A Novel CAPTCHA Recognition System Based on Refined Visual Attention  

作　　者：Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第4期115-136,共22页计算机、材料和连续体(英文)

基　　金：supported by the National Natural Science Foundation of China(Nos.U22A2034,62177047);High Caliber Foreign Experts Introduction Plan funded by MOST,and Central South University Research Programme of Advanced Interdisciplinary Studies(No.2023QYJC020).

摘　　要：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.

关 键 词：Text-based CAPTCHA recognition refined visual attention web security computer vision 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]