Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory
This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/8729440 |
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| _version_ | 1849435063665557504 |
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| author | Laor Boongasame Jindaphon Boonpluk Sunisa Soponmanee Jirapond Muangprathub Karanrat Thammarak |
| author_facet | Laor Boongasame Jindaphon Boonpluk Sunisa Soponmanee Jirapond Muangprathub Karanrat Thammarak |
| author_sort | Laor Boongasame |
| collection | DOAJ |
| description | This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically. |
| format | Article |
| id | doaj-art-e42e5b26727e4f7bb23adce1c58ff516 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-e42e5b26727e4f7bb23adce1c58ff5162025-08-20T03:26:25ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/8729440Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term MemoryLaor Boongasame0Jindaphon Boonpluk1Sunisa Soponmanee2Jirapond Muangprathub3Karanrat Thammarak4Business Innovation and Investment Laboratory: B2I-LabDepartment of MathematicsDepartment of MathematicsFaculty of Science and Industrial TechnologyDepartment of Computer Engineering and ElectronicsThis study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically.http://dx.doi.org/10.1155/2024/8729440 |
| spellingShingle | Laor Boongasame Jindaphon Boonpluk Sunisa Soponmanee Jirapond Muangprathub Karanrat Thammarak Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory Applied Computational Intelligence and Soft Computing |
| title | Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory |
| title_full | Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory |
| title_fullStr | Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory |
| title_full_unstemmed | Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory |
| title_short | Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory |
| title_sort | design and implement deepfake video detection using vgg 16 and long short term memory |
| url | http://dx.doi.org/10.1155/2024/8729440 |
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