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IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering

2024-03-01 14:53:31
Musab Al-Ghadi, Joris Voerman, Souhail Bakkali, Micka\"el Coustaty, Nicolas Sidere, Xavier St-Georges

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 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.

Abstract (translated)

越来越多地使用数字技术和基于移动设备的注册程序强调了个人身份证明文件(IDs)在验证用户和保护敏感信息中的关键作用。然而,伪造身份证生产量的增加给验证方法的发展带来了巨大的挑战,需要开发可靠且高效的自动验证方法。本文介绍了IDTrust,一种基于深度学习的评估ID质量的框架。IDTrust是一个利用深度学习方法增强身份证质量的系统。这种方法消除了依赖原始文档模式进行质量检查和预处理步骤的需要。因此,在数据集适用性方面,它提供了显著的改进。通过利用带通滤波器方法,系统旨在有效地检测和区分ID质量。对MIDV-2020和L3i-ID数据集的全面实验确定了最佳参数,显著提高了分类性能,有效地区分了原始和扫描身份证。

URL

https://arxiv.org/abs/2403.00573

PDF

https://arxiv.org/pdf/2403.00573.pdf


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