Optical Character Recognition (OCR) systems are widely deployed in electronic identity verification, yet their integrity remains susceptible to presentation attacks. The current research investigates authenticity assessment for Vietnamese chip-embedded National ID Card (CCCD) images as a prerequisite step prior to OCR execution. The objective is the detection and mitigation of common spoofing vectors, specifically printed photographs and screen captures. A pre-verification framework utilizing the ResNet50 architecture is introduced, analyzing morphological and texture variations to classify input media. Evaluations were conducted on a proprietary dataset containing 2,029 images, exploring both multi-class and binary configurations. Performance metrics indicate the binary formulation achieves a 98.6% accuracy rate, a 1.00 recall for spoof detection, and a 0% False Acceptance Rate. These empirical findings substantiate the framework’s viability for operational deployment, addressing systemic vulnerabilities in automated identity validation pipelines.
Research Article | Open Access | Download Full Text
Volume 5 | Issue 3 | Year 2026 | Article Id: DST-V5I3P101 DOI: https://doi.org/10.59232/DST-V5I3P101
Vietnamese National ID Card Image Authentication for Integrity Assessment in Optical Character Recognition Systems
Nguyen Minh Man, Tran Duc Tam, Trinh Huy Hoang, Tran Son Hai
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 20 May 2026 | 25 Jun 2026 | 18 Jul 2026 | 25 Aug 2026 |
Citation
Nguyen Minh Man, Tran Duc Tam, Trinh Huy Hoang, Tran Son Hai. “Vietnamese National ID Card Image Authentication for Integrity Assessment in Optical Character Recognition Systems.” DS Journal of Digital Science and Technology, vol. 5, no. 3, pp. 1-10, 2026.
Abstract
Keywords
Citizen Identification Card, Deep Learning, Document Authentication, eKYC, Presentation Attack Detection, ResNet50
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