Name
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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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3378446 | ACM Transactions on Privacy and Security |
Keywords | DocType | Volume |
Text captchas,authentication,generative adversarial networks,security,transfer learning | Journal | 23 |
Issue | ISSN | Citations |
2 | 2471-2566 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guixin Ye | 1 | 3 | 2.39 |
| Zhanyong Tang | 2 | 55 | 12.76 |
| Dingyi Fang | 3 | 75 | 12.37 |
| Zhanxing Zhu | 4 | 199 | 29.61 |
| Yansong Feng | 5 | 735 | 64.17 |
| Pengfei Xu | 6 | 109 | 27.58 |
| Xiaojiang Chen | 7 | 233 | 28.96 |
| Jungong Han | 8 | 1785 | 117.64 |
| Zheng Wang | 9 | 215 | 18.10 |