Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding

Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privac...

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Autores principales:	Yoga Samudra, Tohari Ahmad
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Audio processing Data protection Data hiding Infrastructure Information security Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/e96c37cfa8d147c1b88b3f32d477cf32
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spelling	oai:doaj.org-article:e96c37cfa8d147c1b88b3f32d477cf322021-12-02T05:02:56ZImproved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding2405-844010.1016/j.heliyon.2021.e08381https://doaj.org/article/e96c37cfa8d147c1b88b3f32d477cf322021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021024841https://doaj.org/toc/2405-8440Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privacy of the users will suffer from serious problems. Therefore, security protection is much required in various aspects, and one of how it is done is by embedding the data (payload) in another form of data (cover) such as audio. However, the existing methods do not provide enough space to accommodate the payload, so bigger data can not be taken; the quality of the respective generated data is relatively low, making it much different from its corresponding cover. This research works on these problems by improving a prediction error expansion-based algorithm and designing a mirroring embedded sample scheme. Here, a processed audio sample is forced to be as close as possible to the original one. The experimental results show that this proposed method produces a higher quality of stego data considering the size of the payloads. It achieves more than 100 dB, which is higher than that of the compared algorithms. Additionally, this proposed method is reversible, which means that both the original payload and the audio cover can be fully reconstructed.Yoga SamudraTohari AhmadElsevierarticleAudio processingData protectionData hidingInfrastructureInformation securityScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08381- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Audio processing Data protection Data hiding Infrastructure Information security Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Audio processing Data protection Data hiding Infrastructure Information security Science (General) Q1-390 Social sciences (General) H1-99 Yoga Samudra Tohari Ahmad Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
description	Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privacy of the users will suffer from serious problems. Therefore, security protection is much required in various aspects, and one of how it is done is by embedding the data (payload) in another form of data (cover) such as audio. However, the existing methods do not provide enough space to accommodate the payload, so bigger data can not be taken; the quality of the respective generated data is relatively low, making it much different from its corresponding cover. This research works on these problems by improving a prediction error expansion-based algorithm and designing a mirroring embedded sample scheme. Here, a processed audio sample is forced to be as close as possible to the original one. The experimental results show that this proposed method produces a higher quality of stego data considering the size of the payloads. It achieves more than 100 dB, which is higher than that of the compared algorithms. Additionally, this proposed method is reversible, which means that both the original payload and the audio cover can be fully reconstructed.
format	article
author	Yoga Samudra Tohari Ahmad
author_facet	Yoga Samudra Tohari Ahmad
author_sort	Yoga Samudra
title	Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
title_short	Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
title_full	Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
title_fullStr	Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
title_full_unstemmed	Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
title_sort	improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e96c37cfa8d147c1b88b3f32d477cf32
work_keys_str_mv	AT yogasamudra improvedpredictionerrorexpansionandmirroringembeddedsamplesforenhancingreversibleaudiodatahiding AT tohariahmad improvedpredictionerrorexpansionandmirroringembeddedsamplesforenhancingreversibleaudiodatahiding
_version_	1718400744718073856

Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding

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