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. 2018 Jun 25;8(1):9653.
doi: 10.1038/s41598-018-28008-6.

Physical Unclonable Function based on a Multi-Mode Optical Waveguide

Charis Mesaritakis  1 , Marialena Akriotou  2 , Alexandros Kapsalis  3 , Evangelos Grivas  3 , Charidimos Chaintoutis  2 , Thomas Nikas  2 , Dimitris Syvridis  2
Affiliations

Physical Unclonable Function based on a Multi-Mode Optical Waveguide

Charis Mesaritakis et al. Sci Rep. .

Abstract

Physical unclonable functions are the physical equivalent of one-way mathematical transformations that, upon external excitation, can generate irreversible responses. Exceeding their mathematical counterparts, their inherent physical complexity renders them resilient to cloning and reverse engineering. When these features are combined with their time-invariant and deterministic operation, the necessity to store the responses (keys) in non-volatile means can be alleviated. This pivotal feature, makes them critical components for a wide range of cryptographic-authentication applications, where sensitive data storage is restricted. In this work, a physical unclonable function based on a single optical waveguide is experimentally and numerically validated. The system's responses consist of speckle-like images that stem from mode-mixing and scattering events of multiple guided transverse modes. The proposed configuration enables the system's response to be simultaneously governed by multiple physical scrambling mechanisms, thus offering a radical performance enhancement in terms of physical unclonability compared to conventional optical implementations. Additional features like physical re-configurability, render our scheme suitable for demanding authentication applications.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Basic PUF properties: (a) unclonability output depends on the physical properties of the PUF, (b) unpredictability the output depends on the input, (c) time-invariant operation (robustness) (d) schematic of a typical optical PUF based on an optical diffuser.
Figure 2
Figure 2
Schematic of the proposed PUF implementation, presenting all physical mechanisms associated with response generation. Inset depicting the friction processed fiber’s facet, R and d correspond to the radius of the facet and to the average defect size. Ψin and Ψout are related to the initial excited and filtered modes, due to the core-cladding of the fiber.
Figure 3
Figure 3
(a) Block diagram of the experimental setup, using bulk optics. NIR-DFB corresponds to near infrared distributed feedback laser, EDFA to erbium doped fiber amplifier, POF stands for polymer optical fiber. In the insets from left-to-right: microscope image of the fiber’s processed facet followed by a typical speckle image acquired. Block diagram of the (b) Key-generation procedure used in and (c) Key authentication.
Figure 4
Figure 4
(a) Euclidean distances for normalized raw responses (images 8bit, 340 × 340). “Inter” corresponds to images from different PUF instantiations (103) and “intra” to multiple images for the same PUF-challenge combination. (b) Hamming distances for pairs of code words generated through random binary hashing and (c) through the spatial Gabor filtering. (d) Robustness of the proposed system (with Random and Gabor) and versus the error correction capability (ECC) bits employed. (e) Probability of cloning for 255-bit long code-words generated from 103 POF-PUFs and using the data provided in versus the error correction capabilities. (d) Combined graphs presenting Robustness versus the attained probability of cloning for the proposed scheme and.
Figure 5
Figure 5
(a) Schematic representation of the experimental setup used for testing sensitivity to fiber deformations. (b) Experimental image: the system is equipped with a thermoelectric cooler and a piston that can deform the fiber, attached to a NanoMax TS301 micro-positioner. (c) Cross correlation of consecutive images versus the fiber deformation (d) hamming distances of the generated code-words for the same challenge-PUF under mechanical deformation.
Figure 6
Figure 6
Experimental: (a) cropped image of the speckle with dimension of 200 × 200 (b) autocorrelation function alongside sinc2 fitting providing average speckle size of 6 pixels (c) intensity histogram alongside gamma function fitting. Simulation: (d) image speckle (e) autocorrelation function with d = 6 pixels (f) intensity histogram for the simulation data.
Figure 7
Figure 7
(a) Correlation coefficient variation versus the wavelength of the illumination source (b) Probability density function of hamming distances for 100 different PUF instances. (c) Mean cross correlation (black) for 100 different illumination conditions versus the percentage of output facet defects for 100 TEm, k = 1..10 modes (black square) TEm, k > 100 modes (black-pentagons). A fitting based on a logistic function is assumed in the first case (black slash). The average extractable bits/image were computed in the right y-axis (red-circle) for the low order modes.
Figure 8
Figure 8
Experimental images of a 10 cm PUF’s output where (a) the input facet is highly defective and the output facet is polished and (b) vice versa.
See this image and copyright information in PMC

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