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Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security

The increasingly pervasive problem of counterfeiting affects both individuals and industry. In particular, public health and medical fields face threats to device authenticity and patient privacy, especially in the post-pandemic era. Physical unclonable functions (PUFs) present a modern solution usi...

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Autores principales: Pradhan, Sayantan, Rajagopala, Abhi D., Meno, Emma, Adams, Stephen, Elks, Carl R., Beling, Peter A., Yadavalli, Vamsi K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535045/
https://www.ncbi.nlm.nih.gov/pubmed/37763841
http://dx.doi.org/10.3390/mi14091678
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author Pradhan, Sayantan
Rajagopala, Abhi D.
Meno, Emma
Adams, Stephen
Elks, Carl R.
Beling, Peter A.
Yadavalli, Vamsi K.
author_facet Pradhan, Sayantan
Rajagopala, Abhi D.
Meno, Emma
Adams, Stephen
Elks, Carl R.
Beling, Peter A.
Yadavalli, Vamsi K.
author_sort Pradhan, Sayantan
collection PubMed
description The increasingly pervasive problem of counterfeiting affects both individuals and industry. In particular, public health and medical fields face threats to device authenticity and patient privacy, especially in the post-pandemic era. Physical unclonable functions (PUFs) present a modern solution using counterfeit-proof security labels to securely authenticate and identify physical objects. PUFs harness innately entropic information generators to create a unique fingerprint for an authentication protocol. This paper proposes a facile protein self-assembly process as an entropy generator for a unique biological PUF. The posited image digitization process applies a deep learning model to extract a feature vector from the self-assembly image. This is then binarized and debiased to produce a cryptographic key. The NIST SP 800-22 Statistical Test Suite was used to evaluate the randomness of the generated keys, which proved sufficiently stochastic. To facilitate deployment on physical objects, the PUF images were printed on flexible silk-fibroin-based biodegradable labels using functional protein bioinks. Images from the labels were captured using a cellphone camera and referenced against the source image for error rate comparison. The deep-learning-based biological PUF has potential as a low-cost, scalable, highly randomized strategy for anti-counterfeiting technology.
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spelling pubmed-105350452023-09-29 Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security Pradhan, Sayantan Rajagopala, Abhi D. Meno, Emma Adams, Stephen Elks, Carl R. Beling, Peter A. Yadavalli, Vamsi K. Micromachines (Basel) Article The increasingly pervasive problem of counterfeiting affects both individuals and industry. In particular, public health and medical fields face threats to device authenticity and patient privacy, especially in the post-pandemic era. Physical unclonable functions (PUFs) present a modern solution using counterfeit-proof security labels to securely authenticate and identify physical objects. PUFs harness innately entropic information generators to create a unique fingerprint for an authentication protocol. This paper proposes a facile protein self-assembly process as an entropy generator for a unique biological PUF. The posited image digitization process applies a deep learning model to extract a feature vector from the self-assembly image. This is then binarized and debiased to produce a cryptographic key. The NIST SP 800-22 Statistical Test Suite was used to evaluate the randomness of the generated keys, which proved sufficiently stochastic. To facilitate deployment on physical objects, the PUF images were printed on flexible silk-fibroin-based biodegradable labels using functional protein bioinks. Images from the labels were captured using a cellphone camera and referenced against the source image for error rate comparison. The deep-learning-based biological PUF has potential as a low-cost, scalable, highly randomized strategy for anti-counterfeiting technology. MDPI 2023-08-28 /pmc/articles/PMC10535045/ /pubmed/37763841 http://dx.doi.org/10.3390/mi14091678 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pradhan, Sayantan
Rajagopala, Abhi D.
Meno, Emma
Adams, Stephen
Elks, Carl R.
Beling, Peter A.
Yadavalli, Vamsi K.
Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title_full Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title_fullStr Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title_full_unstemmed Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title_short Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
title_sort deep-learning-based digitization of protein-self-assembly to print biodegradable physically unclonable labels for device security
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535045/
https://www.ncbi.nlm.nih.gov/pubmed/37763841
http://dx.doi.org/10.3390/mi14091678
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