Semi-Supervised Deep Learning Approach For Breaking Geocaching Captchas

but.event.date23.04.2020cs
but.event.titleStudent EEICT 2020cs
dc.contributor.authorBostik, Ondrej
dc.date.accessioned2021-07-15T13:12:39Z
dc.date.available2021-07-15T13:12:39Z
dc.date.issued2020cs
dc.description.abstractFor nearly two decades, a substantial part of developed anti-abuse and anti-spam systems for web applications called CAPTCHA is based on imperfections in OCR (Optical Character Recognition) algorithms. But with improvements in Deep Learning in OCR, these systems are now obsolete. More and more systems can now break various text Captchas with great accuracy. Now with sufficient training dataset, almost every text-based Captcha scheme can be broken. The focus of this work is to present an idea of a semi-supervised method for reading text-based Captcha which needs only a small initial dataset. The main part of this article is dealing with the problem of training a deep learning system with only a small sample of target Captcha scheme via transfer learning.en
dc.formattextcs
dc.format.extent166-170cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 26st Conference STUDENT EEICT 2020: Selected Papers. s. 166-170. ISBN 978-80-214-5868-0cs
dc.identifier.isbn978-80-214-5868-0
dc.identifier.urihttp://hdl.handle.net/11012/200645
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 26st Conference STUDENT EEICT 2020: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/EEICT2020cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectOCRen
dc.subjectCAPTCHAen
dc.subjectDeep learningen
dc.subjectsemi-supervised learningen
dc.subjectMATLABen
dc.titleSemi-Supervised Deep Learning Approach For Breaking Geocaching Captchasen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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