Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture
The widespread use of the internet, coupled with the increasing production of digital content, has caused significant challenges in information security and manipulation. Deepfake detection has become a critical research topic in both academic and practical domains, as it involves identifying forged...
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MDPI AG
2025-01-01
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| author | Bekir Eray Katı Ecir Uğur Küçüksille Güncel Sarıman |
| author_facet | Bekir Eray Katı Ecir Uğur Küçüksille Güncel Sarıman |
| author_sort | Bekir Eray Katı |
| collection | DOAJ |
| description | The widespread use of the internet, coupled with the increasing production of digital content, has caused significant challenges in information security and manipulation. Deepfake detection has become a critical research topic in both academic and practical domains, as it involves identifying forged elements in artificially generated videos using various deep learning and artificial intelligence techniques. In this dissertation, an innovative model was developed for detecting deepfake videos by combining the Quantum Transfer Learning (QTL) and Class-Attention Vision Transformer (CaiT) architectures. The Deepfake Detection Challenge (DFDC) dataset was used for training, and a system capable of detecting spatiotemporal inconsistencies was constructed by integrating QTL and CaiT technologies. In addition to existing preprocessing methods in the literature, a novel preprocessing function tailored to the requirements of deep learning models was developed for the dataset. The advantages of quantum computing offered by QTL were merged with the global feature extraction capabilities of the CaiT. The results demonstrated that the proposed method achieved a remarkable performance in detecting deepfake videos, with an accuracy of 90% and ROC AUC score of 0.94 achieved. The model’s performance was compared with other methods evaluated on the DFDC dataset, highlighting its efficiency in resource utilization and overall effectiveness. The findings reveal that the proposed QTL-CaiT-based system provides a strong foundation for deepfake detection and contributes significantly to the academic literature. Future research should focus on testing the model on real quantum devices and applying it to larger datasets to further enhance its applicability. |
| format | Article |
| id | doaj-art-52921d2e1df04ee9b4cbfba1bf8d735d |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-52921d2e1df04ee9b4cbfba1bf8d735d2025-01-24T13:19:41ZengMDPI AGApplied Sciences2076-34172025-01-0115252510.3390/app15020525Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer ArchitectureBekir Eray Katı0Ecir Uğur Küçüksille1Güncel Sarıman2Institute of Natural and Applied Sciences, Suleyman Demirel University, 32200 Isparta, TürkiyeDepartment of Computer Engineering, Engineering and Natural Sciences Faculty, Suleyman Demirel University, 32200 Isparta, TürkiyeDepartment of Information Systems Engineering, Faculty of Technology, Muğla Sıtkı Koçman University, 48000 Muğla, TürkiyeThe widespread use of the internet, coupled with the increasing production of digital content, has caused significant challenges in information security and manipulation. Deepfake detection has become a critical research topic in both academic and practical domains, as it involves identifying forged elements in artificially generated videos using various deep learning and artificial intelligence techniques. In this dissertation, an innovative model was developed for detecting deepfake videos by combining the Quantum Transfer Learning (QTL) and Class-Attention Vision Transformer (CaiT) architectures. The Deepfake Detection Challenge (DFDC) dataset was used for training, and a system capable of detecting spatiotemporal inconsistencies was constructed by integrating QTL and CaiT technologies. In addition to existing preprocessing methods in the literature, a novel preprocessing function tailored to the requirements of deep learning models was developed for the dataset. The advantages of quantum computing offered by QTL were merged with the global feature extraction capabilities of the CaiT. The results demonstrated that the proposed method achieved a remarkable performance in detecting deepfake videos, with an accuracy of 90% and ROC AUC score of 0.94 achieved. The model’s performance was compared with other methods evaluated on the DFDC dataset, highlighting its efficiency in resource utilization and overall effectiveness. The findings reveal that the proposed QTL-CaiT-based system provides a strong foundation for deepfake detection and contributes significantly to the academic literature. Future research should focus on testing the model on real quantum devices and applying it to larger datasets to further enhance its applicability.https://www.mdpi.com/2076-3417/15/2/525deepfake detectionquantum transfer learningvision transformer |
| spellingShingle | Bekir Eray Katı Ecir Uğur Küçüksille Güncel Sarıman Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture Applied Sciences deepfake detection quantum transfer learning vision transformer |
| title | Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture |
| title_full | Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture |
| title_fullStr | Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture |
| title_full_unstemmed | Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture |
| title_short | Enhancing Deepfake Detection Through Quantum Transfer Learning and Class-Attention Vision Transformer Architecture |
| title_sort | enhancing deepfake detection through quantum transfer learning and class attention vision transformer architecture |
| topic | deepfake detection quantum transfer learning vision transformer |
| url | https://www.mdpi.com/2076-3417/15/2/525 |
| work_keys_str_mv | AT bekireraykatı enhancingdeepfakedetectionthroughquantumtransferlearningandclassattentionvisiontransformerarchitecture AT ecirugurkucuksille enhancingdeepfakedetectionthroughquantumtransferlearningandclassattentionvisiontransformerarchitecture AT guncelsarıman enhancingdeepfakedetectionthroughquantumtransferlearningandclassattentionvisiontransformerarchitecture |