Deepfake detection using neural networks
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Формат: | Дисертація |
| Мова: | English |
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Brac University
2021
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| Онлайн доступ: | http://hdl.handle.net/10361/15678 |
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10361-156782024-03-13T21:01:38Z Deepfake detection using neural networks Sakib, Sadman Abid, Mir Tarid Al Tiana, Nures Saba Asha, Wajida Anwar Huq, Syed Mahbubul Hossain, Muhammad Iqbal Abrar, Mohammed Abid Department of Computer Science and Engineering, Brac University Deepfake Detect Neural networks Video Images Artificial intelligence Digital media--Editing Information technology--Social aspects Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-34). 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. Sadman Sakib Mir Tarid Al Abid Nures Saba Tiana Wajida Anwar Asha Syed Mahbubul Huq B. Computer Science 2021-12-01T05:09:51Z 2021-12-01T05:09:51Z 2021 2021-09 Thesis ID 16301082 ID 17101536 ID 18101229 ID 18101290 ID 21141043 http://hdl.handle.net/10361/15678 en Brac 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. 34 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deepfake Detect Neural networks Video Images Artificial intelligence Digital media--Editing Information technology--Social aspects Neural networks (Computer science) |
| spellingShingle |
Deepfake Detect Neural networks Video Images Artificial intelligence Digital media--Editing Information technology--Social aspects Neural networks (Computer science) Sakib, Sadman Abid, Mir Tarid Al Tiana, Nures Saba Asha, Wajida Anwar Huq, Syed Mahbubul Deepfake detection using neural networks |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Sakib, Sadman Abid, Mir Tarid Al Tiana, Nures Saba Asha, Wajida Anwar Huq, Syed Mahbubul |
| format |
Thesis |
| author |
Sakib, Sadman Abid, Mir Tarid Al Tiana, Nures Saba Asha, Wajida Anwar Huq, Syed Mahbubul |
| author_sort |
Sakib, Sadman |
| title |
Deepfake detection using neural networks |
| title_short |
Deepfake detection using neural networks |
| title_full |
Deepfake detection using neural networks |
| title_fullStr |
Deepfake detection using neural networks |
| title_full_unstemmed |
Deepfake detection using neural networks |
| title_sort |
deepfake detection using neural networks |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15678 |
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