{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:40:18Z","timestamp":1756489218271,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,24]]},"DOI":"10.1145\/3674029.3674052","type":"proceedings-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T12:25:22Z","timestamp":1726057522000},"page":"138-142","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial 3D Objects Against Monocular Depth Estimators"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2227-5442","authenticated-orcid":false,"given":"Tam\u00e1s M\u00e1rk","family":"Feh\u00e9r","sequence":"first","affiliation":[{"name":"Dept. of Control Engineering and Information Technology, Budapest University of Technology and Economics, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-6080","authenticated-orcid":false,"given":"M\u00e1rton","family":"Szemenyei","sequence":"additional","affiliation":[{"name":"Dept. of Control Engineering and Information Technology, Budapest University of Technology and Economics, Hungary"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25090"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Shariq\u00a0Farooq Bhat Reiner Birkl Diana Wofk Peter Wonka and Matthias M\u00fcller. 2023. ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth. https:\/\/doi.org\/10.48550\/arXiv.2302.12288 arXiv:2302.12288 [cs].","DOI":"10.48550\/arXiv.2302.12288"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","unstructured":"Reiner Birkl Diana Wofk and Matthias M\u00fcller. 2023. MiDaS v3.1 \u2013 A Model Zoo for Robust Monocular Relative Depth Estimation. https:\/\/doi.org\/10.48550\/arXiv.2307.14460 arXiv:2307.14460 [cs].","DOI":"10.48550\/arXiv.2307.14460"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19839-7_30"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2022MUL0001"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMC52423.2021.9658898"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJVT.2023.3265363"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.01351"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2308.03108"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","unstructured":"Nikolaus Hansen. 2023. The CMA Evolution Strategy: A Tutorial. https:\/\/doi.org\/10.48550\/arXiv.1604.00772 arXiv:1604.00772 [cs stat].","DOI":"10.48550\/arXiv.1604.00772"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","unstructured":"Nikolaus Hansen Youhei Akimoto and Petr Baudis. 2019. CMA-ES\/pycma on Github. Zenodo DOI:10.5281\/zenodo.2559634. https:\/\/doi.org\/10.5281\/zenodo.2559634","DOI":"10.5281\/zenodo.2559634"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","unstructured":"Alwyn Mathew Aditya\u00a0Prakash Patra and Jimson Mathew. 2020. Monocular Depth Estimators: Vulnerabilities and Attacks. https:\/\/doi.org\/10.48550\/arXiv.2005.14302 arXiv:2005.14302 [cs].","DOI":"10.48550\/arXiv.2005.14302"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","unstructured":"Federico Nesti Giulio Rossolini Gianluca D\u2019Amico Alessandro Biondi and Giorgio Buttazzo. 2022. CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models. https:\/\/doi.org\/10.48550\/arXiv.2206.04365 arXiv:2206.04365 [cs].","DOI":"10.48550\/arXiv.2206.04365"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Ren\u00e9 Ranftl Alexey Bochkovskiy and Vladlen Koltun. 2021. Vision Transformers for Dense Prediction. 12179\u201312188. https:\/\/openaccess.thecvf.com\/content\/ICCV2021\/html\/Ranftl_Vision_Transformers_for_Dense_Prediction_ICCV_2021_paper.html","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3019967"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","unstructured":"Donghua Wang Wen Yao Tingsong Jiang Guijian Tang and Xiaoqian Chen. 2023. A Survey on Physical Adversarial Attack in Computer Vision. https:\/\/doi.org\/10.48550\/arXiv.2209.14262 arXiv:2209.14262 [cs].","DOI":"10.48550\/arXiv.2209.14262"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Chaowei Xiao Dawei Yang Bo Li Jia Deng and Mingyan Liu. 2019. MeshAdv: Adversarial Meshes for Visual Recognition. 6898\u20136907. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Xiao_MeshAdv_Adversarial_Meshes_for_Visual_Recognition_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00706"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Xiaohui Zeng Chenxi Liu Yu-Siang Wang Weichao Qiu Lingxi Xie Yu-Wing Tai Chi-Keung Tang and Alan\u00a0L. Yuille. 2019. Adversarial Attacks Beyond the Image Space. 4302\u20134311. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Zeng_Adversarial_Attacks_Beyond_the_Image_Space_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00443"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","unstructured":"Ziqi Zhang Xinge Zhu Yingwei Li Xiangqun Chen and Yao Guo. 2020. Adversarial Attacks on Monocular Depth Estimation. https:\/\/doi.org\/10.48550\/arXiv.2003.10315 arXiv:2003.10315 [cs].","DOI":"10.48550\/arXiv.2003.10315"}],"event":{"name":"ICMLT 2024: 2024 9th International Conference on Machine Learning Technologies","acronym":"ICMLT 2024","location":"Oslo Norway"},"container-title":["2024 9th International Conference on Machine Learning Technologies (ICMLT)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674029.3674052","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3674029.3674052","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:03:42Z","timestamp":1756487022000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674029.3674052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":20,"alternative-id":["10.1145\/3674029.3674052","10.1145\/3674029"],"URL":"https:\/\/doi.org\/10.1145\/3674029.3674052","relation":{},"subject":[],"published":{"date-parts":[[2024,5,24]]},"assertion":[{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}