Semi-Supervised Approach To Train Captcha Letter Position Detetor
| but.event.date | 27.04.2021 | cs |
| but.event.title | STUDENT EEICT 2021 | cs |
| dc.contributor.author | Bostik, Ondrej | |
| dc.date.accessioned | 2021-07-21T07:07:00Z | |
| dc.date.available | 2021-07-21T07:07:00Z | |
| dc.date.issued | 2021 | cs |
| dc.description.abstract | Common Optical Character Recognition (OCR) methods benefit from the fact, that the text is distributedin images in a predictable pattern. This is not the situation with CAPTCHA systems. UtilizingOCR algorithms to overcome common web anti-abuse CAPTCHA systems is therefore a challengingtask. To train a system to overcome any CAPTCHA scheme, an attacker needs a huge dataset ofannotated images. And for some methods, the attacker needs not only the right answers but also anexact position of the character in the CAPTCHA image.Annotate the positions of the object in an image is a time-consuming task. In this paper, we proposea system, which can help to annotate the position of CAPTCHA character with minimal humaninteraction. After annotating a small sample of targeted CAPTCHA images, a YOLO-based regiondetection deep network is used to search for the characters’ locations. | en |
| dc.format | text | cs |
| dc.format.extent | 436-440 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 436-440. ISBN 978-80-214-5942-7 | cs |
| dc.identifier.isbn | 978-80-214-5942-7 | |
| dc.identifier.uri | http://hdl.handle.net/11012/200796 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers | en |
| dc.relation.uri | https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | OCR | en |
| dc.subject | CAPTCHA | en |
| dc.subject | Deep learning | en |
| dc.subject | YOLO v2 | en |
| dc.subject | semi-supervised learning | en |
| dc.subject | MATLAB | en |
| dc.title | Semi-Supervised Approach To Train Captcha Letter Position Detetor | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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