SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140619" target="_blank" >RIV/00216305:26220/21:PU140619 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Popis výsledku v původním jazyce

Common Optical Character Recognition (OCR) methods benefit from the fact, that the text is distributed in images in a predictable pattern. This is not the situation with CAPTCHA systems. Utilizing OCR algorithms to overcome common web anti-abuse CAPTCHA systems is therefore a challenging task. To train a system to overcome any CAPTCHA scheme, an attacker needs a huge dataset of annotated images. And for some methods, the attacker needs not only the right answers but also an exact 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 propose a system, which can help to annotate the position of CAPTCHA character with minimal human interaction. After annotating a small sample of targeted CAPTCHA images, a YOLO-based region detection deep network is used to search for the characters’ locations.

Název v anglickém jazyce

SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Popis výsledku anglicky

Common Optical Character Recognition (OCR) methods benefit from the fact, that the text is distributed in images in a predictable pattern. This is not the situation with CAPTCHA systems. Utilizing OCR algorithms to overcome common web anti-abuse CAPTCHA systems is therefore a challenging task. To train a system to overcome any CAPTCHA scheme, an attacker needs a huge dataset of annotated images. And for some methods, the attacker needs not only the right answers but also an exact 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 propose a system, which can help to annotate the position of CAPTCHA character with minimal human interaction. After annotating a small sample of targeted CAPTCHA images, a YOLO-based region detection deep network is used to search for the characters’ locations.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 27nd Conference STUDENT EEICT 2018

ISBN

978-80-214-5942-7

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

436-440

Název nakladatele

Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

27. 4. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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