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Published on 8 November 2024
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Tang,S. (2024). Research on CAPTCHA recognition technology based on deep learning. Applied and Computational Engineering,81,41-46.
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Research on CAPTCHA recognition technology based on deep learning

Shengyuan Tang *,1,
  • 1 South China Agricultural University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/81/20240967

Abstract

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), a security technique widely used on the Internet, can be utilized to distinguish human users from automated programs. The rapid development of deep learning technology has led to the emergence of graph-based recognition techniques that demonstrate excellent performance, which has prompted the investigation of CAPTCHA recognition based on deep learning as a research area of significant interest. This paper addresses the challenging problem of CAPTCHA recognition based on deep learning techniques, reviews the development and classification of CAPTCHA, examines traditional CAPTCHA recognition methods, and delves into the application of deep learning in CAPTCHA recognition. Therefore, a CAPTCHA recognition system is designed and its effectiveness is verified through experiments. This paper makes a significant contribution to the field of CAPTCHA recognition by proposing a deep learning-based approach, which not only enhances the accuracy and efficiency of CAPTCHA recognition, but also provides new ideas and methods for the further development. In the future, further research will be conducted in the field of CAPTCHA recognition to explore additional deep learning models and techniques with the aim of boosting the security and user experience of CAPTCHA.

Keywords

CAPTCHA Recognition, Deep Learning, Security Technique, Machine Learning, Image Recognition.

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Cite this article

Tang,S. (2024). Research on CAPTCHA recognition technology based on deep learning. Applied and Computational Engineering,81,41-46.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/
ISBN:978-1-83558-563-4(Print) / 978-1-83558-564-1(Online)
Conference date: 21 November 2024
Editor:Mustafa ISTANBULLU, Xinqing Xiao
Series: Applied and Computational Engineering
Volume number: Vol.81
ISSN:2755-2721(Print) / 2755-273X(Online)

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