{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:47:49Z","timestamp":1759826869366,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Foundation of State Key Laboratory","award":["JCKYS2023LD3"],"award-info":[{"award-number":["JCKYS2023LD3"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>This paper presents HE-DMDeception, a novel adversarial attack network that integrates human visual deception with deep model deception to enhance the security of 3D object detection. Existing patch-based and camouflage methods can mislead deep learning models but struggle to generate visually imperceptible, high-quality textures. Our framework employs a CycleGAN-based camouflage network to generate highly camouflaged background textures, while a dedicated deception module disrupts non-maximum suppression (NMS) and attention mechanisms through optimized constraints that balance attack efficacy and visual fidelity. To overcome the scarcity of annotated vehicle data, an image segmentation module based on the pre-trained Segment Anything (SAM) model is introduced, leveraging a two-stage training strategy combining semi-supervised self-training and supervised fine-tuning. Experimental results show that the minimum P@0.5 values (50%, 55%, 20%, 25%, 25%) were achieved by HE-DMDeception across You Only Look Once version 8 (YOLOv8), Real-Time Detection Transformer (RT-DETR), Fast Region-based Convolutional Neural Network (Faster-RCNN), Single Shot MultiBox Detector (SSD), and MaskRegion-based Convolutional Neural Network (Mask RCNN) detection models, while maintaining high visual consistency with the original camouflage. These findings demonstrate the robustness and practicality of HE-DMDeception, offering new insights into 3D object detection adversarial attacks.<\/jats:p>","DOI":"10.3390\/info16100867","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:05:36Z","timestamp":1759824336000},"page":"867","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HE-DMDeception: Adversarial Attack Network for 3D Object Detection Based on Human Eye and Deep Learning Model Deception"],"prefix":"10.3390","volume":"16","author":[{"given":"Pin","family":"Zhang","sequence":"first","affiliation":[{"name":"Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawen","family":"Liu","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4263-4683","authenticated-orcid":false,"given":"Yichao","family":"Teng","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha 410022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiazheng","family":"Ni","sequence":"additional","affiliation":[{"name":"Naval Research Institute of PLA, Beijing 100161, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuansun","family":"Xiaobo","sequence":"additional","affiliation":[{"name":"Naval Research Institute of PLA, Beijing 100161, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Wang","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjing 210007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, A., Yin, Z., Liu, S., Tang, S., and Liu, X. 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