A Comprehensive Survey on Deepfake Detection Techniques

Authors

  • Duha A. Sultan Dept. of Biology, Education College for Girls University of Mosul, Mosul, Iraq
  • Laheeb M. Ibrahim Dept. of Software Engineering, College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

Keywords:

Machine learning, Deep learning, Deepfake, GAN, Deepfake detection, Forgery detection, Convolutional Neural Network

Abstract

Improving machine learning and artificial intelligence makes it possible to swap someone else's face and voice in a high realism video which made distinguishing the difference between the real and fake videos difficult. Although this technology can be used in many useful fields like advertising, video gaming, and film industry, most of the time it is used for malicious purposes. Therefore, many studies have been done to understand how deepfake works and how to detect these fake videos or images. In this paper, an inclusive study is presented on the existing techniques used for creating and detecting fake materials and analyzing these techniques that are used by several researchers in addition to the great role of artificial intelligence and deep learning on improving them.

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Generalpipeline. (a) Real Datasets (CELEBA) and Deepfake images, (b) Every images in (a) topographies extractedby EM algorithm; (c) classifiers (K-NN, SVM, LDA).

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Published

31.12.2022

How to Cite

Sultan, D. A. ., & Ibrahim, L. M. . (2022). A Comprehensive Survey on Deepfake Detection Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 189–202. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2430

Issue

Vol. 10 No. 3s (2022)

Section

Research Article

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Copyright (c) 2023 Duha A. Sultan, Laheeb M. Ibrahim

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