04342nam 2200769Ia 450 991080845420332120200520144314.01-283-60392-697866139163721-118-43798-51-118-43796-91-118-43795-010.1002/9781118437957(CKB)3190000000032947(EBL)1022347(SSID)ssj0000711543(PQKBManifestationID)11416643(PQKBTitleCode)TC0000711543(PQKBWorkID)10722516(PQKB)11028464(DLC) 2012024649(CaBNVSL)mat06331046(IDAMS)0b0000648193ddab(IEEE)6331046(Au-PeEL)EBL1022347(CaPaEBR)ebr10602086(CaONFJC)MIL391637(OCoLC)809555684(CaSebORM)9781118437988(MiAaPQ)EBC1022347(PPN)244333815(OCoLC)841331224(OCoLC)ocn841331224 (EXLCZ)99319000000003294720120611d2012 uy 0engurunu|||||txtccrMachine learning in image steganalysis /Hans Georg Schaathun1st editionHoboken Wiley20121 online resource (394 p.)Wiley - IEEEDescription based upon print version of record.0-470-66305-7 Includes bibliographical references and index.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.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.Wiley - IEEEMachine learningWavelets (Mathematics)Data encryption (Computer science)Machine learning.Wavelets (Mathematics)Data encryption (Computer science)006.3/1SCI067000bisacshSchaathun Hans Georg1671590MiAaPQMiAaPQMiAaPQBOOK9910808454203321Machine learning in image steganalysis4034261UNINA