A machine learning approach to detect DeepFake videos
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/10361/15754 |
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10361-157542022-01-26T10:23:13Z A machine learning approach to detect DeepFake videos Hassan, Md. Mahedi Nawrin, Na sha Rahman, Md. Khalilur Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Neural networks Deepfake Machine learning 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 32-35). DeepFake detection is important as the internet is a big part of our lives. DeepFake photos and videos can easily mislead us into thinking something that probably did not happen. It can also reduce trust in the media. As these manipulations become more convincing, celebrities are usually the victim of these kinds of misleading photos and videos. To detect fake videos, we will focus on existing methods and build our model to be more accurate as images of small imperceptible perturbations are su cient to fool the most powerful neural network. In our Machine Learning approach, we rst take the sample videos for training. Then, using open CV2, we have generated images from those videos. After that, we have passed these images to PCA for extracting principal component features. Then we applied VGG-16 and nally we have compared the train-test accuracy using di erent classi ers like SVC, RFC, GNB, CNN etc. After analyzing through our model we will be able to infer whether the input video is real or fake. Md. Mahedi Hassan Na sha Nawrin B. Computer Science 2021-12-26T05:00:07Z 2021-12-26T05:00:07Z 2021 2021-06 Thesis ID 17301098 ID 20241064 http://hdl.handle.net/10361/15754 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. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Neural networks Deepfake Machine learning |
| spellingShingle |
Neural networks Deepfake Machine learning Hassan, Md. Mahedi Nawrin, Na sha A machine learning approach to detect DeepFake videos |
| 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 |
Rahman, Md. Khalilur |
| author_facet |
Rahman, Md. Khalilur Hassan, Md. Mahedi Nawrin, Na sha |
| format |
Thesis |
| author |
Hassan, Md. Mahedi Nawrin, Na sha |
| author_sort |
Hassan, Md. Mahedi |
| title |
A machine learning approach to detect DeepFake videos |
| title_short |
A machine learning approach to detect DeepFake videos |
| title_full |
A machine learning approach to detect DeepFake videos |
| title_fullStr |
A machine learning approach to detect DeepFake videos |
| title_full_unstemmed |
A machine learning approach to detect DeepFake videos |
| title_sort |
machine learning approach to detect deepfake videos |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15754 |
| work_keys_str_mv |
AT hassanmdmahedi amachinelearningapproachtodetectdeepfakevideos AT nawrinnasha amachinelearningapproachtodetectdeepfakevideos AT hassanmdmahedi machinelearningapproachtodetectdeepfakevideos AT nawrinnasha machinelearningapproachtodetectdeepfakevideos |
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1814309639290880000 |