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An overview of GAN-DeepFakes detection: proposal, improvement, and evaluation

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Abstract

Image source forensics is commonly regarded as one of the most effective methods for blindly verifying the authenticity and integrity of digital images. The most recent topic related to image source forensics is the detection of DeepFakes generated using Generative adversarial networks (GANs). In recent years, with the rapid growth of GANs, a photo-realistic image can be easily generated from a random vector. Moreover, the images generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties distinguishing artificial from real images. Therefore, detecting DeepFakes generated by GANs is an important task. By reviewing the background of DeepFakes detection methods, this paper aims to provide readers with a systematic understanding of GAN, its variants used to create DeepFakes, and, more crucially, methods proposed to identify DeepFakes in the literature to date.

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Acknowledgements

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R125),Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

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Authors and Affiliations

  1. Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes (ENIG), University of Gabes, Gabes, Tunisia

    Fatma Ben Aissa, Mourad Zaied & Mahmoud Mejdoub

  2. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia

    Monia Hamdi

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  1. Fatma Ben Aissa
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Ben Aissa, F., Hamdi, M., Zaied, M. et al. An overview of GAN-DeepFakes detection: proposal, improvement, and evaluation. Multimed Tools Appl 83, 32343–32365 (2024). https://doi.org/10.1007/s11042-023-16761-4

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