AI-Driven Counterfeit Drug Identification Systems

Abstract

Counterfeit medicines remain a critical public health challenge, particularly in regions with fragmented pharmaceutical supply chains and limited regulatory oversight. Recent advances in artificial intelligence have enabled automated visual inspection of medicines; however, most existing systems rely on isolated image analysis and fail to integrate packaging authentication with tablet-level verification. This study addresses this limitation by proposing a multi-stage AI framework that combines package feature extraction, pill surface characterization, and anomaly-aware decision fusion for counterfeit drug identification. The proposed research gap focuses on improving detection reliability when counterfeit products imitate authentic packaging but differ in microscopic physical characteristics. A hybrid deep learning architecture is conceptualized to jointly analyze multiple visual representations while incorporating uncertainty estimation during prediction. The framework is designed to reduce false authentication of sophisticated counterfeit medicines and improve robustness under varying imaging conditions. The proposed methodology provides a scalable solution for pharmacies, manufacturers, and regulatory agencies. The study contributes toward secure pharmaceutical distribution through intelligent, explainable, and automated counterfeit detection