{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T07:53:11Z","timestamp":1773733991265,"version":"3.50.1"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Security and Privacy"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Deep neural networks (DNNs) have significantly advanced the classification of remote\u2010sensing images (RSIs), achieving remarkable performance in critical applications such as disaster monitoring, urban planning, and environmental assessment. However, despite their sophistication, these systems remain highly vulnerable to adversarial attacks that can undermine their reliability in real\u2010world deployments. Current adversarial attack methods suffer from a critical drawback: they produce visually apparent perturbations characterized by artificial geometric patterns and unnatural textures that are easily identified by both human observers and automated detection systems, limiting their practical threat potential. To overcome these limitations, we introduce the environment\u2010adaptive stealth attack (EASA) framework, which strategically combines stealth patch placement with environment\u2010conscious perturbation generation to create highly effective adversarial examples with high visual fidelity. The framework consists of two complementary components: the stealth region locator (SRL) performs comprehensive environmental analysis using HSV\u2010based sky detection, discrete cosine transform (DCT) frequency\u2010domain texture evaluation, and gradient\u2010aware region identification. It incorporates probability\u2010guided sampling to optimize the placement of attacks while preserving spatial naturalness; the Adaptive Environment Perturbation Optimizer (AEPO) generates physically plausible perturbations via multilayer noise synthesis, integrating environmental color schemes through illumination physics modeling, and utilizing momentum\u2010based optimization with projection constraints to control perturbation intensity. Comprehensive experiments across diverse state\u2010of\u2010the\u2010art deep learning architectures demonstrate that EASA achieves a favorable balance between attack effectiveness and visual naturalness. While maintaining competitive attack success rates, EASA improves visual fidelity and environmental realism compared to existing approaches, enhancing the imperceptibility of adversarial perturbations. This work contributes to a deeper understanding of the trade\u2010offs between attack stealthiness and effectiveness in remote sensing systems, providing valuable insights for developing more comprehensive defense strategies.<\/jats:p>","DOI":"10.1002\/spy2.70195","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:51:58Z","timestamp":1769521918000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Environment\u2010Adaptive Stealth Attack Framework for Natural Camouflage Adversarial Attacks in Remote\u2010Sensing Image"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0003-9758-4750","authenticated-orcid":false,"given":"Dianlong","family":"Fang","sequence":"first","affiliation":[{"name":"School of Computer and Information Anhui Normal University  Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Anhui Normal University  Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Anhui Normal University  Anhui China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3286826"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9554514"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fclim.2025.1520242"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2956318"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15051307"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-57640-w"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10518-023-01716-9"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.3233\/JHS-230037"},{"key":"e_1_2_14_10_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3382159","article-title":"A High Spatial Resolution Aerial Image Dataset and an Efficient Scene Classification Model","volume":"62","author":"Lin Y.","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15194804"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09446-y"},{"key":"e_1_2_14_13_1","first-page":"1","article-title":"Edge Feature Enhancement for Fine\u2010Grained Segmentation of Remote Sensing Images","volume":"62","author":"Chen Z.","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_14_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3314641","article-title":"CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote\u2010Sensing Image Semantic Segmentation","volume":"61","author":"Wu H.","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/22797254.2024.2447344"},{"key":"e_1_2_14_16_1","unstructured":"C.Szegedy W.Zaremba I.Sutskever et al. 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