Text-based CAPTCHAs remains a popular scheme for distinguishing between a legitimate human user and an automated program. This article presents a novel genetic text captcha solver based on the generative adversarial network. As a departure from prior text captcha solvers that require a labor-intensive and time-consuming process to construct, our scheme needs significantly fewer real captchas but yields better performance in solving captchas. Our approach works by first learning a synthesizer to automatically generate synthetic captchas to construct a base solver. It then improves and fine-tunes the base solver using a small number of labeled real captchas. As a result, our attack requires only a small set of manually labeled captchas, which reduces the cost of launching an attack on a captcha scheme. We evaluate our scheme by applying it to 33 captcha schemes, of which 11 are currently used by 32 of the top-50 popular websites. Experimental results demonstrate that our scheme significantly outperforms four prior captcha solvers and can solve captcha schemes where others fail. As a countermeasure, we propose to add imperceptible perturbations onto a captcha image. We demonstrate that our countermeasure can greatly reduce the success rate of the attack.

中文翻译：

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使用生成对抗网络来破解和保护文本验证码

基于文本的验证码仍然是区分合法人类用户和自动化程序的流行方案。本文提出了一种基于生成对抗网络的新型遗传文本验证码求解器。与先前需要劳动密集型和耗时的过程来构建的文本验证码求解器不同，我们的方案需要的实际验证码显着减少，但在求解验证码方面产生了更好的性能。我们的方法通过首先学习合成器来自动生成合成验证码来构建基础求解器。然后，它使用少量标记的真实验证码改进和微调基本求解器。因此，我们的攻击只需要一小组手动标记的验证码，从而降低了对验证码方案发起攻击的成本。我们通过将其应用于 33 个验证码方案来评估我们的方案，其中 11 个目前被 50 个热门网站中的 32 个使用。实验结果表明，我们的方案明显优于四个先前的验证码求解器，并且可以解决其他验证码失败的验证码方案。作为对策，我们建议在验证码图像上添加不易察觉的扰动。我们证明了我们的对策可以大大降低攻击的成功率。