融合多重风格迁移和对抗样本技术的验证码安全性增强方法  

CAPTCHA Security Enhancement Method Incorporating Multiple Style Migration and Adversarial Examples

作　　者：张郅 李欣[1] 叶乃夫 胡凯茜 ZHANG Zhi;LI Xin;YE Naifu;HU Kaixi(School of Information Network Security,Peoples Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《信息网络安全》2022年第10期129-135,共7页Netinfo Security

基　　金：国家重点研发计划[2020AAA0107705]。

摘　　要：验证码对于防御Web服务的自动攻击具有重要作用,但面对具有自动识别技术的破解工具时,难以实现有效的安全保护。如果采用高度失真等方式增强验证码的安全性,会使验证码失去原来的形状,导致人眼也难以识别。文章提出一种融合多重风格迁移和对抗样本技术的验证码安全性增强方法,在保留原有内容的同时利用多重风格迁移方式抵御未知的机器识别,再通过对抗样本技术攻击已知的常见模型,从而欺骗神经网络。在文本验证码数据集上的实验结果表明,文章提出的生成算法具有更低的机器识别率,有效提高了文本验证码的安全性。Completely automated public turing test to tell computers and humans apart(CAPTCHA)plays an important role in preventing automated attacks against Web services,but it is difficult to provide effective security protection when facing cracking tools with automatic recognition technology.If highly distorted and other brute force methods are used,it is difficult to recognize even by human eyes.This paper proposed a CAPTCHA security enhancement method incorporating multiple style migration and adversarial examples to defend unknown machine recognition by multiple style transfer while preserving the original content,and added noise to attack common models by adversarial examples to deceive neural networks.Experimental results on the text CAPTCHA dataset show that the generation algorithm proposed in this paper has a lower machine recognition rate and effectively improves the security of text CAPTCHA.

关 键 词：CAPTCHA 深度学习 图像风格迁移 对抗样本 卷积神经网络 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]