Optimal performance of simple low-cost optical physical unclonable functions resilient to machine learning attacks

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 th...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 40079 - 15
Main Authors: Akriotou, Marialena, Bartsokas, Theodoros, Veinidis, Christos, Syvridis, Dimitris
Format: Journal Article
Language:English
Published: London Nature Publishing Group 01.11.2025
Nature Portfolio
Subjects:
Algorithms
Lasers
Learning algorithms
Machine learning
ISSN:2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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).
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2045-2322
DOI:10.1038/s41598-025-23840-z