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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorDas, Taposh
dc.contributor.authorAzam, Md. Rasel
dc.contributor.authorHasan, Rizbanul
dc.date.accessioned2018-02-18T05:54:24Z
dc.date.available2018-02-18T05:54:24Z
dc.date.copyright2017
dc.date.issued2017-12
dc.identifier.otherID 13101093
dc.identifier.otherID 13101295
dc.identifier.otherID 13301065
dc.identifier.urihttp://hdl.handle.net/10361/9497
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 37-39).
dc.description.abstractIn our proposed model we have implemented copy-move image forgery detection technique. Copy-move image forgery is one of the types of image forgery where a part of image is copied and then it is pasted in the same image having an intention to make a false image or to hide some important object within the image. Our purpose is to make an efficient and robust solution to this kind of image forgery. Our proposed system consists of few steps: (1) Stationary Wavelet Transform (SWT) is used to decompose the input image into four parts from which approximate image is taken as input for the next step. (2) Scale Invariant Feature Transform (SIFT) algorithm is then run on the approximate image extracted by SWT to extract the key point descriptor features. (3) The descriptor features are clustered using linkage method ward. (4) Clustered key points are compared to take decision whether image is tampered or not. (5) In post processing step false positive removal is done using Random Sample Consensus (RANSAC). Our proposed model after implementations performs 93% accurately over a certain dataset.en_US
dc.description.statementofresponsibilityTaposh Das
dc.description.statementofresponsibilityMd. Rasel Azam
dc.description.statementofresponsibilityRizbanul Hasan
dc.format.extent39 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectSWTen_US
dc.subjectSIFTen_US
dc.subjectCopy-move imageen_US
dc.subjectForgery detectionen_US
dc.titleSWT and SIFT based copy-move image forgery detectionen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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