Image-Based Item Authentication Using Segmented Learning Models
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Solution Overview
Problem
Conventional manual authentication of items leads to errors and inefficiencies due to human limitations, resulting in increased computational resources, data breaches, and loss of sensitive information from counterfeit items being incorrectly identified as authentic.
Innovation Solution
An item authentication system uses learning models to analyze images of items, performing feature extraction and classification to determine authenticity with a confidence score, reducing errors and improving efficiency by automating the verification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual authentication is used, then operational simplicity is maintained, but accuracy and reliability deteriorate due to human errors and limitations
Solution Approach 1:
The patent replaces manual human authentication with an automated machine learning-based image analysis system. The learning model processes images of items, extracts features, and classifies them as authentic or counterfeit, eliminating human errors and improving consistency and accuracy in authentication decisions.
Solution Approach 2:
The system creates a digital copy of the physical item through image capture and uses the learning model to analyze the image data. This digital representation allows for consistent, repeatable analysis without the variability inherent in manual inspection, thereby improving authentication reliability.
2Reliability
If manual authentication is used, then computational resources are reduced, but errors increase leading to data breaches and information loss
Solution Approach 1:
The learning model performs self-training by analyzing training data to automatically learn patterns and characteristics of authentic versus counterfeit items. This self-service capability enables the system to improve its authentication accuracy over time without requiring continuous human intervention or computational resources for manual verification.
Solution Approach 2:
The system changes the operational parameters from manual inspection to automated image processing. By using machine learning algorithms to analyze image features, the system achieves higher accuracy while optimizing computational resource usage through efficient image segmentation and feature extraction techniques.
3Measurement precision
If automated learning model authentication is implemented, then accuracy and consistency improve, but device complexity increases
Solution Approach 1:
The authentication process is segmented into distinct stages: image capture, image segmentation into smaller regions, feature extraction from these regions, and final classification by the learning model. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an intermediary feature extraction layer between image processing and classification. This intermediary component extracts and represents the essential features from segmented images, simplifying the input to the learning model and reducing the complexity of direct image-to-decision mappings while improving authentication accuracy.
Data Source
AI summary
Authenticating items using a learning model is described. A set of images of an item is received from an image capture system. A set of image segments corresponding to respective images of the set of images is generated by a computing device. A confidence score and/or a binary value that indicates an authenticity of the item is generated as output from a learning model by providing the image segments as input to the learning model. The confidence score is associated with the authenticity of the item. The confidence score is broadcast by the computing device for displaying the authenticity of the item via a user interface. Additionally, or alternatively, one or more data transactions associated with the item are processed or canceled by the computing device based on the binary value and the confidence score.