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A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture

by Eman Abdelfattah, Ausif Mahmood
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 5
Year of Publication: 2011
Authors: Eman Abdelfattah, Ausif Mahmood
10.5120/3297-4503

Eman Abdelfattah, Ausif Mahmood . A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture. International Journal of Computer Applications. 27, 5 ( August 2011), 18-26. DOI=10.5120/3297-4503

@article{ 10.5120/3297-4503,
author = { Eman Abdelfattah, Ausif Mahmood },
title = { A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 5 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number5/3297-4503/ },
doi = { 10.5120/3297-4503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:58.971808+05:30
%A Eman Abdelfattah
%A Ausif Mahmood
%T A Multi-Algorithm, High Reliability, Extensible Steganalyzer using Services Oriented Architecture
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 5
%P 18-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a unified Steganalyzer that can work with different media types such as images and audios. It is also capable of providing improved accuracy in stego detection through the use of multiple algorithms. The designed system integrates different steganalysis techniques in a reliable Steganalyzer by using a Services Oriented Architecture (SOA). Other contributions of the research done in this paper include, an improved Mel-Cepstrum technique for audio wav files feature extraction that results in better accuracy in stego detection (> 99.9%), improved overall classification system that is based on three individual classifiers; a Neural Network classifier, a Support Vector Machines classifier, and an AdaBoost algorithm based classifier. Finally, an extensible classifier is introduced that allows incorporation of detecting new embedding techniques to the current system, so that the framework will continue to provide reliable stego detection for future embedding algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Mel-Cepstrum Support Vector Machines Neural Networks AdaBoost

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