Advanced Polymeric Physical Unclonable Functions: From Mechanistic Design and Computer Vision to Enhanced Effect of Stimuli

ABSTRACT Commodity fraud is a serious issue leading to significant financial losses for producers, marketers, and consumers. The past anticounterfeiting and security systems were cloneable and predictable. Today, development of advanced, multilayered, and unclonable systems focuses on providing stronger protection against counterfeiting. A physical unclonable function (PUF) generates unique identifiers based on the inherent randomness of the physical system. Polymers with numerous random features and stimuli‐responsive characteristics broaden the options for PUF fabrication while increasing complexity. A combination of PUF polymers with artificial intelligence technology like machine learning, deep learning, and software coding for encryption purposes, makes this system advanced and multi‐level in security. Smart materials are utilized to advance PUF's performance and increasing the security level. This review undertakes a summary of recent advancements in innovative encryption and anticounterfeiting mechanisms with PUF polymers with highlighting the important role of computer vision for encryption. It introduces different mechanisms of PUF polymers, including statistical arrangement, phase separation, self‐assembly, wetting/dewetting, and stress, strain, and modulus‐induced wrinkling. Polymers offer diverse morphologies, undergo various chemical and physical reactions and offer durability, flexibility, and resistance to environmental factors. This makes them ideal for unclonable anticounterfeiting systems, helping protect valuable and luxury products and healthcare.