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dc.contributor.advisorHossain, Muhammad Iqbal
dc.contributor.advisorAbrar, Mohammed Abid
dc.contributor.authorSakib, Sadman
dc.contributor.authorAbid, Mir Tarid Al
dc.contributor.authorTiana, Nures Saba
dc.contributor.authorAsha, Wajida Anwar
dc.contributor.authorHuq, Syed Mahbubul
dc.date.accessioned2021-12-01T05:09:51Z
dc.date.available2021-12-01T05:09:51Z
dc.date.copyright2021
dc.date.issued2021-09
dc.identifier.otherID 16301082
dc.identifier.otherID 17101536
dc.identifier.otherID 18101229
dc.identifier.otherID 18101290
dc.identifier.otherID 21141043
dc.identifier.urihttp://hdl.handle.net/10361/15678
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 33-34).
dc.description.abstractDeepfake is a sort of arti cial intelligence that forge original image or video and create persuading images, audio and video hoaxes by utilizing two contending AI algorithms-the generator and discriminator that form a generative adversarial net- work (GAN). The term `Deepfake' started in 2017, when a mysterious Reddit user called himself "Deepfakes." The user "Deepfakes" supplanted genuine faces with celebrity faces. With the rapid advancement of modern technology, Deepfakes have become an emerging problem, as deepfakes can threaten cybersecurity, political elec- tions, companies, individual and corporate nances, reputations, and more. There- fore, this makes deepfake detection more and more urgent. Although, a lot of techniques has been invented to detect deepfake but not all of them works perfectly and accurately for all cases. Also, as more up to date deepfake creation strategies are grown, ine ectively generalizing methodologies should be continually refreshed to cover these new techniques. Our research focuses on the recent techniques that are used to create manipulated videos and detect them though ensembling di erent CNN models.en_US
dc.description.statementofresponsibilitySadman Sakib
dc.description.statementofresponsibilityMir Tarid Al Abid
dc.description.statementofresponsibilityNures Saba Tiana
dc.description.statementofresponsibilityWajida Anwar Asha
dc.description.statementofresponsibilitySyed Mahbubul Huq
dc.format.extent34 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses 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.subjectDeepfakeen_US
dc.subjectDetecten_US
dc.subjectNeural networksen_US
dc.subjectVideoen_US
dc.subjectImagesen_US
dc.subject.lcshArtificial intelligence
dc.subject.lcshDigital media--Editing
dc.subject.lcshInformation technology--Social aspects
dc.subject.lcshNeural networks (Computer science)
dc.titleDeepfake detection using neural networksen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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