PIN-a-Boo: Revealing Smartphone PINs via Segmentation and Hand Skeleton Tracking from Video Feeds

dc.contributor.authorWeich, Patrick
dc.contributor.authorLobachev, Oleg
dc.contributor.editorSkala, Václav
dc.date.accessioned2025-07-30T10:33:57Z
dc.date.available2025-07-30T10:33:57Z
dc.date.issued2025
dc.description.abstract-translatedIt is crucial to improve smartphone security, given the prevalence of sensitive information stored on them. This study presents an attack strategy that reveals smartphone PIN entries using computer vision and pattern recognition techniques. By leveraging modern segmentation and hand skeleton tracking, our method accurately identifies and analyzes finger movement patterns, even when partially obscured. We can reliably infer the entered PIN by combining these movement patterns with the smartphone’s position and the on-screen keypad layout. This approach significantly enhances shoulder-surfing attacks, requiring only a video recording of the entry process. Our attack requires much less specialized expertise, making it more accessible. We conclude by analyzing the method’s potential impact and its implications for public safety.en
dc.format12 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.doihttp://www.doi.org/10.24132/CSRN.2025-31
dc.identifier.issn2464-4617 (Print)
dc.identifier.issn2464-4625 (online)
dc.identifier.urihttp://hdl.handle.net/11025/62241
dc.language.isoenen
dc.publisherVaclav Skala - UNION Agencyen
dc.rights© Vaclav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectpočítačové viděnícs
dc.subjectrozpoznávání vzorůcs
dc.subjectzabezpečení chytrých telefonůcs
dc.subject„showroom surfing“cs
dc.subjectútok založený na videucs
dc.subjectsoukromí uživatelůcs
dc.subject.translatedcomputer visionen
dc.subject.translatedpattern recognitionen
dc.subject.translatedsmartphone securityen
dc.subject.translatedshoulder-surfingen
dc.subject.translatedvideo-based attacken
dc.subject.translateduser privacyen
dc.titlePIN-a-Boo: Revealing Smartphone PINs via Segmentation and Hand Skeleton Tracking from Video Feedsen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size9579663*
local.has.filesyes*

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