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Article . 2023
License: CC BY
Data sources: Datacite
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Article . 2023
License: CC BY
Data sources: Datacite
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COUNTERFEIT PRODUCT DETECTION SYSTEM USING GRAPHICAL QRCODE IN BLOCKCHAIN

Authors: V. Brindha Devi, Mercy P. and Madhubala R.;

COUNTERFEIT PRODUCT DETECTION SYSTEM USING GRAPHICAL QRCODE IN BLOCKCHAIN

Abstract

Nowadays people buy products through online and even from shops, but could not distinguish which is a counterfeit product and which is a genuine product. There are many famous companies whose product is duplicated and made some changes and are sold in the market, where even the manufacturer get confused with the duplicate product. The customer buys the product and comes to a bad opinion about the company which is completely a negative part on the side of the manufacturer and the seller who sells the product. So in order to overcome this, manufacturers need to generate a graphical Quick response code that will be linked with the blockchain. When there is a usage of a normal Quick response code (QR code) which has a combination of black and white colors it will be easy for the hackers to duplicate the data. So manufacturers need to include a graphical-based QR code where hackers could not duplicate the product which may lead to ink smearing. When they link the Graphical QR code to the blockchain all the data will be stored in the form of blocks and could not change any of the data which is stored in blocks, on the customer side where they could scan the graphical QR code and find whether the Product is genuine or not. For each transaction in the blockchain, aspecific hash value will be generated and this hash when the customer scan needs to match the hash value what the manufacturer who added the block for the product. In this way, the counterfeit product could be eradicated from the market.

Keywords

Counterfeit Blockchain Ethereum Graphical Quick Response Code

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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