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Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Language EnglishEnglish
Book Paperback
Book Multi-Class Classification for Identifying JPEG Steganography Embedding Methods Benjamin M Rodriguez
Libristo code: 08242968
Publishers Biblioscholar, November 2012
Over 725 steganography tools are available over the Internet, each providing a method for covert tra... Full description
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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.

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About the book

Full name Multi-Class Classification for Identifying JPEG Steganography Embedding Methods
Language English
Binding Book - Paperback
Date of issue 2012
Number of pages 194
EAN 9781288311934
ISBN 9781288311934
Libristo code 08242968
Publishers Biblioscholar
Weight 354
Dimensions 189 x 246 x 10
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