Machine Learning in Image Steganalysis

<p><i>Steganography</i> 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. S<i>teganalysis</i> is...

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Bibliographic Details
Main Author Schaathun, Hans Georg
Format eBook Book
LanguageEnglish
Published Chichester, West Sussex Wiley 2012
WILEY
IEEE Press : Wiley
John Wiley & Sons, Incorporated
Wiley-IEEE Press
Wiley-Blackwell
Institution of Electrical Engineers
John Wiley & Sons Ltd
Edition1
SeriesWiley - IEEE
Subjects
Data encryption (Computer science)
Machine learning
SCIENCE
SCIENCE / Waves & Wave Mechanics
Signal Processing and Analysis
Wavelets (Mathematics)
Waves & Wave Mechanics
Online AccessGet full text
ISBN9780470663059
0470663057
9781118437964
1118437969
9781118437988
1118437985
1118437950
9781118437957
DOI10.1002/9781118437957

Cover

Table of Contents:
  • Preface xi PART I OVERVIEW 1 Introduction 3 1.1 Real Threat or Hype? 3 1.2 Artificial Intelligence and Learning 4 1.3 How to Read this Book 5 2 Steganography and Steganalysis 7 2.1 Cryptography versus Steganography 7 2.2 Steganography 8 2.3 Steganalysis 17 2.4 Summary and Notes 23 3 Getting Started with a Classifier 25 3.1 Classification 25 3.2 Estimation and Confidence 28 3.3 Using libSVM 30 3.4 Using Python 33 3.5 Images for Testing 38 3.6 Further Reading 39 PART II FEATURES 4 Histogram Analysis 43 4.1 Early Histogram Analysis 43 4.2 Notation 44 4.3 Additive Independent Noise 44 4.4 Multi-dimensional Histograms 54 4.5 Experiment and Comparison 63 5 Bit-plane Analysis 65 5.1 Visual Steganalysis 65 5.2 Autocorrelation Features 67 5.3 Binary Similarity Measures 69 5.4 Evaluation and Comparison 72 6 More Spatial Domain Features 75 6.1 The Difference Matrix 75 6.2 Image Quality Measures 82 6.3 Colour Images 86 6.4 Experiment and Comparison 86 7 The Wavelets Domain 89 7.1 A Visual View 89 7.2 The Wavelet Domain 90 7.3 Farid’s Features 96 7.4 HCF in the Wavelet Domain 98 7.5 Denoising and the WAM Features 101 7.6 Experiment and Comparison 106 8 Steganalysis in the JPEG Domain 107 8.1 JPEG Compression 107 8.2 Histogram Analysis 114 8.3 Blockiness 122 8.4 Markov Model-based Features 124 8.5 Conditional Probabilities 126 8.6 Experiment and Comparison 128 9 Calibration Techniques 131 9.1 Calibrated Features 131 9.2 JPEG Calibration 133 9.3 Calibration by Downsampling 137 9.4 Calibration in General 146 9.5 Progressive Randomisation 148 PART III CLASSIFIERS 10 Simulation and Evaluation 153 10.1 Estimation and Simulation 153 10.2 Scalar Measures 158 10.3 The Receiver Operating Curve 161 10.4 Experimental Methodology 170 10.5 Comparison and Hypothesis Testing 173 10.6 Summary 176 11 Support Vector Machines 179 11.1 Linear Classifiers 179 11.2 The Kernel Function 186 11.3 ν-SVM 189 11.4 Multi-class Methods 191 11.5 One-class Methods 192 11.6 Summary 196 12 Other Classification Algorithms 197 12.1 Bayesian Classifiers 198 12.2 Estimating Probability Distributions 203 12.3 Multivariate Regression Analysis 209 12.4 Unsupervised Learning 212 12.5 Summary 215 13 Feature Selection and Evaluation 217 13.1 Overfitting and Underfitting 217 13.2 Scalar Feature Selection 220 13.3 Feature Subset Selection 222 13.4 Selection Using Information Theory 225 13.5 Boosting Feature Selection 238 13.6 Applications in Steganalysis 239 14 The Steganalysis Problem 245 14.1 Different Use Cases 245 14.2 Images and Training Sets 250 14.3 Composite Classifier Systems 258 14.4 Summary 262 15 Future of the Field 263 15.1 Image Forensics 263 15.2 Conclusions and Notes 265 Bibliography 267 Index 279
  • Machine learning in image steganalysis -- Contents -- Preface -- Part I: Overview -- Chapter 1. Introduction -- Chapter 2. Steganography and Steganalysis -- Chapter 3. Getting Started with a Classifier -- Part II: Features -- Chapter 4. Histogram Analysis -- Chapter 5. Bit-plane Analysis -- Chapter 6. More Spatial Domain Features -- Chapter 7. The Wavelets Domain -- Chapter 8. Steganalysis in the JPEG Domain -- Chapter 9. Calibration Techniques -- Part III: Classifiers -- Chapter 10. Simulation and Evaluation -- Chapter 11. Support Vector Machines -- Chapter 12. Other Classification Algorithms -- Chapter 13. Feature Selection and Evaluation -- Chapter 14. The Steganalysis Problem -- Chapter 15. Future of the Field -- Bibliography -- Index
  • Cover -- Title Page -- Copyright -- Preface -- Chapter 1: Introduction -- 1.1 Real Threat or Hype? -- 1.2 Artificial Intelligence and Learning -- 1.3 How to Read this Book -- Chapter 2: Steganography and Steganalysis -- 2.1 Cryptography versus Steganography -- 2.2 Steganography -- 2.3 Steganalysis -- 2.4 Summary and Notes -- Chapter 3: Getting Started with a Classifier -- 3.1 Classification -- 3.2 Estimation and Confidence -- 3.3 Using libSVM -- 3.4 Using Python -- 3.5 Images for Testing -- 3.6 Further Reading -- Chapter 4: Histogram Analysis -- 4.1 Early Histogram Analysis -- 4.2 Notation -- 4.3 Additive Independent Noise -- 4.4 Multi-dimensional Histograms -- 4.5 Experiment and Comparison -- Chapter 5: Bit-plane Analysis -- 5.1 Visual Steganalysis -- 5.2 Autocorrelation Features -- 5.3 Binary Similarity Measures -- 5.4 Evaluation and Comparison -- Chapter 6: More Spatial Domain Features -- 6.1 The Difference Matrix -- 6.2 Image Quality Measures -- 6.3 Colour Images -- 6.4 Experiment and Comparison -- Chapter 7: The Wavelets Domain -- 7.1 A Visual View -- 7.2 The Wavelet Domain -- 7.3 Farid's Features -- 7.4 HCF in the Wavelet Domain -- 7.5 Denoising and the WAM Features -- 7.6 Experiment and Comparison -- Chapter 8: Steganalysis in the JPEG Domain -- 8.1 JPEG Compression -- 8.2 Histogram Analysis -- 8.3 Blockiness -- 8.4 Markov Model-based Features -- 8.5 Conditional Probabilities -- 8.6 Experiment and Comparison -- Chapter 9: Calibration Techniques -- 9.1 Calibrated Features -- 9.2 JPEG Calibration -- 9.3 Calibration by Downsampling -- 9.4 Calibration in General -- 9.5 Progressive Randomisation -- Chapter 10: Simulation and Evaluation -- 10.1 Estimation and Simulation -- 10.2 Scalar Measures -- 10.3 The Receiver Operating Curve -- 10.4 Experimental Methodology -- 10.5 Comparison and Hypothesis Testing -- 10.6 Summary
  • Chapter 11: Support Vector Machines -- 11.1 Linear Classifiers -- 11.2 The Kernel Function -- 11.3 ν-SVM -- 11.4 Multi-class Methods -- 11.5 One-class Methods -- 11.6 Summary -- Chapter 12: Other Classification Algorithms -- 12.1 Bayesian Classifiers -- 12.2 Estimating Probability Distributions -- 12.3 Multivariate Regression Analysis -- 12.4 Unsupervised Learning -- 12.5 Summary -- Chapter 13: Feature Selection and Evaluation -- 13.1 Overfitting and Underfitting -- 13.2 Scalar Feature Selection -- 13.3 Feature Subset Selection -- 13.4 Selection Using Information Theory -- 13.5 Boosting Feature Selection -- 13.6 Applications in Steganalysis -- Chapter 14: The Steganalysis Problem -- 14.1 Different Use Cases -- 14.2 Images and Training Sets -- 14.3 Composite Classifier Systems -- 14.4 Summary -- Chapter 15: Future of the Field -- 15.1 Image Forensics -- 15.2 Conclusions and Notes -- Bibliography -- Index