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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959524001218 |
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| Summary: | 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. |
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| ISSN: | 2405-9595 |