Robust lossless data hiding using clustering and statistical quantity histogram

An, L; Gao, X; Yuan, Y; Tao, D

Robust lossless data hiding using clustering and statistical quantity histogram

An, L Gao, X Yuan, Y Tao, D

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2012, 77 (1), pp. 1 - 11
Issue Date:: 2012-02-01

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Field	Value	Language
dc.contributor.author	An, L	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-02-01	en_US
dc.identifier.citation	Neurocomputing, 2012, 77 (1), pp. 1 - 11	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22860
dc.description.abstract	Lossless data hiding methods usually fail to recover the hidden messages completely when the watermarked images are attacked. Therefore, the robust lossless data hiding (RLDH), or the robust reversible watermarking technique, is urgently needed to effectively improve the recovery performance. To date a couple of methods have been developed; however, they have such drawbacks as poor visual quality and low capacity. To solve this problem, we develop a novel statistical quantity histogram shifting and clustering-based RLDH method or SQH-SC for short. The benefits of SQH-SC in comparison with existing typical methods include: (1) strong robustness against lossy compression and random noise due to the usage of k-means clustering; (2) good imperceptibility and reasonable performance tradeoff due to the consideration of the just noticeable distortion of images; (3) high capacity due to the flexible adjustment of the threshold; and (4) wide adaptability and good stability to different kinds of images. Extensive experimental studies based on natural images, medical images, and synthetic aperture radar (SAR) images demonstrate the effectiveness of the proposed SQH-SC. © 2011.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2011.06.012	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Robust lossless data hiding using clustering and statistical quantity histogram	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	77	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	77	en_US

Abstract:

Lossless data hiding methods usually fail to recover the hidden messages completely when the watermarked images are attacked. Therefore, the robust lossless data hiding (RLDH), or the robust reversible watermarking technique, is urgently needed to effectively improve the recovery performance. To date a couple of methods have been developed; however, they have such drawbacks as poor visual quality and low capacity. To solve this problem, we develop a novel statistical quantity histogram shifting and clustering-based RLDH method or SQH-SC for short. The benefits of SQH-SC in comparison with existing typical methods include: (1) strong robustness against lossy compression and random noise due to the usage of k-means clustering; (2) good imperceptibility and reasonable performance tradeoff due to the consideration of the just noticeable distortion of images; (3) high capacity due to the flexible adjustment of the threshold; and (4) wide adaptability and good stability to different kinds of images. Extensive experimental studies based on natural images, medical images, and synthetic aperture radar (SAR) images demonstrate the effectiveness of the proposed SQH-SC. © 2011.

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http://hdl.handle.net/10453/22860