Hossain, Muhammad IqbalAbrar, Mohammed AbidSakib, SadmanAbid, Mir Tarid AlTiana, Nures SabaAsha, Wajida AnwarHuq, Syed Mahbubul2021-12-012021-12-0120212021-09ID 16301082ID 17101536ID 18101229ID 18101290ID 21141043http://hdl.handle.net/10361/15678Cataloged from PDF version of thesis.Includes bibliographical references (pages 33-34).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Deepfake 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.34 pagesenBrac 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.DeepfakeDetectNeural networksVideoImagesArtificial intelligenceDigital media--EditingInformation technology--Social aspectsNeural networks (Computer science)Thesis