{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:19:27Z","timestamp":1790813967102,"version":"4.1.0"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":257,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3304903"],"award-info":[{"award-number":["2023YFC3304903"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    The global economy and society are seriously threatened by the pervasive spread of counterfeit goods. Their high level of simulation makes real and fake goods extremely similar in appearance and difficult to distinguish. The existing identification techniques mostly use CNNs and transformer architectures. However, CNNs have limitations in modeling long\u2010range dependencies, leading to their limited classification performance, while vision transformers (ViTs), although excellent in modeling long\u2010range dependencies, the quadratic computational complexity of their self\u2010attention mechanism makes it difficult to be widely used in real\u2010world scenarios with limited computational resources. According to recent research, long\u2010range relationships can be accurately modeled using the state space model (SSM), which is represented by Mamba, while preserving linear computational complexity. Motivated by this, we proposed CGMamba, a SSM\u2010based intelligent recognition model for counterfeit goods. Specifically, we constructed a novel hybrid basic block called global\u2010local feature aggregation (GLFA). This block greatly enhances the feature extraction capability for counterfeit goods by deeply integrating the local feature extraction capability of the CNN and the global modeling capability of SSM. It is composed of three components: a local feature extractor, a global feature extractor, and an adaptive feature aggregation module (AFAM). In addition, to address the problem of lack of counterfeit goods image data, we constructed a large counterfeit goods dataset containing 101,480 images covering 104 categories for model training and evaluation. The experimental results showed that CGMamba achieved 90.99% Top 1 accuracy on the self\u2010constructed dataset and 79.5% on the public dataset CNFOOD\u2010241, which significantly outperforms the existing methods. The source code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/wth1998\/CGMamba.git\">https:\/\/github.com\/wth1998\/CGMamba.git<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1155\/int\/9939880","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T06:20:09Z","timestamp":1758003609000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["CGMamba: Intelligent Identification of Counterfeit Goods Based on State Space Models"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1849-2728","authenticated-orcid":false,"given":"Yuheng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tinghao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3697-9983","authenticated-orcid":false,"given":"Lijuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9930-2903","authenticated-orcid":false,"given":"Qian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"Trends in Trade in Counterfeit and Pirated Goods","author":"Trade I.","year":"2016","journal-title":"Economic Cooperation And Development (OECD) and the EUIPO 2019 Report"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.5539\/ibr.v16n11p1"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/ijcs.12466"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1108\/jfmm-09-2019-0204"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/joe.22156"},{"key":"e_1_2_11_6_2","volume-title":"Survey of Techniques for the Fight Against Counterfeit Goods and Intellectual Property Rights (IPR) Infringement","author":"Baldini G.","year":"2015"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.04.026"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.3797\/scipharm.1202-03"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.30764\/1819-2785-2018-13-2-88-96"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0141-y"},{"key":"e_1_2_11_11_2","article-title":"Deep Neural Network-Based Detection of Counterfeit Products From Smartphone Images","author":"Garcia-Cotte H.","year":"2024","journal-title":"arXiv preprint arXiv:2410.05969"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00633-1"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-022-02352-7"},{"key":"e_1_2_11_14_2","volume-title":"A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network","author":"Kumar S. 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