Microstructural Surface Authentication Using Fixed-Size Feature Vectors

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Solution Overview

Problem

Existing methods for authenticating and identifying physical objects face challenges due to variations in materials, lighting conditions, camera perspectives, and environmental factors, leading to unreliable image matching and increased counterfeiting, especially for items that cannot be tagged with identifiers.

Innovation Solution

A system using computer vision and machine learning algorithms generates a tamper-proof physical code based on the distinctive microstructural features of an object, extracting and processing tokens from surface images to create a fixed-size representation for authentication and identification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional image matching methods are used for authentication, then the system can identify physical objects, but the reliability decreases due to variations in lighting, camera perspectives, and environmental factors

Engineering Contradiction:

Improveauthentication reliabilityVSAvoidimage matching precision

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent segments the physical object into multiple local microstructural regions and extracts distinctive features from each region. Instead of relying on global image matching, the system divides the object surface into multiple patches and extracts local microstructural features (LMFs) from each patch, which are then aggregated to form a robust authentication representation that is invariant to lighting and camera variations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the authentication approach by changing from pixel-based image matching to feature-based representation. It extracts microstructural features and converts them into a fixed-size vector representation that captures essential object characteristics while being invariant to environmental variations. This parameter transformation from raw pixels to abstract feature vectors resolves the reliability-precision contradiction.

Inventive Principle:

Principle #35Parameter changes

2Ease of manufacture

If identifiers are physically attached to objects for tracking, then object identification is possible, but the ease of manufacture decreases and counterfeiting increases

Engineering Contradiction:

Improveidentification implementation easeVSAvoidcounterfeiting risk

Core Design Contradiction:

Ease of manufactureVSObject-affected harmful factors

Solution Approach 1:

The patent enables objects to authenticate themselves through their inherent microstructural features without requiring external identifiers or tags. The system extracts authentication features directly from the object's surface microstructure, making the object self-identifying. This eliminates the need for separate identifier attachments and prevents counterfeiting since the microstructural features are intrinsic to the object and difficult to replicate.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent extracts authentication information directly from the object's microstructural features rather than relying on attached identifiers. By taking out the identification function from external tags and embedding it within the object's inherent structure, the system simultaneously improves ease of manufacture (no tags needed) and reduces counterfeiting risk (features are intrinsic and hard to copy).

Inventive Principle:

Principle #2Taking out (Extraction)

3Loss of information

If variable-size feature representations are used, then comprehensive object characteristics are captured, but the device complexity increases for processing and comparison

Engineering Contradiction:

Improvefeature information completenessVSAvoidprocessing system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent transforms the feature representation from variable-dimensional patch-based data to a fixed-size vector space. It projects microstructural features from multiple patches of arbitrary sizes into a standardized fixed-size vector representation, enabling efficient comparison and processing. This dimensional transformation maintains comprehensive object characteristics while simplifying the processing system.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12482283B1Authentication and identification of physical objects using fixed-size representation

Publication Date: 2025.11.25 VERACITY PROTOCOL INC

AI summary

The present disclosure relates to a method for generating a fixed-size representation of a surface based on microstructural features. Microstructural features are extracted from surfaces of the physical object using a feature extractor. An input sequence of tokens is determined. A token from the input sequence characterizes the microstructural features of the surface. The input sequence of tokens is augmented with an additional token. The augmented input sequence is iteratively processed using an attention mechanism. Contextual information is exchanged between the tokens of the input sequence of tokens, and the attention mechanism is trained on the processing of the input sequence of tokens to extract the fixed-size representation of the surface of the physical object from a set of arbitrary size representations. An output of the iterative processing of the augmented input sequence is determined as the fixed-size representation of the surface to determine the status of the physical object.