AI-Driven Medicine Authentication System for Detecting Counterfeit Drugs
Prof. Vaishali N. Shelokar
Dept. Of Information Technology,
Prof Ram Meghe College of Engineering And Management, Badnera, Amravati, Maharashtra India, 444701
Sant Gadge Baba Amravati university Amravati Maharashtra
vaishalithakare786@gmail.com
Priti G. Rathod
Dept. Of Information Technology,
Prof Ram Meghe College of Engineering And Management, Badnera, Amravati, Maharashtra India, 444701
Sant Gadge Baba Amravati university Amravati Maharashtra
rathodpriti281@gmail.com
Saloni V. Chapke
Dept. Of Information Technology,
Prof Ram Meghe College of Engineering And Management, Badnera, Amravati, Maharashtra India, 444701
Sant Gadge Baba Amravati university Amravati Maharashtra
salonichapke004@gmail.com
Bhavik V. Ghati
Dept. Of Information Technology,
Prof Ram Meghe College of Engineering And Management, Badnera, Amravati, Maharashtra India, 444701
Sant Gadge Baba Amravati university Amravati Maharashtra
bhavikghati@gmail.com
Yash A. Deshmukh
Dept. Of Information Technology,
Prof Ram Meghe College of Engineering And Management, Badnera, Amravati, Maharashtra India, 444701
Sant Gadge Baba Amravati university Amravati Maharashtra
yashdeshmukh6677@gmail.com
Abstract
The widespread presence of counterfeit medicines has become a critical issue in the healthcare sector, leading to severe risks for patients and reduced trust in pharmaceutical systems. Traditional verification approaches, including manual inspection and barcode systems, often fail to provide accurate and real-time authentication for consumers. This paper presents an intelligent medicine authentication system that utilizes artificial intelligence and mobile technology to detect counterfeit drugs efficiently. The system allows users to scan QR codes or capture images of medicine packaging using a mobile application. The captured data is processed to extract essential details, which are then validated against trusted datasets. Based on the analysis, the system provides a reliability score indicating whether the medicine is genuine or suspicious.
Additionally, the application includes a feature that offers general medicine-related guidance based on user input while encouraging professional consultation. The proposed solution enhances accessibility, improves user awareness, and provides a scalable approach to combating counterfeit medicines.
Keywords: Counterfeit drug detection, artificial intelligence, QR code verification, healthcare security, mobile application
