Using generative adversarial networks to break and protect text captchas

Ye, Guixin, Tang, Zhanyong, Fang, Dingyi, Zhu, Zhanxing, Feng, Yansong, Xu, Pengfei, Chen, Xiaojiang, Han, Jungong and Wang, Zheng (2020) Using generative adversarial networks to break and protect text captchas. ACM Transactions on Privacy and Security, 23 (2), 1-29, [7]. (doi:10.1145/3378446).

Abstract

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.

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