Machine Learning in Image Steganalysis

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.

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 -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