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A data augmentation based deep learning approach for deepfake image detection
| dc.contributor.advisor | Kurnaz, S. | |
| dc.contributor.author | Elebe, T. M. | |
| dc.coverage.spatial | Гомель | ru_RU |
| dc.date.accessioned | 2024-06-04T11:40:37Z | |
| dc.date.available | 2024-06-04T11:40:37Z | |
| dc.date.issued | 2024 | |
| dc.identifier.citation | Elebe, T. M. A data augmentation based deep learning approach for deepfake image detection [Электронный ресурс] / T. M. Elebe ; scientific supervisor S. Kurnaz // E.R.A – Современная наука: электроника, робототехника, автоматизация : материалы I Междунар. науч.-техн. конф, студентов, аспирантов и молодых ученых, Гомель, 29 фев. 2024 г. / Гомел. гос. техн. ун-т им. П. О. Сухого [и др.] ; под общ. ред. А. А. Бойко. – Гомель : ГГТУ им. П. О. Сухого, 2024. – C. 195–196. | ru_RU |
| dc.identifier.uri | https://elib.gstu.by/handle/220612/35821 | |
| dc.description.abstract | Deepfake technology, pushed by advanced deep learning algorithms, poses a serious threat to the integrity of visual content, potentially leading to misinformation, propaganda, and fraudulent evidence fabrication. our research proposes a rigorous framework for real and deepfake picture recognition. The suggested approach merges a transformer-based model, notably the Vision Transformer (ViT), coupled with fine-tuned Convolutional Neural Networks (CNNs). | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | ГГТУ им. П.О. Сухого | ru_RU |
| dc.subject | Index Terms—depfake | ru_RU |
| dc.subject | Deep learning | ru_RU |
| dc.subject | Transformers | ru_RU |
| dc.subject | Convolutional neual network | ru_RU |
| dc.title | A data augmentation based deep learning approach for deepfake image detection | ru_RU |
| dc.type | Article | ru_RU |
