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A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

Xin Liu*, Xiao Chen

Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Xin Liu. Email: email

Journal of Information Hiding and Privacy Protection 2020, 2(2), 87-94. https://doi.org/10.32604/jihpp.2020.09839

Abstract

In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On this basis, the challenges faced in this field and future research directions are discussed.

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Cite This Article

X. Liu and X. Chen, "A survey of gan-generated fake faces detection method based on deep learning," Journal of Information Hiding and Privacy Protection, vol. 2, no.2, pp. 87–94, 2020.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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