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).
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).
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.
The scope of this paper is to demonstrate a fully working and compact photonic Physical Unclonable Function (PUF) device capable of operating in real life scenarios as an authentication mechanism and random number generator. For this purpose, an extensive experimental investigation of a Polymer Optical Fiber (POF) and a diffuser as PUF tokens is performed and the most significant properties are evaluated using the proper mathematical tools. Two different software algorithms, the Random Binary Method (RBM) and Singular Value Decomposition (SVD), were tested for optimized key extraction and error correction codes have been incorporated for enhancing key reproducibility. By taking into consideration the limitations and overall performance derived by the experimental evaluation of the system, the designing details towards the implementation of a miniaturized, energy efficient and low-cost device are extensively discussed. The performance of the final device is thoroughly evaluated, demonstrating a long-term stability of 1 week, an operating temperature range of 50C, an exponentially large pool of unique Challenge-Response Pairs (CRPs), recovery after power failure and capability of generating NIST compliant true random numbers.
Abstract A physical unclonable function (PUF) device leverages manufacturing randomness to generate a unique fingerprint for secure authentication, encryption, and counterfeit prevention. Optical PUFs (OPUFs) have gained prominence due to their strong security, light‐based challenge‐response mechanisms, and potential for quantum‐secure encryption. These devices have promising applications for hardware security, authentication, cryptographic key generation, and anti‐counterfeiting measures across multiple industries. This review enlightens the diverse material platforms used for OPUFs, ranging from fiber optics and liquid crystals to advanced semiconductor nanocrystals and plasmonic metasurfaces, each offering distinct optical characteristics that impact security strength. While no single material system dominates the field, hybrid and multi‐model approaches integrating low‐dimensional materials are being explored to enhance reliability and scalability. Finally, the challenges and future perspectives are outlined, including adaptive OPUFs architectures, utilizing artificial intelligence (AI) for challenge‐response generation, and their integration into quantum communication networks, highlighting how next‐generation semiconductor materials can further advance OPUFs technologies.
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.
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.
Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict.Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions.We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs.This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element.We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing.The sensitivity of the COPUF system is also explored.We explore non-traditional PUF configurations enabled by the COPUF architecture.The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate.This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle.Our results show that it is possible to construct inexpensive optical PUFs using computational imaging.This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.