
	Information Technology Journal
	Year: 2009 \| Volume: 8 \| Issue: 6 \| Page No.: 811-820 DOI: 10.3923/itj.2009.811.820

Feature Selection for Image Steganalysis using Hybrid Genetic Algorithm

Zhihua Xia, Xingming Sun, Jiaohua Qin and Changming Niu

Abstract: Learning-based methodology has been demonstrated to be an effective approach to dispose the steganalysis difficulties due to the variety of image texture. A crucial process of the learning-based steganalysis is to construct a low-dimensional feature set. In this study, a feature selection method based on Hybrid Genetic Algorithm (HGA) is presented to select feature subsets which not only contain fewer features, but also provide better detection performance for steganalysis. First, the general framework about utilizing Genetic Algorithm (GA) to do feature selection for steganalysis is presented. Then, we analyze similarity among individuals (SI) in each generation and the Transformation of Generations (TG) to determine whether the GA has converged into a local area. Next, according to the SI and TG, the restarting operation is incorporated into the HGA to allow the algorithm to escape from the unsatisfactory local area. In the experiments, three feature subsets are formed from a universal feature set for three typical steganography methods, respectively. The experimental results show that the classifiers using the feature subsets gain better detection accuracy and higher speed than those using the universal set.

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How to cite this article:

Zhihua Xia, Xingming Sun, Jiaohua Qin and Changming Niu, 2009. Feature Selection for Image Steganalysis using Hybrid Genetic Algorithm. Information Technology Journal, 8: 811-820.

DOI: 10.3923/itj.2009.811.820

URL: https://scialert.net/abstract/?doi=itj.2009.811.820

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