Acquisition source identification through a blind image classification-Reference-Cited by-同舟云学术

Acquisition source identification through a blind image classification

Published:2015-04 Issue:4 Volume:9 Page:329-337
ISSN:1751-9667
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Amerini Irene¹,Becarelli Rudy¹,Bertini Bruno¹,Caldelli Roberto¹²

Affiliation:

1. Media Integration and Communication Center, University of FlorenceItaly

2. CNIT – National Interuniversity Consortium for TelecommunicationsParmaItaly

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2014.0316

Reference18 articles.

1. Digital image forgery detection using passive techniques: A survey

2. A bibliography on blind methods for identifying image forgery;Mahdian B.;Signal Process.: Image Commun.,2010

3. 2008 N. Khanna G.T.‐C. Chiu J.P. Allebach E.J. Delp Forensic techniques for classifying scanner computer generated and digital camera images 1653 1656

4. 2008 ICASSP'08 C. McKay A. Swaminathan H. Gou M. Wu Image acquisition forensics: Forensic analysis to identify imaging source 1657 1660

5. How realistic is photorealistic?

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