-parts":[[2020,3,23]]},"end":{"date-parts":[[2020,3,27]]}},"container-title":["2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9125449\/9127351\/09127389.pdf?arnumber=9127389","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T01:50:32Z","timestamp":1706061032000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9127389\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/percom45495.2020.9127389","relation":{},"subject":[],"published":{"date-parts":[[2020,3]]}}}