Vis enkel innførsel

dc.contributor.authorOsorio-Roig, Daile
dc.contributor.authorRathgeb, Christian
dc.contributor.authorDrozdowski, Pawel
dc.contributor.authorTerhorst, Philipp
dc.contributor.authorStruc, Vitomir
dc.contributor.authorBusch, Christoph
dc.date.accessioned2024-06-18T09:01:14Z
dc.date.available2024-06-18T09:01:14Z
dc.date.created2022-09-20T13:08:38Z
dc.date.issued2022
dc.identifier.citationIEEE Transactions on Biometrics, Behavior, and Identity Science. 2022, 4 (2), 263-275.en_US
dc.identifier.issn2637-6407
dc.identifier.urihttps://hdl.handle.net/11250/3134463
dc.description.abstractIn the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes; (2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker’s database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAn Attack on Facial Soft-Biometric Privacy Enhancementen_US
dc.title.alternativeAn Attack on Facial Soft-Biometric Privacy Enhancementen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber263-275en_US
dc.source.volume4en_US
dc.source.journalIEEE Transactions on Biometrics, Behavior, and Identity Scienceen_US
dc.source.issue2en_US
dc.identifier.doi10.1109/TBIOM.2022.3172724
dc.identifier.cristin2053524
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal