Solving Google's Continuous Audio CAPTCHA with HMM-Based Automatic Speech Recognition
Solving Google's Continuous Audio CAPTCHA with HMM-Based Automatic Speech Recognition
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DOI：
10.1007/978-3-642-41383-4_3
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Shotaro Sano;Takuma Otsuka;HIroshi G. Okuno
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文献类型：
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作者：
Shotaro Sano;Takuma Otsuka;HIroshi G. Okuno
CAPTCHA在维护各种Web服务的安全性方面发挥着关键作用，它将人类与自动化程序区分开来，并防止Web服务被滥用。CAPTCHA被设计成通过提出对人类容易但对计算机困难的问题来阻止自动化程序，例如，视觉数字或音频话语的识别。最近的音频验证码，如谷歌的音频reCAPTCHA，已经呈现出重叠和失真的目标语音与固定的背景噪声。我们通过开发一个音频reCAPTCHA求解器来研究重叠音频CAPTCHA的安全性。我们的求解器是基于语音识别技术，使用隐马尔可夫模型（HMM）。它是通过使用现成的库HMM工具包实现的。我们的实验揭示了当前版本的音频reCAPTCHA的漏洞，求解器破解了52%的问题。我们进一步解释说，背景静态噪声并没有有助于增强对我们的求解器的安全性。
CAPTCHAs play critical roles in maintaining the security of various Web services by distinguishing humans from automated programs and preventing Web services from being abused. CAPTCHAs are designed to block automated programs by presenting questions that are easy for humans but difficult for computers, e.g., recognition of visual digits or audio utterances. Recent audio CAPTCHAs, such as Google’s audio reCAPTCHA, have presented overlapping and distorted target voices with stationary background noise. We investigate the security of overlapping audio CAPTCHAs by developing an audio reCAPTCHA solver. Our solver is constructed based on speech recognition techniques using hidden Markov models (HMMs). It is implemented by using an off-the-shelf library HMM Toolkit. Our experiments revealed vulnerabilities in the current version of audio reCAPTCHA with the solver cracking 52% of the questions. We further explain that background stationary noise did not contribute to enhance security against our solver.