ISBN-10:
0470663057
ISBN-13:
9780470663059
Pub. Date:
10/12/2012
Publisher:
Wiley
Machine Learning in Image Steganalysis / Edition 1

Machine Learning in Image Steganalysis / Edition 1

by Hans Georg Schaathun, Bogdan Vrusias

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Overview

Machine Learning in Image Steganalysis / Edition 1

Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This 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. It looks at a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Python programs and algorithms are provided to allow readers to modify and reproduce outcomes discussed in the book.

Product Details

ISBN-13: 9780470663059
Publisher: Wiley
Publication date: 10/12/2012
Series: Wiley - IEEE Series
Pages: 296
Product dimensions: 6.80(w) x 9.90(h) x 2.30(d)

About the Author

Hans Georg Schaathun, Department of Computing, University of Surrey, UK
Dr Schaathun was previously a lecturer in coding and cryptography at the University of Bergen. Since February 2006, he has been a lecturer at the University of Surrey, UK, belonging to the research group in Digital Watermarking and Multimedia Security. His main research areas are applications of coding theory in information hiding, and machine learning techniques in steganalysis. He teaches Computer Security and Steganography at MSc level, and Functional Programming Techniques at u/g level. Dr Scaathun has published more than 35 international, peer-reviewed articles, and is an associate editor of EURASIP Journal of Information Security.

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

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