Abstract :

The Rapid Expansion Of E-commerce And Global Supply Chains Has Significantly Increased The Prevalence Of Counterfeit Products, Posing Serious Threats To Consumers, Manufacturers, And Economies. Traditional Product Verification Systems Rely Heavily On Centralized Databases, Which Are Vulnerable To Tampering, Single Points Of Failure, And Unauthorized Access. To Address These Limitations, This Project Proposes A BlockchainBased Fake Product Identification System Integrated With Content Search Using DistilBERT API, Ensuring Enhanced Transparency, Security, And Efficiency.The System Leverages Blockchain Technology To Store Immutable Product Information, Including Product ID, Manufacturing Details, Company Information, And Digital Signatures Generated Using Cryptographic Hashing Algorithms Such As SHA-256. Each Product Is Registered On The Blockchain Through Smart Contracts, Ensuring That Once Data Is Recorded, It Cannot Be Altered Or Deleted. This Immutability Guarantees Trust And Traceability Across The Product Lifecycle.To Further Enhance Verification, The System Incorporates Content-based Search Using DistilBERT, A Lightweight And Efficient Transformer-based Natural Language Processing Model. DistilBERT Enables Semantic Comparison Of Product-related Information, Allowing The System To Identify Similarities And Discrepancies In Product Descriptions, Thus Improving Counterfeit Detection Accuracy Beyond Simple Barcode Matching.The Application Is Developed Using Flask For The Web Interface And Web3.py For Blockchain Interaction. Users Can Register, Login, Add Products, And Verify Product Authenticity By Uploading Barcode Images. The System Generates A Unique Digital Signature For Each Product Using Its Barcode Data. During Verification, The Uploaded Barcode Is Hashed And Compared With Stored Blockchain Records To Determine Authenticity.Additionally, The System Provides Administrative Functionalities Such As Viewing Registered Users And Products, Ensuring Complete Control And Monitoring. The Decentralized Architecture Eliminates Dependency On A Central Authority, Making The System Resilient Against Cyberattacks And Data Manipulation.Experimental Results Demonstrate That Integrating Blockchain With NLP-based Content Analysis Significantly Improves The Reliability And Robustness Of Counterfeit Detection Systems. The Proposed Solution Not Only Ensures Data Integrity But Also Enhances User Trust By Providing Transparent And Verifiable Product Information.In Conclusion, This System Offers A Scalable And Secure Framework For Combating Counterfeit Products Using Advanced Technologies Such As Blockchain And Natural Language Processing, Paving The Way For Future Innovations In Supply Chain Security

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1522-1534

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite