Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

A Multi-Layer Trust Framework for Self-Sovereign Identity on Blockchain

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  • M Jyothi,
  • N Haji bablu,
  • M Pavani,
  • K Kalyan Kumar,
  • S Mohammed Jabeer
M Jyothi
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
N Haji bablu
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
M Pavani
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Kalyan Kumar
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Mohammed Jabeer
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15250577

Published 2025-04-20

Keywords

  • KYC,
  • Blockchain,
  • deepfake,
  • customer identification

How to Cite

M Jyothi, N Haji bablu, M Pavani, K Kalyan Kumar, & S Mohammed Jabeer. (2025). A Multi-Layer Trust Framework for Self-Sovereign Identity on Blockchain. International Journal of Computational Learning & Intelligence, 4(4), 645–657. https://doi.org/10.5281/zenodo.15250577

Copyright (c) 2025 M Jyothi, N Haji bablu, M Pavani, K Kalyan Kumar, S Mohammed Jabeer

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The growing sophistication of deepfake technology poses a significant challenge to remote identity verification systems, particularly in electronic Know Your Customer (eKYC) applications. Many existing deepfake detection datasets lack the necessary features to assess eKYC systems effectively, as they do not include essential factors like head movements and facial verification protocols. To address this gap, we introduce eKYC-DF, a large-scale dataset comprising over 228,000 high-quality synthetic and real videos, representing diverse demographics. This dataset is designed to facilitate the development and evaluation of eKYC systems by incorporating various head poses, facial expressions, and verification benchmarks. Additionally, our dataset provides protocols for both deepfake detection and facial recognition assessments, making it a valuable resource for enhancing identity-proofing security. The eKYC-DF dataset, along with evaluation tools and pre-trained models, is publicly available to researchers for further study and development.

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