Detail publikačního výsledku

SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

BOŠTÍK, O.

Originální název

SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Anglický název

SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

OCR, CAPTCHA, Deep learning, YOLO v2, semi-supervised learning, MATLAB

Klíčová slova v angličtině

OCR, CAPTCHA, Deep learning, YOLO v2, semi-supervised learning, MATLAB

Autoři

BOŠTÍK, O.

Rok RIV

2021

Vydáno

26.04.2021

Nakladatel

Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5942-7

Kniha

Proceedings of the 27nd Conference STUDENT EEICT 2018

Strany od

436

Strany do

440

Strany počet

5

BibTex

@inproceedings{BUT171159,
  author="Ondřej {Boštík}",
  title="SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR",
  booktitle="Proceedings of the 27nd Conference STUDENT EEICT 2018",
  year="2021",
  number="1",
  pages="436--440",
  publisher="Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5942-7"
}

Dokumenty

EEICT_2021