{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:27:15Z","timestamp":1775323635515,"version":"3.50.1"},"reference-count":11,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2023,1,1]]},"DOI":"10.1587\/transinf.2022mul0001","type":"journal-article","created":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T22:17:24Z","timestamp":1672525044000},"page":"31-35","source":"Crossref","is-referenced-by-count":4,"title":["Projection-Based Physical Adversarial Attack for Monocular Depth Estimation"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Renya","family":"DAIMO","sequence":"first","affiliation":[{"name":"Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoshi","family":"ONO","sequence":"additional","affiliation":[{"name":"Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] C. Zhao, Q. Sun, C. Zhang, Y. Tang, and F. Qian, \u201cMonocular depth estimation based on deep learning: An overview,\u201d Science China Technological Sciences, vol.63, no.9, pp.1612-1627, 2020. 10.1007\/s11431-020-1582-8","DOI":"10.1007\/s11431-020-1582-8"},{"key":"2","unstructured":"[2] I.J. Goodfellow, J. Shlens, and C. Szegedy, \u201cExplaining and harnessing adversarial examples,\u201d arXiv preprint arXiv:1412.6572, 2014. 10.48550\/arXiv.1412.6572"},{"key":"3","unstructured":"[3] K. Yamanaka, R. Matsumoto, K. Takahashi, and S. Fujii, \u201cAdversarial examples for monocular depth estimation [in Japanese],\u201d IPSJ SIG Tech. Rep. AVM, 2019."},{"key":"4","unstructured":"[4] Z. Zhang, X. Zhu, Y. Li, X. Chen, and Y. Guo, \u201cAdversarial attacks on monocular depth estimation,\u201d arXiv preprint arXiv:2003.10315, 2020. 10.48550\/arXiv.2003.10315"},{"key":"5","unstructured":"[5] A. Mathew, A.P. Patra, and J. Mathew, \u201cMonocular depth estimators: Vulnerabilities and attacks,\u201d arXiv preprint arXiv:2005.14302, 2020. 10.48550\/arXiv.2005.14302"},{"key":"6","unstructured":"[6] A. Wong, S. Cicek, and S. Soatto, \u201cTargeted adversarial perturbations for monocular depth prediction,\u201d arXiv preprint arXiv:2006. 08602, 2020. 10.48550\/arXiv.2006.08602"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, \u201cRobust physical-world attacks on deep learning visual classification,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1625-1634, 2018. 10.1109\/cvpr.2018.00175","DOI":"10.1109\/CVPR.2018.00175"},{"key":"8","unstructured":"[8] G. Lovisotto, H. Turner, I. Sluganovic, M. Strohmeier, and I. Martinovic, \u201cSlap: Improving physical adversarial examples with short-lived adversarial perturbations,\u201d arXiv preprint arXiv:2007. 04137, 2020. 10.48550\/arXiv.2007.04137"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] T. Suzuki, S. Takeshita, and S. Ono, \u201cAdversarial example generation using evolutionary multi-objective optimization,\u201d IEEE Congress on Evolutionary Computation, pp.2136-2144, 2019. 10.1109\/cec.2019.8790123","DOI":"10.1109\/CEC.2019.8790123"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, \u201cDeeper depth prediction with fully convolutional residual networks,\u201d Int&apos;l Conf. 3D Vision, pp.239-248, 2016. 10.1109\/3dv.2016.32","DOI":"10.1109\/3DV.2016.32"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Q. Zhang and H. Li, \u201cMOEA\/D: A multiobjective evolutionary algorithm based on decomposition,\u201d IEEE Trans. Evol. Comput., vol.11, no.6, pp.712-731, 2007. 10.1109\/tevc.2007.892759","DOI":"10.1109\/TEVC.2007.892759"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/1\/E106.D_2022MUL0001\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T04:08:53Z","timestamp":1673064533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/1\/E106.D_2022MUL0001\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,1]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022mul0001","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,1]]},"article-number":"2022MUL0001"}}