Paper Info
Title
Image forgery detection through residual-based local descriptors and block-matching
Abstract
We propose a new image forgery detection technique which fuses the outputs of two very diverse tools, based on machine learning and block-matching, respectively. The machine-learning tool builds upon some local descriptors recently proposed in the steganalysis field, which are selected and merged based on an ad hoc measure of reliability. The block-matching tool leverages on the patchmatch algorithm for fast search of candidate matchings. Both tools are fine-tuned so as to optimize their fusion which, in turn, exploits the respective strengths and weaknesses of each tool. The proposed technique ranked first in phase 1 of the first Image Forensics Challenge organized in 2013 by the IEEE Signal Processing Society.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7026072
Image Processing
Keywords
Field
DocType
image forensics,image fusion,image matching,learning (artificial intelligence),IEEE Signal Processing Society,Image Forensics Challenge,ad hoc reliability measure,block-matching tool,image forgery detection technique,local descriptors,machine learning,output fusion optimization,patchmatch algorithm,residual-based local descriptors,steganalysis field,Digital forensics,forgery detection,machine learning
Residual,Data mining,Signal processing,Pattern recognition,Digital forensics,Ranking,Computer science,Exploit,Artificial intelligence,Steganalysis,Fuse (electrical),Strengths and weaknesses
Conference
ISSN
Citations 
PageRank 
1522-4880
30
0.94
References 
Authors
17
3
Name
Order
Citations
PageRank
Davide Cozzolino135819.37
Diego Gragnaniello216212.51
Luisa Verdoliva397157.12