Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Proceedings of the First International Workshop on Information Hiding
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
Hide and Seek: An Introduction to Steganography
IEEE Security and Privacy
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
International Journal of Computer Mathematics - Bioinformatics
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Optimized Feature Extraction for Learning-Based Image Steganalysis
IEEE Transactions on Information Forensics and Security
A feature-based classification technique for blind image steganalysis
IEEE Transactions on Multimedia
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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.