Cellular Neural Network-Based Medical Image Encryption
This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic opera...
Gespeichert in:
Veröffentlicht in: | SN computer science 2020-11, Vol.1 (6), p.346, Article 346 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 6 |
container_start_page | 346 |
container_title | SN computer science |
container_volume | 1 |
creator | Sheela, S. J. Suresh, K. V. Tandur, Deepaknath Sanjay, A. |
description | This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes. |
doi_str_mv | 10.1007/s42979-020-00371-0 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2933155007</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2933155007</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2780-427a3ed4c74e8fbe7c1ae14ba9bd8f9232b945aa1b594e65b2bc455bcbfd5e263</originalsourceid><addsrcrecordid>eNp9kE9LAzEUxIMoWGq_gKeC5-jLv83mqKVqoepFwVtIsm9L63a3Jl2k397YFbx5muExMw9-hFwyuGYA-iZJbrShwIECCM0onJARLwpGSwP69Og5NUa9n5NJShsA4AqkLNSIFDNsmr5xcfqMfXRNlv1XFz_onUtYTZ-wWod8XWzdCqfzNsTDbr_u2gtyVrsm4eRXx-Ttfv46e6TLl4fF7HZJA9clUMm1E1jJoCWWtUcdmEMmvTO-KmvDBfdGKueYV0ZioTz3QSrlg68rhbwQY3I17O5i99lj2ttN18c2v7TcCMGUygByig-pELuUItZ2F9dbFw-Wgf1BZAdENiOyR0QWckkMpZTD7Qrj3_Q_rW_9OGhm</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2933155007</pqid></control><display><type>article</type><title>Cellular Neural Network-Based Medical Image Encryption</title><source>ProQuest Central UK/Ireland</source><source>SpringerLink Journals - AutoHoldings</source><source>ProQuest Central</source><creator>Sheela, S. J. ; Suresh, K. V. ; Tandur, Deepaknath ; Sanjay, A.</creator><creatorcontrib>Sheela, S. J. ; Suresh, K. V. ; Tandur, Deepaknath ; Sanjay, A.</creatorcontrib><description>This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.</description><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-020-00371-0</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Algorithms ; Artificial neural networks ; Cellular communication ; Communication ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Confidentiality ; Cryptography ; Data encryption ; Data Structures and Information Theory ; Diffusion barriers ; Encryption ; Evolution ; Hydrogen bonds ; Information storage ; Information Systems and Communication Service ; Medical imaging ; Neural networks ; Original Research ; Partial differential equations ; Pattern Recognition and Graphics ; Pixels ; Software Engineering/Programming and Operating Systems ; Telemedicine ; Vision</subject><ispartof>SN computer science, 2020-11, Vol.1 (6), p.346, Article 346</ispartof><rights>Springer Nature Singapore Pte Ltd 2020</rights><rights>Springer Nature Singapore Pte Ltd 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2780-427a3ed4c74e8fbe7c1ae14ba9bd8f9232b945aa1b594e65b2bc455bcbfd5e263</citedby><cites>FETCH-LOGICAL-c2780-427a3ed4c74e8fbe7c1ae14ba9bd8f9232b945aa1b594e65b2bc455bcbfd5e263</cites><orcidid>0000-0001-6793-1182</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s42979-020-00371-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2933155007?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Sheela, S. J.</creatorcontrib><creatorcontrib>Suresh, K. V.</creatorcontrib><creatorcontrib>Tandur, Deepaknath</creatorcontrib><creatorcontrib>Sanjay, A.</creatorcontrib><title>Cellular Neural Network-Based Medical Image Encryption</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Cellular communication</subject><subject>Communication</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Confidentiality</subject><subject>Cryptography</subject><subject>Data encryption</subject><subject>Data Structures and Information Theory</subject><subject>Diffusion barriers</subject><subject>Encryption</subject><subject>Evolution</subject><subject>Hydrogen bonds</subject><subject>Information storage</subject><subject>Information Systems and Communication Service</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Original Research</subject><subject>Partial differential equations</subject><subject>Pattern Recognition and Graphics</subject><subject>Pixels</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Telemedicine</subject><subject>Vision</subject><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE9LAzEUxIMoWGq_gKeC5-jLv83mqKVqoepFwVtIsm9L63a3Jl2k397YFbx5muExMw9-hFwyuGYA-iZJbrShwIECCM0onJARLwpGSwP69Og5NUa9n5NJShsA4AqkLNSIFDNsmr5xcfqMfXRNlv1XFz_onUtYTZ-wWod8XWzdCqfzNsTDbr_u2gtyVrsm4eRXx-Ttfv46e6TLl4fF7HZJA9clUMm1E1jJoCWWtUcdmEMmvTO-KmvDBfdGKueYV0ZioTz3QSrlg68rhbwQY3I17O5i99lj2ttN18c2v7TcCMGUygByig-pELuUItZ2F9dbFw-Wgf1BZAdENiOyR0QWckkMpZTD7Qrj3_Q_rW_9OGhm</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Sheela, S. J.</creator><creator>Suresh, K. V.</creator><creator>Tandur, Deepaknath</creator><creator>Sanjay, A.</creator><general>Springer Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0001-6793-1182</orcidid></search><sort><creationdate>20201101</creationdate><title>Cellular Neural Network-Based Medical Image Encryption</title><author>Sheela, S. J. ; Suresh, K. V. ; Tandur, Deepaknath ; Sanjay, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2780-427a3ed4c74e8fbe7c1ae14ba9bd8f9232b945aa1b594e65b2bc455bcbfd5e263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Cellular communication</topic><topic>Communication</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Confidentiality</topic><topic>Cryptography</topic><topic>Data encryption</topic><topic>Data Structures and Information Theory</topic><topic>Diffusion barriers</topic><topic>Encryption</topic><topic>Evolution</topic><topic>Hydrogen bonds</topic><topic>Information storage</topic><topic>Information Systems and Communication Service</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Original Research</topic><topic>Partial differential equations</topic><topic>Pattern Recognition and Graphics</topic><topic>Pixels</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Telemedicine</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sheela, S. J.</creatorcontrib><creatorcontrib>Suresh, K. V.</creatorcontrib><creatorcontrib>Tandur, Deepaknath</creatorcontrib><creatorcontrib>Sanjay, A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sheela, S. J.</au><au>Suresh, K. V.</au><au>Tandur, Deepaknath</au><au>Sanjay, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cellular Neural Network-Based Medical Image Encryption</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. SCI</stitle><date>2020-11-01</date><risdate>2020</risdate><volume>1</volume><issue>6</issue><spage>346</spage><pages>346-</pages><artnum>346</artnum><issn>2662-995X</issn><eissn>2661-8907</eissn><abstract>This article introduces a novel cryptosystem for the protection of medical images which is very essential in teleradiology applications. The proposed cryptosystem is based on Fridrich architecture which uses hyperchaotic cellular neural network (CNN) and DNA technology to perform cryptographic operations. In this paper, cellular neural network crumb coding transform (CNN-CCT) is proposed to perform confusion operation. It is used to shuffle the pixel values randomly. The diffusion operation is achieved by employing cipher block chain (CBC) mode of XOR operation which provides greater efficiency in hardware platforms. The diffusion operation is used to change the pixel values, thereby achieving the higher security. Simulation and comparison results infer that the proposed cryptosystem is robust against various cryptographic attacks and competitive with the state-of-the-art encryption schemes.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><doi>10.1007/s42979-020-00371-0</doi><orcidid>https://orcid.org/0000-0001-6793-1182</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2662-995X |
ispartof | SN computer science, 2020-11, Vol.1 (6), p.346, Article 346 |
issn | 2662-995X 2661-8907 |
language | eng |
recordid | cdi_proquest_journals_2933155007 |
source | ProQuest Central UK/Ireland; SpringerLink Journals - AutoHoldings; ProQuest Central |
subjects | Algorithms Artificial neural networks Cellular communication Communication Computer Imaging Computer Science Computer Systems Organization and Communication Networks Confidentiality Cryptography Data encryption Data Structures and Information Theory Diffusion barriers Encryption Evolution Hydrogen bonds Information storage Information Systems and Communication Service Medical imaging Neural networks Original Research Partial differential equations Pattern Recognition and Graphics Pixels Software Engineering/Programming and Operating Systems Telemedicine Vision |
title | Cellular Neural Network-Based Medical Image Encryption |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T22%3A47%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Cellular%20Neural%20Network-Based%20Medical%20Image%20Encryption&rft.jtitle=SN%20computer%20science&rft.au=Sheela,%20S.%20J.&rft.date=2020-11-01&rft.volume=1&rft.issue=6&rft.spage=346&rft.pages=346-&rft.artnum=346&rft.issn=2662-995X&rft.eissn=2661-8907&rft_id=info:doi/10.1007/s42979-020-00371-0&rft_dat=%3Cproquest_cross%3E2933155007%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2933155007&rft_id=info:pmid/&rfr_iscdi=true |