Microstructural Object Authentication Under Variable Imaging Conditions
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Solution Overview
Problem
Existing methods for authenticating and identifying physical objects face challenges due to variations in materials, lighting conditions, and environmental factors, leading to unreliable image matching and the inability to track critical items without identifiers, which can be easily duplicated or lost, resulting in counterfeiting and loss of identity information.
Innovation Solution
A system using any camera to generate a tamper-proof physical code based on the distinctive material structure of an object, employing computer vision and machine learning algorithms to authenticate or identify objects by extracting and matching microstructural features through a deep neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional identifiers (barcodes, RFID tags) are attached to physical objects, then object identification is enabled, but the identifiers can be lost, damaged, or counterfeited, compromising reliability
Solution Approach 1:
The patent extracts the identification function from external physical identifiers and embeds it directly into the object's material structure through microstructural features. The deep neural network extracts distinctive microstructural patterns (texture, grain structure, molecular arrangement) that are inherently part of the object, eliminating the need for separate identifier components that can be lost or counterfeited.
Solution Approach 2:
The patent creates a digital copy of the object's unique microstructural fingerprint through image capture and processing. The extracted microstructural features are stored as immutable reference data, enabling authentication without physical contact or additional hardware on the object itself.
2Measurement precision
If image matching is used for object authentication, then identification is possible, but variations in lighting, materials, and environmental factors reduce measurement precision
Solution Approach 1:
The patent focuses on local microstructural features rather than global object appearance. By extracting specific texture patterns, grain structures, and material characteristics at the micro-level, the system achieves authentication that is insensitive to macro-level variations in lighting, camera angles, and environmental conditions.
Solution Approach 2:
The patent transforms the authentication approach by changing from comparing overall image parameters (color, shape, size) to comparing microstructural parameters (texture frequency, grain density, molecular pattern). This parameter transformation makes the authentication robust against environmental variations while maintaining high precision.
3Loss of information
If physical identifiers are permanently attached to objects, then tracking is enabled, but the identifiers can fade with usage and time, leading to loss of information
Solution Approach 1:
The patent performs preliminary capture of the object's microstructural characteristics during manufacturing or initial registration. The microstructural fingerprint is extracted and stored as reference data before the object enters service, ensuring that the authentication reference is available even if the object's appearance changes over time due to wear or aging.
Data Source
AI summary
The present disclosure relates to a method for authentication of an object based on microstructural features extracted from an input surface of the object. Input surfaces of the object are processed by a feature extractor using a deep neural network from images of the object captured by a device camera. The input surfaces vary in dimensions. The feature extractor outputs an input surface uniformly sized with respect to sizes of the input surfaces. The input surface is divided into micro-surfaces which are used to train the feature extractor to identify microstructural features of the input surface. The feature extractor is applied on surfaces of other objects that have different dimensions from the input surfaces. Microstructural features of the surfaces of the objects are stored in a database. The feature extractor authenticates query objects by matching the microstructural features of a query object with the microstructural features of the other objects.