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Review
. 2020 Mar 4;6(3):9.
doi: 10.3390/jimaging6030009.

A Survey of Deep Learning-Based Source Image Forensics

Pengpeng Yang  1   2 , Daniele Baracchi  3 , Rongrong Ni  1   2 , Yao Zhao  1   2 , Fabrizio Argenti  3 , Alessandro Piva  3
Affiliations
Review

A Survey of Deep Learning-Based Source Image Forensics

Pengpeng Yang et al. J Imaging. .

Abstract

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

Keywords: data driven methods; image forensics; multimedia forensics; source identification.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The structure of the presented review.
Figure 2
Figure 2
The framework of the deep learning-based algorithms for source forensics. (Section X.X) indicates the subsection where the related technique is described in detail.
See this image and copyright information in PMC

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