This survey is extracted from my PhD thesis "Machine Learning Techniques for Image Forensics in Adversarial Setting" but I tried to describe new materials such as counter forensics and anti-counter forensics. This Survey gives to hint regarding Adversarial Image/Multimedia forensics ML-DL.
The paper also is accessible on Sciencedirect, arXiv and my phd thesis is accessible on http://clem.dii.unisi.it/~vipp/thesis_nowroozi.html or https://theses.eurasip.org/theses/859/machine-learning-techniques-for-image-forensics/
In the future I will try to import new methods regarding counter forensics and anti-counter forensics in ML/DL.
See also Thesis Machine Learning Techniques for Image Forensics in Adversarial Setting
http://clem.dii.unisi.it/~vipp/website_resources/publications/conferences/2018_Adversarial%20Multimedia%20Forensics%20Overview%20and%20Challenges%20Ahead.pdf and https://arxiv.org/pdf/1811.01629.pdf
Thanks for sharing my PhD thesis link also you can find my PhD thesis on (http://clem.dii.unisi.it/~vipp/thesis_nowroozi.html ) and (https://theses.eurasip.org/theses/859/machine-learning-techniques-for-image-forensics/).
In this Survey I tried to describe more regarding Counter and Anti-Counter Forensics in Multimedia Forensics that I called Adversarial Multimedia/Image Forensics especially in machine/deep learning. A new part in this survey devoted to secure architectures in multimedia forensics which all models approximately more robust against different kinds of adversarial attacks (you can find in the section of Anti-Counter Forensics). Although this area one of the hop topic in multimedia forensics, in the future I will try to update new methodologies especially in Anti-C-Forensics. In the future also I would like to improve these sections for DeepFake.
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