Several websites improve their security and avoid dangerous Internet attacks by implementing CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), a type of verification to identify whether the end-user is human or a robot. The most prevalent type of CAPTCHA is text-based, designed to be easily recognized by humans while being unsolvable towards machines or robots. However, as deep learning technology progresses, development of convolutional neural network (CNN) models that predict text-based CAPTCHAs becomes easier. The purpose of this research is to investigate the flaws and vulnerabilities in the CAPTCHA generating systems in order to design more resilient CAPTCHAs. To achieve this, we created CapNet, a Convolutional Neural Network. The proposed platform can evaluate both numerical and alphanumerical CAPTCHAs

翻译：许多网站通过实施CAPTCHA（全自动区分计算机和人类的图灵测试）来提升安全性并避免危险的网络攻击，这是一种用于识别终端用户是人还是机器人的验证方式。最常见的CAPTCHA类型是基于文本的，其设计目标是易于人类识别，同时使机器或机器人无法解决。然而，随着深度学习技术的进步，开发能够预测文本型CAPTCHA的卷积神经网络（CNN）模型变得愈发容易。本研究旨在探究CAPTCHA生成系统中的缺陷与漏洞，以设计更具鲁棒性的CAPTCHA。为此，我们构建了CapNet——一种卷积神经网络。所提出的平台能够同时评估数字型与字母数字型CAPTCHA。