{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:41Z","timestamp":1761176261523,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Monocular Depth Estimation (MDE) is a core component in safety-critical systems such as autonomous driving. However, its vulnerability to physical adversarial attacks under practical black-box settings\u2014where attackers cannot access model parameters\u2014has not been thoroughly investigated. This paper proposes SpectraShift3D, a novel black-box physical attack framework that generates 3D-perceptual adversarial textures without requiring any model access. The framework integrates three key innovations: (1) a Feature Consistency Loss to enhance transferability across diverse MDE architectures; (2) an Adaptive Dynamic Depth Error Loss that selectively maximizes depth distortion in safety-critical regions (e.g., road obstacles); and (3) a Dynamic Occlusion Shape module to generate physically plausible occlusions for real-world robustness. Extensive experiments on six state-of-the-art MDE models (including MonoDepth2, MiDaS, and DepthAnything) show that SpectraShift3D misleads depth estimation by 13.31 meters on average (surpassing safe driving thresholds) with a 53.6% attack success rate, significantly outperforming existing methods. The attack remains effective under varying viewpoints, lighting, and distances. Our work highlights critical security risks in real-world MDE systems and establishes a standardized benchmark for black-box physical attacks, urging the community to reconsider the robustness of vision-based autonomous systems.<\/jats:p>","DOI":"10.3233\/faia251282","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:59Z","timestamp":1761127019000},"source":"Crossref","is-referenced-by-count":0,"title":["SpectraShift3D: Black-Box Physical Attacks on Monocular Depth Estimation via Robust 3D-Aware Textures"],"prefix":"10.3233","author":[{"given":"Changda","family":"Shi","sequence":"first","affiliation":[{"name":"Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shaanxi University of Science and Technology, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Wei","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:59Z","timestamp":1761127019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251282","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}