Forged Video Detection Using Deep Learning: A SLR
In today’s digital landscape, video and image data have emerged as pivotal and widely adopted means of communication. They serve not only as a ubiquitous mode of conveying information but also as indispensable evidential and substantiating elements across diverse domains, encompassing law enforcemen...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/6661192 |
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| author | Maryam Munawar Iram Noreen Raed S. Alharthi Nadeem Sarwar |
| author_facet | Maryam Munawar Iram Noreen Raed S. Alharthi Nadeem Sarwar |
| author_sort | Maryam Munawar |
| collection | DOAJ |
| description | In today’s digital landscape, video and image data have emerged as pivotal and widely adopted means of communication. They serve not only as a ubiquitous mode of conveying information but also as indispensable evidential and substantiating elements across diverse domains, encompassing law enforcement, forensic investigations, media, and numerous others. This study employs a systematic literature review (SLR) methodology to meticulously investigate the existing body of knowledge. An exhaustive review and analysis of precisely 90 primary research studies were conducted, unveiling a range of research methodologies instrumental in detecting forged videos. The study’s findings shed light on several research methodologies integral to the detection of forged videos, including deep neural networks, convolutional neural networks, Deepfake analysis, watermarking networks, and clustering, amongst others. This array of techniques highlights the field and emphasizes the need to combat the evolving challenges posed by forged video content. The study shows that videos are susceptible to an array of manipulations, with key issues including frame insertion, deletion, and duplication due to their dynamic nature. The main limitations of the domain are copy-move forgery, object-based forgery, and frame-based forgery. This study serves as a comprehensive repository of the latest advancements and techniques, structured, and summarized to benefit researchers and practitioners in the field. It elucidates the complex challenges inherent to video forensics. |
| format | Article |
| id | doaj-art-d489b7051497482bbf70eb38165189c9 |
| institution | DOAJ |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-d489b7051497482bbf70eb38165189c92025-08-20T03:23:46ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/6661192Forged Video Detection Using Deep Learning: A SLRMaryam Munawar0Iram Noreen1Raed S. Alharthi2Nadeem Sarwar3Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer Science and EngineeringDepartment of Computer ScienceIn today’s digital landscape, video and image data have emerged as pivotal and widely adopted means of communication. They serve not only as a ubiquitous mode of conveying information but also as indispensable evidential and substantiating elements across diverse domains, encompassing law enforcement, forensic investigations, media, and numerous others. This study employs a systematic literature review (SLR) methodology to meticulously investigate the existing body of knowledge. An exhaustive review and analysis of precisely 90 primary research studies were conducted, unveiling a range of research methodologies instrumental in detecting forged videos. The study’s findings shed light on several research methodologies integral to the detection of forged videos, including deep neural networks, convolutional neural networks, Deepfake analysis, watermarking networks, and clustering, amongst others. This array of techniques highlights the field and emphasizes the need to combat the evolving challenges posed by forged video content. The study shows that videos are susceptible to an array of manipulations, with key issues including frame insertion, deletion, and duplication due to their dynamic nature. The main limitations of the domain are copy-move forgery, object-based forgery, and frame-based forgery. This study serves as a comprehensive repository of the latest advancements and techniques, structured, and summarized to benefit researchers and practitioners in the field. It elucidates the complex challenges inherent to video forensics.http://dx.doi.org/10.1155/2023/6661192 |
| spellingShingle | Maryam Munawar Iram Noreen Raed S. Alharthi Nadeem Sarwar Forged Video Detection Using Deep Learning: A SLR Applied Computational Intelligence and Soft Computing |
| title | Forged Video Detection Using Deep Learning: A SLR |
| title_full | Forged Video Detection Using Deep Learning: A SLR |
| title_fullStr | Forged Video Detection Using Deep Learning: A SLR |
| title_full_unstemmed | Forged Video Detection Using Deep Learning: A SLR |
| title_short | Forged Video Detection Using Deep Learning: A SLR |
| title_sort | forged video detection using deep learning a slr |
| url | http://dx.doi.org/10.1155/2023/6661192 |
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