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- Vol. 5 Issue 3
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Lightweight Hybrid CNN-Transformer Architecture for Real-Time Image-Based Deep fake Detection on Resource-Constrained Systems
Authors
1 Department of Computer Science & Engineering, Amritsar Group of Colleges, Amritsar, Punjab, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 589-593
- Article Type
- Review Article
References
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11. G. Petmezas, V. Vanian, K. Konstantoudakis, E. E. I. Almaloglou, and D. Zarpalas, "Video deepfake detection using a hybrid CNN-LSTM-transformer model for identity verification," Multimedia Tools Appl., vol. 84, pp. 40617–40636, 2025.
12. K. Sudarshana and Y. Vamsidhar, "UAM-Net: Robust deepfake detection through hybrid attention into scalable convolutional network," Expert Systems, vol. 42, e70009, 2025.
13. G. Rani, A. Kothekar, S. G. Philip, V. S. Dhaka, E. Zumpano, and E. Vocaturo, "Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection," Frontiers in Big Data, vol. 8, 1521653, 2025.
14. W. Foster and Intel OpenVINO Team, "OpenVINO 2026.1: New models, more performance," Intel OpenVINO Toolkit Blog, 2026.
15. F. Zhao, J. Gong, and E. Wang, "Accelerating PyTorch with Intel Extension for PyTorch," Intel Developer technical article, n.d.
16. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks," IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1499–1503, 2016. DOI: 10.1109/LSP.2016.2603342.
17. Intel Corporation, “Intel Extension for PyTorch Installation Guide (EOL),” official project documentation, n.d. [Online]. Available: https://pytorch-extension.intel.com/. Accessed: 9 Oct. 2026.
2. A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, "FaceForensics++: Learning to detect manipulated facial images," in Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV), 2019.
3. A. Howard et al., "Searching for MobileNetV3," in Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV), 2019, pp. 1314–1324.
4. Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, "Celeb-DF: A large-scale challenging dataset for deepfake forensics," in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020, pp. 3204–3213.
5. A. Kozlov, I. Lazarevich, V. Shamporov, N. Lyalyushkin, and Y. Gorbachev, "Neural network compression framework for fast model inference," arXiv:2002.08679, 2020.
6. A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," in Proc. Int. Conf. Learning Representations (ICLR), 2021.
7. S. Mehta and M. Rastegari, "MobileViT: A lightweight, general-purpose, and mobile-friendly vision transformer," in Proc. Int. Conf. Learning Representations (ICLR), 2022.
8. D. A. Coccomini, N. Messina, C. Gennaro, and F. Falchi, "Combining EfficientNet and vision transformers for video deepfake detection," in Proc. Int. Conf. Image Analysis and Processing (ICIAP), 2022, pp. 219–229.
9. E. Altuncu, V. N. L. Franqueira, and S. Li, "Deepfake: Definitions, performance metrics and standards, datasets, and a meta-review," Frontiers in Big Data, 2024.
10. A. Luo, R. Cai, C. Kong, X. Kang, J. Huang, and A. C. Kot, "Forgery-aware adaptive learning with vision transformer for generalized face forgery detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, pp. 4116–4129, 2025.
11. G. Petmezas, V. Vanian, K. Konstantoudakis, E. E. I. Almaloglou, and D. Zarpalas, "Video deepfake detection using a hybrid CNN-LSTM-transformer model for identity verification," Multimedia Tools Appl., vol. 84, pp. 40617–40636, 2025.
12. K. Sudarshana and Y. Vamsidhar, "UAM-Net: Robust deepfake detection through hybrid attention into scalable convolutional network," Expert Systems, vol. 42, e70009, 2025.
13. G. Rani, A. Kothekar, S. G. Philip, V. S. Dhaka, E. Zumpano, and E. Vocaturo, "Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection," Frontiers in Big Data, vol. 8, 1521653, 2025.
14. W. Foster and Intel OpenVINO Team, "OpenVINO 2026.1: New models, more performance," Intel OpenVINO Toolkit Blog, 2026.
15. F. Zhao, J. Gong, and E. Wang, "Accelerating PyTorch with Intel Extension for PyTorch," Intel Developer technical article, n.d.
16. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks," IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1499–1503, 2016. DOI: 10.1109/LSP.2016.2603342.
17. Intel Corporation, “Intel Extension for PyTorch Installation Guide (EOL),” official project documentation, n.d. [Online]. Available: https://pytorch-extension.intel.com/. Accessed: 9 Oct. 2026.
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