论文标题
通过深度学习欺骗计算机进行反向图丁测试
Deceiving computers in Reverse Turing Test through Deep Learning
论文作者
论文摘要
在不经过反向图灵测试过程的情况下，人类在日常生活中工作变得越来越困难，而计算机会测试用户是否是人类。如今，几乎每个网站和服务提供商都有检查他们的网站是否被自动机器人爬行，这些机器人可以从其网站中提取有价值的信息。在此过程中，通过使用深度学习技术来破译这些测试并获得不需要的自动化数据，同时通过发布垃圾邮件创造滋扰，机器人越来越聪明。当试图破译验证码时，人类几乎每天都花费大量时间。这项调查的目的是检查使用一部分常用验证码（称为文本验证码）是否是验证其人类客户的可靠过程。我们主要集中于每个验证验证的预处理步骤，该步骤以二进制强度转换它们，并尽可能地消除混乱，并开发了各种模型，以正确标记尽可能多的验证码。我们还提出了一些方法来改善验证人类的过程，这使得人类很容易解决现有的验证码，并且很难让机器人这样做。
It is increasingly becoming difficult for human beings to work on their day to day life without going through the process of reverse Turing test, where the Computers tests the users to be humans or not. Almost every website and service providers today have the process of checking whether their website is being crawled or not by automated bots which could extract valuable information from their site. In the process the bots are getting more intelligent by the use of Deep Learning techniques to decipher those tests and gain unwanted automated access to data while create nuisance by posting spam. Humans spend a considerable amount of time almost every day when trying to decipher CAPTCHAs. The aim of this investigation is to check whether the use of a subset of commonly used CAPTCHAs, known as the text CAPTCHA is a reliable process for verifying their human customers. We mainly focused on the preprocessing step for every CAPTCHA which converts them in binary intensity and removes the confusion as much as possible and developed various models to correctly label as many CAPTCHAs as possible. We also suggested some ways to improve the process of verifying the humans which makes it easy for humans to solve the existing CAPTCHAs and difficult for bots to do the same.
