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Review

A Survey of Deep Learning-Based Source Image Forensics

1
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
2
Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China
3
Department of Information Engineering, University of Florence, Via di S. Marta, 3, 50139 Florence, Italy
*
Authors to whom correspondence should be addressed.
Received: 7 February 2020 / Revised: 26 February 2020 / Accepted: 27 February 2020 / Published: 4 March 2020
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. View Full-Text
Keywords: image forensics; multimedia forensics; source identification; data driven methods image forensics; multimedia forensics; source identification; data driven methods
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MDPI and ACS Style

Yang, P.; Baracchi, D.; Ni, R.; Zhao, Y.; Argenti, F.; Piva, A. A Survey of Deep Learning-Based Source Image Forensics. J. Imaging 2020, 6, 9. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030009

AMA Style

Yang P, Baracchi D, Ni R, Zhao Y, Argenti F, Piva A. A Survey of Deep Learning-Based Source Image Forensics. Journal of Imaging. 2020; 6(3):9. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030009

Chicago/Turabian Style

Yang, Pengpeng; Baracchi, Daniele; Ni, Rongrong; Zhao, Yao; Argenti, Fabrizio; Piva, Alessandro. 2020. "A Survey of Deep Learning-Based Source Image Forensics" J. Imaging 6, no. 3: 9. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030009

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