Convolutional Neural Network Item Authentication System
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Solution Overview
Problem
The existing methods for authenticating luxury goods are time-consuming, costly, and lack accuracy, with no standardized professional qualification or formal training, leading to subjective judgments and potential black market interests, while current deep learning technologies have not effectively addressed the need for quick and reliable authentication.
Innovation Solution
An item authentication method using pre-processing techniques such as size modification and feature approximation degree clustering, combined with convolutional neural networks like VGG19, RESNET54, and WRESNET16, to identify target authentication points and determine authenticity through a decision tree algorithm, setting thresholds for authentication results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual authentication is used for luxury goods, then authentication can be performed with basic knowledge, but authentication accuracy is low and results are subjective
Solution Approach 1:
The patent replaces manual mechanical authentication with an automated image processing system using convolutional neural networks. The system automatically extracts features from images of luxury goods and performs authentication without human intervention, thereby eliminating subjectivity while maintaining ease of operation through automated workflows.
Solution Approach 2:
The patent introduces an image-based intermediate representation as a mediator between the luxury good and the authentication decision. Instead of direct human inspection, the system uses processed images and extracted features as an intermediary that enables objective, consistent authentication while remaining accessible to users through simple image uploads.
2Measurement precision
If multiple deep learning models are used for authentication, then authentication accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple convolutional neural network models (VGG19, RESNET54, WRESNET16) into a unified authentication system. The models work together to extract complementary features from images, and their results are integrated through a decision tree algorithm, achieving higher accuracy while managing complexity through systematic integration.
Solution Approach 2:
The patent divides the authentication task into separate specialized models, each trained to extract specific types of features from images. This segmentation allows each model to focus on particular authentication aspects, improving overall accuracy while enabling modular system architecture that manages complexity through division of labor.
3Productivity
If image-based authentication is implemented, then authentication speed is improved, but authentication reliability may be compromised without professional oversight
Solution Approach 1:
The patent implements a self-service authentication system where the automated model performs authentication without requiring professional human oversight. The system independently processes images, extracts features, and generates authentication results, achieving both high speed and reliability through sophisticated algorithmic decision-making that eliminates human error and bias.
Data Source
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AI summary
The application provides an item authentication method, system, device and storage medium. The method comprises: acquiring a plurality of images taken on an item to be authenticated, each of the images contains at least one target authentication point; acquiring a single-point score corresponding to each of a plurality of target authentication points through identifying the plurality of images by a plurality of trained convolutional neural network models respectively, each of the trained convolutional neural network models is corresponding to the at least one target authentication point; obtaining a total score through performing a weighted summation process on single-point scores of the plurality of target authentication points according to weights obtained from a testing of a trained training set; and authenticating the authenticity of the item according to the single-point scores or/and the total score. The problem that counterfeit goods are not be quickly authenticated can be solved effectively through identifying target authentication points used to describing the item in the images by convolutional neural networks, constructing an assessment mechanism, and determining the authenticity of the item through assessing identification results.