Deep learning-based optical authentication using the structural coloration of metals with femtosecond laser-induced periodic surface structures

Abstract

Structurally colored materials present potential technological applications including anticounterfeiting tags for authentication due to the ability to controllably manipulate colors through nanostructuring. Yet, no applications of deep learning algorithms, known to discover meaningful structures in data with far-reaching optimization capabilities, to such optical authentication applications involving low-spatial-frequency laser-induced periodic surface structures (LSFLs) have been demonstrated to date. In this work, by fine-tuning one of the lightweight convolutional neural networks, MobileNetV1, we investigate the optical authentication capabilities of the structurally colorized images on metal surfaces fabricated by controlling the orientation of femtosecond LSFLs. We show that the structural color variations due to a broad range of the illumination incident angles combined with both the controlled orientations of LSFLs and differences in features captured in the image make this system suitable for deep learning-based optical authentication.