Security Document Image Classification for Copy Prevention
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Solution Overview
Problem
Existing security document authentication methods are complex, vulnerable to counterfeiting, and require sophisticated devices to evaluate security features, which can be compromised by counterfeiters.
Innovation Solution
A neural network trained to classify digital images as security documents or not without authenticating security features, using altered digital training images to mimic human perception, reducing the need for security features and enhancing copy prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security features are evaluated using sophisticated detectors, then authentication accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent extracts the essential visual characteristics of security documents that human observers use for identification, separating these from the complex authentication processes. The neural network is trained to detect only the most salient visual features that define a security document's appearance, eliminating the need for sophisticated detectors while maintaining authentication effectiveness.
Solution Approach 2:
The patent replaces expensive, sophisticated authentication devices with a simpler, more accessible neural network model. The model uses standard image processing capabilities rather than specialized hardware, making the system cheaper and more widely deployable while maintaining adequate security document identification.
2Reliability
If security features are made visible or detectable, then copy prevention is improved, but the document design is distorted and usability deteriorates
Solution Approach 1:
Instead of adding visible security features to documents, the patent inverts the approach by training the neural network to recognize security documents through their natural visual characteristics. The model learns to identify security documents without any modifications to the document design, preserving aesthetic integrity while maintaining copy prevention effectiveness.
3Device complexity
If authentication processes are performed downstream, then security feature evaluation is simplified, but copy prevention effectiveness is reduced
Solution Approach 1:
The patent performs authentication evaluation upstream, before copying or reproduction actions occur. The neural network analyzes the digital image to determine if it represents a security document, and this evaluation result is used to prevent copying before the reproduction process begins, thereby maintaining copy prevention effectiveness while keeping the process simple.
4Reliability
If specialized security features are included in documents, then copy prevention is improved, but evaluation complexity and device requirements increase
Solution Approach 1:
The patent creates a universal neural network model that can evaluate various types of security documents using the same basic architecture. The model handles different document formats and security features through a single multi-functional system, eliminating the need for specialized devices for each document type while maintaining copy prevention effectiveness.
Data Source
AI summary
A computer-implemented method for training an artificial intelligence based neural network is provided. At least one digital image of at least one security document is provided as a reference. A set of digital training images is provided, wherein these are altered compared to the digital image of the security document. The set of digital training images includes a first subset of positive digital training images having a visual impact of an alteration such that an unbiased human observer would consider a reproduction of the respective digital training image to represent the security document or multiple security documents. The set of digital training images includes a second subset of negative digital training images. Ground truth is provided to the neural network as to whether a reproduction of the respective digital training image is to be considered representing the security document or multiple security documents or not.