Identification of Fake Products Using Blockchain
DOI:
https://doi.org/10.5281/zenodo.7900499Keywords:
Blockchain, Counterfeit, Decentralized network, ImmutableAbstract
Fake products are something which is like producing the same product as that of the original product but by degrading the quality of the product by using the cheap materials. Even though the quality may be low but the product may look same asthat of the original product. This makes it merely impossible for the consumer to validate and know whether the product is real or fake. There are very less ways to keep the track of the original products. The customer thinks twice when he needs to buy any product as he or she will have very less way to track the product’s originality. This may cause a hugeloss for customer as well as the company. Along with that the customers trust on the company will also get hindered. Toavoid this Blockchain can be used in order to have a proper tracking of the product as Blockchain is known for its immutability a proper immutable, transparent tracking can be done. Therefore the main aim is to develop a system which will keep a track of the products main details such as manufacturer name, product ID, product manufactured date, place within the Blockchain, making the system decentralised and immutable with peer to peer transaction so that when the customer receives the product they can verify it by scanning it through the QR code and thereby do the validation of the product
References
Ma, J., Lin, S. Y., Chen, X., Sun, H. M., Chen, Y. C., & Wang, H. (2020). A blockchain-based application system for product anti-counterfeiting. IEEE Access, 8, 77642-77652.
Shreekumar, T., Mittal, P., Sharma, S., Kamath, R. N., Rajesh, S., & Ganapathy, B. N. (2022). Fake Product Detection Using Blockchain Technology. JOURNAL OF ALGEBRAIC STATISTICS, 13(3), 2815-2821.
Funde, A., Nahar, P., Khilari, A., Marne, N., & Nerkar, N. (2019). Blockchain Based Fake Product Identification in Supply Chain. International Research Journal of Engineering and Technology (IRJET), 6(5), 5367-5369.
NABI, S. A., Reddy, K. S., Reddy, M. R., Harish, J., Kumar, D. V., & Manasvi, A. (2023). AUTHENTICATION OF PRODUCT & COUNTERFEITS ELIMINATION USING BLOCK CHAIN. International Journal of Early Childhood Special Education, 15(1).
Jayaprasanna, M. C., Soundharya, V. A., Suhana, M., & Sujatha, S. (2021, February). A Block Chain based Management System for Detecting Counterfeit Product in Supply Chain. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 253-257). IEEE.
Yun, Y. (2020). The influence of blockchain technology on fraud and fake protection. OUR Journal: ODU Undergraduate Research Journal, 7(1), 8.
Nakamoto, S., & Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf, 4(2).
Al-Shammari, N. K., Syed, T. H., & Syed, M. B. (2021). An Edge–IoT framework and prototype based on blockchain for smart healthcare applications. Engineering, Technology & Applied Science Research, 11(4), 7326-7331.
Beck, R., Czepluch, J. S., Lollike, N., & Malone, S. (2016). Blockchain–the gateway to trust-free cryptographic transactions. In Twenty-Fourth European Conference on Information Systems (ECIS), İstanbul, Turkey, 2016 (pp. 1-14). Springer Publishing Company.
Yadav, A. S., & Kushwaha, D. S. (2021). Query Optimization in a Blockchain-Based Land Registry Management System. Ingénierie des Systèmes d Inf., 26(1), 13-21.
Yadav, A. S., Singh, N., & Kushwaha, D. S. (2022). Sidechain: storage land registry data using blockchain improve performance of search records. Cluster Computing, 25(2), 1475-1495.
Ahmed, S. T., Sreedhar Kumar, S., Anusha, B., Bhumika, P., Gunashree, M., & Ishwarya, B. (2020). A generalized study on data mining and clustering algorithms. New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India, 1121-1129.
Gunashree, M., Ahmed, S. T., Sindhuja, M., Bhumika, P., Anusha, B., & Ishwarya, B. (2020). A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set. Procedia Computer Science, 171, 1624-1633.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Antony Roshan Dsouza, Shantala Devi Patil, Amuthabala K
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
