{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:34:43Z","timestamp":1773002083493,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":209,"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"}],"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>The increasing complexity of video tampering techniques poses a significant threat to the integrity and security of Internet of Multimedia Things (IoMT) ecosystems, particularly in resource\u2010constrained edge\u2010cloud infrastructures. This paper introduces Multiscale Gated Multihead Attention Depthwise Separable CNN (MGMA\u2010DSCNN), an advanced deep learning framework specifically optimized for real\u2010time tampered video detection in IoMT environments. By integrating lightweight convolutional neural networks (CNNs) with multihead attention mechanisms, MGMA\u2010DSCNN significantly enhances feature extraction while maintaining computational efficiency. Unlike conventional methods, this approach employs a multiscale attention mechanism to refine feature representations, effectively identifying deepfake manipulations, frame insertions, splicing, and adversarial forgeries across diverse multimedia streams. Extensive experiments on multiple forensic video datasets\u2014including the HTVD dataset\u2014demonstrate that MGMA\u2010DSCNN outperforms state\u2010of\u2010the\u2010art architectures such as VGGNet\u201016, ResNet, and DenseNet, achieving an unprecedented detection accuracy of 98.1%. Furthermore, by leveraging edge\u2010cloud synergy, our framework optimally distributes computational loads, effectively reducing latency and energy consumption, making it highly suitable for real\u2010time security surveillance and forensic investigations. These advancements position MGMA\u2010DSCNN as a scalable, high\u2010performance solution for next\u2010generation intelligent video authentication, offering robust, low\u2010latency detection capabilities in dynamic and resource\u2010constrained IoMT environments.<\/jats:p>","DOI":"10.1155\/int\/3744881","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T09:19:31Z","timestamp":1753780771000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Intelligent Surveillance Platform With Deep Tampered Video Detection in Secure Edge\u2010Cloud Services"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6719-5185","authenticated-orcid":false,"given":"Yuwen","family":"Shao","sequence":"first","affiliation":[]},{"given":"Qiuling","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5990-0834","authenticated-orcid":false,"given":"Junsong","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9620-6908","authenticated-orcid":false,"given":"Haiying","family":"Tian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1389-4581","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"e_1_2_14_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04272-z"},{"key":"e_1_2_14_2_2","doi-asserted-by":"crossref","unstructured":"NguyenT. 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