# IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering

arXiv: https://arxiv.org/abs/2403.00573 · HTML: https://mlsanity.com/paper/2403.00573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Masked Frequency Modeling for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2206.07706/) (2022)
- [MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis](https://mlsanity.com/paper/2107.00396/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection](https://mlsanity.com/paper/2607.01442/) (2026)
- [Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality](https://mlsanity.com/paper/2606.11884/) (2026)
- [Video Transformer for Remote Identity Document Hologram Detection](https://mlsanity.com/paper/2607.11419/) (2026)

## Abstract (arXiv)

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.