用深度学习破解谷歌reCAPTCHAv2，准确率达100%。
Breaking reCAPTCHAv2
- 采用先进YOLO模型进行图像分割与分类，突破传统方法限制。
- 成功解决100%的验证码，远超此前68%-71%的水平。
- 揭示系统依赖浏览器历史与cookie判断真人，安全性存疑。
本研究评估了利用先进机器学习方法破解谷歌reCAPTCHAv2系统的有效性。通过采用先进的YOLO模型进行图像分割与分类，实验结果显示可实现100%的验证码破解率，显著高于以往研究的68%-71%。此外，研究发现人类用户与机器人在通过验证时所需应对的挑战数量无显著差异，表明当前AI技术已能有效绕过基于图像的验证码。我们还深入分析了reCAPTCHAv2机制，发现其对用户身份判断高度依赖于浏览器历史和cookie数据。相关代码已公开。
原文摘要 · Abstract (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.
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