{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:56:37Z","timestamp":1772452597339,"version":"3.50.1"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>\n                    \n                    \n                    The availability of picture editing software makes it simple to adjust and modify digital photos. A copy-paste forgery, which is used to hide items or create a scene that does not exist, is the most popular method of picture manipulation. There are numerous ways to spot this fake, but it is a laborious and challenging process. To address this problem, multiple forgery detection utilizing copy-move images is discovered and deep learning is enabled through a hybrid optimization technique. The deep learning for copy-move image multiple forgery detection using hybrid teacher learning optimization is presented in this research. Here, the input image is initially taken from Many Images Splicing Dataset, where multiple items are found using the You Only Look Once v3 algorithm to generate an anchor box. Additionally, ZF-Net is used to extract features from each item. Here, ZF-Net parameters are altered utilizing Hybrid Teacher-Learning-Based Optimization (HTLBO). Additionally, the performance of HTLBO_ZF-Net is examined using a variety of evaluation metrics, including accuracy, True Positive Rate, True Negative Rate, Positive Predictive Value and Negative Predictive Value, and it is discovered that these metrics have achieved values of 95.8%, 89.3%, 89.1%, 96.9% and 96.6%, respectively.\n                  <\/jats:p>","DOI":"10.1177\/24056456251320116","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T01:08:41Z","timestamp":1740445721000},"page":"237-254","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Teacher Learning Optimization Enabled Deep Learning for Copy-Move Image Multiple Forgery Detection"],"prefix":"10.1177","volume":"23","author":[{"given":"Chaitra","family":"Basavaraj","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Global Academy of Technology, Bangalore, Karnataka, India"}]},{"given":"PV","family":"Bhaskar Reddy","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, REVA University, Bangalore, Karnataka, India"}]}],"member":"179","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRIIS48246.2019.9073569"},{"key":"e_1_3_2_3_1","first-page":"687","volume-title":"YOLO v3-Tiny: Object detection and recognition using one stage improved model2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)","author":"Adarsh P.","year":"2020","unstructured":"Adarsh P., Rathi P., Kumar M. (2020). YOLO v3-Tiny: Object detection and recognition using one stage improved model. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 687\u2013694). IEEE."},{"key":"e_1_3_2_4_1","first-page":"3305","volume-title":"A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition2009 16th IEEE International Conference on Image Processing (ICIP)","author":"Bai Y.","year":"2009","unstructured":"Bai Y., Guo L., Jin L., Huang Q. (2009). A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 3305\u20133308). 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(1999). Methods for tamper detection in digital images. In Multimedia and Security, Workshop at ACM Multimedia (vol. 99, pp. 29\u201334)."},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/30.267415"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.08.059"},{"key":"e_1_3_2_17_1","volume-title":"Adapter-Based Incremental Learning for Face Forgery DetectionProceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Gao C.","year":"2024","unstructured":"Gao C., Xu Q., Qiao P., Xu K., Qian X., Dou Y. (2024). Adapter-Based Incremental Learning for Face Forgery Detection. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea."},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12051"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10620-9"},{"issue":"21","key":"e_1_3_2_20_1","first-page":"11640","article-title":"Image manipulation detection using convolutional neural network","volume":"12","author":"Kim D. H.","year":"2017","unstructured":"Kim D. H., Lee H. Y. (2017). Image manipulation detection using convolutional neural network. 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In Proceedings of 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China."},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103057"},{"key":"e_1_3_2_24_1","unstructured":"The Multiple Image Dataset (MISD) will be taken from \u201chttps:\/\/zenodo.org\/record\/5525829#.YkG52-dBzIW\u201d assessed on December 2022."},{"issue":"3","key":"e_1_3_2_25_1","first-page":"2000","article-title":"Copy-move forgery detection-a hybrid approach","volume":"17","author":"Patel J. J.","year":"2022","unstructured":"Patel J. J., Bhatt N. S. (2022). Copy-move forgery detection-a hybrid approach. 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