Advantages of using Feature Selection Techniques on Steganalysis Schemes
Abstract
Steganalysis Consists in Classifying Documents as Steganographied or Genuine. This Paper Presents a Methodology for Steganalysis based on a Set of 193 Features with Two Main Goals: Determine a Sufficient Number of Images for Effective Training of a Classifier in the Obtained High-Dimensional Space, and Use Feature Selection to Select Most Relevant Features for the Desired Classification. Dimensionality Reduction is Performed using a Forward Selection and Reduces the Original 193 Features Set by a Factor of 13, with overall Same Performance. © Springer-Verlag Berlin Heidelberg 2007.
Recommended Citation
Y. Miche et al., "Advantages of using Feature Selection Techniques on Steganalysis Schemes," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4507 LNCS, pp. 606 - 613, Springer, Jan 2007.
The definitive version is available at https://doi.org/10.1007/978-3-540-73007-1_73
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-354073006-4
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 2007
