Deep Learning ID Card Authenticity Verification with Class Activation Maps
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Solution Overview
Problem
The reliability of Internet banking security is low due to the inability to effectively authenticate identification cards, often resulting in false authentications using falsified or unauthorized cards, which requires manual verification by personnel, leading to inefficiencies and missed falsifications.
Innovation Solution
An identification card authenticity determining apparatus and method using deep learning, which inputs card data into a feature extraction model to extract features, then uses a classification model to determine authenticity and generates a class activation map to identify falsification regions, allowing for automated and reliable verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification by bank clerks or system managers is used to check identification card authenticity, then authentication can be performed, but many personnel are needed and falsified cards cannot be properly detected
Solution Approach 1:
The system enables self-service automated authentication by allowing users to capture and submit identification card images through mobile devices, eliminating the need for manual verification by bank clerks. The deep learning model automatically processes these images to verify authenticity, replacing human inspectors with an autonomous system that performs verification without human intervention.
Solution Approach 2:
The patent replaces the mechanical manual verification process with an automated deep learning-based image analysis system. The neural network model processes identification card images to detect falsification, substituting human visual inspection with computational algorithms that can identify subtle signs of forgery that are imperceptible to the human eye.
2Measurement precision
If manual verification by bank clerks or system managers is used to check identification card authenticity, then authentication can be performed, but authenticity of identification card is not normally checked due to precise falsification
Solution Approach 1:
The system changes the parameters of verification by analyzing multiple features of the identification card image simultaneously, including hologram characteristics, ultraviolet response, magnetic strip data, and visual security elements. The deep learning model processes these parameters at different levels of abstraction, transforming raw pixel data into meaningful authentication features that reveal falsification patterns invisible to human inspectors.
Solution Approach 2:
The system creates a digital copy of the identification card through image capture and processes this copy through multiple analysis layers. The deep learning model generates feature maps and class activation maps that replicate and emphasize different aspects of the card's authenticity, allowing comprehensive verification without handling the physical card, thereby preventing contamination or damage while maintaining verification accuracy.
3Productivity
If automated deep learning verification is implemented, then personnel requirements are reduced and verification speed increases, but system complexity increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: image capture module, preprocessing module, deep learning inference module, and result output module. Each module performs a specific function in the verification pipeline, allowing independent optimization and maintenance. The segmentation enables parallel processing of different card features and facilitates deployment on resource-constrained mobile devices by dividing computational tasks across multiple processing stages.
Data Source
AI summary
An identification card authenticity determining method based on deep learning according to the disclosure for automatically checking authenticity of an identification card includes: inputting identification card data to a feature information extraction model to extract pieces of feature information, expressing an indicator for checking authenticity of the identification card, from the identification card data; inputting the extracted pieces of feature information to a classification model to determine authenticity of the identification card; and when it is determined that the identification card is falsified, extracting a class activation map, where a falsification region of the identification card data is activated, from the pieces of feature information.