Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

Face identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to b...

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Bibliographic Details
Authors: Carabe, Luis, Cermeño Mediavilla, Eduardo
Format: article
Publication Date:2021
Country:España
Institution:Universidad Autónoma de Madrid
Repository:Biblos-e Archivo. Repositorio Institucional de la UAM
Language:English
OAI Identifier:oai:repositorio.uam.es:10486/705004
Online Access:http://hdl.handle.net/10486/705004
https://dx.doi.org/10.1109/ACCESS.2021.3088786
Access Level:Open access
Keyword:Access control
ArcFace
biometrics
deep learning
face recognition
FaceNet
identification
morphing
security
spoofing attack
Informática
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spelling Stegano-Morphing: Concealing Attacks on Face Identification AlgorithmsCarabe, LuisCermeño Mediavilla, EduardoAccess controlArcFacebiometricsdeep learningface recognitionFaceNetidentificationmorphingsecurityspoofing attackInformáticaFace identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject's image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects' images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneouslyThis work was supported by the Consejería De Ciencia, Universidad e Innovación, Comunidad de MadridInstitute of Electrical and Electronics Engineers Inc. (IEEE)Departamento de Ingeniería InformáticaEscuela Politécnica Superior20212021-06-14research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/705004https://dx.doi.org/10.1109/ACCESS.2021.3088786reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7050042026-06-23T12:46:27Z
dc.title.none.fl_str_mv Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
title Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
spellingShingle Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
Carabe, Luis
Access control
ArcFace
biometrics
deep learning
face recognition
FaceNet
identification
morphing
security
spoofing attack
Informática
title_short Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
title_full Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
title_fullStr Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
title_full_unstemmed Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
title_sort Stegano-Morphing: Concealing Attacks on Face Identification Algorithms
dc.creator.none.fl_str_mv Carabe, Luis
Cermeño Mediavilla, Eduardo
author Carabe, Luis
author_facet Carabe, Luis
Cermeño Mediavilla, Eduardo
author_role author
author2 Cermeño Mediavilla, Eduardo
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
dc.subject.none.fl_str_mv Access control
ArcFace
biometrics
deep learning
face recognition
FaceNet
identification
morphing
security
spoofing attack
Informática
topic Access control
ArcFace
biometrics
deep learning
face recognition
FaceNet
identification
morphing
security
spoofing attack
Informática
description Face identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject's image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects' images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneously
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-06-14
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/705004
https://dx.doi.org/10.1109/ACCESS.2021.3088786
url http://hdl.handle.net/10486/705004
https://dx.doi.org/10.1109/ACCESS.2021.3088786
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc. (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc. (IEEE)
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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