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On Brightness Agnostic Adversarial Examples Against Face Recognition Systems

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dc.contributor.authorSingh, Inderjeet
dc.contributor.authorMomiyama, Satoru
dc.contributor.authorKakizaki, Kazuya
dc.contributor.authorAraki, Toshinori
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:46Z
dc.date.available2021-10-04T08:43:46Z
dc.date.issued2021
dc.description.abstractThis paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37455
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-318
dc.subjectAdversarial examples
dc.subjectFace recognition
dc.subjectBrightness variations
dc.subjectCurriculum learning
dc.titleOn Brightness Agnostic Adversarial Examples Against Face Recognition Systemsen
dc.typeText/Conference Paper
gi.citation.endPage204
gi.citation.publisherPlaceBonn
gi.citation.startPage197
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group