Year: 2025
Type: conference-paper
Abstract: The increasing use of digital technologies and mobile-based registration procedures highlights the vital role of personal identity documents (IDs) in verifying users and safeguarding sensitive information. However, the rise in counterfeit ID production poses a significant challenge, necessitating the development of reliable and efficient automated verification methods. This paper introduces IDTrust, a deep-learning framework for assessing the quality of IDs. IDTrust is a system that detects the quality of identity documents using a deep learning-based approach. This method eliminates relying on original document patterns for quality checks and pre-processing steps for alignment. As a result, it offers significant improvements in terms of dataset applicability. Using a bandpass filtering-based method, the system aims to detect and differentiate ID quality effectively. Comprehensive experiments on the MIDV-2020 and L3i-ID datasets identify optimal parameters, significantly improving discrimination performance, and effectively distinguishing between original and scanned ID documents.
Authors: Musab Al-Ghadi, Joris Voerman, Mickaël Coustaty, Olivier Lessard, Nicolas Sidere
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409917550