Machine Learning Seal Authentication Using Scale-Invariant Feature Matching
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Solution Overview
Problem
Existing technologies face challenges in efficiently and accurately authenticating seals on electronic documents, leading to issues such as invalid agreements, fraud, and resource wastage.
Innovation Solution
A machine learning model, potentially incorporating convolutional neural networks and faster regional proposal networks, is trained to detect and authenticate seals by preprocessing images, transforming them into a scale-invariant domain using SIFT features, and comparing them to model seals to determine a similarity score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual seal authentication is performed, then authenticity can be verified, but time consumption and resource wastage increase
Solution Approach 1:
The patent replaces manual mechanical authentication processes with an automated computer-based system that uses machine learning models and image processing algorithms to detect and verify seals, thereby eliminating time-consuming manual verification while maintaining authentication accuracy
Solution Approach 2:
The system enables self-service authentication by automatically detecting seals in documents and comparing them against stored reference seals without requiring human intervention, allowing the authentication process to serve itself through automated decision-making algorithms
2Productivity
If seal detection automation is implemented, then efficiency increases, but detection accuracy may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing images to enhance seal features before detection, and by pre-training machine learning models on extensive seal datasets, thereby ensuring high detection accuracy is established beforehand before automated processing begins
Solution Approach 2:
The patent introduces intermediary elements including machine learning models that act as mediators between raw images and detection results, and image processing algorithms that serve as intermediaries to enhance seal features, thereby maintaining high accuracy throughout the automated detection process
3Adaptability or versatility
If scale variant seal comparison is performed, then all seal variations can be detected, but computational complexity increases
Solution Approach 1:
The patent transforms the seal comparison problem from a two-dimensional pixel-by-pixel comparison into a multi-dimensional feature space using scale-invariant feature transforms, allowing seals of different sizes and orientations to be compared by mapping them to equivalent feature representations across multiple dimensional parameters
Data Source
AI summary
In some implementations, there is provided seal authentication using machine learning. There may be provided a method including receiving, by a trained machine learning model, a document to be authenticated; detecting, by the trained machine learning model, whether the document contains a seal; in response to detecting the seal, providing the seal extracted by the trained machine learning model for authentication; authenticating the extracted seal in a scale invariant domain by at least using a comparison of the extracted seal and a model seal to determine a similarity score; and providing the similarity score as an indication of whether the extracted seal is authentic. Related systems, methods, and articles of manufacture are also disclosed.