Comparative study of deep learning techniques for DeepFake video detection
Deep learning addresses a wide range of complex challenges, spanning from computer vision to data analytics. It is also employed to develop softwares that pose threats to privacy and security. To develop a DeepFake video, an individual in the original video is replaced with someone else using deep l...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959524001218 |
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| author | Rozi Khan Muhammad Sohail Imran Usman Moid Sandhu Mohsin Raza Muhammad Azfar Yaqub Antonio Liotta |
| author_facet | Rozi Khan Muhammad Sohail Imran Usman Moid Sandhu Mohsin Raza Muhammad Azfar Yaqub Antonio Liotta |
| author_sort | Rozi Khan |
| collection | DOAJ |
| description | Deep learning addresses a wide range of complex challenges, spanning from computer vision to data analytics. It is also employed to develop softwares that pose threats to privacy and security. To develop a DeepFake video, an individual in the original video is replaced with someone else using deep learning. Various deep learning-based techniques have been proposed to detect DeepFakes. In this work, we extensively analyse DeepFake video detection techniques considering their strengths and limitations. We provide a comparative analysis along with discussing their architectures and performances. Finally, we propose hyperparameter settings that improve deep learning model’s overall accuracy and efficiency. |
| format | Article |
| id | doaj-art-19e9cd8c85a946f9a00d2bbe0f31aaff |
| institution | Kabale University |
| issn | 2405-9595 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj-art-19e9cd8c85a946f9a00d2bbe0f31aaff2024-12-10T04:14:25ZengElsevierICT Express2405-95952024-12-0110612261239Comparative study of deep learning techniques for DeepFake video detectionRozi Khan0Muhammad Sohail1Imran Usman2Moid Sandhu3Mohsin Raza4Muhammad Azfar Yaqub5Antonio Liotta6Department of Computer Sciences, National University of Sciences and Technology, Balochistan Campus, 87300, PakistanRiphah College of Computing, Riphah International University, Faisalabad Campus, 38000, PakistanDepartment of Computer Sciences, National University of Sciences and Technology, Balochistan Campus, 87300, PakistanAustralian e-Health Research Centre, CSIRO, Queensland, AustraliaDepartment of Computer Sciences, National University of Sciences and Technology, Balochistan Campus, 87300, PakistanFaculty of Engineering, Free University of Bozen-Bolzano, Bolzano, 39100, Italy; Corresponding author.Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, 39100, ItalyDeep learning addresses a wide range of complex challenges, spanning from computer vision to data analytics. It is also employed to develop softwares that pose threats to privacy and security. To develop a DeepFake video, an individual in the original video is replaced with someone else using deep learning. Various deep learning-based techniques have been proposed to detect DeepFakes. In this work, we extensively analyse DeepFake video detection techniques considering their strengths and limitations. We provide a comparative analysis along with discussing their architectures and performances. Finally, we propose hyperparameter settings that improve deep learning model’s overall accuracy and efficiency.http://www.sciencedirect.com/science/article/pii/S2405959524001218DeepFakeArtificial intelligenceDeep learningConvolutional neural networksGenerative adversarial networksRecurrent convolutional neural networks |
| spellingShingle | Rozi Khan Muhammad Sohail Imran Usman Moid Sandhu Mohsin Raza Muhammad Azfar Yaqub Antonio Liotta Comparative study of deep learning techniques for DeepFake video detection ICT Express DeepFake Artificial intelligence Deep learning Convolutional neural networks Generative adversarial networks Recurrent convolutional neural networks |
| title | Comparative study of deep learning techniques for DeepFake video detection |
| title_full | Comparative study of deep learning techniques for DeepFake video detection |
| title_fullStr | Comparative study of deep learning techniques for DeepFake video detection |
| title_full_unstemmed | Comparative study of deep learning techniques for DeepFake video detection |
| title_short | Comparative study of deep learning techniques for DeepFake video detection |
| title_sort | comparative study of deep learning techniques for deepfake video detection |
| topic | DeepFake Artificial intelligence Deep learning Convolutional neural networks Generative adversarial networks Recurrent convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2405959524001218 |
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