A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection
Abstract Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers. Digital image fakes...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88592-2 |
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| author | Hessa Alfraihi Muhammad Swaileh A. Alzaidi Hamed Alqahtani Abdulbasit A. Darem Ali M. Al-Sharafi Ahmad A. Alzahrani Menwa Alshammeri Abdulwhab Alkharashi |
| author_facet | Hessa Alfraihi Muhammad Swaileh A. Alzaidi Hamed Alqahtani Abdulbasit A. Darem Ali M. Al-Sharafi Ahmad A. Alzahrani Menwa Alshammeri Abdulwhab Alkharashi |
| author_sort | Hessa Alfraihi |
| collection | DOAJ |
| description | Abstract Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers. Digital image fakes mean adding infrequent patterns to the unique images, which causes a heterogeneous method in image properties. Copy move forgery is the firmest kind of image forgeries to be perceived. It occurs by duplicating the image part and then inserting it again in the image itself but in any other place. If original content is not accessible, then the forgery recognition technique is employed in image security. In contrast, methods that depend on deep learning (DL) have exposed good performance and suggested outcomes. Still, they provide general issues with a higher dependency on training data for a suitable range of hyperparameters. This manuscript presents an Enhancing Copy-Move Video Forgery Detection through Fusion-Based Transfer Learning Models with the Tasmanian Devil Optimizer (ECMVFD-FTLTDO) model. The objective of the ECMVFD-FTLTDO model is to perceive and classify copy-move forgery in video content. At first, the videos are transformed into distinct frames, and noise is removed using a modified wiener filter (MWF). Next, the ECMVFD-FTLTDO technique employs a fusion-based transfer learning (TL) process comprising three models: ResNet50, MobileNetV3, and EfficientNetB7 to capture diverse spatial features across various scales, thereby enhancing the capability of the model to distinguish authentic content from tampered regions. The ECMVFD-FTLTDO approach utilizes an Elman recurrent neural network (ERNN) classifier for the detection process. The Tasmanian devil optimizer (TDO) method is implemented to optimize the parameters of the ERNN classifier, ensuring superior convergence and performance. A wide range of simulation analyses is performed under GRIP and VTD datasets. The performance validation of the ECMVFD-FTLTDO technique portrayed a superior accuracy value of 95.26% and 92.67% compared to existing approaches under GRIP and VTD datasets. |
| format | Article |
| id | doaj-art-888f0a18441f4ad3a1b4ab227042b329 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-888f0a18441f4ad3a1b4ab227042b3292025-02-09T12:29:34ZengNature PortfolioScientific Reports2045-23222025-02-0115112710.1038/s41598-025-88592-2A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detectionHessa Alfraihi0Muhammad Swaileh A. Alzaidi1Hamed Alqahtani2Abdulbasit A. Darem3Ali M. Al-Sharafi4Ahmad A. Alzahrani5Menwa Alshammeri6Abdulwhab Alkharashi7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityDepartment of English Language, College of Language Sciences, King Saud UniversityDepartment of Information Systems, College of Computer Science, Center of Artificial Intelligence, King Khalid UniversityCenter for Scientific Research and Entrepreneurship, Northern Border UniversityDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of BishaDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura UniversityDepartment of Computer Science, College of Computer and Information Sciences, Jouf UniversityDepartment of Computer Science, College of Computing and Informatics, Saudi Electronic UniversityAbstract Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers. Digital image fakes mean adding infrequent patterns to the unique images, which causes a heterogeneous method in image properties. Copy move forgery is the firmest kind of image forgeries to be perceived. It occurs by duplicating the image part and then inserting it again in the image itself but in any other place. If original content is not accessible, then the forgery recognition technique is employed in image security. In contrast, methods that depend on deep learning (DL) have exposed good performance and suggested outcomes. Still, they provide general issues with a higher dependency on training data for a suitable range of hyperparameters. This manuscript presents an Enhancing Copy-Move Video Forgery Detection through Fusion-Based Transfer Learning Models with the Tasmanian Devil Optimizer (ECMVFD-FTLTDO) model. The objective of the ECMVFD-FTLTDO model is to perceive and classify copy-move forgery in video content. At first, the videos are transformed into distinct frames, and noise is removed using a modified wiener filter (MWF). Next, the ECMVFD-FTLTDO technique employs a fusion-based transfer learning (TL) process comprising three models: ResNet50, MobileNetV3, and EfficientNetB7 to capture diverse spatial features across various scales, thereby enhancing the capability of the model to distinguish authentic content from tampered regions. The ECMVFD-FTLTDO approach utilizes an Elman recurrent neural network (ERNN) classifier for the detection process. The Tasmanian devil optimizer (TDO) method is implemented to optimize the parameters of the ERNN classifier, ensuring superior convergence and performance. A wide range of simulation analyses is performed under GRIP and VTD datasets. The performance validation of the ECMVFD-FTLTDO technique portrayed a superior accuracy value of 95.26% and 92.67% compared to existing approaches under GRIP and VTD datasets.https://doi.org/10.1038/s41598-025-88592-2Forgery detectionTasmanian devil optimizerTransfer learningFake imagesElman recurrent neural network |
| spellingShingle | Hessa Alfraihi Muhammad Swaileh A. Alzaidi Hamed Alqahtani Abdulbasit A. Darem Ali M. Al-Sharafi Ahmad A. Alzahrani Menwa Alshammeri Abdulwhab Alkharashi A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection Scientific Reports Forgery detection Tasmanian devil optimizer Transfer learning Fake images Elman recurrent neural network |
| title | A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection |
| title_full | A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection |
| title_fullStr | A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection |
| title_full_unstemmed | A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection |
| title_short | A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection |
| title_sort | multi model feature fusion based transfer learning with heuristic search for copy move video forgery detection |
| topic | Forgery detection Tasmanian devil optimizer Transfer learning Fake images Elman recurrent neural network |
| url | https://doi.org/10.1038/s41598-025-88592-2 |
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