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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66719
Volume:
10 |
8 |
Aug 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and Data Science
Avinashilingam Institute for Home Science and Higher Education for Women
Artificial Intelligence and Data Science
Avinashilingam Institute for Home Science and Higher Education for Women
Artificial Intelligence and Data Science
Avinashilingam Institute for Home Science and Higher Education for Women
Artificial Intelligence and Data Science
Avinashilingam Institute for Home Science and Higher Education for Women
Computer Science And Engineering
Avinashilingam Institute for Home Science and Higher Education for Women
The increase in counterfeit products has affected consumer trust and caused financial losses across industries. Traditional methods such as holograms and static barcodes can be copied and cannot identify whether a product has already been purchased. The proposed QR Code-Based Product Verification and Purchase System is a secure web application that assigns a unique QR code to each product. The Admin registers products and generates QR codes, while Users scan the codes to verify product authenticity. Each product is initially marked as “Unused” and changes to “Used” after a successful purchase. The system identifies products as genuine, already used, or counterfeit using a centralized database. This prevents duplicate purchases and provides fast and reliable product verification, helping reduce counterfeit products in the supply chain.
KEYWORDS
The developed QR code-based product verification and purchase system provides a practical way to address the problem of counterfeit products. It combines a unique digital identity for each product, real-time database verification, and single-use purchase control within one web application. The system uses a three-tier MERN architecture, allowing administrators to register products, generate the above image and QR code, and manage product details. Customers can scan the QR code using a camera to check the product status before making a purchase. During testing, the three product states, "Valid", "Already Used", and "Fake", helped the system identify genuine, previously purchased, and counterfeit products correctly. The status update after a successful purchase also prevented the same QR code from being reused. By connecting product verification with the purchase process, the system provides better control than methods that only verify product codes. The current system has some limitations. It requires an internet connection to access the database and perform real-time verification. The above image generated through the AI service may also require administrator review in some cases. In addition, the current product identifier provides a good level of security for the prototype, but further security improvements would be required before using the system on a large commercial scale. Several improvements can be considered for future development. A mobile application using React Native or Flutter could make QR code scanning more convenient and support limited offline functionality. Blockchain technology could also be explored to maintain a secure record of product history and transactions. Other possible improvements include online payment integration, AI-based detection of suspicious verification patterns, role-based access for multiple administrators, and an analytics dashboard for monitoring sales, verification attempts, and counterfeit products. Overall, the developed system provides a simple and cost-effective approach to product authentication. By combining QR code verification with purchase control, it can help consumers identify genuine products and reduce the repeated use of product codes. With further improvements and testing, the system could be extended for wider use in different industries.
1. ISO/IEC 18004:2015, Information Technology – Automatic Identification and Data Capture Techniques – QR Code Bar Code Symbology Specification, International Organization for Standardisation, 2015. 2. OECD/EUIPO, Trends in Trade in Counterfeit and Pirated Goods, OECD Publishing, 2019. 3. I. Sommerville, Software Engineering, 10th ed. Pearson, 2015. 4. R. S. Pressman, Software Engineering: A Practitioner's Approach, 8th ed. McGraw-Hill Education, 2014. 5. A. Sharma and V. Rao, "QR Code-Based Anti-Counterfeiting Systems for Retail Supply Chains," International Journal of Computer Applications, vol. 176, no. 12, pp. 22–29, 2024. 6. N. Gupta, S. Iyer, and R. Menon, "Blockchain-Enabled Product Traceability for Counterfeit Prevention," IEEE Transactions on Engineering Management, vol. 71, pp. 3120–3131, 2024. 7. K. Fernandes and P. Nair, "Design of a Real-Time Product Authentication System Using QR Codes and Cloud Databases," Journal of Web Engineering and Technology, vol. 15, no. 3, pp. 88–97, 2023. 8. M. Ibrahim and J. Park, "Secure Single-Use Token Verification for Anti-Counterfeit Applications," IEEE Access, vol. 12, pp. 45210–45221, 2024.
Publication Date
8/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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