Publication detail

SEMI-SUPERVISED DEEP LEARNING APPROACH FOR BREAKING GEOCACHING CAPTCHAS

BOŠTÍK, O.

Original Title

SEMI-SUPERVISED DEEP LEARNING APPROACH FOR BREAKING GEOCACHING CAPTCHAS

Type

conference paper

Language

English

Original Abstract

For 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.

Keywords

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

Authors

BOŠTÍK, O.

Released

23. 4. 2020

Publisher

Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5868-0

Book

Proceedings II of the 26th Conference STUDENT EEICT 2020 - Selected papers

Edition

1

Edition number

1

Pages from

166

Pages to

170

Pages count

5

BibTex

@inproceedings{BUT164004,
  author="Ondřej {Boštík}",
  title="SEMI-SUPERVISED DEEP LEARNING APPROACH FOR BREAKING GEOCACHING CAPTCHAS",
  booktitle="Proceedings II of the 26th Conference STUDENT EEICT 2020 - Selected papers",
  year="2020",
  series="1",
  number="1",
  pages="166--170",
  publisher="Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5868-0"
}