dc.contributor.advisor | Uddin, Dr. Jia | |
dc.contributor.author | Das, Taposh | |
dc.contributor.author | Azam, Md. Rasel | |
dc.contributor.author | Hasan, Rizbanul | |
dc.date.accessioned | 2018-02-18T05:54:24Z | |
dc.date.available | 2018-02-18T05:54:24Z | |
dc.date.copyright | 2017 | |
dc.date.issued | 2017-12 | |
dc.identifier.other | ID 13101093 | |
dc.identifier.other | ID 13101295 | |
dc.identifier.other | ID 13301065 | |
dc.identifier.uri | http://hdl.handle.net/10361/9497 | |
dc.description | This 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.description | Cataloged from PDF version of thesis report. | |
dc.description | Includes bibliographical references (pages 37-39). | |
dc.description.abstract | In 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.statementofresponsibility | Taposh Das | |
dc.description.statementofresponsibility | Md. Rasel Azam | |
dc.description.statementofresponsibility | Rizbanul Hasan | |
dc.format.extent | 39 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | BRAC 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.subject | SWT | en_US |
dc.subject | SIFT | en_US |
dc.subject | Copy-move image | en_US |
dc.subject | Forgery detection | en_US |
dc.title | SWT and SIFT based copy-move image forgery detection | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering | |