LEADER 04342nam 2200769Ia 450 001 9910808454203321 005 20200520144314.0 010 $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(OCoLC)841331224 035 $a(OCoLC)ocn841331224 035 $a(EXLCZ)993190000000032947 100 $a20120611d2012 uy 0 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning in image steganalysis /$fHans Georg Schaathun 205 $a1st edition 210 $aHoboken $cWiley$d2012 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$01671590 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808454203321 996 $aMachine learning in image steganalysis$94034261 997 $aUNINA