LEADER 04373nam 2200757 450 001 9910137612703321 005 20230125234627.0 010 $a1-283-60392-6 010 $a9786613916372 010 $a1-118-43798-5 010 $a1-118-43796-9 010 $a1-118-43795-0 024 7 $a10.1002/9781118437957 035 $a(CKB)3190000000032947 035 $a(EBL)1022347 035 $a(SSID)ssj0000711543 035 $a(PQKBManifestationID)11416643 035 $a(PQKBTitleCode)TC0000711543 035 $a(PQKBWorkID)10722516 035 $a(PQKB)11028464 035 $a(DLC) 2012024649 035 $a(CaBNVSL)mat06331046 035 $a(IDAMS)0b0000648193ddab 035 $a(IEEE)6331046 035 $a(Au-PeEL)EBL1022347 035 $a(CaPaEBR)ebr10602086 035 $a(CaONFJC)MIL391637 035 $a(OCoLC)809555684 035 $a(CaSebORM)9781118437988 035 $a(MiAaPQ)EBC1022347 035 $a(PPN)244333815 035 $a(EXLCZ)993190000000032947 100 $a20151222d2012 uy 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning in image steganalysis /$fHans Georg Schaathun 205 $a1st edition 210 1$aChichester, West Sussex, U.K. :$cJohn Wiley,$d2012. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2012] 215 $a1 online resource (394 p.) 225 1 $aWiley - IEEE 300 $aDescription based upon print version of record. 311 $a0-470-66305-7 320 $aIncludes bibliographical references and index. 327 $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. 330 $aSteganography is the art of communicating a secret message, hiding the very existence of a secret message. This is typically done by hiding the message within a non-sensitive document. Steganalysis is the art and science of detecting such hidden messages. The task in steganalysis is to take an object (communication) and classify it as either a steganogram or a clean document. Most recent solutions apply classification algorithms from machine learning and pattern recognition, which tackle problems too complex for analytical solution by teaching computers to learn from empirical data. Part 1of the book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context. Part 2 is a survey of a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Part 3 is an in-depth study of machine learning techniques and classifier algorithms, and presents a critical assessment of the experimental methodology and applications in steganalysis.Key features: . Serves as a tutorial on the topic of steganalysis with brief introductions to much of the basic theory provided, and also presents a survey of the latest research.. Develops and formalises the application of machine learning in steganalysis; with much of the understanding of machine learning to be gained from this book adaptable for future study of machine learning in other applications. . Contains Python programs and algorithms to allow the reader to modify and reproduce outcomes discussed in the book.. Includes companion software available from the author's website. 410 0$aWiley - IEEE 606 $aMachine learning 606 $aWavelets (Mathematics) 606 $aData encryption (Computer science) 615 0$aMachine learning. 615 0$aWavelets (Mathematics) 615 0$aData encryption (Computer science) 676 $a006.3/1 686 $aSCI067000$2bisacsh 700 $aSchaathun$b Hans Georg$0845658 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910137612703321 996 $aMachine learning in image steganalysis$91887878 997 $aUNINA LEADER 06474nam 2200757 a 450 001 9910140977003321 005 20240516062953.0 010 $a9786613126368 010 $a9781283126366 010 $a1283126362 010 $a9781118033807 010 $a1118033809 010 $a9781118033623 010 $a1118033620 010 $a9781118033609 010 $a1118033604 035 $a(CKB)2670000000083421 035 $a(EBL)697635 035 $a(SSID)ssj0000535879 035 $a(PQKBManifestationID)11346955 035 $a(PQKBTitleCode)TC0000535879 035 $a(PQKBWorkID)10545984 035 $a(PQKB)10094907 035 $a(OCoLC)729726215 035 $a(CaSebORM)9780470880913 035 $a(MiAaPQ)EBC697635 035 $a(OCoLC)801812936 035 $a(OCoLC)ocn801812936 035 $a(Perlego)1011594 035 $a(EXLCZ)992670000000083421 100 $a20101115d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital signal processing using MATLAB for students and researchers /$fJohn W. Leis 205 $a1st ed. 210 $aHoboken, New Jersey $cWiley$d[2011] 215 $a1 online resource (382 pages) 300 $aDescription based upon print version of record. 311 08$a9780470880913 311 08$a0470880910 320 $aIncludes bibliographical references and index. 327 $aDIGITAL SIGNAL PROCESSING USING MATLAB FOR STUDENTS AND RESEARCHERS; CONTENTS; PREFACE; CHAPTER 1: WHAT IS SIGNAL PROCESSING?; 1.1 CHAPTER OBJECTIVES; 1.2 INTRODUCTION; 1.3 BOOK OBJECTIVES; 1.4 DSP AND ITS APPLICATIONS; 1.5 APPLICATION CASE STUDIES USING DSP; 1.6 OVERVIEW OF LEARNING OBJECTIVES; 1.7 CONVENTIONS USED IN THIS BOOK; 1.8 CHAPTER SUMMARY; CHAPTER 2: MATLAB FOR SIGNAL PROCESSING; 2.1 CHAPTER OBJECTIVES; 2.2 INTRODUCTION; 2.3 WHAT IS MATLAB?; 2.4 GETTING STARTED; 2.5 EVERYTHING IS A MATRIX; 2.6 INTERACTIVE USE; 2.7 TESTING AND LOOPING; 2.8 FUNCTIONS AND VARIABLES 327 $a2.9 PLOTTING AND GRAPHING2.10 LOADING AND SAVING DATA; 2.11 MULTIDIMENSIONAL ARRAYS; 2.12 BITWISE OPERATORS; 2.13 VECTORIZING CODE; 2.14 USING MATLAB FOR PROCESSING SIGNALS; 2.15 CHAPTER SUMMARY; CHAPTER 3: SAMPLED SIGNALS AND DIGITAL PROCESSING; 3.1 CHAPTER OBJECTIVES; 3.2 INTRODUCTION; 3.3 PROCESSING SIGNALS USING COMPUTER ALGORITHMS; 3.4 DIGITAL REPRESENTATION OF NUMBERS; 3.5 SAMPLING; 3.6 QUANTIZATION; 3.7 IMAGE DISPLAY; 3.8 ALIASING; 3.9 RECONSTRUCTION; 3.10 BLOCK DIAGRAMS AND DIFFERENCE EQUATIONS; 3.11 LINEARITY, SUPERPOSITION, AND TIME INVARIANCE 327 $a3.12 PRACTICAL ISSUES AND COMPUTATIONAL EFFICIENCY3.13 CHAPTER SUMMARY; CHAPTER 4: RANDOM SIGNALS; 4.1 CHAPTER OBJECTIVES; 4.2 INTRODUCTION; 4.3 RANDOM AND DETERMINISTIC SIGNALS; 4.4 RANDOM NUMBER GENERATION; 4.5 STATISTICAL PARAMETERS; 4.6 PROBABILITY FUNCTIONS; 4.7 COMMON DISTRIBUTIONS; 4.8 CONTINUOUS AND DISCRETE VARIABLES; 4.9 SIGNAL CHARACTERIZATION; 4.10 HISTOGRAM OPERATORS; 4.11 MEDIAN FILTERS; 4.12 CHAPTER SUMMARY; CHAPTER 5: REPRESENTING SIGNALS AND SYSTEMS; 5.1 CHAPTER OBJECTIVES; 5.2 INTRODUCTION; 5.3 DISCRETE-TIME WAVEFORM GENERATION; 5.4 THE z TRANSFORM 327 $a5.5 POLYNOMIAL APPROACHThe previous section showed how to iterate a difference equation in order to determinethe output sequence. It is particularly important to understand the relationshipbetween difference equations and their transforms. The z transform of a linear systemgives us the key to combining systems together to form more complex systems, sincethe z transforms in combined blocks are able to be multiplied or added together asnecessary. We now give another insight into this approach.S...5.6 POLES, ZEROS, AND STABILITY; 5.7 TRANSFER FUNCTIONS AND FREQUENCY RESPONSE 327 $a5.8 VECTOR INTERPRETATION OF FREQUENCY RESPONSE5.9 CONVOLUTION; 5.10 CHAPTER SUMMARY; CHAPTER 6: TEMPORAL AND SPATIAL SIGNAL PROCESSING; 6.1 CHAPTER OBJECTIVES; 6.2 INTRODUCTION; 6.3 CORRELATION; 6.4 LINEAR PREDICTION; 6.5 NOISE ESTIMATION AND OPTIMAL FILTERING; 6.6 TOMOGRAPHY; 6.7 CHAPTER SUMMARY; CHAPTER 7: FREQUENCY ANALYSIS OF SIGNALS; 7.1 CHAPTER OBJECTIVES; 7.2 INTRODUCTION; 7.3 FOURIER SERIES; 7.4 HOW DO THE FOURIER SERIES COEFFICIENT EQUATIONS COME ABOUT?; 7.5 PHASE-SHIFTED WAVEFORMS; 7.6 THE FOURIER TRANSFORM; 7.7 ALIASING IN DISCRETE-TIME SAMPLING 327 $a7.8 THE FFT AS A SAMPLE INTERPOLATOR 330 $a"This book uses an active learning approach to the topic of digital signal processing (DSP). DSP is a fundamental technology with wide ranging applications as, for example, digital downloads of movies, mobile and broadband communications, digital television, and many other areas. In this book the subject is taught using a "hands-on" experimental approach with MATLAB examples throughout the text to illustrate the mathematical concepts and DSP algorithms developed and explained. Existing books in this area tends to fall into one of two camps - either a highly mathematical treatment with few practical examples of the everyday uses of DSP, or else very verbose and descriptive treatments with little or no mathematical content to back up the topics. The former type of text is excellent as a standard reference, but poor as a learning vehicle. The latter type of text is good for general knowledge, but fails to meet the needs of University students and practicing industry professionals. Students need a learn-by-doing approach, and industry professionals need to come up to speed as rapidly as possible. The book uses MATLAB throughout, inn an established industry and University programming environment. 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