CAPTCHaStar！基于交互式形状发现的CAPTCHA
CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery
摘要：
在过去的几年里，用户可以注册的大多数网站（例如，电子邮件提供商和社交网络）采用CAPTCHA（区分计算机和人类的完全自动化公共图灵测试）作为对抗自动化攻击的对策。CAPTCHA的设计者和攻击者之间的斗智斗勇导致当前的CAPTCHA令人讨厌，难以为用户解决，同时仍然容易受到自动化攻击。在本文中，我们提出了CAPTCHaStar，一个新的基于图像的验证码，依赖于用户交互。这种新颖的CAPTCHA利用了人类在混乱环境中识别形状的先天能力。我们评估我们的建议对验证码的两个关键方面的有效性，即，可用性和对自动化攻击的弹性。特别是，我们评估了可用性，进行了全面的用户研究，并测试了我们的提案对几种类型的自动化攻击的弹性：传统攻击;为我们的提案设计的ad-hoc攻击;以及基于机器学习的攻击。与现有技术相比，我们的建议更加用户友好（例如，只有大约35%的用户更喜欢当前的解决方案，例如基于文本的CAPTCHA），并且对自动化攻击更具弹性。
英文摘要：
Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks. In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35% of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
