| 000 | 06032nam a2201297 i 4500 | ||
|---|---|---|---|
| 001 | 6331046 | ||
| 003 | IEEE | ||
| 005 | 20220712205835.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151222s2012 njua ob 001 eng d | ||
| 020 |
_a9781118437957 _qebook |
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| 020 |
_z9780470663059 _qprint |
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| 020 |
_z9781118437964 _qAdobe PDF |
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| 020 |
_z9781118437988 _qePub |
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| 020 |
_z1118437969 _qAdobe PDF |
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| 020 |
_z1118437985 _qePub |
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| 020 |
_z9781118438008 _qMobiPocket |
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| 020 |
_z1118438000 _qMobiPocket |
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| 020 |
_z1118437950 _qelectronic |
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| 024 | 7 |
_a10.1002/9781118437957 _2doi |
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| 035 | _a(CaBNVSL)mat06331046 | ||
| 035 | _a(IDAMS)0b0000648193ddab | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 | _aQ325.5b.S35 2012eb | |
| 082 | 0 | 0 |
_a006.3/1 _223 |
| 100 | 1 |
_aSchaathun, Hans Georg, _eauthor. _928065 |
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| 245 | 1 | 0 |
_aMachine learning in image steganalysis / _cHans Georg Schaathun. |
| 264 | 1 |
_aChichester, West Sussex, U.K. : _bJohn Wiley, _c2012. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012] |
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| 300 |
_a1 PDF (x, 284 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aWiley - IEEE | |
| 504 | _aIncludes bibliographical references. | ||
| 505 | 8 | _aFront 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. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aSteganography 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. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/22/2015. | ||
| 650 | 0 |
_aMachine learning. _91831 |
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| 650 | 0 |
_aWavelets (Mathematics) _910807 |
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| 650 | 0 |
_aData encryption (Computer science) _99168 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 695 | _aCalibration | ||
| 695 | _aCameras | ||
| 695 | _aCommunities | ||
| 695 | _aComputers | ||
| 695 | _aContext | ||
| 695 | _aConvolution | ||
| 695 | _aCorrelation | ||
| 695 | _aDiscrete cosine transforms | ||
| 695 | _aDiscrete wavelet transforms | ||
| 695 | _aEncryption | ||
| 695 | _aError analysis | ||
| 695 | _aError probability | ||
| 695 | _aFeature extraction | ||
| 695 | _aForensics | ||
| 695 | _aHistograms | ||
| 695 | _aHistory | ||
| 695 | _aImage coding | ||
| 695 | _aImage color analysis | ||
| 695 | _aIndexes | ||
| 695 | _aIntegrated circuits | ||
| 695 | _aJoints | ||
| 695 | _aKernel | ||
| 695 | _aLearning systems | ||
| 695 | _aMachine learning | ||
| 695 | _aMachine learning algorithms | ||
| 695 | _aMarkov processes | ||
| 695 | _aMedia | ||
| 695 | _aMonitoring | ||
| 695 | _aMonte Carlo methods | ||
| 695 | _aNoise | ||
| 695 | _aPresses | ||
| 695 | _aPrinters | ||
| 695 | _aProbability | ||
| 695 | _aProbability distribution | ||
| 695 | _aQuantization | ||
| 695 | _aRandom processes | ||
| 695 | _aRobots | ||
| 695 | _aSociology | ||
| 695 | _aSoftware | ||
| 695 | _aStandards | ||
| 695 | _aStreaming media | ||
| 695 | _aSupport vector machine classification | ||
| 695 | _aSupport vector machines | ||
| 695 | _aTesting | ||
| 695 | _aTraining | ||
| 695 | _aTransform coding | ||
| 695 | _aTransforms | ||
| 695 | _aUnsolicited electronic mail | ||
| 695 | _aVectors | ||
| 695 | _aVisualization | ||
| 695 | _aWavelet domain | ||
| 695 | _aWriting | ||
| 695 | _aAccuracy | ||
| 695 | _aAdditives | ||
| 695 | _aAlgorithm design and analysis | ||
| 695 | _aAnalysis of variance | ||
| 695 | _aBayesian methods | ||
| 695 | _aBibliographies | ||
| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _928066 |
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| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. _96902 |
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| 776 | 0 | 8 |
_iPrint version: _z9780470663059 |
| 830 | 0 |
_aWiley - IEEE _97628 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6331046 |
| 942 | _cEBK | ||
| 999 |
_c74260 _d74260 |
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