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Nano Letters

Reversible and Reconfigurable Optical Physical Unclonable Functions with Elastic Liquid Crystal Scatterers

Zhen He, Bing Cao, Yu Zhang, Hongyang Zhu, Xiuxiu Wang, Chensha Li, Weili Zhang

Published Oct 14, 2025• 4 citations
Publisher

Abstract

In high-throughput network security applications, physical unclonable functions (PUFs) employing fixed mapping mechanisms are vulnerable to machine learning attacks. Reconfigurable PUFs can improve security by altering their mapping states, but often face constraints of limited reconfigurability, irreversible controllability, or high hardware overhead. Here, reversible and reconfigurable optical PUFs, 2RO-PUFs, were developed by two-beam manipulation of photothermal-sensitive scatterers with a precisely controllable phase transition. Experimental results verify that the programmed scatters and resultant speckle patterns exhibit deterministic and repeatable reconfigurability while maintaining cryptographic robustness. As demonstrated in a novel Internet of Things framework, the introduction of optically controlled speckle formation into PUFs frameworks enhances cryptographic security and flexibility while alleviating storage and computational overhead.

DOI
10.1021/acs.nanolett.5c03711

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In high-throughput network security applications, physical unclonable functions (PUFs) employing fixed mapping mechanisms are vulnerable to machine learning attacks. Reconfigurable PUFs can improve security by altering their mapping states, but often face constraints of limited reconfigurability, irreversible controllability, or high hardware overhead. Here, reversible and reconfigurable optical PUFs, 2RO-PUFs, were developed by two-beam manipulation of photothermal-sensitive scatterers with a precisely controllable phase transition. Experimental results verify that the programmed scatters and resultant speckle patterns exhibit deterministic and repeatable reconfigurability while maintaining cryptographic robustness. As demonstrated in a novel Internet of Things framework, the introduction of optically controlled speckle formation into PUFs frameworks enhances cryptographic security and flexibility while alleviating storage and computational overhead.
Random number generators (RNG) are fundamental to cryptographic systems, requiring high entropy, unpredictability, and resistance to cloning attacks. This work proposes RNG based on optical physical unclonable function (PUF) that uses random scattering of structured vortex beams through rough surface. The inherent disorder of the scattering medium with tunable beam parameters like vortex order, provides a rich source of physical randomness. The generated speckle patterns are processed using a post processing algorithm to produce random sequences at a rate of 1.42 Gbps. The statistical quality of the sequences is evaluated using the NIST SP 800-22, AIS-31, and Dieharder test suites, which meet the required randomness criteria. Further assessment using entropies and Kolmogorov complexity shows high entropy and algorithmic randomness. The physical unclonability and uniqueness are verified through inter-device hamming distances, Pearson correlation of sequences obtained from different spatial regions and vortex orders, revealing negligible correlations. To assess resilience against data based adversaries, machine learning modeling attacks are performed. The prediction accuracies remain close to random guessing, showing resistance to learning based cloning and response prediction attacks. These results establish the proposed optical PUF method as a high speed, physically unclonable, and machine learning resistant source of random numbers for secure cryptographic applications.
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The increased prevalence of the Internet of Things (IoT) and the integration of digital technology into our daily lives have given rise to heightened security risks and the need for more robust security measures. In response to these challenges, physical unclonable functions (PUFs) have emerged as promising solution, offering a highly secure method to generate unpredictable and unique random digital values by leveraging inherent physical characteristics. However, traditional PUFs implementations often require complex hardware and circuitry, which can add to the cost and complexity of the system. We present a novel approach using a random wrinkles PUF (rw-PUF) based on an optically anisotropic, facile, simple, and cost-effective material. These wrinkles contain randomly oriented liquid crystal molecules, resulting in a two-dimensional retardation map corresponding to a complex birefringence pattern. Additionally, our proposed technique allows for customization based on specific requirements using a spatial light modulator, enabling fast fabrication. The random wrinkles PUF has the capability to store multiple data sets within a single PUF without the need for physical alterations. Furthermore, we introduce a concept called 'polyhedron authentication,' which utilizes three-dimensional information storage in a voxelated random wrinkles PUF. This approach demonstrates the feasibility of implementing high-level security technology by leveraging the unique properties of the rw-PUF.
Optical physical unclonable functions (PUFs) have emerged as a promising cryptographic primitive for next-generation security. However, to harmonize with various modern networks, conventional optical PUF is inadequate due to a rigid key space with a fixed specification. This study implements hierarchically controllable randomness sources for multi-level key generation, exploiting speckle characteristics. By adjusting illumination diameter, the speckle size can be modulated, allowing the extraction of three-level keys with various lengths: 64, 256, and 1,024 bits. Performance evaluation reveals that all levels are superb in uniformity, uniqueness, reproducibility, and randomness. Moreover, it is proposed two feasible applications. The first is a hierarchical authentication architecture that spans from Internet of Things (IoT) devices to sensitive information, balancing security and resources. The second is a multi-stage image encryption algorithm, exhibiting holocryptic performance superior to conventional XOR-based encryption. This versatile platform sets a new paradigm for optical PUF, establishing a foundation for robust and expandable next-generation security.
In this paper we reconsider Physical Unclonable Functions based on the traditional approach of optical scattering to randomly disordered optical media. These devices have the major advantage of utilization of simple and very low-cost technology and therefore the potential to be installed all over the network providing critical cybersecurity operations such authentication, real time cryptographic key generation and generation of trues random sequences. To comply with the requirements of the aforementioned operations, critical issues must be resolved. We propose and implement algorithms for the generation of an almost unlimited number of uncorrelated optical challenges. We show experimentally that the uncorrelated challenges result in optical speckle which, after the proper numerical processing, produce true random sequences. Moreover, we determine the optimal illumination conditions to achieve the best possible performance in terms of robustness and unpredictability. Last but not least, we studied the resilience of the PUF against machine learning attacks. We conclude experimentally that under certain illuminating conditions and using the aforementioned uncorrelated challenges, the network cannot predict the responses even after being trained with a very large number of challenge responses (24,000 pairs).