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Secure Deep Learning for Intelligent Terahertz Metamaterial Identification

Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not ye...

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Detalles Bibliográficos
Autores principales: Liu, Feifei, Zhang, Weihao, Sun, Yu, Liu, Jianwei, Miao, Jungang, He, Feng, Wu, Xiaojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583053/
https://www.ncbi.nlm.nih.gov/pubmed/33027897
http://dx.doi.org/10.3390/s20195673
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author Liu, Feifei
Zhang, Weihao
Sun, Yu
Liu, Jianwei
Miao, Jungang
He, Feng
Wu, Xiaojun
author_facet Liu, Feifei
Zhang, Weihao
Sun, Yu
Liu, Jianwei
Miao, Jungang
He, Feng
Wu, Xiaojun
author_sort Liu, Feifei
collection PubMed
description Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification.
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spelling pubmed-75830532020-10-28 Secure Deep Learning for Intelligent Terahertz Metamaterial Identification Liu, Feifei Zhang, Weihao Sun, Yu Liu, Jianwei Miao, Jungang He, Feng Wu, Xiaojun Sensors (Basel) Letter Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification. MDPI 2020-10-05 /pmc/articles/PMC7583053/ /pubmed/33027897 http://dx.doi.org/10.3390/s20195673 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Liu, Feifei
Zhang, Weihao
Sun, Yu
Liu, Jianwei
Miao, Jungang
He, Feng
Wu, Xiaojun
Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title_full Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title_fullStr Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title_full_unstemmed Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title_short Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
title_sort secure deep learning for intelligent terahertz metamaterial identification
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583053/
https://www.ncbi.nlm.nih.gov/pubmed/33027897
http://dx.doi.org/10.3390/s20195673
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