Evaluation of Feature Selection Measures for Steganalysis

Steganalysis has attracted researchers' attention overwhelmingly in last few years which discriminate stego images from non-stego images. The performance of a Steganalysis depends not only on the choice of classifier but also on features that are used to represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalization. In this paper, kullback divergence measure, chernoff distance measure and linear regression are used for relevant feature selection. The performance of steganalysis using different measures used for feature selection is compared and evaluated in terms of classification error and computation time of training classifier. Experimental results show that Linear regression measure used for feature selection outperforms other measures used for feature selection in terms of both classification error and compilation time.

[1]  Niels Provos,et al.  Hide and Seek: An Introduction to Steganography , 2003, IEEE Secur. Priv..

[2]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[4]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[5]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[6]  David Kahn,et al.  The History of Steganography , 1996, Information Hiding.

[7]  Sung-Bae Cho,et al.  Forward selection method with regression analysis for optimal gene selection in cancer classification , 2007, Int. J. Comput. Math..

[8]  Andreas Pfitzmann,et al.  Attacks on Steganographic Systems , 1999, Information Hiding.

[9]  Hany Farid,et al.  Detecting hidden messages using higher-order statistical models , 2002, Proceedings. International Conference on Image Processing.

[10]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[11]  Ying Wang,et al.  Optimized Feature Extraction for Learning-Based Image Steganalysis , 2007, IEEE Transactions on Information Forensics and Security.

[12]  Guo-Shiang Lin,et al.  A feature-based classification technique for blind image steganalysis , 2005, IEEE Transactions on Multimedia.

[13]  Sushil Jajodia,et al.  Exploring steganography: Seeing the unseen , 1998 .

[14]  Jesús S. Aguilar-Ruiz,et al.  Incremental wrapper-based gene selection from microarray data for cancer classification , 2006, Pattern Recognit..

[15]  Karim Faez,et al.  Image Steganalysis Based on Statistical Moments of Wavelet Subband Histograms in Different Frequencies and Support Vector Machine , 2007, Third International Conference on Natural Computation (ICNC 2007).

[16]  Jessica J. Fridrich,et al.  Practical steganalysis of digital images: state of the art , 2002, IS&T/SPIE Electronic Imaging.

[17]  R Kahavi,et al.  Wrapper for feature subset selection , 1997 .

[18]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[19]  Siwei Lyu,et al.  Steganalysis using higher-order image statistics , 2006, IEEE Transactions on Information Forensics and Security.

[20]  Jessica Fridrich,et al.  Determining the stego algorithm for JPEG images , 2006 .

[21]  William A. Pearlman,et al.  Steganalysis of additive-noise modelable information hiding , 2003, IS&T/SPIE Electronic Imaging.