Product Authenticity Verification via Multi-Region Label Analysis
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Solution Overview
Problem
Current methods for authenticating products, such as using barcodes and unique identifiers, are vulnerable to replication and manipulation, making it difficult to accurately distinguish between authentic and counterfeit products.
Innovation Solution
A method utilizing a trained machine learning model to analyze image data from product labels and exteriors, identifying and classifying regions of interest, comparing attributes against expected values, and providing an authenticity indication, which includes optical character recognition and comparison of display characteristics, color, shape, and surface roughness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If barcodes and unique identifiers are used for product authentication, then the authentication process becomes simpler and more direct, but the reliability of authentication deteriorates because barcodes can be easily replicated and manipulated
Solution Approach 1:
The authentication system segments the label into multiple regions of interest (text regions, barcode regions, logo regions, image regions) and analyzes each region independently using specialized machine learning models. This segmentation allows the system to examine multiple authentication features simultaneously, increasing reliability while maintaining ease of operation through automated multi-point verification.
Solution Approach 2:
The system transitions from one-dimensional barcode scanning to two-dimensional image analysis by capturing and analyzing entire label images. This dimensional expansion enables simultaneous verification of text content, barcode accuracy, logo authenticity, and image integrity, thereby improving authentication reliability while keeping the user interface simple.
2Measurement precision
If multiple attributes and regions of the label are analyzed to improve authentication accuracy, then the measurement precision improves, but the device complexity and processing time increase
Solution Approach 1:
The complex analysis task is segmented into independent region-processing units, each handled by specialized machine learning models trained for specific tasks (text recognition, barcode verification, logo authentication). This modular segmentation improves measurement precision through focused analysis while managing device complexity by dividing the overall system into manageable, independent components.
Solution Approach 2:
The machine learning models are pre-trained on extensive datasets during manufacturing, enabling them to perform their specific analysis tasks autonomously without requiring complex real-time coordination. This self-service capability allows each region to be analyzed independently, improving overall authentication accuracy while reducing the computational complexity required during actual product verification.
Data Source
AI summary
Some embodiments relate to a method of determining authenticity of a product, such as an electrical product. The method includes acquiring image sensor data indicative of an image including a label including information related to the product. The method includes identifying regions of the label that include information related to the product, and classifying each identified region based on a determined form of information included in the region, by using a trained machine learning model based on the acquired image data. For each classified region of the label, the method involves identifying attributes of the classified region based on the acquired image data, and comparing the identified attributes against respective expected attributes for a label region of an authentic product that includes the determined form of information of the classified region. The method involves providing an indication of authenticity of the product determined based on the comparison.