Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to...

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Autores: Tlelo-Cuautle, Esteban, Díaz-Muñoz, Jonathan Daniel, González-Zapata, Astrid Maritza, Li, Rui, León-Salas, Walter Daniel, Fernández Fernández, Francisco Vidal, Guillén-Fernández, Omar, Cruz-Vega, Israel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/95844
Acceso en línea:https://hdl.handle.net/11441/95844
https://doi.org/10.3390/s20051326
Access Level:acceso abierto
Palabra clave:Chaos
Correlation
FPGA
Hindmarsh-Rose neuron
Hopfield neuron
Image encryption
Lyapunov exponent
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spelling Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGATlelo-Cuautle, EstebanDíaz-Muñoz, Jonathan DanielGonzález-Zapata, Astrid MaritzaLi, RuiLeón-Salas, Walter DanielFernández Fernández, Francisco VidalGuillén-Fernández, OmarCruz-Vega, IsraelChaosCorrelationFPGAHindmarsh-Rose neuronHopfield neuronImage encryptionLyapunov exponentChaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.Multidisciplinary Digital Publishing Institute (MDPI)Electrónica y Electromagnetismo2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/95844https://doi.org/10.3390/s20051326reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSensors, 20 (5), 1326.http://dx.doi.org/10.3390/s20051326info:eu-repo/semantics/openAccessoai:idus.us.es:11441/958442026-06-17T12:51:07Z
dc.title.none.fl_str_mv Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
title Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
spellingShingle Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
Tlelo-Cuautle, Esteban
Chaos
Correlation
FPGA
Hindmarsh-Rose neuron
Hopfield neuron
Image encryption
Lyapunov exponent
title_short Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
title_full Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
title_fullStr Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
title_full_unstemmed Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
title_sort Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA
dc.creator.none.fl_str_mv Tlelo-Cuautle, Esteban
Díaz-Muñoz, Jonathan Daniel
González-Zapata, Astrid Maritza
Li, Rui
León-Salas, Walter Daniel
Fernández Fernández, Francisco Vidal
Guillén-Fernández, Omar
Cruz-Vega, Israel
author Tlelo-Cuautle, Esteban
author_facet Tlelo-Cuautle, Esteban
Díaz-Muñoz, Jonathan Daniel
González-Zapata, Astrid Maritza
Li, Rui
León-Salas, Walter Daniel
Fernández Fernández, Francisco Vidal
Guillén-Fernández, Omar
Cruz-Vega, Israel
author_role author
author2 Díaz-Muñoz, Jonathan Daniel
González-Zapata, Astrid Maritza
Li, Rui
León-Salas, Walter Daniel
Fernández Fernández, Francisco Vidal
Guillén-Fernández, Omar
Cruz-Vega, Israel
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Electrónica y Electromagnetismo
dc.subject.none.fl_str_mv Chaos
Correlation
FPGA
Hindmarsh-Rose neuron
Hopfield neuron
Image encryption
Lyapunov exponent
topic Chaos
Correlation
FPGA
Hindmarsh-Rose neuron
Hopfield neuron
Image encryption
Lyapunov exponent
description Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/95844
https://doi.org/10.3390/s20051326
url https://hdl.handle.net/11441/95844
https://doi.org/10.3390/s20051326
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, 20 (5), 1326.
http://dx.doi.org/10.3390/s20051326
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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