A Simple Generic Attack on Text Captchas
Haichang Gao, Jeff Yan, Fang Cao, Zhengya Zhang, Lei Lei, Mengyun Tang, Ping Zhang, Xin Zhou, Xuqin Wang, Jiawei Li
Abstract
Text-based Captchas have been widely deployed across the Internet to defend against undesirable or malicious bot programs. Many attacks have been proposed; these fine prior art advanced the scientific understanding of Captcha robustness, but most of them have a limited applicability. In this paper, we report a simple, low-cost but powerful attack that effectively breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon and other Internet giants. For all the schemes, our attack achieved a success rate ranging from 5% to 77%, and achieved an average speed of solving a puzzle in less than 15 seconds on a standard desktop computer (with a 3.3GHz Intel Core i3 CPU and 2 GB RAM). This is to date the simplest generic attack on text Captchas. Our attack is based on Log-Gabor filters; a famed application of Gabor filters in computer security is John Daugman's iris recognition algorithm. Our work is the first to apply Gabor filters for breaking Captchas. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
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