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Publication detail
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
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
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" }