A Feature Selection Methodology for Steganalysis
Abstract
This Paper Presents a Methodology to Select Features Before Training a Classifier based on Support Vector Machines (Svm). in This Study 23 Features Presented in [1] Are Analysed. a Feature Ranking is Performed using a Fast Classifier Called K-Nearest-Neighbours Combined with a Forward Selection. the Result of the Feature Selection is afterward Tested on Svm to Select the Optimal Number of Features. This Method is Tested with the Outguess Steganographic Software and 14 Features Are Selected While Keeping the Same Classification Performances. Results Confirm that the Selected Features Are Efficient for a Wide Variety of Embedding Rates. the Same Methodology is Also Applied for Steghide and F5 to See If Feature Selection is Possible on These Schemes. © Springer-Verlag Berlin Heidelberg 2006.
Recommended Citation
Y. Miche et al., "A Feature Selection Methodology for Steganalysis," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4105 LNCS, pp. 49 - 56, Springer, Jan 2006.
The definitive version is available at https://doi.org/10.1007/11848035_9
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-354039392-4
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2006
