Seam carving detection using convolutional neural networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SACI.2018.8441016 http://hdl.handle.net/11449/171461 |
Resumo: | Deep Learning techniques have been widely used in the recent years, primarily because of their efficiency in several applications, such as engineering, medicine, and data security. Seam carving is a content-aware image resizing method that can also be used for image tampering, being not straightforward to be identified. In this paper, we combine Convolutional Neural Networks and Local Binary Patterns to recognize whether an image has been modified automatically or not by seam carving. The experimental results show that the proposed approach can achieve accuracies within the range [81%-98%] depending on the severity of the tampering procedure. |
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Repositório Institucional da UNESP |
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2946 |
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Seam carving detection using convolutional neural networksComputer ForensicsConvolutional Neural NetworksDeep LearningSeam CarvingDeep Learning techniques have been widely used in the recent years, primarily because of their efficiency in several applications, such as engineering, medicine, and data security. Seam carving is a content-aware image resizing method that can also be used for image tampering, being not straightforward to be identified. In this paper, we combine Convolutional Neural Networks and Local Binary Patterns to recognize whether an image has been modified automatically or not by seam carving. The experimental results show that the proposed approach can achieve accuracies within the range [81%-98%] depending on the severity of the tampering procedure.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State University-UNESPSao Paulo State University-UNESPFAPESP: #2013/07375-0FAPESP: #2014/12236-1FAPESP: #2016/19403-6FAPESP: #2016/25687-7CNPq: #306166/2014-3CNPq: #307066/2017-7Universidade Estadual Paulista (Unesp)Cieslak, Luiz Fernandoda Silva [UNESP]Da Costa, Kelton Augustopontara [UNESP]Paulopapa, Joao [UNESP]2018-12-11T16:55:25Z2018-12-11T16:55:25Z2018-08-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject195-199http://dx.doi.org/10.1109/SACI.2018.8441016SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 195-199.http://hdl.handle.net/11449/17146110.1109/SACI.2018.84410162-s2.0-85053394453Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedingsinfo:eu-repo/semantics/openAccess2021-10-23T21:47:02Zoai:repositorio.unesp.br:11449/171461Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:47:02Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Seam carving detection using convolutional neural networks |
title |
Seam carving detection using convolutional neural networks |
spellingShingle |
Seam carving detection using convolutional neural networks Cieslak, Luiz Fernandoda Silva [UNESP] Computer Forensics Convolutional Neural Networks Deep Learning Seam Carving |
title_short |
Seam carving detection using convolutional neural networks |
title_full |
Seam carving detection using convolutional neural networks |
title_fullStr |
Seam carving detection using convolutional neural networks |
title_full_unstemmed |
Seam carving detection using convolutional neural networks |
title_sort |
Seam carving detection using convolutional neural networks |
author |
Cieslak, Luiz Fernandoda Silva [UNESP] |
author_facet |
Cieslak, Luiz Fernandoda Silva [UNESP] Da Costa, Kelton Augustopontara [UNESP] Paulopapa, Joao [UNESP] |
author_role |
author |
author2 |
Da Costa, Kelton Augustopontara [UNESP] Paulopapa, Joao [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Cieslak, Luiz Fernandoda Silva [UNESP] Da Costa, Kelton Augustopontara [UNESP] Paulopapa, Joao [UNESP] |
dc.subject.por.fl_str_mv |
Computer Forensics Convolutional Neural Networks Deep Learning Seam Carving |
topic |
Computer Forensics Convolutional Neural Networks Deep Learning Seam Carving |
description |
Deep Learning techniques have been widely used in the recent years, primarily because of their efficiency in several applications, such as engineering, medicine, and data security. Seam carving is a content-aware image resizing method that can also be used for image tampering, being not straightforward to be identified. In this paper, we combine Convolutional Neural Networks and Local Binary Patterns to recognize whether an image has been modified automatically or not by seam carving. The experimental results show that the proposed approach can achieve accuracies within the range [81%-98%] depending on the severity of the tampering procedure. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T16:55:25Z 2018-12-11T16:55:25Z 2018-08-20 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SACI.2018.8441016 SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 195-199. http://hdl.handle.net/11449/171461 10.1109/SACI.2018.8441016 2-s2.0-85053394453 |
url |
http://dx.doi.org/10.1109/SACI.2018.8441016 http://hdl.handle.net/11449/171461 |
identifier_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 195-199. 10.1109/SACI.2018.8441016 2-s2.0-85053394453 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
195-199 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964641666793472 |