论文标题
深入CAPTCHA：基于深度学习的验证验证器漏洞评估
Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment
论文作者
论文摘要
CAPTCHA是以人为本的测试，可将人类操作员与机器人，攻击程序或其他试图模仿人类智能的计算机代理区分开。在这项研究中，我们研究了一种通过基于深度学习的解决方案来破解视觉验证测试的方法。这项研究的目的是研究验证码发电机系统的弱点和脆弱性；因此，开发更强大的验证码，而没有冒着手动尝试和失败的努力的风险。我们开发了一个称为DeepCaptcha的卷积神经网络，以实现这一目标。所提出的平台能够研究数值和字母码。为了培训和开发一个高效的模型，我们已经生成了500,000个验证码的数据集来培训我们的模型。在本文中，我们介绍了定制的深神经网络模型，我们回顾了解决问题的研究差距，现有的挑战以及解决方案。我们网络的破裂精度分别导致数值和α数字测试数据集的高率为98.94％和98.31％。这意味着要开发强大的验证码需要更多的工作，以免针对自动人工代理。作为这项研究的结果，我们根据深入Captcha模型进行的性能分析，确定一些有效的技术来提高验证码的安全性。
CAPTCHA is a human-centred test to distinguish a human operator from bots, attacking programs, or other computerised agents that tries to imitate human intelligence. In this research, we investigate a way to crack visual CAPTCHA tests by an automated deep learning based solution. The goal of this research is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator systems; hence, developing more robust CAPTCHAs, without taking the risks of manual try and fail efforts. We develop a Convolutional Neural Network called Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate both numerical and alphanumerical CAPTCHAs. To train and develop an efficient model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues. Our network's cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets, respectively. That means more works is required to develop robust CAPTCHAs, to be non-crackable against automated artificial agents. As the outcome of this research, we identify some efficient techniques to improve the security of the CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA model.
