Classification of masked image data.

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algo...

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Autores principales:	Kamila Lis, Mateusz Koryciński, Konrad A Ciecierski
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5c8d452dd714ef5a4048fd26d9bc978
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id	oai:doaj.org-article:b5c8d452dd714ef5a4048fd26d9bc978
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spelling	oai:doaj.org-article:b5c8d452dd714ef5a4048fd26d9bc9782021-12-02T20:09:39ZClassification of masked image data.1932-620310.1371/journal.pone.0254181https://doaj.org/article/b5c8d452dd714ef5a4048fd26d9bc9782021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254181https://doaj.org/toc/1932-6203Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation-called a masked form-can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.Kamila LisMateusz KorycińskiKonrad A CiecierskiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254181 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kamila Lis Mateusz Koryciński Konrad A Ciecierski Classification of masked image data.
description	Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation-called a masked form-can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.
format	article
author	Kamila Lis Mateusz Koryciński Konrad A Ciecierski
author_facet	Kamila Lis Mateusz Koryciński Konrad A Ciecierski
author_sort	Kamila Lis
title	Classification of masked image data.
title_short	Classification of masked image data.
title_full	Classification of masked image data.
title_fullStr	Classification of masked image data.
title_full_unstemmed	Classification of masked image data.
title_sort	classification of masked image data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b5c8d452dd714ef5a4048fd26d9bc978
work_keys_str_mv	AT kamilalis classificationofmaskedimagedata AT mateuszkorycinski classificationofmaskedimagedata AT konradaciecierski classificationofmaskedimagedata
_version_	1718375085995196416

Classification of masked image data.

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