The findings from this research can be directly applied to enhance real-world identity verification systems by improving the accuracy and efficiency of document authentication processes. IDTrust, with its DeepQD and GuidedDeepQD models, offers a more reliable method for assessing the quality of identification documents without relying on original patterns or extensive pre-processing steps. By incorporating deep learning techniques like convolutional neural networks (CNNs) and bandpass filtering, these models can effectively differentiate between original and scanned IDs.
In practical applications, implementing IDTrust could streamline identity verification procedures in various industries such as banking, healthcare, travel, and government services. The enhanced accuracy provided by these models reduces the risk of fraudulent activities related to counterfeit document production. This technology could lead to faster processing times for user registrations, secure access control systems based on document verification, and overall improved data security measures.
Furthermore, integrating IDTrust into existing identity verification systems can enhance their capabilities in detecting forged or altered documents. By leveraging advanced pattern recognition algorithms and feature extraction methods offered by deep learning frameworks like IDTrust, organizations can strengthen their fraud prevention strategies while ensuring a seamless user experience during the authentication process.