Toward agent-based LSB image steganography system

In a digital communication environment, information security is mandatory. Three essential parameters used in the design process of a steganography algorithm are Payload, security, and fidelity. However, several methods are implemented in information hiding, such as Least Significant Bit (LBS), Disc...

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Autores principales:	Baothman Fatmah Abdulrahman, Edhah Budoor Salem
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	steganography digital security software agents lsb steganography ensemble classifier steganography datasets Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/ee0e238c2a7b4e85baf91323259779a9
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id	oai:doaj.org-article:ee0e238c2a7b4e85baf91323259779a9
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spelling	oai:doaj.org-article:ee0e238c2a7b4e85baf91323259779a92021-12-05T14:10:51ZToward agent-based LSB image steganography system2191-026X10.1515/jisys-2021-0044https://doaj.org/article/ee0e238c2a7b4e85baf91323259779a92021-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0044https://doaj.org/toc/2191-026XIn a digital communication environment, information security is mandatory. Three essential parameters used in the design process of a steganography algorithm are Payload, security, and fidelity. However, several methods are implemented in information hiding, such as Least Significant Bit (LBS), Discrete Wavelet Transform, Masking, and Discrete Cosine Transform. The paper aims to investigate novel steganography techniques based on agent technology. It proposes a Framework of Steganography based on agent for secret communication using LSB. The most common image steganography databases are explored for training and testing. The methodology in this work is based on the statistical properties of the developed agent software using Matlab. The experiment design is based on six statistical feature measures, including Histogram, Mean, Standard deviation, Entropy, Variance and Energy. For steganography, an Ensemble classifier is used to test two scenarios: embedding a single language message and inserting bilingual messages. ROC Curve represents the evaluation metrics. The result shows that the designed agent-based system with 50% training/testing sample set and 0.2 Payload can pick out the best cover image for the provided hidden message size to avoid visual artifact.Baothman Fatmah AbdulrahmanEdhah Budoor SalemDe Gruyterarticlesteganographydigital securitysoftware agents lsb steganographyensemble classifiersteganography datasetsScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 903-919 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	steganography digital security software agents lsb steganography ensemble classifier steganography datasets Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	steganography digital security software agents lsb steganography ensemble classifier steganography datasets Science Q Electronic computers. Computer science QA75.5-76.95 Baothman Fatmah Abdulrahman Edhah Budoor Salem Toward agent-based LSB image steganography system
description	In a digital communication environment, information security is mandatory. Three essential parameters used in the design process of a steganography algorithm are Payload, security, and fidelity. However, several methods are implemented in information hiding, such as Least Significant Bit (LBS), Discrete Wavelet Transform, Masking, and Discrete Cosine Transform. The paper aims to investigate novel steganography techniques based on agent technology. It proposes a Framework of Steganography based on agent for secret communication using LSB. The most common image steganography databases are explored for training and testing. The methodology in this work is based on the statistical properties of the developed agent software using Matlab. The experiment design is based on six statistical feature measures, including Histogram, Mean, Standard deviation, Entropy, Variance and Energy. For steganography, an Ensemble classifier is used to test two scenarios: embedding a single language message and inserting bilingual messages. ROC Curve represents the evaluation metrics. The result shows that the designed agent-based system with 50% training/testing sample set and 0.2 Payload can pick out the best cover image for the provided hidden message size to avoid visual artifact.
format	article
author	Baothman Fatmah Abdulrahman Edhah Budoor Salem
author_facet	Baothman Fatmah Abdulrahman Edhah Budoor Salem
author_sort	Baothman Fatmah Abdulrahman
title	Toward agent-based LSB image steganography system
title_short	Toward agent-based LSB image steganography system
title_full	Toward agent-based LSB image steganography system
title_fullStr	Toward agent-based LSB image steganography system
title_full_unstemmed	Toward agent-based LSB image steganography system
title_sort	toward agent-based lsb image steganography system
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/ee0e238c2a7b4e85baf91323259779a9
work_keys_str_mv	AT baothmanfatmahabdulrahman towardagentbasedlsbimagesteganographysystem AT edhahbudoorsalem towardagentbasedlsbimagesteganographysystem
_version_	1718371695975202816

Toward agent-based LSB image steganography system

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