Mean Best Basis Algorithm for Wavelet Speech Parameterization

In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA...

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Main Authors: Galka, J., Ziolko, M.
Format: Conference Proceeding
Language:English
Subjects:
Basis algorithms
best basis
Cepstral analysis
Discrete wavelet transforms
Frequency conversion
Signal processing algorithms
Speech processing
Speech recognition
Wavelet coefficients
Wavelet packets
Wavelet transforms
wavelets
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Ziolko, M.
description In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients of the logistic regression model for blind image steganalysis. The logistic regression model is then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm.
doi_str_mv 10.1109/IIH-MSP.2009.298
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subjects Basis algorithms
best basis
Cepstral analysis
Discrete wavelet transforms
Frequency conversion
Signal processing algorithms
Speech processing
Speech recognition
Wavelet coefficients
Wavelet packets
Wavelet transforms
wavelets
title Mean Best Basis Algorithm for Wavelet Speech Parameterization
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