Mobile Authentication System Using Machine Learning for Product Verification
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Solution Overview
Problem
Conventional product authentication systems are vulnerable to counterfeiting, as they can be easily deceived by fake verification portals, and they burden servers by performing authentication checks, rather than determining product authenticity on the mobile device.
Innovation Solution
A system that uses a machine learning model trained on product image data to identify designs on packaging, including visual codes, and redirects users to verification servers or platforms to authenticate products, preventing redirection to fake portals and determining authenticity on the mobile device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If server-based authentication is used, then verification can be centralized, but server load increases and authentication speed decreases
Solution Approach 1:
The system performs preliminary actions by pre-loading authentication data and machine learning models into the mobile device before authentication is needed. The device locally stores product information, verification rules, and visual code patterns, enabling immediate authentication without server delays. This preliminary preparation resolves the contradiction by allowing fast local verification while maintaining reliability through pre-established verification protocols.
Solution Approach 2:
The mobile device acts as an intermediary between the consumer and the authentication server. It performs initial authentication checks locally using stored data and machine learning models, then only communicates with the server for final verification or complex validations. This intermediary approach reduces server load and improves authentication speed while maintaining reliability through the coordinated verification process.
2Ease of operation
If visual codes are made easily accessible, then consumer verification is simplified, but counterfeiting becomes easier
Solution Approach 1:
The system applies local quality by embedding verification capabilities directly into the mobile device's local environment. Each device stores unique authentication data, machine learning models trained on genuine product patterns, and verification rules locally. This allows easy operation through local machine learning inference while maintaining high anti-counterfeiting reliability through localized security measures that are difficult to replicate.
Solution Approach 2:
The system replaces traditional mechanical verification methods with machine learning-based automated recognition. Instead of simple visual inspection, the mobile device uses trained neural networks to analyze product images, detect visual codes, and verify authenticity through pattern recognition. This substitution maintains ease of operation through automated processing while significantly improving anti-counterfeiting reliability through intelligent verification.
3Productivity
If authentication data is stored on device, then verification speed increases, but device storage requirements increase
Solution Approach 1:
The system applies partial action by storing only the essential authentication data, visual code patterns, and critical machine learning models locally on the device. Non-essential data is accessed from remote servers. This selective local storage approach increases verification speed for critical authentication steps while minimizing storage requirements by only caching what is immediately needed for authentication operations.
Solution Approach 2:
The system performs preliminary action by pre-loading and caching essential authentication data, verification rules, and machine learning models into the device before authentication is needed. This preliminary storage of critical components enables fast local verification without requiring large amounts of storage space for all possible data, as only the necessary authentication-related information is pre-loaded.
Data Source
AI summary
The present disclosure relates to the field of product authentication and verification and discloses a system (100) and method (200) to authenticate a product. The system (100) comprises a memory (102), a scanning module (104), a product identification module (108), a first re-direction module (106) and a second re-direction module (110). The product identification module (108) detects a design printed on the packaging of products and identifies products based on pre-trained machine learning model and detected design. The scanning module (104) facilitates user to scan visual codes printed on products and extracts UIDs and verification URLs from scanned visual code. The first re-direction module (106) receives extracted UID and verification URL if product is identified and screens extracted URL to identify its authenticity. The second re-direction module (110) re-directs users to a third-party verification platform at the embedded URL for product verification if product is not identified.