Recent achievements of the team - Fast and Accurate Recognition of Perovskite Fluorescent Anti-Counterfeiting Labels Based on Lightweight Convolutional Neural Networks
Our team published the paper “Fast and Accurate Recognition of Perovskite Fluorescent Anti-Counterfeiting Labels Based on Lightweight Convolutional Neural Networks” in the international journal ACS Applied Materials & Interfaces (IF:8.3, CAS Region II). Counterfeiting Labels Based on Lightweight Convolutional Neural Networks”. The School of Computer Engineering and Science of Shanghai University is the first department of the paper, with Yuexing Han as the first author, Bao Shengqi as the second author, Bozhi Shi as the third author, and Qiaochuan Chen as the corresponding author.
Anti-counterfeiting technology has always been a key issue in the field of information security. Physical unclonable function (PUF) labels, which are random patterns generated by a stochastic process, are an effective anti-counterfeiting strategy due to the inherent randomness of their physical patterns. In this study, a high-throughput droplet array generation technique based on surface tension constraints was developed for the preparation of chalcogenide crystal films with controllable shapes and sizes. The texture of PUF labels is constructed by utilizing the random distribution of chalcogenide nanocrystal grains. Compared with other anti-counterfeiting labels, the labels in this study not only have fluorescent properties, but also have micrometer size, low cost, and high encoding capacity, which provide support for multilevel anti-counterfeiting protection. In addition, this study introduces an innovative PUF recognition method based on Partial Convolutional Network (PaCoNet), which effectively addresses the limitations of previous methods in terms of recognition accuracy and speed. Experimental validation of a dataset of chalcocite nanocrystal films containing up to 60 different macro shapes and unique micro textures shows that the method in this study achieves recognition accuracy of up to 99.65% and significantly reduces the recognition time per image to only 0.177 seconds, highlighting the potential application of these tags in the field of anti-counterfeiting.
