A Survey of Deep Learning-Based Source Image Forensics

A Survey of Deep Learning-Based Source Image Forensics

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 technique...

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Bibliographic Details
Main Authors: Pengpeng Yang, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti, Alessandro Piva
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Journal of Imaging
Subjects:
image forensics
multimedia forensics
source identification
data driven methods
Online Access:https://www.mdpi.com/2313-433X/6/3/9
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spelling doaj-2f88e0e44ca94d04bd836135233b19a82020-11-24T21:53:48ZengMDPI AGJournal of Imaging2313-433X2020-03-0163910.3390/jimaging6030009jimaging6030009A Survey of Deep Learning-Based Source Image ForensicsPengpeng Yang0Daniele Baracchi1Rongrong Ni2Yao Zhao3Fabrizio Argenti4Alessandro Piva5Institute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Information Engineering, University of Florence, Via di S. Marta, 3, 50139 Florence, ItalyInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information Science, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Information Engineering, University of Florence, Via di S. Marta, 3, 50139 Florence, ItalyDepartment of Information Engineering, University of Florence, Via di S. Marta, 3, 50139 Florence, ItalyImage 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.https://www.mdpi.com/2313-433X/6/3/9image forensicsmultimedia forensicssource identificationdata driven methods
collection DOAJ
language English
format Article
sources DOAJ
author Pengpeng Yang
Daniele Baracchi
Rongrong Ni
Yao Zhao
Fabrizio Argenti
Alessandro Piva
spellingShingle Pengpeng Yang
Daniele Baracchi
Rongrong Ni
Yao Zhao
Fabrizio Argenti
Alessandro Piva
A Survey of Deep Learning-Based Source Image Forensics
Journal of Imaging
image forensics
multimedia forensics
source identification
data driven methods
author_facet Pengpeng Yang
Daniele Baracchi
Rongrong Ni
Yao Zhao
Fabrizio Argenti
Alessandro Piva
author_sort Pengpeng Yang
title A Survey of Deep Learning-Based Source Image Forensics
title_short A Survey of Deep Learning-Based Source Image Forensics
title_full A Survey of Deep Learning-Based Source Image Forensics
title_fullStr A Survey of Deep Learning-Based Source Image Forensics
title_full_unstemmed A Survey of Deep Learning-Based Source Image Forensics
title_sort survey of deep learning-based source image forensics
publisher MDPI AG
series Journal of Imaging
issn 2313-433X
publishDate 2020-03-01
description 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.
topic image forensics
multimedia forensics
source identification
data driven methods
url https://www.mdpi.com/2313-433X/6/3/9
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