{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:52:07Z","timestamp":1773805927490,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Culture and Innovation of Hungary","award":["2022-2.1.1-NL-2022-00012"],"award-info":[{"award-number":["2022-2.1.1-NL-2022-00012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing effective adversarial methodologies is increasingly challenging. We present Truck Adversarial Camouflage Optimization (TACO), a novel framework that generates adversarial camouflage patterns on 3D vehicle models to deceive state-of-the-art object detectors. Adopting Unreal Engine 5, TACO integrates differentiable rendering with a Photorealistic Rendering Network to optimize adversarial textures targeted at YOLOv8. To ensure the generated textures are both effective in deceiving detectors and visually plausible, we introduce the Convolutional Smooth Loss function, a generalized smooth loss function. Experimental evaluations demonstrate that TACO significantly degrades YOLOv8\u2019s detection performance, achieving an AP@0.5 of 0.0099 on unseen test data. Furthermore, these adversarial patterns exhibit strong transferability to other object detection models such as Faster R-CNN and earlier YOLO versions.<\/jats:p>","DOI":"10.3390\/bdcc9030072","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T10:38:48Z","timestamp":1742380728000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4002-3231","authenticated-orcid":false,"given":"Adonisz","family":"Dimitriu","sequence":"first","affiliation":[{"name":"Techtra Technology Transfer Institute, Sz\u00e9chenyi Istv\u00e1n University, 9026 Gy\u0151r, Hungary"}]},{"given":"Tam\u00e1s Vilmos","family":"Michaletzky","sequence":"additional","affiliation":[{"name":"Techtra Technology Transfer Institute, Sz\u00e9chenyi Istv\u00e1n University, 9026 Gy\u0151r, Hungary"}]},{"given":"Viktor","family":"Remeli","sequence":"additional","affiliation":[{"name":"Techtra Technology Transfer Institute, Sz\u00e9chenyi Istv\u00e1n University, 9026 Gy\u0151r, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. 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