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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62130
Volume:
10 |
4 |
April 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
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.
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
Department, Computer Networking
PSG Polytechnic College
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
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.
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Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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