Artificial optical microfingerprints for advanced anticounterfeiting

Artificial optical microfingerprints,known as physically unclonable functions(PUFs)offer a groundbreaking approach for anti-counterfeiting.However,these PUFs artificial optical microfingerprints suffer from a limited number of challenge-response pairs,making them vulnerable to machine learning(ML)attacks when additional error-correcting units are introduced.This study presents a pioneering demonstration of artificial optical microfingerprints that combine the advantages of PUFs,a large encoding capacity algorithm,and reliable deep learning authentication against ML attacks.Our approach utilizes the triple-mode PUFs,incorporating bright-field,multicolor fluorescence wrinkles,and the topography of surface enhanced Raman scattering in the mechanical and optical layers.Notably,the quaternary encoding of these PUFs artificial microfingerprints allows for an encoding capacity of 6.43×10^(24082) and achieves 100%deep learning recognition accuracy.Furthermore,the PUFs artificial optical microfingerprints exhibit high resilience against ML attacks,facilitated by generative adversarial networks(GAN)(with mean prediction accuracy of~85.0%).The results of this study highlight the potential of utilizing up to three PUFs in conjunction with a GAN training system,paving the way for achieving encoded information that remains resilient to ML attacks.