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Machine learning in image steganalysis / Hans Georg Schaathun.

By: Schaathun, Hans Georg [author.].
Contributor(s): IEEE Xplore (Online Service) [distributor.] | John Wiley & Sons [publisher.].
Material type: materialTypeLabelBookSeries: Wiley - IEEE: Publisher: Chichester, West Sussex, U.K. : John Wiley, 2012Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2012]Description: 1 PDF (x, 284 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9781118437957.Subject(s): Machine learning | Wavelets (Mathematics) | Data encryption (Computer science) | Calibration | Cameras | Communities | Computers | Context | Convolution | Correlation | Discrete cosine transforms | Discrete wavelet transforms | Encryption | Error analysis | Error probability | Feature extraction | Forensics | Histograms | History | Image coding | Image color analysis | Indexes | Integrated circuits | Joints | Kernel | Learning systems | Machine learning | Machine learning algorithms | Markov processes | Media | Monitoring | Monte Carlo methods | Noise | Presses | Printers | Probability | Probability distribution | Quantization | Random processes | Robots | Sociology | Software | Standards | Streaming media | Support vector machine classification | Support vector machines | Testing | Training | Transform coding | Transforms | Unsolicited electronic mail | Vectors | Visualization | Wavelet domain | Writing | Accuracy | Additives | Algorithm design and analysis | Analysis of variance | Bayesian methods | BibliographiesGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3/1 Online resources: Abstract with links to resource Also available in print.
Contents:
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.
Summary: 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.
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Includes bibliographical references.

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.

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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.

Also available in print.

Mode of access: World Wide Web

Description based on PDF viewed 12/22/2015.

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