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Review Article

Lightweight Hybrid CNN-Transformer Architecture for Real-Time Image-Based Deep fake Detection on Resource-Constrained Systems

Open Access Peer Reviewed Crossref DOI

Authors

  • Vishvas1

1 Department of Computer Science & Engineering, Amritsar Group of Colleges, Amritsar, Punjab, India.

Article Information

Published Online
September-December 2026
Pages
589-593
Article Type
Review Article
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References

1. F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017.
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.
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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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