Machine learning in image steganalysis / Hans Georg Schaathun.

Format:

Book

Author/Creator:

Schaathun, Hans Georg.

Series:

Wiley - IEEE

Language:

English

Subjects (All):

Machine learning.

Wavelets (Mathematics).

Data encryption (Computer science).

Physical Description:

1 online resource (394 p.)

Edition:

1st edition

Place of Publication:

Hoboken : Wiley, 2012.

Language Note:

English

System Details:

text file

Summary:

Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This is typically done by hiding the message within a non-sensitive document. Steganalysis is the art and science of detecting such hidden messages. The task in steganalysis is to take an object (communication) and classify it as either a steganogram or a clean document. Most recent solutions apply classification algorithms from machine learning and pattern recognition, which tackle problems too complex for analytical solution by teaching computers to learn from empirical data. Part 1of the book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context. Part 2 is a survey of a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Part 3 is an in-depth study of machine learning techniques and classifier algorithms, and presents a critical assessment of the experimental methodology and applications in steganalysis.Key features: . Serves as a tutorial on the topic of steganalysis with brief introductions to much of the basic theory provided, and also presents a survey of the latest research.. Develops and formalises the application of machine learning in steganalysis; with much of the understanding of machine learning to be gained from this book adaptable for future study of machine learning in other applications. . Contains Python programs and algorithms to allow the reader to modify and reproduce outcomes discussed in the book.. Includes companion software available from the author's website.

Contents:

Front Matter

Overview. Introduction

Steganography and Steganalysis

Getting Started with a Classifier

Features. Histogram Analysis

Bit-Plane Analysis

More Spatial Domain Features

The Wavelets Domain

Steganalysis in the JPEG Domain

Calibration Techniques

Classifiers. Simulation and Evaluation

Support Vector Machines

Other Classification Algorithms

Feature Selection and Evaluation

The Steganalysis Problem

Future of the Field

Bibliography

Index.

Notes:

Description based upon print version of record.

Description based on PDF viewed 12/22/2015.

Includes bibliographical references and index.

ISBN:

9786613916372

9781283603928

1283603926

9781118437988

1118437985

9781118437964

1118437969

9781118437957

1118437950

OCLC:

809555684