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首页> 外文期刊>International journal of ad hoc and ubiquitous computing >Security analysis of image CAPTCHA using a mask R-CNN-based attack model
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Security analysis of image CAPTCHA using a mask R-CNN-based attack model

机译：基于掩模R-CNN的攻击模型图像CAPTCHA的安全分析

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摘要

The CAPTCHAs are attacked by automated programs to break their underlying design principle. Therefore, analysing the robustness of CAPTCHA is the critical requirement. Recently, a neural style transfer-based image CAPTCHA called style area CAPTCHA (SACAPTCHA) has been reported. Though the security of SACAPTCHA is evaluated on R-CNN and FCN attack models using accuracy metrics, they are ineffective to analyse its robustness. Therefore, we propose a mask R-CNN-based attack model to critically analyse the robustness of SACAPTCHA. The proposed model performs a shape-wise analysis to test the usability of different shapes and quantifies the model performance using the F1-score. The simulation results show the highest F1 score of 0.962 and 0.828 for star and circle shapes in dataset-1 and dataset-2 respectively. The results show that model prediction is independent of the regularities of the shape. The observations prove that SACAPTCHA is vulnerable to object detection attack even after using irregular shapes.
机译：CAPTCHAS受自动化程序攻击以打破其潜在的设计原则。因此，分析CAPTCHA的稳健性是关键要求。最近，已经报道了一种称为样式区域CAPTCHA（SACAPTCHA）的神经样式传输图像CAPTCHA。尽管SACAPTCHA的安全性评估了使用精度度量的R-CNN和FCN攻击模型，但它们无效地分析其稳健性。因此，我们提出了一种基于掩模基于R-CNN的攻击模型，以批判性地分析Sacaptcha的鲁棒性。所提出的模型执行形状明智的分析，以测试不同形状的可用性，并使用F1分数定量模型性能。仿真结果分别显示了DataSet-1和数据集-2中的星形和圆形的最高F1分数为0.962和0.828。结果表明，模型预测与形状的规律无关。即使在使用不规则形状之后，观察结果证明了SAVAPTCHA易于对象检测攻击。

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