NDLI: Quantitative Steganalysis Based on Wavelet Domain HMT and PLSR

Content Provider	IEEE Xplore Digital Library
Author	Ziwen Sun Hui Li
Copyright Year	2011
Abstract	Aiming at the problem of estimation of secret message length in steganalysis, this paper presents a quantitative steganalysis method based on HMT (Hidden Markov Tree) and PLSR (Partial Least Squares Regression) to solve the problem. In this paper, three 2-State HMT models are modeled respectively for wavelet coefficients in the horizontal, vertical and diagonal directions. In order to calculate the parameters of HMT, EM (Estimation and Maximization) algorithm is adopted to train the HMT models. The parameters are used as the 66-D feature of image. Then, the quantitative steganalyzer which is used to estimate the message length is established by combining HMT with PLSR. The proposed scheme is evaluated by constructing quantitative steganalyzers for F5, outguess and MB, simulation results demonstrate that these quantitative steaganalyzers can estimate the message embedding rates accurately and fast.
Starting Page	216
Ending Page	220
File Size	367534
Page Count	5
File Format	PDF
ISBN	9781457703270
DOI	10.1109/DCABES.2011.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Wavelet domain Computational modeling Quantitative steganalysis IEEE Press Embedding rate Hidden Markov models Estimation Hidden markov tree model Partial least squares regression Wavelet coefficients
Content Type	Text
Resource Type	Article

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