IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering
<|reference_start|>IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering: 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.<|reference_end|>
@article{al-ghadi2024idtrust:,
    title={IDTrust: Deep Identity Document Quality Detection with Bandpass
  Filtering},
    author={Musab Al-Ghadi, Joris Voerman, Souhail Bakkali, Micka"el Coustaty,
  Nicolas Sidere, Xavier St-Georges},
    journal={arXiv preprint arXiv:2403.00573},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00573},
    primaryClass={cs.CV}
}