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Machine Learning in Image Steganalysis

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Machine Learning in Image Steganalysis Cover

 

Synopses & Reviews

Publisher Comments:

This text will develop and formalize the applications of machine learning in steganalysis.  Researchers in steganalysis typically have a background in signal and image processing, or multimedia coding, and lack the high level statistical knowledge required for using machine learning; this book will provide an accessible introduction to the subject, as applied specifically to steganalysis.  Much of the understanding of machine learning that can be gained from this book can be adapted for future study of machine learning in other applications.

 

The book begins with an overview of current systems and theory within steganalysis, followed by an introduction to the concepts and uses of machine learning.  Feature selection and classifiers are then discussed in depth.  The coverage of feature selection will include evaluation, completeness, wavelet features, JPEG features, and a brief exploration of less commonly known feature sets. The section on classifiers will cover Support Vector Machines, kernel methods in general, non-learning classifiers, ANN, unsupervised learning, and evaluation heuristics. Whilst the focus is on steganalysis, it will include some steganographic methods (eg F5, YASS) where these are necessary to evaluate steganalytic techniques. Whilst steganalysis and machine learning are integrated throughout the book and illustrated with numerous examples, a final section brings them together to examine training set selection, fusion in steganalysis, and comparisons and evaluations. The book concludes with discussion of future directions of the discipline, and the role of machine learning in steganalysis as part of the wider trend on digital forensics.

 

Theory will be discussed in the text, supported by complete source codes provided in an appendix and on a supporting website.

Synopsis:

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.

Table of Contents

Part One Overview 3

1 Introduction 5

1.1 Real threat or hype? 5

1.2 Artificial Intelligence and Learning 6

1.3 How to read this book 7

2 Steganography and Steganalysis 9

2.1 Cryptography versus Steganography 9

2.2 Steganography 10

2.2.1 The Prisoners’ Problem 10

2.2.2 Covers – Synthesis and Modification 12

2.2.3 Keys and Kerckhoffs’ Principle 13

2.2.4 LSB embedding 15

2.2.5 Steganography and Watermarking 17

2.2.6 Different media types 18

2.3 Steganalysis 19

2.3.1 The Objective of Steganalysis 19

2.3.2 Blind and Targeted Steganalysis 20

2.3.3 Main approaches to steganalysis 21

2.3.4 Example: pairs of values 24

2.4 Summary and Notes 26

3 Getting Started with a Classifier 27

3.1 Classification 27

3.1.1 Learning Classifiers 28

3.1.2 Accuracy 29

3.2 Estimation and Confidence 29

3.3 Using libSVM 32

3.3.1 Training and testing 32

3.3.2 Grid search and Cross-Validation 33

3.4 Using Python 35

3.4.1 Why we use Python 35

3.4.2 Getting started with Python 36

3.4.3 Scientific Computing 37

3.4.4 Python Imaging Library 38

3.4.5 An example: Image Histogram 38

3.5 Images for Testing 39

3.6 Further Reading 41

Part Two Features 43

4 Histogram Analysis 45

4.1 Early Histogram Analysis 45

4.2 Notation 46

4.3 Additive Independent Noise 46

4.3.1 The effect of noise 47

4.3.2 The Histogram Characteristic Function 48

4.3.3 Moments of the Characteristic Function 50

4.3.4 Amplitude of Local Extrema 54

4.4 Multi-dimensional Histograms 56

4.4.1 HCF Features for Colour Images 57

4.4.2 The Co-occurrence Matrix 58

4.5 Experiment and Comparison 64

5 Bit Plane Analysis 65

5.1 Visual Steganalysis 65

5.2 Auto-correlation 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

6.1.1 The EM features of Chen et al. 76

6.1.2 Markov Models and the SPAM features 78

6.1.3 Higher-order differences 80

6.1.4 Run-length analysis 81

6.2 Image Quality Measures 81

6.3 Colour Images 85

6.4 Experiment and Comparison 85

7 The Wavelets Domain 87

7.1 A Visual View 87

7.2 The Wavelet Domain 89

7.2.1 The Fast Wavelet Transform 89

7.2.2 Example: The Haar Wavelet 90

7.2.3 The Wavelet Transform in Python 91

7.2.4 Other Wavelet Transforms 92

7.3 Farid’s Features 94

7.3.1 The image statistics 94

7.3.2 The linear predictor 94

7.3.3 Notes 96

7.4 HCF in the wavelet domain 96

7.4.1 Notes and further reading 99

7.5 Denoising and the WAM features 99

7.5.1 The denoising algorithm 100

7.5.2 Locally Adaptive LAW-ML 101

7.5.3 Wavelet Absolute Moments 103

7.6 Experiment and Comparison 104

8 Steganalysis in the JPEG domain 105

8.1 JPEG compression 106

8.1.1 The compression 106

8.1.2 Programming JPEG steganography 108

8.1.3 Embedding in JPEG 110

8.2 Histogram Analysis 111

8.2.1 The JPEG histogram 112

8.2.2 First-order Features 115

8.2.3 Second-order Features 117

8.2.4 Histogram Characteristic Function 118

8.3 Blockiness 120

8.4 Markov model based features 122

8.5 Conditional Probabilities 124

8.6 Experiment and Comparison 125

9 Calibration Techniques 127

9.1 Calibrated Features 127

9.2 JPEG Calibration 129

9.2.1 The FRI-23 feature set 129

9.2.2 The Pevný features and Cartesian Calibration 131

9.3 Calibration by Downsampling 132

9.3.1 Down-sampling as calibration 133

9.3.2 Calibrated HCF-COM 134

9.3.3 The sum and difference images 136

9.3.4 Features for colour images 138

9.3.5 Pixel Selection 139

9.3.6 Other Features based on Downsampling 141

9.3.7 Evaluation and Notes 142

9.4 Calibration in General 142

9.5 Progressive Randomisation 143

Part Three Classifiers 145

10 Simulation and Evaluation 147

10.1 Estimation and Simulation 147

10.1.1 The binomial distribution 147

10.1.2 Probabilities and Sampling 148

10.1.3 Monte Carlo simulations 150

10.1.4 Confidence intervals 151

10.2 Scalar measures 152

10.2.1 Two error types 152

10.2.2 Common scalar measures 154

10.3 The Receiver Operating Curve 155

10.3.1 The libSVM API for Python 156

10.3.2 The ROC curve 158

10.3.3 Choosing a Point on the ROC Curve 160

10.3.4 Confidence and variance 161

10.3.5 The area under the curve 163

10.4 Experimental Methodology 164

10.4.1 Feature Storage 165

10.4.2 Parallel computation 166

10.4.3 The dangers of large-scale experiments 167

10.5 Comparison and hypothesis testing 167

10.5.1 The hypothesis test 168

10.5.2 Comparing two binomial proportions 168

10.6 Summary 170

11 Support Vector Machines 171

11.1 Linear Classifiers 171

11.1.1 Linearly Separable Problems 172

11.1.2 Non-separable Problems 175

11.2 The kernel function 179

11.2.1 Example: the XOR function 179

11.2.2 The SVM algorithm 180

11.3 _-SVM 182

11.4 Multi-class methods 183

11.5 One-class methods 184

11.5.1 The one-class SVM solution 185

11.5.2 Practical problems 186

11.5.3 Multiple hyperspheres 187

11.6 Summary 187

12 Other Classification Algorithms 189

12.1 Bayesian Classifiers 190

12.1.1 Classification Regions and Errors 191

12.1.2 Misclassification risk 192

12.1.3 The naïve Bayes classifier 193

12.1.4 A security criterion 194

12.2 Estimating Probability Distributions 195

12.2.1 The histogram 195

12.2.2 The kernel density estimator 196

12.3 Multivariate Regression Analysis 201

12.3.1 Linear Regression 201

12.3.2 Support Vector Regression 202

12.4 Unsupervised Learning 204

12.4.1 K-means clustering 204

12.5 Summary 206

13 Feature Selection and Evaluation 207

13.1 Overfitting and Underfitting 207

13.1.1 Feature Selection and Feature Extraction 209

13.2 Scalar feature selection 209

13.2.1 Analysis of Variance 210

13.3 Feature Subset Selection 212

13.3.1 Subset Evaluation 213

13.3.2 Search Algorithms 213

13.4 Selection using Information Theory 214

13.4.1 Entropy 215

13.4.2 Mutual Information 216

13.4.3 Multivariate Information 219

13.4.4 Information Theory with Continuous Sets 221

13.4.5 Estimation of entropy and information 222

13.4.6 Ranking Features 223

13.5 Boosting feature selection 225

13.6 Applications in Steganalysis 228

13.6.1 Correlation coefficient 229

13.6.2 Optimised feature vectors for JPEG 229

14 The Steganalysis Problem 233

14.1 Different use cases 233

14.1.1 Who are Alice and Bob? 233

14.1.2 Wendy’s role 235

14.1.3 Pooled Steganalysis 236

14.1.4 Quantitative Steganalysis 237

14.2 Images and Training Sets 238

14.2.1 Choosing Cover Source 238

14.2.2 The Training Scenario 241

14.2.3 The Steganalytic Game 244

14.3 Composite Classifier Systems 246

14.3.1 Fusion 246

14.3.2 A multi-layer classifier for JPEG 248

14.3.3 Benefits of composite classifiers 249

14.4 Summary 249

15 Future of the Field 251

15.1 Image Forensics 251

15.2 Conclusions and notes 253

Bibliography 255

Index 263

Product Details

ISBN:
9781118437988
Publisher:
Wiley-IEEE Press
Subject:
Science : Waves & Wave Mechanics
Author:
Schaathun, Hans Georg
Subject:
Waves & Wave Mechanics
Subject:
Signal processing
Subject:
Data encryption (Computer science)
Subject:
Machine learning
Copyright:
Series:
Wiley - IEEE
Publication Date:
20120905
Binding:
Electronic book text in proprietary or open standard format
Language:
English
Pages:
296
Dimensions:
250 x 150 x 15 mm 24 oz

Related Subjects

Science and Mathematics » Physics » General

Machine Learning in Image Steganalysis
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Product details 296 pages Wiley-IEEE Press - English 9781118437988 Reviews:
"Synopsis" by , 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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