Open Access

ARTICLE

A Novel CAPTCHA Recognition System Based on Refined Visual Attention

Zaid Derea^1,2,*, Beiji Zou¹, Xiaoyan Kui^1,*, Monir Abdullah³, Alaa Thobhani¹, Amr Abdussalam⁴

1 School of Computer Science and Engineering, Central South University, Changsha, 410083, China
2 College of Computer Science and Information Technology, Wasit University, Wasit, 52001, Iraq
3 Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia
4 Electronic Engineering and Information Science Department, University of Science and Technology of China, Hefei, 230026, China

* Corresponding Authors: Zaid Derea. Email: ; ; Xiaoyan Kui. Email:

Computers, Materials & Continua 2025, 83(1), 115-136. https://doi.org/10.32604/cmc.2025.062729

Received 26 December 2024; Accepted 02 January 2025; Issue published 26 March 2025

Abstract

Improving website security to prevent malicious online activities is crucial, and CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has emerged as a key strategy for distinguishing human users from automated bots. Text-based CAPTCHAs, designed to be easily decipherable by humans yet challenging for machines, are a common form of this verification. However, advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency. In our comprehensive investigation into CAPTCHA recognition, we have tailored the renowned UpDown image captioning model specifically for this purpose. Our approach innovatively combines an encoder to extract both global and local features, significantly boosting the model’s capability to identify complex details within CAPTCHA images. For the decoding phase, we have adopted a refined attention mechanism, integrating enhanced visual attention with dual layers of Long Short-Term Memory (LSTM) networks to elevate CAPTCHA recognition accuracy. Our rigorous testing across four varied datasets, including those from Weibo, BoC, Gregwar, and Captcha 0.3, demonstrates the versatility and effectiveness of our method. The results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types, contributing to a deeper understanding of CAPTCHA recognition technology.

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Keywords

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