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首页> 外文期刊>ACM transactions on Asian and low-resource language information processing >A Novel Attack on Monochrome and Greyscale Devanagari CAPTCHAs
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A Novel Attack on Monochrome and Greyscale Devanagari CAPTCHAs

机译:对单色和灰度德南省魔术码的小说攻击

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摘要

The use of computer programs in breaching web site security is common today. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) and human interaction proofs are the cost-effective solution to these kinds of computer attacks on web sites. These CAPTCHAs are available in many forms, such as those based on text, images and audio. A CAPTCHA must be secure enough that it cannot be broken by a computer program, and it must be usable enough that humans can easily understand it. The most popular is the text-based scheme. Most text-based CAPTCHAs are based on the English language and are not usable by the native people of India. Research has proven that native people are more comfortable with native language-based CAPTCHA. Devanagari-based CAPTCHAs are also available, but the security aspect has not been tested. Unfortunately, English language-based CAPTCHAs are successfully broken. Therefore, it is important to test the security of Devanagari script-based CAPTCHAs. We picked five unique monochrome CAPTCHAs and five unique greyscale CAPTCHAs for testing security. We achieved 88.13% to 97.6% segmentation rates on these schemes and generated six types of features for these segmented characters, such as raw pixels, zoning, projection, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented Fast and Rotated BRIEF (ORB). For classification, we used three classifiers for comparative analyses. Using k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Random Forest, we achieved high recognition on monochrome and greyscale schemes. For monochrome Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 64.78% to 82.39%, SVM ranges from 76.46% to 91.34% and Random Forest ranges from 80.34% to 91.28%. For greyscale Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 67.52% to 85.47%, SVM ranges from 76.9% to 91.71% and Random Forest ranges from 83.07% to 92.13%. We achieved a breaking rate for monochrome schemes of 66% to 85% and for greyscale schemes of 73% to 93%.
机译:今天在违反网站安全中使用计算机程序很常见。 CAPTCHA(完全自动化的公共图灵测试,告诉计算机和人类分开)和人类的互动证明是对网站上这些计算机攻击的成本效益的解决方案。这些CAPTCHAS以多种形式提供,例如基于文本,图像和音频的形式提供。 CAPTCHA必须足够安全,即它无法通过计算机程序打破,必须可以使用,以便人类可以很容易地理解它。最受欢迎的是基于文本的方案。基于大多数文本的CAPTCHA基于英语语言,不受印度本地人使用的。研究证明,本地人对基于母语的CAPTCHA更舒适。基于Devanagari的CAPTCHA也可用,但安全方面尚未测试。不幸的是,英语语言的CAPTCHA已成功损坏。因此,重要的是测试基于Devanagari脚本的CAPTCHA的安全性。我们选择了五个独特的单色CAPTCHAS和五个独特的灰度CAPTCHAS进行测试安全性。我们在这些方案上实现了88.13%至97.6%的分割率,并为这些分段字符生成了六种类型的功能,例如原始像素,分区,投影,鳞片不变特征变换(SIFT),加速强大的功能(冲浪)和定向快速和旋转的简短(ORB)。对于分类,我们使用三个分类器进行比较分析。使用K-COMBERY邻(K-NN),支持向量机(SVM)和随机森林,我们对单色和灰度方案进行了高识别。对于单色Devanagari CAPTCHA,K-NN的识别率从64.78%到82.39%,SVM范围为76.46%至91.34%,随机森林的范围从80.34%到91.28%。对于灰度德那戈阿里人CAPTCHA,K-NN的识别率为67.52%至85.47%,SVM范围为76.9%至91.71%和随机森林的范围从83.07%到92.13%。我们实现了66%至85%的单色方案的断裂率,并且灰度方案为73%至93%。

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