Central Library

Machine learning in image steganalysis / (Record no. 74260)

000 -LEADER
fixed length control field 06032nam a2201297 i 4500
001 - CONTROL NUMBER
control field 6331046
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712205835.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151222s2012 njua ob 001 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781118437957
-- ebook
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- print
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- Adobe PDF
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- ePub
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- Adobe PDF
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- ePub
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- MobiPocket
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- MobiPocket
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-- electronic
082 00 - CLASSIFICATION NUMBER
Call Number 006.3/1
100 1# - AUTHOR NAME
Author Schaathun, Hans Georg,
245 10 - TITLE STATEMENT
Title Machine learning in image steganalysis /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (x, 284 pages) :
490 1# - SERIES STATEMENT
Series statement Wiley - IEEE
505 8# - FORMATTED CONTENTS NOTE
Remark 2 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Wavelets (Mathematics)
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Data encryption (Computer science)
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6331046
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Chichester, West Sussex, U.K. :
-- John Wiley,
-- 2012.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [2012]
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-- text
-- rdacontent
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-- electronic
-- isbdmedia
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-- online resource
-- rdacarrier
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-- Description based on PDF viewed 12/22/2015.
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-- Calibration
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-- Cameras
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-- Communities
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-- Computers
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-- Context
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-- Convolution
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-- Correlation
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-- Discrete cosine transforms
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-- Discrete wavelet transforms
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-- Encryption
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-- Error analysis
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-- Error probability
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-- Feature extraction
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-- Forensics
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-- Histograms
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-- History
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-- Image coding
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-- Image color analysis
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-- Indexes
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-- Integrated circuits
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-- Joints
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-- Kernel
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-- Learning systems
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-- Machine learning
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-- Machine learning algorithms
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-- Markov processes
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-- Media
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-- Monitoring
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-- Monte Carlo methods
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-- Noise
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-- Presses
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-- Printers
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-- Probability
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-- Probability distribution
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-- Quantization
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-- Random processes
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-- Robots
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-- Sociology
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-- Software
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-- Standards
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-- Streaming media
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-- Support vector machine classification
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-- Support vector machines
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-- Testing
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-- Training
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-- Transform coding
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-- Transforms
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-- Unsolicited electronic mail
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-- Vectors
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-- Visualization
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-- Wavelet domain
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-- Writing
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-- Accuracy
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-- Additives
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-- Algorithm design and analysis
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-- Analysis of variance
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-- Bayesian methods
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-- Bibliographies

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