Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 4 — Issue 1 — March 2022

Extensive Analysis of Deep Learning-based Deepfake Video Detection

https://doi.org/10.36548/jucct.2022.1.001
D. Myvizhi
Department of Computer Science and Engineering, Government College of Technology, Anna University, Chennai, India
J. C. Miraclin Joyce Pamila
Professor, Department of Computer Science and Engineering, Government College of Technology, Anna University, Chennai, India
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How to Cite

Myvizhi, D., and J. C. Miraclin Joyce Pamila. 2022. “Extensive Analysis of Deep Learning-Based Deepfake Video Detection”. Journal of Ubiquitous Computing and Communication Technologies 4 (1): 1-8. https://doi.org/10.36548/jucct.2022.1.001.
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Keywords

— Deepfake detection
— face-swapping
— deep learning
— CNN
— RNN
Published: 28-04-2022

Abstract

Deepfake is the practice of replacing an existing image or video with someone else’s likeness. Currently, the spread of face-swapping deepfake strategies is increasing, producing a considerable range of naturalistic fake videos that cause danger to everyone. Due to the issue made by deepfake, recognizing between real and fake videos becomes a major issue. Deep learning is an efficient and useful approach for detecting deepfake videos and images. Research in recent years has focused on understanding how deepfake works and a number of deep learning-based approaches that are developed to do so. The aim of this study is to give an in-depth look at how several architectures work, along with the challenges encountered when detecting deepfake videos. It examines the existing deepfake detection methods using machine learning and deep learning approaches.

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