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 enhances the quality of identification
documents by using a deep learning-based approach. This method eliminates the
need for 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. By utilizing a bandpass
filtering-based method, the system aims to effectively detect and differentiate
ID quality. 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.

IDTrust 是一个基于深度学习的系统，通过使用深度学习方法来改善身份证明文件的质量，从而提供了数据集适用性的重大改进。通过利用基于带通滤波的方法，该系统旨在有效检测和区分身份证明文件的质量，并通过在 MIDV-2020 和 L3i-ID 数据集上进行广泛实验，识别出最佳参数，显著改善了区分性能，有效区分原始和扫描身份证明文件。