{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T14:31:54Z","timestamp":1793197914565,"version":"4.5.0"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forensic Sciences"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the nascent reliance on unmanned aerial systems as a means of surveillance, security, and monitoring, it is essential to ensure that video data legitimacy is assured. Object\u2010based Copy\u2010Move Forgery (CMF), in which an object is either copied or moved in the frame or across a frame to alter the visual description, is one of the major threats to video integrity. Such manipulations are a problem for traditional detection methods because object motion, occlusion, and irregular background are complex. This research presents an interpretable and robust deep learning architecture to identify such forgeries, that is, a combination of transfer learning with a new Multi\u2010Aspect Fossa Graph Transformer enhanced with Shuffle Attention (MAFGTN\u2010SA). To achieve this, a better generative adversarial network, which is built upon GoogLeNet, is used to extract deep spatial\u2010semantic features using normalized grayscale video frames. These characteristics are then represented by MAFGTN\u2010SA to represent complex object relationships between the frames and spatial inconsistencies to accurately classify genuine and tampered frames. In order to increase interpretability, the model incorporates Local Interpretable Model\u2010Agnostic Explanations (LIME) for saliency\u2010based visualization of tampered regions. Experimental evaluations on three benchmark datasets, SULFA, GRIP, VTD, CG\u20101050 v2.0, and COVERAGE, validate the effectiveness of the proposed method, attaining an accuracy of 98.73%, 96.78%, 98.45%, 97.92%, and 97.36%, respectively, and with high F1\u2010scores of 98.73%, 98.5%, 97.98%, 97.41%, and 96.93%. The results highlight the framework's superior detection capability, reliability, and explainability across diverse video forgery scenarios.<\/jats:p>","DOI":"10.1111\/1556-4029.70437","type":"journal-article","created":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T18:01:24Z","timestamp":1786903284000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable copy\u2010move forgery detection in videos: Generative adversarial network\u2010based transfer learning and multi\u2010aspect transformer with shuffle attention"],"prefix":"10.1111","author":[{"given":"M.","family":"Raghavendra Reddy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Annamalai University  Chidambaram Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Anbu Ananth","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Annamalai University  Chidambaram Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Santhosh Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering G Pulla Reddy Engineering College  Kurnool Andhra Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2024.07.005"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010023\u201015609\u20101"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11196\u2010023\u201010030\u20101"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003546153-9"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi16030097"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11760\u2010025\u201003972\u20105"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201001112\u20100"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3452609"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010025\u201011009\u20108"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.10.030"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130110"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3357395"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2024.103863"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010024\u201010558\u20108"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277\u2010024\u201011028\u2010z"},{"key":"e_1_2_12_17_1","unstructured":"CastroM BallesterosDM RenzaD.CG\u20101050: Original and tampered images (color and grayscale). 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