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A Survey of Anti-forensic for Face Image Forgery

Haitao Zhang*

Engineering Research Center of Digital Forensics of Ministry of Education, School of Computer, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Haitao Zhang. Email: email

Journal of Information Hiding and Privacy Protection 2022, 4(1), 41-51. https://doi.org/10.32604/jihpp.2022.031707

Abstract

Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face generation, face editing and face swapping; The latter is mainly the relevant forensic means based on spatial domain and frequency domain using deep learning technology. Then, this paper divides the existing anti-forensic works into three categories according to their method characteristics, namely hiding operation traces, forgery reconstruction and adversarial attack. Finally, this paper summarizes the limitations and prospects of the existing anti-forensic technologies.

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Cite This Article

H. Zhang, "A survey of anti-forensic for face image forgery," Journal of Information Hiding and Privacy Protection, vol. 4, no.1, pp. 41–51, 2022.



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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