EA based Heuristic Segmentation for Efficient Data Hiding

Wazirali, R; Chaczko, Z

EA based Heuristic Segmentation for Efficient Data Hiding

Wazirali, R Chaczko, Z

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Publisher:: Foundation of Computer Science
Publication Type:: Journal Article
Citation:: International Journal of Computer Applications, 2015
Issue Date:: 2015-05-20

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Field	Value	Language
dc.contributor.author	Wazirali, R	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.date.available	2015-05-01	en_US
dc.date.issued	2015-05-20	en_US
dc.identifier.citation	International Journal of Computer Applications, 2015	en_US
dc.identifier.issn	0975-8887	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37170
dc.identifier.uri	http://hdl.handle.net/10453/35788
dc.identifier.uri	http://hdl.handle.net/10453/37171
dc.identifier.uri	http://hdl.handle.net/10453/40695
dc.description.abstract	Information concealment is of paramount importance in information and communication security. For the protection of information and intellectual property, effective techniques are needed. Steganography is the art of writing concealed information in a way that it does not arouse suspicion about its existence. This ensures that only the sender and the recipient are aware of the concealed message existence. Capacity and Stego image imperceptibility are the most crucial aspects. This paper provides a heuristic approach of choosing the right-most regions for embedding that ensure minimum changes to stego object. Then, different percentages of secret data will be hidden on the cluster based on the characteristic of the region. Therefore, the sharp edge region will hide more data while the smooth will hide data. The proposed approach use K-mean clustering to categorized the segmentation and then genetic algorithm will be used to boost the PSNR (peak signal to-noise ratio) value while optimizing high capacity information. The obtained stego object is virtually indistinguishable from the cover object. The experimental results show a significant enhancement over other previous work.	en_US
dc.publisher	Foundation of Computer Science	en_US
dc.relation.ispartof	International Journal of Computer Applications	en_US
dc.relation.isbasedon	10.5120/20738-3126	en_US
dc.title	EA based Heuristic Segmentation for Efficient Data Hiding	en_US
dc.type	Journal Article
utslib.for	080504 Ubiquitous Computing	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	080110 Simulation and Modelling	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.publication-status	Published	en_US

Abstract:

Information concealment is of paramount importance in information and communication security. For the protection of information and intellectual property, effective techniques are needed. Steganography is the art of writing concealed information in a way that it does not arouse suspicion about its existence. This ensures that only the sender and the recipient are aware of the concealed message existence. Capacity and Stego image imperceptibility are the most crucial aspects. This paper provides a heuristic approach of choosing the right-most regions for embedding that ensure minimum changes to stego object. Then, different percentages of secret data will be hidden on the cluster based on the characteristic of the region. Therefore, the sharp edge region will hide more data while the smooth will hide data. The proposed approach use K-mean clustering to categorized the segmentation and then genetic algorithm will be used to boost the PSNR (peak signal to-noise ratio) value while optimizing high capacity information. The obtained stego object is virtually indistinguishable from the cover object. The experimental results show a significant enhancement over other previous work.

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http://hdl.handle.net/10453/35788