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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62130

DEEPFAKE FORENSICS USING VISION TRANSFORMER MODELS VIDEO

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62130

Volume:

10 |

4 |

April 2026

Pages:

1-9

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SV

Sundar V

Department, Computer Networking

PSG Polytechnic College

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MNU

Mr. Nitin UA

Department, Computer Networking

PSG Polytechnic College

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MRK

Mr. Revanth K

Department, Computer Networking

PSG Polytechnic College

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DB

Dr Brindha.S

Department, Computer Networking

PSG Polytechnic College

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KC

Kanishq C

Department, Computer Networking

PSG Polytechnic College

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MNB

Mr. Nitheesh BR

Department, Computer Networking

PSG Polytechnic College

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Mentors

AP

Abirami P

Department, Computer Networking

PSG Polytechnic College

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Abstract

Deepfake generation has grown at an unprecedented rate due to advancements in GANs, autoencoders, and diffusion models. These synthetic media pose significant threats to identity, privacy, and digital trust. This paper presents a comprehensive framework for Deepfake Forensics using Vision Transformer (ViT) models, integrating meta-learning–based generalization and 3D decomposition techniques. The hybrid approach combines EfficientNet-ViT backbone architectures with structured 3D face modeling, lighting estimation, and identity texture analysis to capture subtle inconsistencies in manipulated content. The resulting system significantly improves robustness against unseen manipulation methods, cross-domain shifts, and compression artifacts. This work synthesizes insights from MEViT and 3D decomposition-based forensic research to highlight an advanced and generalizable deepfake detection methodology.

KEYWORDS

Key Words: Deepfake Detection Vision Transformer Meta-Learning 3D Decomposition Face Forensics Domain Generalization

Cite This Article

Sundar V, Mr. Nitin UA, Mr. Revanth K, Dr Brindha.S, Kanishq C, Mr. Nitheesh BR, Abirami P, "DEEPFAKE FORENSICS USING VISION TRANSFORMER MODELS VIDEO," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-9, April 2026.

Full Article/Conclusion

The integration of Vision Transformers, meta-learning, and 3D decomposition techniques offers a powerful solution for modern deepfake forensics. ViTs provide global spatial modeling, while physical 3D decomposition reveals lighting and geometric inconsistencies. Meta-learning enables strong domain generalization, ensuring stable detection even against unseen manipulations. This unified framework represents a major step toward reliable and future-proof deepfake detection systems.

References

[1]   Bennett, C. H., & Brassard, G. (1984). Quantum cryptography: Public key distribution and coin tossing. Proceedings of IEEE International Conference on Computers, Systems and Signal Processing, Bangalore, India. [2]   Heo, Y., Kim, Y., Kim, H., & Kim, J. (2023). MEViT: Generalization of Deepfake Detection with Meta-Learning EfficientNet-Vision Transformer. IEEE Access. [3]   Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2020). Face forgery detection by 3D decomposition and composition search. IEEE Transactions on Pattern Analysis and Machine Intelligence. [4]   Li, T., & Zhang, H. (2020). Deep learning applications in video security. Journal of Information Security and Applications. [5]   Yuan, X., Li, Z., & Lyu, S. (2019). 3D- based face analysis for deepfake detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. [6]   Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). MesoNet: A compact facial video forgery detection network. IEEE International Workshop on Information Forensics and Security. [7]   Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). FaceForensics++: Learning to detect manipulated facial images. IEEE International Conference on Computer Vision (ICCV). [8]    Li, Y., Chang, M., & Lyu, S. (2018). In Ictu Oculi: Exposing AI generated fake face videos by detecting eye blinking. IEEE International Workshop on Information Forensics and Security. [9]    Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019). Capsule-Forensics: Using capsule networks to detect forged images and videos. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [10]    Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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Publication Date

5/1/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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