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A Machine Learning Attack against the Civil Rights CAPTCHA

机译:针对民权CAPTCHA的机器学习攻击

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摘要

Human Interactive Proofs (HIPs) are a basic security measure on the Internet to avoid several types of automatic attacks. Recently, a new HIP has been designed to increase security: the Civil Rights CAPTCHA. It employs the empathy capacity of humans to further strengthen the security of a well known OCR CAPTCHA, Securimage. In this paper, we analyse it from a security perspective, pointing out its design flaws. Then, we create a successful side-channel attack, leveraging some well-known machine learning algorithms.
机译:人工交互证明(HIP)是Internet上的一种基本安全措施,可避免多种类型的自动攻击。最近,已设计出一种新的HIP以提高安全性:民权CAPTCHA。它利用人类的同理能力进一步增强了著名的OCR CAPTCHA Securimage的安全性。在本文中,我们从安全角度分析了它,并指出了其设计缺陷。然后,我们利用一些著名的机器学习算法创建成功的边信道攻击。

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