ABSTRACT
In recent years, pharmaceutical counterfeiting has become an increasingly dangerous situation. A patient who unknowingly consumes a counterfeit drug is at a serious health risk. To address this problem, a low-cost and robust approach for authentication that can be administered at the point-of-care is required. Our proposed solution uses Optical Physical Unclonable Functions (PUFs); patterns formed by a stochastic process that can be used for authentication. We create edible PUFs (ePUFs) using electrospray deposition, which utilizes strong electric fields to atomize a liquid suspension into a plume of micro-scale droplets that are delivered to the target. The ePUFs are electrospray-deposited from an edible ink directly onto the surface of the drug tablets. The process parameters (flow rate, translation speed, and suspension concentration) govern the characteristics of the ePUF to provide highly stochastic patterns. To evaluate our approach, 200 ePUFs were deposited onto tablets at various conditions, followed by imaging and storage of the patterns in a database. For ePUF authentication, a machine vision approach was created using the open source SIFT pattern matching algorithm. Using optimized pattern-matching constraints, our algorithm was shown to be 100% successful in authenticating the cellphone images of the ePUFs to the database. Additionally, the algorithm was found to be robust against changes in illumination and orientation of the cellphone images.
