Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications

Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with laboratory data. However, applying machine-l...

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Detalles Bibliográficos
Autores: Megias, David, Lerch-Hostalot, Daniel
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2022
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/146673
Acceso en línea:http://hdl.handle.net/10609/146673
http://doi.org/10.1109/TDSC.2022.3154967
Access Level:acceso abierto
Palabra clave:steganography
steganalysis
machine learning
cover source mismatch
stego source mismatch
uncertainty
esteganografia
esteganàlisi
aprenentatge automàtic
coberta desajust de fonts
desajust de fonts stego
incertesa
esteganografía
esteganálisis
aprendizaje automático
cubrir desajuste de fuente
desajuste de fuente stego
incertidumbre
id ES_d0785ed0bbccaba6de98acf42833f79f
oai_identifier_str oai:openaccess.uoc.edu:10609/146673
network_acronym_str ES
network_name_str España
repository_id_str
spelling Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applicationsMegias, DavidLerch-Hostalot, Danielsteganographysteganalysismachine learningcover source mismatchstego source mismatchuncertaintyesteganografiaesteganàlisiaprenentatge automàticcoberta desajust de fontsdesajust de fonts stegoincertesaesteganografíaesteganálisisaprendizaje automáticocubrir desajuste de fuentedesajuste de fuente stegoincertidumbremachine learningaprenentatge automàticaprendizaje automáticoSteganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with laboratory data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms, unknown parameters such as the message length and even having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This paper presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the directionality property of features with respect to data embedding. Once this strategy is sustained by a consistent theoretical framework, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions.IEEE Transactions on Dependable and Secure ComputingUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)Universitat Oberta de Catalunya. Estudis d'Informàtica, Multimèdia i Telecomunicació202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/146673http://doi.org/10.1109/TDSC.2022.3154967reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésIEEE Transactions on Dependable and Secure Computing, 2022https://ieeexplore.ieee.org/document/9722958info:eu-repo/grantAgreement/ES/RTI2018-095094-B-C22/info:eu-repo/grantAgreement/ES/PCI2020-120689-2/CC BY-NC-ND 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/esinfo:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1466732026-05-28T12:42:01Z
dc.title.none.fl_str_mv Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
title Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
spellingShingle Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
Megias, David
steganography
steganalysis
machine learning
cover source mismatch
stego source mismatch
uncertainty
esteganografia
esteganàlisi
aprenentatge automàtic
coberta desajust de fonts
desajust de fonts stego
incertesa
esteganografía
esteganálisis
aprendizaje automático
cubrir desajuste de fuente
desajuste de fuente stego
incertidumbre
machine learning
aprenentatge automàtic
aprendizaje automático
title_short Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
title_full Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
title_fullStr Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
title_full_unstemmed Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
title_sort Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
dc.creator.none.fl_str_mv Megias, David
Lerch-Hostalot, Daniel
author Megias, David
author_facet Megias, David
Lerch-Hostalot, Daniel
author_role author
author2 Lerch-Hostalot, Daniel
author2_role author
dc.contributor.none.fl_str_mv Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universitat Oberta de Catalunya. Estudis d'Informàtica, Multimèdia i Telecomunicació
dc.subject.none.fl_str_mv steganography
steganalysis
machine learning
cover source mismatch
stego source mismatch
uncertainty
esteganografia
esteganàlisi
aprenentatge automàtic
coberta desajust de fonts
desajust de fonts stego
incertesa
esteganografía
esteganálisis
aprendizaje automático
cubrir desajuste de fuente
desajuste de fuente stego
incertidumbre
machine learning
aprenentatge automàtic
aprendizaje automático
topic steganography
steganalysis
machine learning
cover source mismatch
stego source mismatch
uncertainty
esteganografia
esteganàlisi
aprenentatge automàtic
coberta desajust de fonts
desajust de fonts stego
incertesa
esteganografía
esteganálisis
aprendizaje automático
cubrir desajuste de fuente
desajuste de fuente stego
incertidumbre
machine learning
aprenentatge automàtic
aprendizaje automático
description Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with laboratory data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms, unknown parameters such as the message length and even having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This paper presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the directionality property of features with respect to data embedding. Once this strategy is sustained by a consistent theoretical framework, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/146673
http://doi.org/10.1109/TDSC.2022.3154967
url http://hdl.handle.net/10609/146673
http://doi.org/10.1109/TDSC.2022.3154967
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Dependable and Secure Computing, 2022
https://ieeexplore.ieee.org/document/9722958
info:eu-repo/grantAgreement/ES/RTI2018-095094-B-C22/
info:eu-repo/grantAgreement/ES/PCI2020-120689-2/
dc.rights.none.fl_str_mv CC BY-NC-ND 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC-ND 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE Transactions on Dependable and Secure Computing
publisher.none.fl_str_mv IEEE Transactions on Dependable and Secure Computing
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420176985817088
score 15.300719

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