| Description |
1 online resource |
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text file |
| Summary |
"The only book to look at steganalysis from the perspective of machine learning theory, and to apply the common technique of machine learning to the particular field of steganalysis; ideal for people working in both disciplines"-- Provided by publisher. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Steganography and Steganalysis -- Getting Started with a Classifier -- Features. Histogram Analysis -- Bit-Plane Analysis -- More Spatial Domain Features -- The Wavelets Domain -- Steganalysis in the JPEG Domain -- Calibration Techniques -- Classifiers. Simulation and Evaluation -- Support Vector Machines -- Other Classification Algorithms -- Feature Selection and Evaluation -- The Steganalysis Problem -- Future of the Field. |
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Machine generated contents note: pt. I OVERVIEW -- 1. Introduction -- 1.1. Real Threat or Hype-- 1.2. Artificial Intelligence and Learning -- 1.3. How to Read this Book -- 2. Steganography and Steganalysis -- 2.1. Cryptography versus Steganography -- 2.2. Steganography -- 2.2.1. Prisoners' Problem -- 2.2.2. Covers -- Synthesis and Modification -- 2.2.3. Keys and Kerckhoffs' Principle -- 2.2.4. LSB Embedding -- 2.2.5. Steganography and Watermarking -- 2.2.6. Different Media Types -- 2.3. Steganalysis -- 2.3.1. Objective of Steganalysis -- 2.3.2. Blind and Targeted Steganalysis -- 2.3.3. Main Approaches to Steganalysis -- 2.3.4. Example: Pairs of Values -- 2.4. Summary and Notes -- 3. Getting Started with a Classifier -- 3.1. Classification -- 3.1.1. Learning Classifiers -- 3.1.2. Accuracy -- 3.2. Estimation and Confidence -- 3.3. Using libSVM -- 3.3.1. Training and Testing -- 3.3.2. Grid Search and Cross-validation -- 3.4. Using Python -- 3.4.1. Why we use Python -- 3.4.2. Getting Started with Python -- 3.4.3. Scientific Computing -- 3.4.4. Python Imaging Library -- 3.4.5. Example: Image Histogram -- 3.5. Images for Testing -- 3.6. Further Reading -- pt. II FEATURES -- 4. Histogram Analysis -- 4.1. Early Histogram Analysis -- 4.2. Notation -- 4.3. Additive Independent Noise -- 4.3.1. Effect of Noise -- 4.3.2. Histogram Characteristic Function -- 4.3.3. Moments of the Characteristic Function -- 4.3.4. Amplitude of Local Extrema -- 4.4. Multi-dimensional Histograms -- 4.4.1. HCF Features for Colour Images -- 4.4.2. Co-occurrence Matrix -- 4.5. Experiment and Comparison -- 5. Bit-plane Analysis -- 5.1. Visual Steganalysis -- 5.2. Autocorrelation Features -- 5.3. Binary Similarity Measures -- 5.4. Evaluation and Comparison -- 6. More Spatial Domain Features -- 6.1. Difference Matrix -- 6.1.1. EM Features of Chen et al. -- 6.1.2. Markov Models and the SPAM Features -- 6.1.3. Higher-order Differences -- 6.1.4. Run-length Analysis -- 6.2. Image Quality Measures -- 6.3. Colour Images -- 6.4. Experiment and Comparison -- 7. Wavelets Domain -- 7.1. Visual View -- 7.2. Wavelet Domain -- 7.2.1. Fast Wavelet Transform -- 7.2.2. Example: The Haar Wavelet -- 7.2.3. Wavelet Transform in Python -- 7.2.4. Other Wavelet Transforms -- 7.3. Farid's Features -- 7.3.1. Image Statistics -- 7.3.2. Linear Predictor -- 7.3.3. Notes -- 7.4. HCF in the Wavelet Domain -- 7.4.1. Notes and Further Reading -- 7.5. Denoising and the WAM Features -- 7.5.1. Denoising Algorithm -- 7.5.2. Locally Adaptive LAW-ML -- 7.5.3. Wavelet Absolute Moments -- 7.6. Experiment and Comparison -- 8. Steganalysis in the JPEG Domain -- 8.1. JPEG Compression -- 8.1.1. Compression -- 8.1.2. Programming JPEG Steganography -- 8.1.3. Embedding in JPEG -- 8.2. Histogram Analysis -- 8.2.1. JPEG Histogram -- 8.2.2. First-order Features -- 8.2.3. Second-order Features -- 8.2.4. Histogram Characteristic Function -- 8.3. Blockiness -- 8.4. Markov Model-based Features -- 8.5. Conditional Probabilities -- 8.6. Experiment and Comparison -- 9. Calibration Techniques -- 9.1. Calibrated Features -- 9.2. JPEG Calibration -- 9.2.1. FRI-23 Feature Set -- 9.2.2. Pevny Features and Cartesian Calibration -- 9.3. Calibration by Downsampling -- 9.3.1. Downsampling as Calibration -- 9.3.2. Calibrated HCF-COM -- 9.3.3. Sum and Difference Images -- 9.3.4. Features for Colour Images -- 9.3.5. Pixel Selection -- 9.3.6. Other Features Based on Downsampling -- 9.3.7. Evaluation and Notes -- 9.4. Calibration in General -- 9.5. Progressive Randomisation -- pt. III CLASSIFIERS -- 10. Simulation and Evaluation -- 10.1. Estimation and Simulation -- 10.1.1. Binomial Distribution -- 10.1.2. Probabilities and Sampling -- 10.1.3. Monte Carlo Simulations -- 10.1.4. Confidence Intervals -- 10.2. Scalar Measures -- 10.2.1. Two Error Types -- 10.2.2. Common Scalar Measures -- 10.3. Receiver Operating Curve -- 10.3.1. libSVM API for Python -- 10.3.2. ROC Curve -- 10.3.3. Choosing a Point on the ROC Curve -- 10.3.4. Confidence and Variance -- 10.3.5. Area Under the Curve -- 10.4. Experimental Methodology -- 10.4.1. Feature Storage -- 10.4.2. Parallel Computation -- 10.4.3. Dangers of Large-scale Experiments -- 10.5. Comparison and Hypothesis Testing -- 10.5.1. Hypothesis Test -- 10.5.2. Comparing Two Binomial Proportions -- 10.6. Summary -- 11. Support Vector Machines -- 11.1. Linear Classifiers -- 11.1.1. Linearly Separable Problems -- 11.1.2. Non-separable Problems -- 11.2. Kernel Function -- 11.2.1. Example: The XOR Function -- 11.2.2. SVM Algorithm -- 11.3. ν-SVM -- 11.4. Multi-class Methods -- 11.5. One-class Methods -- 11.5.1. One-class SVM Solution -- 11.5.2. Practical Problems -- 11.5.3. Multiple Hyperspheres -- 11.6. Summary -- 12. Other Classification Algorithms -- 12.1. Bayesian Classifiers -- 12.1.1. Classification Regions and Errors -- 12.1.2. Misclassification Risk -- 12.1.3. Naive Bayes Classifier -- 12.1.4. Security Criterion -- 12.2. Estimating Probability Distributions -- 12.2.1. Histogram -- 12.2.2. Kernel Density Estimator -- 12.3. Multivariate Regression Analysis -- 12.3.1. Linear Regression -- 12.3.2. Support Vector Regression -- 12.4. Unsupervised Learning -- 12.4.1. K-means Clustering -- 12.5. Summary -- 13. Feature Selection and Evaluation -- 13.1. Overfitting and Underfitting -- 13.1.1. Feature Selection and Feature Extraction -- 13.2. Scalar Feature Selection -- 13.2.1. Analysis of Variance -- 13.3. Feature Subset Selection -- 13.3.1. Subset Evaluation -- 13.3.2. Search Algorithms -- 13.4. Selection Using Information Theory -- 13.4.1. Entropy -- 13.4.2. Mutual Information -- 13.4.3. Multivariate Information -- 13.4.4. Information Theory with Continuous Sets -- 13.4.5. Estimation of Entropy and Information -- 13.4.6. Ranking Features -- 13.5. Boosting Feature Selection -- 13.6. Applications in Steganalysis -- 13.6.1. Correlation Coefficient -- 13.6.2. Optimised Feature Vectors for JPEG -- 14. Steganalysis Problem -- 14.1. Different Use Cases -- 14.1.1. Who are Alice and Bob-- 14.1.2. Wendy's Role -- 14.1.3. Pooled Steganalysis -- 14.1.4. Quantitative Steganalysis -- 14.2. Images and Training Sets -- 14.2.1. Choosing the Cover Source -- 14.2.2. Training Scenario -- 14.2.3. Steganalytic Game -- 14.3. Composite Classifier Systems -- 14.3.1. Fusion -- 14.3.2. Multi-layer Classifier for JPEG -- 14.3.3. Benefits of Composite Classifiers -- 14.4. Summary -- 15. Future of the Field -- 15.1. Image Forensics -- 15.2. Conclusions and Notes. |
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Front Matter -- Overview. Introduction -- Steganography and Steganalysis -- Getting Started with a Classifier -- Features. Histogram Analysis -- Bit-Plane Analysis -- More Spatial Domain Features -- The Wavelets Domain -- Steganalysis in the JPEG Domain -- Calibration Techniques -- Classifiers. Simulation and Evaluation -- Support Vector Machines -- Other Classification Algorithms -- Feature Selection and Evaluation -- The Steganalysis Problem -- Future of the Field -- Bibliography -- Index. |
| Subject |
Machine learning.
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Wavelets (Mathematics)
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Data encryption (Computer science)
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Apprentissage automatique. |
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Ondelettes. |
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Chiffrement (Informatique) |
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Data encryption (Computer science) |
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Machine learning |
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Wavelets (Mathematics) |
| Other Form: |
Print version: Schaathun, Hans Georg. Machine learning in image steganalysis. Hoboken : Wiley, 2012 9780470663059 (DLC) 2012016642 |
| ISBN |
9781118437988 (ePub) |
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1118437985 (ePub) |
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9781118437964 (Adobe PDF) |
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1118437969 (Adobe PDF) |
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9781118438008 (MobiPocket) |
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1118438000 (MobiPocket) |
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9781118437957 (electronic bk.) |
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1118437950 (electronic bk.) |
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(hardback) |
| Standard No. |
9786613916372 |
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9781118437988 |
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