Coating objects with microscopic droplets of liquid crystals makes it possible to identify and authenticate objects as if they had biometric-like features: this is extremely valuable as an anti-counterfeiting measure. How to extract features from images has been studied elsewhere, but exchanging data about features is not enough if we wish to build secure cryptographic authentication protocols . What we need are authentication tokens (i.e., bitstrings), strategies to cope with noise, always present when processing images , and solutions to protect the original features so that it is impossible to reproduce them from the tokens. Secure sketches and fuzzy extractors are the cryptographic toolkits that offer these functionalities, but they must be instantiated to work with the peculiar specific features extracted from images of liquid crystals. We show how this can work and how we can obtain uniform, error-tolerant, and random strings, and how they are used to authenticate liquid crystal coated objects. Our protocol reminds an existing biometric-based protocol, but only apparently. Using the original protocol as-it-is would make the process vulnerable to an attack that exploits certain physical peculiarities of our liquid crystal coatings. Instead, our protocol is robust against the attack. We prove all our security claims formally, by modeling and verifying in Proverif, our protocol and its cryptographic schemes. We implement and benchmark our solution, measuring both the performance and the quality of authentication .

peer reviewed

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.