Schaathun Machine Learning in Image Steganalysis

1. Auflage 2012
ISBN: 978-1-118-43796-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 296 Seiten, E-Book

Reihe: Wiley - IEEE

Steganography is the art of communicating a secretmessage, hiding the very existence of a secret message. This istypically done by hiding the message within a non-sensitivedocument. Steganalysis is the art and science ofdetecting such hidden messages. The task in steganalysis isto take an object (communication) and classify it as either asteganogram or a clean document. Most recent solutions applyclassification algorithms from machine learning and patternrecognition, which tackle problems too complex for analyticalsolution by teaching computers to learn from empiricaldata.
Part 1of the book is an introduction to steganalysis aspart of the wider trend of multimedia forensics, as well as apractical tutorial on machine learning in this context. Part2 is a survey of a wide range of feature vectors proposed forsteganalysis with performance tests and comparisons. Part 3is an in-depth study of machine learning techniques and classifieralgorithms, and presents a critical assessment of the experimentalmethodology and applications in steganalysis.
Key features:
* Serves as a tutorial on the topic of steganalysis with briefintroductions to much of the basic theory provided, and alsopresents a survey of the latest research.
* Develops and formalises the application of machine learning insteganalysis; with much of the understanding of machine learning tobe gained from this book adaptable for future study of machinelearning in other applications.
* Contains Python programs and algorithms to allow the reader tomodify and reproduce outcomes discussed in the book.
* Includes companion software available from the author'swebsite.

Autoren/Hrsg.

Schaathun, Hans Georg

Inhaltsverzeichnis

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 nu-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