Detecting Deepfake images using deep convolutional neural network
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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| Langue: | English |
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Brac University
2022
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| Accès en ligne: | http://hdl.handle.net/10361/15933 |
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10361-159332022-01-26T10:04:51Z Detecting Deepfake images using deep convolutional neural network Dhar, Arpita Acharjee, Prima Biswas, Likhan Ahmed, Shemonti Sultana, Abida Parvez, Mohammad Zavid Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University CNN Deepfake Deep learning Image processing Transfer learning Neural networks (Computer science) Artificial intelligence Image processing -- Digital techniques. 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 36-40). In recent years, advancement in the realm of machine learning has introduced a feature known as Deepfake pictures, which allows users to substitute a genuine face with a fake one that seems real. As a result, distinguishing between authentic and fraudulent pictures has become di cult. There have been several cases in recent years where Deepfake pictures have been used to defame famous leaders and even regular people. Furthermore, cases have been documented in which Deepfake yet realistic pictures were used to promote political discontent, blackmail, spread fake news, and even carry out false terrorism attacks. The objective of our model is to di erentiate between real and Deepfake images so that the above mentioned situations can be avoided. This project represents a deep CNN model with 13000 images divided in two segments: Training and Testing. The dataset was prepared using necessary image augmentation techniques. A total of 2 categories are considered (real image category and fake image category). For testing purpose we have used a total number of 3000 images divided into two parts for real and fake class, each consisting 1500 images. 75% of the whole data was used as Testing data and remaining 25% as Training data. The dataset was tested against a custom CNN model referred to in the paper as the 18-layered CNN model and ve of the transfer learning models. Our suggested model was successful in achieving 98.77% accuracy whereas the best result out of the transfer learning model was achieved by InceptionV3 with 97.10% testing accuracy. The custom CNN model shows promising results in the case of detecting real and DeepFake images than all the other models used before. Arpita Dhar Prima Acharjee Likhan Biswas Shemonti Ahmed Abida Sultana B. Computer Science 2022-01-17T04:34:17Z 2022-01-17T04:34:17Z 2021 2021-09 Thesis ID 17101069 ID 18301293 ID 17101405 ID 21341062 ID 16201085 http://hdl.handle.net/10361/15933 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. 40 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
CNN Deepfake Deep learning Image processing Transfer learning Neural networks (Computer science) Artificial intelligence Image processing -- Digital techniques. |
| spellingShingle |
CNN Deepfake Deep learning Image processing Transfer learning Neural networks (Computer science) Artificial intelligence Image processing -- Digital techniques. Dhar, Arpita Acharjee, Prima Biswas, Likhan Ahmed, Shemonti Sultana, Abida Detecting Deepfake images using deep convolutional neural network |
| 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 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Dhar, Arpita Acharjee, Prima Biswas, Likhan Ahmed, Shemonti Sultana, Abida |
| format |
Thesis |
| author |
Dhar, Arpita Acharjee, Prima Biswas, Likhan Ahmed, Shemonti Sultana, Abida |
| author_sort |
Dhar, Arpita |
| title |
Detecting Deepfake images using deep convolutional neural network |
| title_short |
Detecting Deepfake images using deep convolutional neural network |
| title_full |
Detecting Deepfake images using deep convolutional neural network |
| title_fullStr |
Detecting Deepfake images using deep convolutional neural network |
| title_full_unstemmed |
Detecting Deepfake images using deep convolutional neural network |
| title_sort |
detecting deepfake images using deep convolutional neural network |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/15933 |
| work_keys_str_mv |
AT dhararpita detectingdeepfakeimagesusingdeepconvolutionalneuralnetwork AT acharjeeprima detectingdeepfakeimagesusingdeepconvolutionalneuralnetwork AT biswaslikhan detectingdeepfakeimagesusingdeepconvolutionalneuralnetwork AT ahmedshemonti detectingdeepfakeimagesusingdeepconvolutionalneuralnetwork AT sultanaabida detectingdeepfakeimagesusingdeepconvolutionalneuralnetwork |
| _version_ |
1814306876144222208 |