Machine Learning in Image Steganalysis

Cover Image Copyright Year: 2012
Author(s): Hans Georg Schaathun
Publisher: Wiley-IEEE Press
Content Type : Books & eBooks
Topics: Communication, Networking & Broadcasting ;  Components, Circuits, Devices & Systems ;  Computing & Processing ;  Robotics & Control Systems ;  Signal Processing & Analysis
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Abstract

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.

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      Front Matter

      Page(s): i - xi
      Copyright Year: 2012

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      The prelims comprise:
      Half-Title Page
      Title Page
      Copyright Page
      Table of Contents
      Preface View full abstract»

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      Introduction

      Page(s): 1 - 6
      Copyright Year: 2012

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      This chapter contains sections titled:
      Real Threat or Hype?
      Artificial Intelligence and Learning
      How to Read this Book View full abstract»

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      Steganography and Steganalysis

      Page(s): 7 - 24
      Copyright Year: 2012

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      This chapter contains sections titled:
      Cryptography versus Steganography
      Steganography
      Steganalysis
      Summary and Notes View full abstract»

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      Getting Started with a Classifier

      Page(s): 25 - 39
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      This chapter contains sections titled:
      Classification
      Estimation and Confidence
      Using libSVM
      Using Python
      Images for Testing
      Further Reading View full abstract»

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      Histogram Analysis

      Page(s): 43 - 63
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      This chapter contains sections titled:
      Early Histogram Analysis
      Notation
      Additive Independent Noise
      Multi-dimensional Histograms
      Experiment and Comparison View full abstract»

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      Bit-Plane Analysis

      Page(s): 65 - 73
      Copyright Year: 2012

      Wiley-IEEE Press eBook Chapters

      This chapter contains sections titled:
      Visual Steganalysis
      Autocorrelation Features
      Binary Similarity Measures
      Evaluation and Comparison View full abstract»

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      More Spatial Domain Features

      Page(s): 75 - 87
      Copyright Year: 2012

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      This chapter contains sections titled:
      The Difference Matrix
      Image Quality Measures
      Colour Images
      Experiment and Comparison View full abstract»

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      The Wavelets Domain

      Page(s): 89 - 106
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      This chapter contains sections titled:
      A Visual View
      The Wavelet Domain
      Farid's Features
      HCF in the Wavelet Domain
      Denoising and the WAM Features
      Experiment and Comparison View full abstract»

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      Steganalysis in the JPEG Domain

      Page(s): 107 - 129
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      This chapter contains sections titled:
      JPEG Compression
      Histogram Analysis
      Blockiness
      Markov Model-based Features
      Conditional Probabilities
      Experiment and Comparison View full abstract»

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      Calibration Techniques

      Page(s): 131 - 149
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      This chapter contains sections titled:
      Calibrated Features
      JPEG Calibration
      Calibration by Downsampling
      Calibration in General
      Progressive Randomisation View full abstract»

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      Simulation and Evaluation

      Page(s): 153 - 177
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      This chapter contains sections titled:
      Estimation and Simulation
      Scalar Measures
      The Receiver Operating Curve
      Experimental Methodology
      Comparison and Hypothesis Testing
      Summary View full abstract»

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      Support Vector Machines

      Page(s): 179 - 196
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      This chapter contains sections titled:
      Linear Classifiers
      The Kernel Function
      í-SVM
      Multi-class Methods
      One-class Methods
      Summary View full abstract»

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      Other Classification Algorithms

      Page(s): 197 - 215
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      This chapter contains sections titled:
      Bayesian Classifiers
      Estimating Probability Distributions
      Multivariate Regression Analysis
      Unsupervised Learning
      Summary View full abstract»

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      Feature Selection and Evaluation

      Page(s): 217 - 243
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      This chapter contains sections titled:
      Overfitting and Underfitting
      Scalar Feature Selection
      Feature Subset Selection
      Selection Using Information Theory
      Boosting Feature Selection
      Applications in Steganalysis View full abstract»

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      The Steganalysis Problem

      Page(s): 245 - 262
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      This chapter contains sections titled:
      Different Use Cases
      Images and Training Sets
      Composite Classifier Systems
      Summary View full abstract»

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      Future of the Field

      Page(s): 263 - 266
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      This chapter contains sections titled:
      Image Forensics
      Conclusions and Notes View full abstract»

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      Bibliography

      Page(s): 267 - 277
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      Index

      Page(s): 279 - 284
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