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Proceedings Paper

A cost-effective decision tree based approach to steganalysis
Author(s): Liyun Li; Husrev Taha Sencar; Nasir Memon
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Paper Abstract

An important issue concerning real-world deployment of steganalysis systems is the computational cost of ac- quiring features used in building steganalyzers. Conventional approach to steganalyzer design crucially assumes that all features required for steganalysis have to be computed in advance. However, as the number of features used by typical steganalyzers grow into thousands and timing constraints are imposed on how fast a decision has to be made, this approach becomes impractical. To address this problem, we focus on machine learning aspect of steganalyzer design and introduce a decision tree based approach to steganalysis. The proposed steganalyzer system can minimize the average computational cost for making a steganalysis decision while still maintaining the detection accuracy. To demonstrate the potential of this approach, a series of experiments are performed on well known steganography and steganalysis techniques.

Paper Details

Date Published: 22 March 2013
PDF: 7 pages
Proc. SPIE 8665, Media Watermarking, Security, and Forensics 2013, 86650P (22 March 2013); doi: 10.1117/12.2008527
Show Author Affiliations
Liyun Li, Polytechnic Institute of New York Univ. (United States)
Husrev Taha Sencar, TOBB Univ. of Economics and Technology (Turkey)
Nasir Memon, Polytechnic Institute of New York Univ. (United States)

Published in SPIE Proceedings Vol. 8665:
Media Watermarking, Security, and Forensics 2013
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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