A Review on Deepfake generation and Detection based on Deep learning: Approaches, and Future Challenges

Authors

  • Israa Mishkhal University of diyala Author
  • Nibras Abdullah Universiti Sains Malaysia Author

DOI:

https://doi.org/10.69923/jxhytj79

Keywords:

Artificial intelligence, deep learning, Deepfake generation, deepfake detection,: Face manipulation techniques, Fake images

Abstract

In recent years, applications of deepfake, particularly to achieve political, economic, or social reputation aims, have been become widespread. These applications do not require high-level professional technical skills. Also, deep learning techniques like Generative Adversarial networks (GANs) have enhanced deepfake, making it more realistic. So, several researchers are looking for developing an effective method to detect a fake image or video. This paper provides a comprehensive overview of several proposed deepfake generation approaches and the approaches used to detect any manipulation. Based on feature extraction methods, this study provides an extensive review of face manipulation, especially focusing on facial swap, re-enactment, and attribute manipulation. Additionally, the study describes all existing deepfake methods and evaluates the presented detection models based on the most effective deep learning algorithms by comparing their respective evaluation metrics. Moreover, it presents the challenges and gapes in trying to enhance and develop deepfake detection techniques. It assists readers in understanding the generation and detection of deepfake mechanisms and presents the field limitations and future works.

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Author Biographies

  • Israa Mishkhal, University of diyala

    Israa Mishkhal was born in Iraq, in Baqubah. She obtained a bachelor’s degree in computer science from Diyala University. She holds a master's degree in computer science from Ball State University (BSU) in the United States of America. She is a Ph.D. student at Universiti Sains Malaysia, Computer Science. She is a lecturer at College Science/Diyala University, Iraq (Diyala). She has many research papers in national and international conferences.

    email: israaadnan@uodiyala.edu.iq, israa_adnan85@student.usm.my

  • Nibras Abdullah, Universiti Sains Malaysia

    Dr. Nibras Abdullah Born in Yemen .He is a distinguished academic and researcher, currently serving as a permanent faculty member at Hodeidah University, Yemen, and as an Assistant Professor (Senior Lecturer) at the School of Computer Sciences, Universiti Sains Malaysia, in Penang, Malaysia. He obtained a bachelor of Engineering from College of Engineering and Petroleum, Hadhramout University of Science and Technology, Yemen, 2003. He holds a master of Computer Science in Computer Sciences from Universiti Sains Malaysia, 2010and a PhD in Computer Science: Specializing in Multimedia Network Protocols, National Advanced IPv6 Center of Excellence from Universiti Sains Malaysia, 2017. :Email: nibras@usm.my

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Published

09/30/2024

Issue

Section

Articles

How to Cite

[1]
“A Review on Deepfake generation and Detection based on Deep learning: Approaches, and Future Challenges”, IJApSc, vol. 1, no. 2, pp. 12–29, Sep. 2024, doi: 10.69923/jxhytj79.

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