首页|用于文本验证码生成的随机扰动优化网络

用于文本验证码生成的随机扰动优化网络

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
文本验证码具有友好和易于理解的特点,被广泛应用于众多互联网应用的安全防御机制中.传统的文本验证码通过将字符扭曲变形或者增加背景噪声来提高安全性,但随着深度学习技术的发展,其安全性难以为继且过度变形的字符会对人类用户识别带来新的难题.为了解决上述问题,本文提出了一种带有随机化策略的扰动优化框架(Perturbation Optimization Network with Randomization for Text-based CAPTCHAs Generation,PORG),用于生成对人类友好但机器难以破解的文本验证码图像.该框架基于先进的扰动方法设计出扰动生成网络(Perturbation Generation Network,PGN),构建丰富的图像扰动因子并应用随机化策略生成多样化的验证码图像.现有方法所生成的扰动因子容易破坏验证码图像传递的视觉信息,对人类用户的识别造成负面影响,违背了验证码设计的初衷,因此本文提出了一种扰动优化网络(Perturbation Optimization Network,PON)对生成的扰动因子进行优化,通过在图像特征层面上扩展距离并在全局层面上缩小差距,使得所生成的验证码在保持人类友好性的同时有效地对抗攻击者模型.本研究在8个真实世界数据集上进行的大量实验,证明了所提出框架模型的优越性,例如在CNKI数据集上,攻击模型的准确率从90.03%降至0.12%.
Perturbation Optimization Network with Randomization for Text-based CAPTCHAs Generation
Text-based CAPTCHAs are friendly and easy to understand,which have been widely used in the security defense mechanism of many Internet applications.Traditional text-based CAPTCHAs improve security by distorting characters or adding background noise.With the development of deep learning,its security is threatened and over-deformed characters will bring new problems to human.To address this,this paper designs a perturbation optimization framework with randomization strategy for text-based CAPTCHAs generation(denoted as PORG),which is friendly for human but difficult for machines.Specifically,the proposed PORG devises a perturbation generation network(PGN)based on current advanced and stable perturbation methods to construct multiple perturbation factors and applies a randomization strategy to generate diverse perturbed images.In particular,the perturbation factors generated by existing methods destroy the visual information conveyed by the CAPTCHA images.To this end,a perturbation optimization network(PON)is designed to control the introduced perturbation factors by extending the distance at feature-level and narrowing the gap at global-level,which makes the generated CAPTCHAs remain human-friendly while effectively treating the attacker model.Extensive experiments conducted on eight real-world datasets show the outperformance of the proposed PORG(e.g.,attack accuracy is dropped from 90.03%to 0.12%on the CNKI dataset).

text-based CAPTCHAsCAPTCHAs generationperturbation optimizationinformation securityimage encryption

曾嘉琪、吴焯婷、吴泽楷、杨振国、刘文印

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广东工业大学 计算机学院,广东 广州 510006

文本类验证码 验证码生成 扰动优化 信息安全 图像加密

国家自然科学基金国家自然科学基金广东省引进创新科研团队计划广东省基础与应用基础研究基金广东省科技创新战略专项广州市科技计划

91748107619020772014ZT05G1572020A1515010616pdjh2020a0173202102020524

2024

10.12052/gdutxb.230051

广东工业大学学报
广东工业大学

广东工业大学学报

影响因子：0.628
ISSN：1007-7162
年,卷(期)：2024.41(3)
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