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dc.contributor.advisorHossain, Dr.Muhammad Iqbal
dc.contributor.authorBin Moshiur, Tasnimul
dc.contributor.authorUllah, Mohammad Zafar
dc.contributor.authorNawar, Nahian
dc.contributor.authorTazwar, Tawsif Muhammed
dc.contributor.authorNanjiba, Rifah
dc.date.accessioned2023-12-05T09:40:01Z
dc.date.available2023-12-05T09:40:01Z
dc.date.copyright2023
dc.date.issued2023-01
dc.identifier.otherID: 18301014
dc.identifier.otherID: 18201153
dc.identifier.otherID: 19241015
dc.identifier.otherID: 18301012
dc.identifier.otherID: 19101522
dc.identifier.urihttp://hdl.handle.net/10361/21925
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 39-41).
dc.description.abstractWith the advancement in technology, fraudulent videos are becoming harder to de tect and easier to produce. Surveillance footage can serve as circumstantial evidence when dealing with crimes, however when this footage is tampered with, there is a great loss in evidence and the footage loses its value. To combat this growing prob lem, in this paper, we aim to find a new system to determine authenticity in a video for security measures based on Blockchain & Deep Learning Tools. The importance of Blockchain in this era of time is gradually increasing due its decentralized features, fault-tolerance attribute, immutability. This paper is looking forward to introducing a system which protects the surveillance footage gathered from a camera in a faster and optimal approach so that the authenticity can be checked and protected. Our goal is to implement a system which would secure the importance of crucial footage as evidence.en_US
dc.description.statementofresponsibilityTasnimul Bin Moshiur
dc.description.statementofresponsibilityMohammad Zafar Ullah
dc.description.statementofresponsibilityNahian Nawar
dc.description.statementofresponsibilityTawsif Muhammed Tazwar
dc.description.statementofresponsibilityRifah Nanjiba
dc.format.extent42 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.subjectBlockchainen_US
dc.subjectSurveillance footageen_US
dc.subjectHashingen_US
dc.subjectCryptographyen_US
dc.subjectDeep learningen_US
dc.subjectCNNen_US
dc.subject.lcshTraffic signs and signals.
dc.subject.lcshBlockchains (Databases)
dc.titleBlockchain-based traffic surveillance footage authenticity detection systemen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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