Affine Invariant Watermark Scheme using Genetic Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108 |
Resumo: | Geometrical attacks are among the most challenging problems in present day digital watermark. Such attacks are very simple to implement yet they can defeat most of the existing digital watermark algorithms without causing serious perceptual image distortion. Geometric attacks can very easily confuse the decoder unless it transforms the image back to its original size/orientation, i.e., recover the lost synchronism. To be able do so, the decoder needs to know how the image has been manipulated, i.e., needs to know geometric transformation parameters. In this research, we report a novel method to estimate the geometric manipulation. We compute the point pattern matching measure for the geometric manipulation. The shape-specific points of the original image are computed. The point matching is realized by genetic algorithm. The proposed scheme does not require the original image because SSP information of the original image has been memorized by the neural network. This method has been proved its robustness to geometric attacks through experiments. |
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INFOCOMP: Jornal de Ciência da Computação |
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Affine Invariant Watermark Scheme using Genetic Algorithmgeometrical transformationshape-specific pointspoints matchinggenetic algorithmneural networkGeometrical attacks are among the most challenging problems in present day digital watermark. Such attacks are very simple to implement yet they can defeat most of the existing digital watermark algorithms without causing serious perceptual image distortion. Geometric attacks can very easily confuse the decoder unless it transforms the image back to its original size/orientation, i.e., recover the lost synchronism. To be able do so, the decoder needs to know how the image has been manipulated, i.e., needs to know geometric transformation parameters. In this research, we report a novel method to estimate the geometric manipulation. We compute the point pattern matching measure for the geometric manipulation. The shape-specific points of the original image are computed. The point matching is realized by genetic algorithm. The proposed scheme does not require the original image because SSP information of the original image has been memorized by the neural network. This method has been proved its robustness to geometric attacks through experiments.Editora da UFLA2005-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108INFOCOMP Journal of Computer Science; Vol. 4 No. 4 (2005): December, 2005; 20-261982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108/93Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessCong, JinPeng, Jiaxiong2015-06-22T12:47:09Zoai:infocomp.dcc.ufla.br:article/108Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:17.638561INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Affine Invariant Watermark Scheme using Genetic Algorithm |
title |
Affine Invariant Watermark Scheme using Genetic Algorithm |
spellingShingle |
Affine Invariant Watermark Scheme using Genetic Algorithm Cong, Jin geometrical transformation shape-specific points points matching genetic algorithm neural network |
title_short |
Affine Invariant Watermark Scheme using Genetic Algorithm |
title_full |
Affine Invariant Watermark Scheme using Genetic Algorithm |
title_fullStr |
Affine Invariant Watermark Scheme using Genetic Algorithm |
title_full_unstemmed |
Affine Invariant Watermark Scheme using Genetic Algorithm |
title_sort |
Affine Invariant Watermark Scheme using Genetic Algorithm |
author |
Cong, Jin |
author_facet |
Cong, Jin Peng, Jiaxiong |
author_role |
author |
author2 |
Peng, Jiaxiong |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Cong, Jin Peng, Jiaxiong |
dc.subject.por.fl_str_mv |
geometrical transformation shape-specific points points matching genetic algorithm neural network |
topic |
geometrical transformation shape-specific points points matching genetic algorithm neural network |
description |
Geometrical attacks are among the most challenging problems in present day digital watermark. Such attacks are very simple to implement yet they can defeat most of the existing digital watermark algorithms without causing serious perceptual image distortion. Geometric attacks can very easily confuse the decoder unless it transforms the image back to its original size/orientation, i.e., recover the lost synchronism. To be able do so, the decoder needs to know how the image has been manipulated, i.e., needs to know geometric transformation parameters. In this research, we report a novel method to estimate the geometric manipulation. We compute the point pattern matching measure for the geometric manipulation. The shape-specific points of the original image are computed. The point matching is realized by genetic algorithm. The proposed scheme does not require the original image because SSP information of the original image has been memorized by the neural network. This method has been proved its robustness to geometric attacks through experiments. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/108/93 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 4 No. 4 (2005): December, 2005; 20-26 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740074053632 |