CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/101064 https://doi.org/10.1109/ACCESS.2021.3132581 |
Resumo: | Identity Documents (IDs) containing a facial portrait constitute a prominent form of personal identi cation. Photograph substitution in of cial documents (a genuine photo replaced by a non-genuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still ef cient ways of misleading the national authorities in in-person identi cation processes. Therefore, in order to con rm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper. The encoded face image looks like the original image to a naked eye. Our architecture is called CodeFace. CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras. The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images. Extensive experiments were performed with real printed documents and smartphone cameras. The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits. |
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CodeFace: A Deep Learning Printer-Proof Steganography for Face PortraitsSteganographymachine-readable travel documentsdeep neural networkhiding message into imagesIdentity Documents (IDs) containing a facial portrait constitute a prominent form of personal identi cation. Photograph substitution in of cial documents (a genuine photo replaced by a non-genuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still ef cient ways of misleading the national authorities in in-person identi cation processes. Therefore, in order to con rm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper. The encoded face image looks like the original image to a naked eye. Our architecture is called CodeFace. CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras. The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images. Extensive experiments were performed with real printed documents and smartphone cameras. The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101064http://hdl.handle.net/10316/101064https://doi.org/10.1109/ACCESS.2021.3132581eng2169-3536Shadmand, FarhadMedvedev, IuriiGonçalves, Nunoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-07-30T02:42:04Zoai:estudogeral.uc.pt:10316/101064Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:20.749419Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
title |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
spellingShingle |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits Shadmand, Farhad Steganography machine-readable travel documents deep neural network hiding message into images |
title_short |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
title_full |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
title_fullStr |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
title_full_unstemmed |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
title_sort |
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits |
author |
Shadmand, Farhad |
author_facet |
Shadmand, Farhad Medvedev, Iurii Gonçalves, Nuno |
author_role |
author |
author2 |
Medvedev, Iurii Gonçalves, Nuno |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Shadmand, Farhad Medvedev, Iurii Gonçalves, Nuno |
dc.subject.por.fl_str_mv |
Steganography machine-readable travel documents deep neural network hiding message into images |
topic |
Steganography machine-readable travel documents deep neural network hiding message into images |
description |
Identity Documents (IDs) containing a facial portrait constitute a prominent form of personal identi cation. Photograph substitution in of cial documents (a genuine photo replaced by a non-genuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still ef cient ways of misleading the national authorities in in-person identi cation processes. Therefore, in order to con rm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper. The encoded face image looks like the original image to a naked eye. Our architecture is called CodeFace. CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras. The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images. Extensive experiments were performed with real printed documents and smartphone cameras. The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/101064 http://hdl.handle.net/10316/101064 https://doi.org/10.1109/ACCESS.2021.3132581 |
url |
http://hdl.handle.net/10316/101064 https://doi.org/10.1109/ACCESS.2021.3132581 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2169-3536 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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