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Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network

Author

Listed:
  • Muhammad Asad Arshed

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
    School of Systems and Technology, University of Management and Technology, Lahore 54770, 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
    Department of Computer and Information Sciences, Universiti Teknologi Petronas, Seri Iskandar 32160, Malaysia)

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.

Suggested Citation

  • Muhammad Asad Arshed & Ayed Alwadain & Rao Faizan Ali & Shahzad Mumtaz & Muhammad Ibrahim & Amgad Muneer, 2023. "Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3710-:d:1227657
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    References listed on IDEAS

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    1. Bo Wang & Xiaohan Wu & Yeling Tang & Yanyan Ma & Zihao Shan & Fei Wei, 2023. "Frequency Domain Filtered Residual Network for Deepfake Detection," Mathematics, MDPI, vol. 11(4), pages 1-13, February.
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