验证码识别技术总结

先说说写这个的背景

      最近有在搞一个东西,已经做的挺不错了,最后想再完美一点,于是乎就提议把这种验证码给OK了,于是乎就KO了这个验证码。达到单个图片识别时间小于200ms,500个样本人工统计正确率为95%。由于本人没有相关经验,是摸着石头过河。本着经验分享的精神,分享一下整个分析的思路。在各位大神面前献丑了。下面是部分总结,欢迎共同讨论交流。

Microsoft Captcha Decoder 验证码识别技术 Abstract: CAPTCHA is now almost a standard security technology. The most widely used CAPTCHAs rely on the sophisticated distortion of text images rendering them unrecognisable to the state of the art of pattern recognition techniques, and these text-based schemes have found widespread applications in commercial websites. The state of the art of CAPTCHA design suggests that such text-based schemes should rely on segmentation resistance to provide security guarantee, as individual character recognition after segmentation can be solved with a high success rate by standard methods such as neural networks. In this paper, we analyse the security of a text-based CAPTCHA designed by Microsoft and deployed for years at many of their online services including Hotmail, MSN and Windows Live. This scheme was designed to be segmentation-resistant, and it has been well studied and tuned by its designers over the years. However, our simple attack has achieved a segmentation success rate of higher than 90% against this scheme. It took on average ~80 ms for the attack to completely segment a challenge on a desktop computer with a 1.86 GHz Intel Core 2 CPU and 2 GB RAM. As a result, we estimate that this Microsoft scheme can be broken with an overall (segmentation and then recognition) success rate of more than 60%. On the contrary, its design goal was that "automatic scripts should not be more successful than 1 in 10,000" attempts (i.e. a success rate of 0.01%). For the first time, we show that a CAPTCHA that is carefully designed to be segmentation-resistant is vulnerable to novel but simple attacks. Our results show that it is not a trivial task to design a CAPTCHA scheme that is both usable and robust.
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值