Product Authenticity Verification Using Digitized Surface Fingerprint and Blockchain
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Solution Overview
Problem
Existing methods for certifying product authenticity, such as using infrared/ultraviolet patterns, NFC chips, and RFID tags, are unreliable due to ease of copying and duplication, leading to potential misuse in verifying genuine products, and require precise imaging equipment which is not universally accessible or practical.
Innovation Solution
A method and system that utilizes digitized surface fingerprint information and blockchain to package unique product features, allowing for automatic selection of certification regions from product images, extraction of surface fingerprint data, and comparison with stored information to verify authenticity, even with low-precision cameras and without precise distance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional certification marks (infrared/ultraviolet patterns, NFC chips, RFID tags, serial numbers, bar codes) are used to guarantee product authenticity, then product verification can be performed, but these marks can be easily copied or duplicated, compromising reliability
Solution Approach 1:
The patent divides the product surface into multiple certification regions and extracts multiple feature points within each region. Instead of relying on a single certification mark, the system segments the verification process into multiple independent feature points (e.g., 5-10 points per region, 2-5 regions per product), making complete copying significantly more difficult while maintaining verification reliability.
Solution Approach 2:
The patent applies different verification strategies to different parts of the product surface. Each certification region contains multiple feature points with unique spatial relationships, and the system selectively verifies specific regions based on product type and certification level. This local differentiation ensures that even if some regions are copied, the unique local features remain unverifiable.
2Measurement precision
If precise imaging equipment is used to capture product features for authenticity verification, then measurement precision is improved, but the complexity and accessibility of the verification system deteriorates
Solution Approach 1:
The patent employs dynamic adaptation of verification parameters based on the captured image quality. The system automatically adjusts the number of feature points to extract, the size of certification regions, and the strictness of verification thresholds based on the actual imaging conditions. This allows standard cameras to achieve sufficient precision by dynamically optimizing verification parameters rather than relying on fixed high-precision equipment requirements.
Solution Approach 2:
The system changes verification parameters (number of feature points, region size, distance thresholds) based on image quality and product characteristics. Instead of requiring constant high-precision imaging equipment, the patent adjusts parameters to maintain adequate verification accuracy across different camera qualities, making the system accessible to consumers with standard mobile phone cameras.
3Ease of operation
If product images are used to verify authenticity, then verification convenience is improved, but image distortion due to shooting angle and environment compromises consistency
Solution Approach 1:
The patent deliberately uses asymmetric spatial relationships between multiple feature points within certification regions rather than symmetric patterns. The asymmetric arrangement of feature points creates unique geometric signatures that are invariant to rotation and scaling, allowing the system to distinguish genuine products from distorted images while maintaining verification convenience with standard camera angles.
Solution Approach 2:
The patent transitions from two-dimensional image pattern matching to three-dimensional spatial relationship verification. By extracting and comparing the spatial coordinates and relative distances between multiple feature points in 3D space (even from 2D images), the system achieves distortion invariance. The verification relies on the consistent spatial relationships between feature points rather than absolute image positions, making it robust to shooting angle and environmental variations.
Data Source
AI summary
Disclosed is a method and system for certifying product authenticity using physical feature information including digitized surface fingerprint and blockchain, each of which manages certification information, packaged by binding together unique product information and physical feature information including surface feature of a product as a unique product feature, based on a distributed ledger of a blockchain to reliably determine whether the product is authentic or not in various ways. The authenticity certification information is packaged as a unique product feature by binding a serial number of the product for which the certification information is generated, together with physical feature information of the product, including image-based surface fingerprint. The authenticity certification information is stored in the blockchain. Determination on authenticity is made by comparing the identification information and the physical feature information with the authenticity certification information stored in the blockchain.