AI-Based Digital Document Authenticity Verifier: Enhancing Document Security at Jesus Reigns Christian College

Authors

Aliah Turla Evangelista

Department of Information Technology, Jesus Reigns Christian College, Malate Manila (Philippines)

John Michael Garra Amad

Department of Information Technology, Jesus Reigns Christian College, Malate Manila (Philippines)

Diana Joss Conda Sualog

Department of Information Technology, Jesus Reigns Christian College, Malate Manila (Philippines)

Vivien Accad Agustin

La Consolacion University (Philippines)

Ronald Burdios Fernandez

La Consolacion University (Philippines)

Article Information

DOI: 10.51244/IJRSI.2026.1305000276

Subject Category: Social science

Volume/Issue: 13/5 | Page No: 3206-3233

Publication Timeline

Submitted: 2026-05-18

Accepted: 2026-05-23

Published: 2026-06-15

Abstract

The increasing use of digital academic documents has raised concerns regarding document tampering, forgery, and the inefficiency of manual verification processes in educational institutions. Traditional methods of verifying academic records are often time-consuming, prone to human error, and vulnerable to unauthorized modifications. In response to these issues, this study entitled “AI-Based Digital Document Authenticity Verifier: Enhancing Document Security at Jesus Reigns Christian College” aimed to develop a secure, efficient, and automated system capable of verifying the authenticity of digital academic documents.

Keywords

Artificial Intelligence (AI), Digital Document Verification, SHA-256, Blockchain Technology

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References

1. Boonkrong, S. (2025). Design of an academic document forgery detection system. International Journal of Information Technology, 17, 5175–5187. https://doi.org/10.1007/s41870-024-02006-6 [Google Scholar] [Crossref]

2. Darem, A., Al-Hashmi, A., Javed, M., & AbuBaker, B. A. (2020). Digital forgery detection of official document images in compressed domain. International Journal of Computer Science and Network Security, 20(12), 115–123. https://doi.org/10.22937/IJCSNS.2020.20.12.12 [Google Scholar] [Crossref]

3. Jagtap, A., Sawat, D. D., & Hegadi, R. S. (2020). Verification of genuine and forged offline signatures using Siamese Neural Network (SNN). Multimedia Tools and Applications, 79(1). https://doi.org/10.1007/s11042-020-08857-y [Google Scholar] [Crossref]

4. Rane, M., Singh, S., Singh, R., & Amarsinh, V. (2020). Integrity and authenticity of academic documents using blockchain approach. ITM Web of Conferences, 32, 03038. https://doi.org/10.1051/itmconf/20203203038 [Google Scholar] [Crossref]

5. Sirajudeen, M., Anitha, R., Varadarajan, V., Kommers, P., Piuri, V., & Subramaniyaswamy, V. (2020). Forgery document detection in information management system using cognitive techniques. Journal of Intelligent & Fuzzy Systems, 39(6), 8057–8068. https://doi.org/10.3233/JIFS-189128 [Google Scholar] [Crossref]

6. Wang, X., Pang, S., Qiao, S., & Lv, Z. (2023). TVS: A trusted verification scheme for office documents based on blockchain. Complex & Intelligent Systems, 9, 2865–2877. https://doi.org/10.1007/s40747-021-00617-1 [Google Scholar] [Crossref]

7. Wei, J., Chen, H., & Zhang, Y. (2021). Authenticity verification on social data outsourcing. Computers & Security, 100, 102077. https://doi.org/10.1016/j.cose.2020.102077 [Google Scholar] [Crossref]

8. Aldwairi, M., Badra, M., & Borghol, R. (2023). DocCert: Nostrification, document verification and authenticity blockchain solution. arXiv. https://arxiv.org/abs/2310.09136 [Google Scholar] [Crossref]

9. Vinogradov, A. (2026). Can generative models actually forge realistic identity documents? arXiv. https://arxiv.org/abs/2601.00829 [Google Scholar] [Crossref]

10. Mohit, A., Aggarwal, B., & Gondhalekar, C. (2026). Provenance verification of AI-generated images via a perceptual hash registry anchored on blockchain. arXiv. https://arxiv.org/abs/2602.02412 [Google Scholar] [Crossref]

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