Detection of Deepfake using computer vision and deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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10361-219322023-12-06T21:02:31Z Detection of Deepfake using computer vision and deep learning Rahman, Anisur Uschash, Ehteshamul Islam Rahman, Faria Adiba, Shihaba Jamal Chowdhury Labib, Tahmidul Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University DeepFake CNN LSTM LR scheduler Digital media--Editing Artificial intelligence Cognitive learning theory Computer vision This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-57). In this era of Metaverse and technological advancement, Deepfakes are one of the most alarming concepts. Deepfakes are mostly synthetically-generated manipulated photos or videos which is created swapping a face of a person using Deep learning, Generated Adversarial Network (GAN), autoencoder-decoder pairing structure. There are several other Deepfaking tools such as; FaceSwap, DeepFaceLab, DFaker, DeepFake-tensorflow etc. Using Generative Adversarial Network (GAN), Deepfakes have become smoother and more realistic in making the fake videos. DeepFakes can become concerning if it is used for political purpose, committing fraud, spreading misinformation, pornography, defamation on social media etc. This possesses a security threat on people’s lives knowingly or unknowingly. As a result, it is visible that DeepFakes can be very distressing on the wrong hand if not detected properly. Our purpose is to detect the DeepFake videos as successfully as possible. We want to focus on detection using Deep learning approaches also using Image recognition and computer vision. For this detection, we used a dataset of videos, which included both real and fake videos. We have successfully extracted it from Kaggle where we have found dataset of more than 2352 videos from DeepFake Detection Challenge(DFDC) and FaceForensics++. To detect the fake videos, we followed the method of employing temporal feature and exploring visual artifacts within frames. Employing temporal feature uses LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) whereas visual artifacts within frames mostly employs deep learning method to detect DeepFakes. We ensembled LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to detect DeepFakes successfully. ResNeXt101_32x8d have been used to extract features and a custom CNN model is added with LSTM for better accuracy for detecting DeepFake. The accuracy of the model was 94.05%. After further improvement and with the introduction of learning rate schedulers, we were able to achieve better accuracy. We have used CosineAnnealingLR, CyclicLR, MultiStepLR and ReduceLRonPlateau among which MultiStepLR gave the highest accuracy of 95.33%. Anisur Rahman Ehteshamul Islam Uschash Faria Rahman Shihaba Jamal Chowdhury Adiba Tahmidul Labib B.Sc. in Computer Science and Engineering 2023-12-06T09:14:51Z 2023-12-06T09:14:51Z 2023 2023-05 Thesis ID 19101631 ID 19101343 ID 22241142 ID 19101629 ID 22241136 http://hdl.handle.net/10361/21932 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. 57 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
DeepFake CNN LSTM LR scheduler Digital media--Editing Artificial intelligence Cognitive learning theory Computer vision |
| spellingShingle |
DeepFake CNN LSTM LR scheduler Digital media--Editing Artificial intelligence Cognitive learning theory Computer vision Rahman, Anisur Uschash, Ehteshamul Islam Rahman, Faria Adiba, Shihaba Jamal Chowdhury Labib, Tahmidul Detection of Deepfake using computer vision and deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Karim, Dewan Ziaul |
| author_facet |
Karim, Dewan Ziaul Rahman, Anisur Uschash, Ehteshamul Islam Rahman, Faria Adiba, Shihaba Jamal Chowdhury Labib, Tahmidul |
| format |
Thesis |
| author |
Rahman, Anisur Uschash, Ehteshamul Islam Rahman, Faria Adiba, Shihaba Jamal Chowdhury Labib, Tahmidul |
| author_sort |
Rahman, Anisur |
| title |
Detection of Deepfake using computer vision and deep learning |
| title_short |
Detection of Deepfake using computer vision and deep learning |
| title_full |
Detection of Deepfake using computer vision and deep learning |
| title_fullStr |
Detection of Deepfake using computer vision and deep learning |
| title_full_unstemmed |
Detection of Deepfake using computer vision and deep learning |
| title_sort |
detection of deepfake using computer vision and deep learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21932 |
| work_keys_str_mv |
AT rahmananisur detectionofdeepfakeusingcomputervisionanddeeplearning AT uschashehteshamulislam detectionofdeepfakeusingcomputervisionanddeeplearning AT rahmanfaria detectionofdeepfakeusingcomputervisionanddeeplearning AT adibashihabajamalchowdhury detectionofdeepfakeusingcomputervisionanddeeplearning AT labibtahmidul detectionofdeepfakeusingcomputervisionanddeeplearning |
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1814309590228008960 |