A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention  

作　　者：Zaid Derea Beiji Zou Xiaoyan Kui 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]Electronic Engineering and Information Science Department,University of Science and Technology of China,Hefei,230026,China

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期2841-2867,共27页工程与科学中的计算机建模(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：Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell Computers and Humans Apart)has become a key method to distinguish humans from bots.While text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable efficiency.In this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual features.We have specifically adapted the well-established image captioning task to address this need.Our approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA recognition.Our extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our method.Our approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for Webo.These results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关 键 词：Text-based CAPTCHA image recognition guided 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]