Machine Learning in Image Steganalysis

Hans Georg Schaathun 지음

Wiley-IEEE Press 2012년 09월 05일 출간

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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.

목차

Preface xiPART I OVERVIEW1 Introduction 31.1 Real Threat or Hype? 31.2 Artificial Intelligence and Learning 41.3 How to Read this Book 52 Steganography and Steganalysis 72.1 Cryptography versus Steganography 72.2 Steganography 82.3 Steganalysis 172.4 Summary and Notes 233 Getting Started with a Classifier 253.1 Classification 253.2 Estimation and Confidence 283.3 Using libSVM 303.4 Using Python 333.5 Images for Testing 383.6 Further Reading 39PART II FEATURES4 Histogram Analysis 434.1 Early Histogram Analysis 434.2 Notation 444.3 Additive Independent Noise 444.4 Multi-dimensional Histograms 544.5 Experiment and Comparison 635 Bit-plane Analysis 655.1 Visual Steganalysis 655.2 Autocorrelation Features 675.3 Binary Similarity Measures 695.4 Evaluation and Comparison 726 More Spatial Domain Features 756.1 The Difference Matrix 756.2 Image Quality Measures 826.3 Colour Images 866.4 Experiment and Comparison 867 The Wavelets Domain 897.1 A Visual View 897.2 The Wavelet Domain 907.3 Farid&'s Features 967.4 HCF in the Wavelet Domain 987.5 Denoising and the WAM Features 1017.6 Experiment and Comparison 1068 Steganalysis in the JPEG Domain 1078.1 JPEG Compression 1078.2 Histogram Analysis 1148.3 Blockiness 1228.4 Markov Model-based Features 1248.5 Conditional Probabilities 1268.6 Experiment and Comparison 1289 Calibration Techniques 1319.1 Calibrated Features 1319.2 JPEG Calibration 1339.3 Calibration by Downsampling 1379.4 Calibration in General 1469.5 Progressive Randomisation 148PART III CLASSIFIERS

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저자(글) Hans Georg Schaathun

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