{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:11:06Z","timestamp":1743117066884,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030736705"},{"type":"electronic","value":"9783030736712"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73671-2_1","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:12:10Z","timestamp":1625569930000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Camdar-Adv: Method for Generating Adversarial Patches on 3D Object"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-3545","authenticated-orcid":false,"given":"Chang","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-6398","authenticated-orcid":false,"given":"Teng","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"1_CR2","unstructured":"Sun, M., et al.: Data poisoning attack against unsupervised node embedding methods. arXiv preprint arXiv:1810.12881 (2018)"},{"key":"1_CR3","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR 2014: International Conference on Learning Representations (ICLR) (2014)"},{"key":"1_CR4","unstructured":"Brown, T.B., et al.: Adversarial patch. arXiv: Computer Vision and Pattern Recognition (2018)"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Liu, A., et al.: Perceptual-sensitive GAN for generating adversarial patches. In: AAAI 2019: Thirty-Third AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 1028\u20131035 (2019)","DOI":"10.1609\/aaai.v33i01.33011028"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Kurakin, A., et al.: Adversarial examples in the physical world. In: ICLR (Workshop) (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"1_CR7","unstructured":"Luo, B., Xu, Q.: Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples. arXiv Preprint arXiv:1912.02598 (2019)"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Thys, S., et al.: Fooling automated surveillance cameras: adversarial patches to attack person detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 49\u201355 (2019)","DOI":"10.1109\/CVPRW.2019.00012"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: AdvPattern: physical-world attacks on deep person re-identification via adversarially transformable patterns. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8340\u20138349 (2019)","DOI":"10.1109\/ICCV.2019.00843"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: Seeing isn\u2019t believing: towards more robust adversarial attack against real world object detectors. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1989\u20132004 (2019)","DOI":"10.1145\/3319535.3354259"},{"key":"1_CR11","unstructured":"Cao, Y., et al.: Adversarial Objects Against LiDARBased Autonomous Driving Systems. arXiv Preprint arXiv:1907.05418 (2019)"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Tu, J., et al.: Physically Realizable Adversarial Examples for LiDAR Object Detection. arXiv Preprint arXiv:2004.00543 (2020)","DOI":"10.1109\/CVPR42600.2020.01373"},{"key":"1_CR13","unstructured":"Cui, Y., et al.: Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review. arXiv Preprint arXiv:2004.05224 (2020)"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Ma, F., Cavalheiro, G.V., Karaman, S.: Selfsupervised sparse-to-dense: self-supervised depth completion from lidar and monocular camera. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)","DOI":"10.1109\/ICRA.2019.8793637"},{"key":"1_CR15","unstructured":"Tang, J., Tian, F.-P., Feng, W., Li, J., Tan, P.: Learning guided convolutional network for depth completion (2019)"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Cao, Y., et al.: Adversarial sensor attack on LiDAR-based perception in autonomous driving. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2267\u20132281 (2019)","DOI":"10.1145\/3319535.3339815"},{"issue":"6","key":"1_CR17","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Redmon, J., et al.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1_CR19","unstructured":"Van Etten, A.: You Only Look Twice: Rapid MultiScale Object Detection In Satellite Imagery. arXiv Preprint arXiv:1805.09512 (2018)"},{"key":"1_CR20","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv Preprint arXiv:1804.02767 (2018)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Ma, F., Karaman, S.: Sparse-to-dense: depth prediction from sparse depth samples and a single image. In: 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018)","DOI":"10.1109\/ICRA.2018.8460184"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Jaritz, M., Charette, R.D., Wirbel, E., Perrotton, X., Nashashibi, F.: Sparse and dense data with CNNs: depth completion and semantic segmentation. In: 2018 International Conference on 3D Vision (3DV) (2018)","DOI":"10.1109\/3DV.2018.00017"},{"issue":"4","key":"1_CR23","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1109\/TITS.2018.2849505","volume":"20","author":"J-G Wang","year":"2019","unstructured":"Wang, J.-G., Zhou, L.-B.: Traffic light recognition with high dynamic range imaging and deep learning. IEEE Trans. Intell. Transp. Syst. 20(4), 1341\u20131352 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR24","unstructured":"Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference, vol. 2008, pp. 129\u2013136 (2008)"}],"container-title":["Lecture Notes in Computer Science","Cyberspace Safety and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73671-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:15:13Z","timestamp":1625570113000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73671-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030736705","9783030736712"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73671-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyberspace Safety and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"css2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.hainanu.edu.cn\/scscs\/css2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"82","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}