IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering
This addresses the challenge of automated verification for personal identity documents, which is crucial for security in digital and mobile registration processes, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of counterfeit identity document detection by introducing IDTrust, a deep-learning framework that uses bandpass filtering to assess ID quality, achieving significant improvements in discrimination performance on the MIDV-2020 and L3i-ID datasets.
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.