Authentication Imaging for High-Fidelity Counterfeit Detection
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Solution Overview
Problem
Existing authentication technologies struggle to differentiate between authentic and high-fidelity counterfeit authentication devices due to limitations in image capturing processes, especially with increasingly higher definition data-coded image patterns.
Innovation Solution
An authentication apparatus and method using a neural network structure, specifically a convolutional neural network (CNN) combined with a fully connected network (FCN), to analyze image imperfections and determine authenticity based on covertly encoded data patterns, trained with controlled image imperfections to enhance recognition rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data-coded image patterns are formed with increasingly higher resolution and definition, then the ability to outperform counterfeiting means is improved, but the difficulty for authentication machines to differentiate between authentic and high-fidelity counterfeit devices increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network with controlled image imperfections before actual authentication. The training process intentionally introduces various types of image degradation (blur, noise, compression artifacts) to prepare the authentication system for real-world conditions. This preliminary training enables the neural network to learn robust features that persist even when image quality varies, thereby resolving the contradiction between high-resolution patterns and authentication accuracy under varying image conditions.
2Device complexity
If traditional authentication methods are used, then the system is simpler to implement, but the reliability of determining authenticity deteriorates when facing high-fidelity counterfeits
Solution Approach 1:
The patent replaces traditional mechanical/optical authentication methods with a neural network-based computational approach. Instead of relying on fixed threshold comparisons or hand-crafted feature extraction, the system uses deep learning models that automatically learn discriminative features from training data. This substitution enables the system to achieve high reliability in distinguishing authentic from counterfeit devices, even when using simple image capture hardware, thereby resolving the contradiction between system complexity and authentication reliability.
Data Source
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AI summary
An authentication apparatus and a method to devise an authentication tool is provided for facilitating determination of authenticity or genuineness of an article with reference to a captured image or a purported primary image of an information bearing device on the article.The authenticity or genuineness of an article is determined with reference to whether a captured image is a primary image of an authentic information bearing device using a trained neural network. The neural network is trained using training images comprising imperfect images having varying degrees of image imperfections which are introduced under controlled conditions.