Artificial Intelligence Techniques for Real-Time Detection and Prevention of Counterfeit Products

Counterfeit products have become a major concern especially because they are easy to produce and their impacts are far reaching. The purpose of this study is to survey the use of artificial intelligence techniques towards identifying and preventing counterfeit items in real-time. By integrating the use of machine learning, deep learning, IoT, and blockchain technology the proposed system is able to yield very high accuracy when it is determining the difference between original and fake products thus enabling the assurance of the authenticity of the products in the supply chain. Tracking and metadata images, IoT sensor data, and blockchain information are integrated in the AI training and validation processes. The findings revealed a marked enhancement in the detection efficiency and the security of the supply chain management system, with satisfaction among buyers regarding the functionality of the system. The case elaborated in this research suggests that the counterfeiting detection could significantly be improved by applying artificial intelligence and stresses that timely modification of new counterfeiting approaches is imperative.