Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Benjamin M Rodriguez

ISBN 10: 1288311931 / ISBN 13: 9781288311934

Published by Creative Media Partners, LLC, 2012

About this Item

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods | Benjamin M Rodriguez | Taschenbuch | Kartoniert / Broschiert | Englisch | 2012 | Creative Media Partners, LLC | EAN 9781288311934 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 122188328

Bibliographic Details

• Title: Multi-Class Classification for Identifying JPEG Steganography Embedding Methods
• Publisher: Creative Media Partners, LLC
• Publication Date: 2012
• Language: English
• ISBN 10: 1288311931
• ISBN 13: 9781288311934
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 246W x 189H x 10D millimeters 246 millimeters width by 189 millimeters height by 10 millimeters depth
• Weight: 354 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Over 725 steganography tools are available over the Internet, each providing a method for covert transmission of secret messages. This research presents four steganalysis advancements that result in an algorithm that identifies the steganalysis tool used to embed a secret message in a JPEG image file. The algorithm includes feature generation, feature preprocessing, multi-class classification and classifier fusion. The first contribution is a new feature generation method which is based on the decomposition of discrete cosine transform (DCT) coefficients used in the JPEG image encoder. The generated features are better suited to identifying discrepancies in each area of the decomposed DCT coefficients. Second, the classification accuracy is further improved with the development of a feature ranking technique in the preprocessing stage for the kernel Fisher's discriminant (KFD) and support vector machines (SVM) classifiers in the kernel space during the training process. Third, for the KFD and SVM two-class classifiers a classification tree is designed from the kernel space to provide a multi-class classification solution for both methods. Fourth, by analyzing a set of classifiers, signature detectors, and multi-class classification methods a classifier fusion system is developed to increase the detection accuracy of identifying the embedding method used in generating the steganography images.