Fast and Accurate Recognition of Perovskite Fluorescent Anti-counterfeiting Labels Based on Lightweight Convolutional Neural Networks

Anti-counterfeiting technology has always been a key issue in the field of information security. Physical Unclonable Function (PUF) labels, which are random patterns produced by a stochastic process, emerge as an effective anti-counterfeiting strategy due to the inherent randomness of their physical patterns. In this study, we developed a high-throughput droplet array generation technique based on surface tension confinement to prepare perovskite crystal films with controllable shapes and sizes. We utilized the random distribution of perovskite nanocrystal particles to construct the PUF textures of the labels. Compared to other anti-counterfeiting labels, our labels not only possess fluorescent properties but also feature microscale dimensions (less than 5.3 × 10–2mm2), low cost (less than 3 × 10–4 USD), and high encoding capacity (1.7 × 101956), providing support for multilevel anti-counterfeiting protection. Additionally, we introduce an innovative PUF recognition method based on a Partial Convolutional Network (PaCoNet), effectively addressing the limitations of previous methods, in terms of recognition accuracy and speed. Experimental validation on a data set of perovskite nanocrystal films with up to 60 different macroscopic shapes and unique microscopic textures demonstrates that our method achieves a recognition accuracy of up to 99.65% and significantly reduces the recognition time per image to just 0.177 s, highlighting the potential application of these labels in the field of anti-counterfeiting.