Cholesteric Spherical Reflectors as Physical Unclonable Identifiers in Anti-counterfeiting

Abstract

Cholesteric Spherical Reflectors (CSRs) are made of droplets of cholesteric liquid crystals (the same material under the screen of our mobile phones) but molded in a spherical shape and hardened into a solid. CSRs have a peculiar behavior when illuminated: they reflect light and produce unique optical patterns whose full display is hardly predictable. They have been argued to behave like an optical Physical Unclonable Function (PUF), therefore finding application in anti-counterfeiting, in particular for object authentication. However, a fundamental challenge remains open: to understand what makes each optical response unique and how to extract this identifying information reliably and repeatedly. We study the problem, and we design and discuss two pivotal procedures to build authentication protocols for objects coated with CSRs. We test the quality of our procedures against large data sets of pattern images: images from CSRs are used to calculate inter-and intra-distance; simulated patterns created artificially are used to measure security in terms of false positive ratio. Our procedures successfully cluster images coming from the same CSR, distinguishing them from images of different CSRs and decoys. Our work is one of the few that has studied procedures of information extraction for materials derived from CSRs. It advances the state of the art in this area, closing the gap between the research on optical PUFs and practical applications. CCS CONCEPTS• Security and privacy → Authentication; Access control.

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“…Speci cally, in the practical authentication process, manufacturers provide images taken under ideal laboratory conditions, but end users may employ images taken in environments saturated with various additional noise sources. Even if the optical signal from a PUF label is stable, these noise sources in uence the readout process, which might prevent the authentication algorithms (i.e., point-by-point comparison) from working 42 .…”

“…This implies that it is impossible to nd an appropriate threshold to distinguish PUF labels. In noise resilience evaluation results for other PUF labels based on point-by-point comparison 42 , negative results have also been found and were attributed to the widespread limitation of the point-by-point comparison method, i.e., sensitive to some noise sources. Therefore, this highlights the need for a more robust authentication algorithm.…”

“…Firstly, due to broad applicability and a low false-positive rate 5 , the point-by-point comparison method (evaluated by the similarity index 11 or Hamming distance 36 ) is widely used 11,[37][38][39][40][41] . However, this method shows poor noise tolerance, due to sensitivity to pixel level intensity information 42 which is prone to being in uenced by some common noise sources. Secondly, contemporary arti cial intelligence (AI)-driven methods 9,12,16,43 can achieve a low false-positive rate and be noiseresilient by learning robust feature representations with deep neural networks.…”