Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network

Muhammad Asad Arshed (), Ayed Alwadain, Rao Faizan Ali, Shahzad Mumtaz, Muhammad Ibrahim and Amgad Muneer ()
Additional contact information
Muhammad Asad Arshed: Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Ayed Alwadain: Computer Science Department, Community College, King Saud University, Riyadh 145111, Saudi Arabia
Rao Faizan Ali: School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
Shahzad Mumtaz: Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Muhammad Ibrahim: Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Amgad Muneer: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Mathematics, 2023, vol. 11, issue 17, 1-13

Abstract: With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images.

Keywords: deepfake; identification; Vision Transformer; pretrained; fine tuning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/17/3710/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/17/3710/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:17:p:3710-:d:1227657

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().