破解 reCAPTCHAv2

Breaking reCAPTCHAv2

September 13, 2024

作者: Andreas Plesner, Tobias Vontobel, Roger Wattenhofer

cs.AI

摘要

我们的研究探讨了利用先进的机器学习方法来解决谷歌reCAPTCHAv2系统的验证码的有效性。我们通过利用先进的YOLO模型进行图像分割和分类来评估自动化系统解决验证码的效果。我们的主要结果是我们可以解决100%的验证码，而先前的研究只解决了68-71%。此外，我们的研究发现表明，在reCAPTCHAv2中，人类和机器人必须解决的挑战数量没有显著差异。这意味着当前的人工智能技术可以利用先进的基于图像的验证码。我们还深入研究了reCAPTCHAv2的内部机制，并发现证据表明reCAPTCHAv2在评估用户是否为人类时主要基于cookie和浏览器历史数据。本文附带了代码。

English

Our work examines the efficacy of employing advanced machine learning methods to solve captchas from Google's reCAPTCHAv2 system. We evaluate the effectiveness of automated systems in solving captchas by utilizing advanced YOLO models for image segmentation and classification. Our main result is that we can solve 100% of the captchas, while previous work only solved 68-71%. Furthermore, our findings suggest that there is no significant difference in the number of challenges humans and bots must solve to pass the captchas in reCAPTCHAv2. This implies that current AI technologies can exploit advanced image-based captchas. We also look under the hood of reCAPTCHAv2, and find evidence that reCAPTCHAv2 is heavily based on cookie and browser history data when evaluating whether a user is human or not. The code is provided alongside this paper.