Computer Vision Authentication for Counterfeit Detection
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Solution Overview
Problem
Current methods for authenticating material samples suspected of being counterfeit are time-consuming and inefficient, requiring laboratory testing and manual inspection, making it difficult to locate and track counterfeit products within supply chains.
Innovation Solution
A computer-implemented method and system using computer-vision features extracted from digital images of material samples, combined with location and time information, to identify and track counterfeit products through a trained classification algorithm, enabling proactive and non-invasive authentication without the need for laboratory testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional laboratory testing and manual inspection methods are used to authenticate material samples, then authentication accuracy can be maintained, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated computer vision systems. Digital images of material samples are processed through algorithms that extract visual features and compare them against reference databases, eliminating the need for time-consuming manual laboratory testing while maintaining authentication accuracy.
Solution Approach 2:
The system creates digital copies (images) of material samples and authenticates these copies through computer vision analysis rather than requiring physical handling and testing of the actual samples. This allows parallel processing of multiple samples and eliminates sequential manual inspection bottlenecks.
2Loss of information
If manual inspection processes are used, then detailed examination can be performed, but it becomes difficult to locate and track counterfeit products throughout the supply chain
Solution Approach 1:
The patent implements a feedback system where authentication results, location data, and timestamp information are continuously captured and fed back into a centralized database. This creates a real-time tracking network that monitors material samples throughout the supply chain, enabling authorities to locate and trace counterfeit products efficiently by querying the database with location or sample identifiers.
Solution Approach 2:
The system serves multiple functions simultaneously: it authenticates material samples, tracks their location, records timestamps, and maintains supply chain provenance information all through a single integrated platform. This multi-functional approach eliminates the need for separate tracking systems and provides comprehensive counterfeit detection capabilities.
3Measurement precision
If comprehensive authentication testing is performed on all suspected counterfeits, then detection accuracy improves, but the cost and time resources required increase significantly
Solution Approach 1:
The patent employs a tiered authentication approach where computer vision analysis performs initial screening of material samples. Only samples that fail this preliminary automated assessment or exhibit high-risk characteristics are escalated to more comprehensive laboratory testing. This partial action strategy maintains high detection accuracy while reducing the overall number of samples requiring resource-intensive full authentication.
Data Source
AI summary
Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm along with location and optional time information. The extracted features/information of a test sample are evaluated by the trained classification algorithm to identify the test sample. The results of the evaluation are used to track and locate counterfeits or authentic products.