NDLI: Blind detection of image splicing based on run length matrix combined properties

Content Provider	IEEE Xplore Digital Library
Author	Han Liu Yun Yang Minqing Shang
Copyright Year	2012
Description	Author affiliation: School of Automation and Information Engineering, Xi'an University of Technology, 710048, China (Han Liu; Yun Yang; Minqing Shang)
Abstract	Image splicing is a technique commonly used in image tampering. In order to achieve image splicing blind detection, a blind, passive, yet effective splicing detection method is proposed in this paper. In this method run length matrix is used to extract image feature and generate the identification model with combination of Neighborhood DCT Coefficient Co-occurrence Matrix Feature and Markov Feature. Support vector machines (SVM) also is selected as classifier for training and testing while genetic algorithm is used to optimize parameters based on evaluation criteria AUC. Experimental results show that there is high classification accuracy for obtained model by this method.
Starting Page	4545
Ending Page	4550
File Size	132967
Page Count	6
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6359340
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Discrete cosine transforms Markov processes Splicing Support vector machines Genetic algorithms Accuracy AUC blind detection run length matrix Markov
Content Type	Text
Resource Type	Article

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