Original title: Semi-Supervised Deep Learning Approach For Breaking Geocaching Captchas
Authors: Bostik, Ondrej
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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: CAPTCHA; Deep learning; MATLAB; OCR; semi-supervised learning
Host item entry: Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected papers, ISBN 978-80-214-5868-0

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200645

Permalink: http://www.nusl.cz/ntk/nusl-447697


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