{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:29:47Z","timestamp":1783193387368,"version":"3.54.6"},"reference-count":63,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0831100"],"award-info":[{"award-number":["2018YFC0831100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Natural Science Foundation of China","award":["ZR2020MF027 568 and ZR2020MF143"],"award-info":[{"award-number":["ZR2020MF027 568 and ZR2020MF143"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["11170032008069"],"award-info":[{"award-number":["11170032008069"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/s21144701","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T10:50:38Z","timestamp":1625827838000},"page":"4701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0531-9064","authenticated-orcid":false,"given":"Yunxia","family":"Liu","sequence":"first","affiliation":[{"name":"Center for Optics Research and Engineering (CORE), Shandong University, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5280-7370","authenticated-orcid":false,"given":"Zeyu","family":"Zou","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Storage and Transportation Technology of Agricultural Products, Shandong Institute of Commerce and Technology, Jinan 250103, China"},{"name":"National Engineering Research Center for Agricultural Products Logistics, Jinan 250103, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5825-2575","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong University, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9809-5110","authenticated-orcid":false,"given":"Ngai-Fong Bonnie","family":"Law","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8808-2714","authenticated-orcid":false,"given":"Anil Anthony","family":"Bharath","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/ACCESS.2013.2260814","article-title":"Information forensics: An overview of the first decade","volume":"1","author":"Stamm","year":"2013","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arjona, R., Prada-Delgado, M., Arcenegui, J., and Baturone, I. 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