Paper Info
Title
Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques
Abstract
We propose an image forgery localization technique which fuses the outputs of three complementary tools, based on sensor noise, machine-learning and block-matching, respectively. To apply the sensor noise tool, a preliminary camera identification phase was required, followed by estimation of the camera fingerprint, and then forgery detection and localization. The machine-learning is based on a suitable local descriptor, while block-matching relies on the PatchMatch algorithm. A decision fusion strategy is then implemented, based on suitable reliability indexes associated with the binary masks. The proposed technique ranked first in phase 2 of the first Image Forensics Challenge organized in 2013 by the IEEE Information Forensics and Security Technical Committee (IFS-TC).
Year
DOI
Venue
2014
10.1109/ICIP.2014.7026073
Image Processing
Keywords
Field
DocType
cameras,digital forensics,image fusion,learning (artificial intelligence),IEEE Information Forensics and Security Technical Committee,PatchMatch algorithm,block-matching,camera fingerprint estimation,camera identification,camera-based techniques,decision fusion,feature-based techniques,forgery detection,image forensics challenge,image forgery localization,machine-learning,pixel-based techniques,sensor noise,Digital forensics,forgery detection,forgery localization,machine learning,sensor noise
Computer vision,Image forgery,Digital forensics,Pattern recognition,Ranking,Computer science,Fusion,Fingerprint,Artificial intelligence,Pixel,Fuse (electrical),Binary number
Conference
ISSN
Citations 
PageRank 
1522-4880
29
0.90
References 
Authors
21
3
Name
Order
Citations
PageRank
Davide Cozzolino135819.37
Diego Gragnaniello216212.51
Luisa Verdoliva397157.12