Buch, Englisch, 304 Seiten, Format (B × H): 172 mm x 251 mm, Gewicht: 591 g
ISBN: 978-0-470-66305-9
Verlag: John Wiley & Sons
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.
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Weitere Infos & Material
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
2.2.1 The Prisoners’ Problem 9
2.2.2 Covers – Synthesis and Modification 10
2.2.3 Keys and Kerckhoffs’ Principle 12
2.2.4 LSB Embedding 13
2.2.5 Steganography and Watermarking 15
2.2.6 Different Media Types 16
2.3 Steganalysis 17
2.3.1 The Objective of Steganalysis 17
2.3.2 Blind and Targeted Steganalysis 18
2.3.3 Main Approaches to Steganalysis 19
2.3.4 Example: Pairs of Values 22
2.4 Summary and Notes 23
3 Getting Started with a Classifier 25
3.1 Classification 25
3.1.1 Learning Classifiers 26
3.1.2 Accuracy 27
3.2 Estimation and Confidence 28
3.3 Using libSVM 30
3.3.1 Training and Testing 30
3.3.2 Grid Search and Cross-validation 31
3.4 Using Python 33
3.4.1 Why we use Python 33
3.4.2 Getting Started with Python 34
3.4.3 Scientific Computing 35
3.4.4 Python Imaging Library 36
3.4.5 An Example: Image Histogram 37
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
4.3.1 The Effect of Noise 45
4.3.2 The Histogram Characteristic Function 47
4.3.3 Moments of the Characteristic Function 48
4.3.4 Amplitude of Local Extrema 51
4.4 Multi-dimensional Histograms 54
4.4.1 HCF Features for Colour Images 55
4.4.2 The Co-occurrence Matrix 57
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
6.1.1 The EM Features of Chen et al. 76
6.1.2 Markov Models and the SPAM Features 79
6.1.3 Higher-order Differences 81
6.1.4 Run-length Analysis 81
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
7.2.1 The Fast Wavelet Transform 91
7.2.2 Example: The Haar Wavelet 92
7.2.3 The Wavelet Transform in Python 93
7.2.4 Other Wavelet Transforms 94
7.3 Farid’s Features 96
7.3.1 The Image Statistics 96
7.3.2 The Linear Predictor 96
7.3.3 Notes 98
7.4 HCF in the Wavelet Domain 98
7.4.1 Notes and Further Reading 100
7.5 Denoising and the WAM Features 101
7.5.1 The Denoising Algorithm 101
7.5.2 Locally Adaptive LAW-ML 103
7.5.3 Wavelet Absolute Moments 104
7.6 Experiment and Comparison 106
8 Steganalysis in the JPEG Domain 107
8.1 JPEG Compression 107
8.1.1 The Compression 108
8.1.2 Programming JPEG Steganography 110
8.1.3 Embedding in JPEG 111
8.2 Histogram Analysis 114
8.2.1 The JPEG Histogram 114
8.2.2 First-order Features 118
8.2.3 Second-order Features 119
8.2.4 Histogram Characteristic Function 121
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
9.2.1 The FRI-23 Feature Set 133
9.2.2 The Pevný Features and Cartesian Calibration 135
9.3 Calibration by Downsampling 137
9.3.1 Downsampling as Calibration 137
9.3.2 Calibrated HCF-COM 138
9.3.3 The Sum and Difference Im
