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Machine learning in image steganalysis /
"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"--
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, United Kingdom :
IEEE/Wiley,
2012.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Schaathun, Hans Georg. | |
| 245 | 1 | 0 | |a Machine learning in image steganalysis / |c Hans Georg Schaathun. |
| 260 | |a Chichester, West Sussex, United Kingdom : |b IEEE/Wiley, |c 2012. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 347 | |a text file | ||
| 520 | |a "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"-- |c Provided by publisher. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | 0 | |6 880-01 |t Steganography and Steganalysis -- |t Getting Started with a Classifier -- |t Features. Histogram Analysis -- |t Bit-Plane Analysis -- |t More Spatial Domain Features -- |t The Wavelets Domain -- |t Steganalysis in the JPEG Domain -- |t Calibration Techniques -- |t Classifiers. Simulation and Evaluation -- |t Support Vector Machines -- |t Other Classification Algorithms -- |t Feature Selection and Evaluation -- |t The Steganalysis Problem -- |t Future of the Field. |
| 505 | 0 | |a 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. | |
| 542 | |f Copyright © Wiley-IEEE Press |g 2012 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Wavelets (Mathematics) | |
| 650 | 0 | |a Data encryption (Computer science) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Ondelettes. | |
| 650 | 6 | |a Chiffrement (Informatique) | |
| 650 | 7 | |a SCIENCE |x Waves & Wave Mechanics. |2 bisacsh | |
| 650 | 7 | |a Data encryption (Computer science) |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Wavelets (Mathematics) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Schaathun, Hans Georg. |t Machine learning in image steganalysis. |d Hoboken : Wiley, 2012 |z 9780470663059 |w (DLC) 2012016642 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118437988/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g pt. I |t OVERVIEW -- |g 1. |t Introduction -- |g 1.1. |t Real Threat or Hype-- |g 1.2. |t Artificial Intelligence and Learning -- |g 1.3. |t How to Read this Book -- |g 2. |t Steganography and Steganalysis -- |g 2.1. |t Cryptography versus Steganography -- |g 2.2. |t Steganography -- |g 2.2.1. |t Prisoners' Problem -- |g 2.2.2. |t Covers -- Synthesis and Modification -- |g 2.2.3. |t Keys and Kerckhoffs' Principle -- |g 2.2.4. |t LSB Embedding -- |g 2.2.5. |t Steganography and Watermarking -- |g 2.2.6. |t Different Media Types -- |g 2.3. |t Steganalysis -- |g 2.3.1. |t Objective of Steganalysis -- |g 2.3.2. |t Blind and Targeted Steganalysis -- |g 2.3.3. |t Main Approaches to Steganalysis -- |g 2.3.4. |t Example: Pairs of Values -- |g 2.4. |t Summary and Notes -- |g 3. |t Getting Started with a Classifier -- |g 3.1. |t Classification -- |g 3.1.1. |t Learning Classifiers -- |g 3.1.2. |t Accuracy -- |g 3.2. |t Estimation and Confidence -- |g 3.3. |t Using libSVM -- |g 3.3.1. |t Training and Testing -- |g 3.3.2. |t Grid Search and Cross-validation -- |g 3.4. |t Using Python -- |g 3.4.1. |t Why we use Python -- |g 3.4.2. |t Getting Started with Python -- |g 3.4.3. |t Scientific Computing -- |g 3.4.4. |t Python Imaging Library -- |g 3.4.5. |t Example: Image Histogram -- |g 3.5. |t Images for Testing -- |g 3.6. |t Further Reading -- |g pt. II |t FEATURES -- |g 4. |t Histogram Analysis -- |g 4.1. |t Early Histogram Analysis -- |g 4.2. |t Notation -- |g 4.3. |t Additive Independent Noise -- |g 4.3.1. |t Effect of Noise -- |g 4.3.2. |t Histogram Characteristic Function -- |g 4.3.3. |t Moments of the Characteristic Function -- |g 4.3.4. |t Amplitude of Local Extrema -- |g 4.4. |t Multi-dimensional Histograms -- |g 4.4.1. |t HCF Features for Colour Images -- |g 4.4.2. |t Co-occurrence Matrix -- |g 4.5. |t Experiment and Comparison -- |g 5. |t Bit-plane Analysis -- |g 5.1. |t Visual Steganalysis -- |g 5.2. |t Autocorrelation Features -- |g 5.3. |t Binary Similarity Measures -- |g 5.4. |t Evaluation and Comparison -- |g 6. |t More Spatial Domain Features -- |g 6.1. |t Difference Matrix -- |g 6.1.1. |t EM Features of Chen et al. -- |g 6.1.2. |t Markov Models and the SPAM Features -- |g 6.1.3. |t Higher-order Differences -- |g 6.1.4. |t Run-length Analysis -- |g 6.2. |t Image Quality Measures -- |g 6.3. |t Colour Images -- |g 6.4. |t Experiment and Comparison -- |g 7. |t Wavelets Domain -- |g 7.1. |t Visual View -- |g 7.2. |t Wavelet Domain -- |g 7.2.1. |t Fast Wavelet Transform -- |g 7.2.2. |t Example: The Haar Wavelet -- |g 7.2.3. |t Wavelet Transform in Python -- |g 7.2.4. |t Other Wavelet Transforms -- |g 7.3. |t Farid's Features -- |g 7.3.1. |t Image Statistics -- |g 7.3.2. |t Linear Predictor -- |g 7.3.3. |t Notes -- |g 7.4. |t HCF in the Wavelet Domain -- |g 7.4.1. |t Notes and Further Reading -- |g 7.5. |t Denoising and the WAM Features -- |g 7.5.1. |t Denoising Algorithm -- |g 7.5.2. |t Locally Adaptive LAW-ML -- |g 7.5.3. |t Wavelet Absolute Moments -- |g 7.6. |t Experiment and Comparison -- |g 8. |t Steganalysis in the JPEG Domain -- |g 8.1. |t JPEG Compression -- |g 8.1.1. |t Compression -- |g 8.1.2. |t Programming JPEG Steganography -- |g 8.1.3. |t Embedding in JPEG -- |g 8.2. |t Histogram Analysis -- |g 8.2.1. |t JPEG Histogram -- |g 8.2.2. |t First-order Features -- |g 8.2.3. |t Second-order Features -- |g 8.2.4. |t Histogram Characteristic Function -- |g 8.3. |t Blockiness -- |g 8.4. |t Markov Model-based Features -- |g 8.5. |t Conditional Probabilities -- |g 8.6. |t Experiment and Comparison -- |g 9. |t Calibration Techniques -- |g 9.1. |t Calibrated Features -- |g 9.2. |t JPEG Calibration -- |g 9.2.1. |t FRI-23 Feature Set -- |g 9.2.2. |t Pevny Features and Cartesian Calibration -- |g 9.3. |t Calibration by Downsampling -- |g 9.3.1. |t Downsampling as Calibration -- |g 9.3.2. |t Calibrated HCF-COM -- |g 9.3.3. |t Sum and Difference Images -- |g 9.3.4. |t Features for Colour Images -- |g 9.3.5. |t Pixel Selection -- |g 9.3.6. |t Other Features Based on Downsampling -- |g 9.3.7. |t Evaluation and Notes -- |g 9.4. |t Calibration in General -- |g 9.5. |t Progressive Randomisation -- |g pt. III |t CLASSIFIERS -- |g 10. |t Simulation and Evaluation -- |g 10.1. |t Estimation and Simulation -- |g 10.1.1. |t Binomial Distribution -- |g 10.1.2. |t Probabilities and Sampling -- |g 10.1.3. |t Monte Carlo Simulations -- |g 10.1.4. |t Confidence Intervals -- |g 10.2. |t Scalar Measures -- |g 10.2.1. |t Two Error Types -- |g 10.2.2. |t Common Scalar Measures -- |g 10.3. |t Receiver Operating Curve -- |g 10.3.1. |t libSVM API for Python -- |g 10.3.2. |t ROC Curve -- |g 10.3.3. |t Choosing a Point on the ROC Curve -- |g 10.3.4. |t Confidence and Variance -- |g 10.3.5. |t Area Under the Curve -- |g 10.4. |t Experimental Methodology -- |g 10.4.1. |t Feature Storage -- |g 10.4.2. |t Parallel Computation -- |g 10.4.3. |t Dangers of Large-scale Experiments -- |g 10.5. |t Comparison and Hypothesis Testing -- |g 10.5.1. |t Hypothesis Test -- |g 10.5.2. |t Comparing Two Binomial Proportions -- |g 10.6. |t Summary -- |g 11. |t Support Vector Machines -- |g 11.1. |t Linear Classifiers -- |g 11.1.1. |t Linearly Separable Problems -- |g 11.1.2. |t Non-separable Problems -- |g 11.2. |t Kernel Function -- |g 11.2.1. |t Example: The XOR Function -- |g 11.2.2. |t SVM Algorithm -- |g 11.3. |t ν-SVM -- |g 11.4. |t Multi-class Methods -- |g 11.5. |t One-class Methods -- |g 11.5.1. |t One-class SVM Solution -- |g 11.5.2. |t Practical Problems -- |g 11.5.3. |t Multiple Hyperspheres -- |g 11.6. |t Summary -- |g 12. |t Other Classification Algorithms -- |g 12.1. |t Bayesian Classifiers -- |g 12.1.1. |t Classification Regions and Errors -- |g 12.1.2. |t Misclassification Risk -- |g 12.1.3. |t Naive Bayes Classifier -- |g 12.1.4. |t Security Criterion -- |g 12.2. |t Estimating Probability Distributions -- |g 12.2.1. |t Histogram -- |g 12.2.2. |t Kernel Density Estimator -- |g 12.3. |t Multivariate Regression Analysis -- |g 12.3.1. |t Linear Regression -- |g 12.3.2. |t Support Vector Regression -- |g 12.4. |t Unsupervised Learning -- |g 12.4.1. |t K-means Clustering -- |g 12.5. |t Summary -- |g 13. |t Feature Selection and Evaluation -- |g 13.1. |t Overfitting and Underfitting -- |g 13.1.1. |t Feature Selection and Feature Extraction -- |g 13.2. |t Scalar Feature Selection -- |g 13.2.1. |t Analysis of Variance -- |g 13.3. |t Feature Subset Selection -- |g 13.3.1. |t Subset Evaluation -- |g 13.3.2. |t Search Algorithms -- |g 13.4. |t Selection Using Information Theory -- |g 13.4.1. |t Entropy -- |g 13.4.2. |t Mutual Information -- |g 13.4.3. |t Multivariate Information -- |g 13.4.4. |t Information Theory with Continuous Sets -- |g 13.4.5. |t Estimation of Entropy and Information -- |g 13.4.6. |t Ranking Features -- |g 13.5. |t Boosting Feature Selection -- |g 13.6. |t Applications in Steganalysis -- |g 13.6.1. |t Correlation Coefficient -- |g 13.6.2. |t Optimised Feature Vectors for JPEG -- |g 14. |t Steganalysis Problem -- |g 14.1. |t Different Use Cases -- |g 14.1.1. |t Who are Alice and Bob-- |g 14.1.2. |t Wendy's Role -- |g 14.1.3. |t Pooled Steganalysis -- |g 14.1.4. |t Quantitative Steganalysis -- |g 14.2. |t Images and Training Sets -- |g 14.2.1. |t Choosing the Cover Source -- |g 14.2.2. |t Training Scenario -- |g 14.2.3. |t Steganalytic Game -- |g 14.3. |t Composite Classifier Systems -- |g 14.3.1. |t Fusion -- |g 14.3.2. |t Multi-layer Classifier for JPEG -- |g 14.3.3. |t Benefits of Composite Classifiers -- |g 14.4. |t Summary -- |g 15. |t Future of the Field -- |g 15.1. |t Image Forensics -- |g 15.2. |t Conclusions and Notes. |
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