Toward remote and secure authentication: Disambiguation of magnetic microwire signatures using neural networks

Secure and high-throughput authentication systems require materials with uniquely identifiable responses that can be remotely detected and rapidly disambiguated. To this end, complex electromagnetic responses from arrangements of amorphous ferromagnetic microwires were analyzed using machine learning. These novel materials deliver maximal spectral dispersion when the frequency of incident electromagnetic radiation matches the microwire resonance. Utilizing data obtained from 225 unique microwire arrangements, a neural network reproduced the response distribution of unseen data to a confidence level of 90%, with a mean square error less than 0.01. This favorable performance affirms the potential of magnetic microwires for use in tags for secure article surveillance systems. Graphical Abstract