基于3D的CAPTCHA验证码研究

使用验证码的主要目的是保证网络的用户是真实的用户而非机器自动产生的用户,这有利于保证网站的安全,而CAPTCHA技术主要用于验证码的生成,传统的验证码生成采用的是2D-CAPTCHA技术,目前该技术已经比较成熟,因此,针对此技术的攻击的研究也比较多,导致了该技术使用的不安全性系数增加.针对这一问题,文章研究了CAPTCHA技术的发展新动向3D-CAPTCHA技术.

基于二维RNN的CAPTCHA识别

全自动开放式人机区分图灵测试(CAPTCHA)是基于人工智能领域开放性问题而设计的网络安全技术,CAPTCHA识别是该研究领域的重要分支.长短时记忆(Long Short Term Memory,LSTM)型递归神经网络(Recurrent Neural Network,RNN)已被成功应用于CAPTCHA识别,LSTM型RNN实质上是一维RNN,而文本型CAPTCHA为二维图像.提出使用二维RNN对CAPTCHA进行识别.二维RNN能够很好的将特征提取同识别相结合,同时具有较好的上下文保持特性,从而更适合文本型CAPTCHA识别.同时为了进一步提高识别的可靠性,提出一种基于支持向量机(Support vector machine,SVM)的拒识策略,实验结果表明二维RNN较一维RNN能够获得更好的识别率,并且新的拒识策略较其他拒识策略取得更好的拒识效果.

一种基于拒识的高可靠性CAPTCHA识别算法

CAPTCHA是一种阻止机器人滥用自然人资源的网络安全机制.研究CAPTCHA识别技术有助于发现CAPTCHA自身的缺陷,促使其变得更加安全.针对现有方法难以识别的高粘着CAPTCHA,本文提出了一种新的识别算法.该算法首先使用递归神经网络(Recurrent neural network,RNN)对CAPTCHA进行识别,然后为了提高识别结果的可靠性,提出了一种基于SVM的拒识新算法,并使用数据降维方法对拒识特征进行降维.实验结果表明:1)本文所提识别算法能够识别高粘着型CAPTCHA,并且识别结果具有高可靠性;2)新的拒识算法相对于其他拒识算法具有明显优势;3)数据降维方法能够进一步改善拒识算法的性能,从而取得更高的可靠性.

覆盖约简算法在Captcha识别方面的应用

Captcha是用来区分计算机与人类的一种程序,图像验证码是一种典型的Captcha。对图像验证码的发展历程进行了总结,比较了几种目前最常见验证码的特点和设计思路,提出了对一般验证码的破解方法,并设计实现了提取验证码数据的图片信息提取系统;应用基于条件信息熵的覆盖约简算法,对验证码进行识别,对比实验结果证明,识别效果良好。

结合Ajax技术开发符合JSF规范的Captcha组件

本文将JSF定制组件技术与Ajax技术结合起来开发制作了一个Captcha组件。整个组件分为两大部分:Ajax支持部分和JSF支持部分。前者主要利用Ajax技术完成组件显示图片部分的即时切换,能够实现Web页面的局部刷新,可以在切换图片的同时不对页面中其他部分产生影响;后者主要利用JSF组件封装机制将Captcha模块封装成符合JSF规范的组件,从而使该组件可以像其他JSF组件一样在Web系统中方便地部署。最后,结合具体实例介绍了该组件在Web系统中的具体使用方式。

一种使用JCAPTCHA框架的验证码实现方法

对验证码识别技术进行介绍,例示了使用JCAPTCHA开源框架实现验证码的方法,并基于该框架设计完成了一个验证码构件,构件将验证码的实现方法进行封装。验证码构件可以方便地集成到Java EE项目中,实践证明,该构件的确能让验证码开发工作变得更为简单有效。

CAPTCHA技术的应用研究——从识别技术原理说起

本文通过对CAPTCHA验证码技术的介绍与研究,引出了reCAPTCHA这一更具现实意义的验证手段,并进一步对CAPTCHA技术的前景进行了展望。

基于CAPTCHA和Winnow算法的垃圾短信过滤研究

为识别并过滤掉日益增多的垃圾短信,提出了基于全自动人机识别系统(CAPTCHA)和Winnow算法的过滤方法。在CAPTCHA方法中,根据用户能否正确辨认图片,人类和计算机能被辨别,该方法能有效地过滤计算机发送的组垃圾短信。改进的Winnow过滤器可以直接处理原始文本,节省了中文分词时间,而且利用了复合分类思想,提高了分类精度。实验结果表明,CAPTCHA和改进的Winnow算法相结合能较准确地过滤掉垃圾短信。

Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.

Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

Individuals and PCs(personal computers)can be recognized using CAPTCHAs(Completely Automated Public Turing test to distinguish Computers and Humans)which are mechanized for distinguishing them.Further,CAPTCHAs are intended to be solved by the people,but are unsolvable by the machines.As a result,using Convolutional Neural Networks(CNNs)these tests can similarly be unraveled.Moreover,the CNNs quality depends majorly on:the size of preparation set and the information that the classifier is found out on.Next,it is almost unmanageable to handle issue with CNNs.A new method of detecting CAPTCHA has been proposed,which simultaneously solves the challenges like preprocessing of images,proper segmentation of CAPTCHA using strokes,and the data training.The hyper parameters such as:Recall,Precision,Accuracy,Execution time,F-Measure(H-mean)and Error Rate are used for computation and comparison.In preprocessing,image enhancement and binarization are performed based on the stroke region of the CAPTCHA.The key points of these areas are based on the SURF feature.The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.

An Improved Text-Based and Image-Based CAPTCHA Based on Solving and Response Time

CAPTCHA is an acronym that stands for Completely Automated Public Turing Test to tell Computers and Humans Apart(CAPTCHA),it is a good example of an authentication system that can be used to determine the true identity of any user.It serves as a security measure to prevent an attack caused by web bots(automatic programs)during an online transaction.It can come as text-based or image-based depending on the project and the programmer.The usability and robustness,as well as level of security,provided each of the varies and call for the development of an improved system.Hence,this paper studied and improved two different CAPTCHA systems(the text-based CAPTCHA and image-based CAPTCHA).The textbased and image-based CAPTCHAwere designed using JavaScript.Response time and solving time are the two metrics used to determine the effectiveness and efficiency of the two CAPTCHA systems.The inclusion of response time and solving time improved the shortfall of the usability and robustness of the existing system.The developed system was tested using 200 students from the Federal College of Animal Health and Production Technology.The results of each of the participants,for the two CAPTCHAs,were extracted from the database and subjected to analysis using SPSS.The result shows that textbased CAPTCHAhas the lowest average solving time(21.3333 s)with a 47.8%success rate while image-based CAPTCHA has the highest average solving time was 23.5138 s with a 52.8%success rate.The average response time for the image-based CAPTCHA was 2.1855 s with a 37.9%success rate lower than the text-based CAPTCHA response time(3.5561 s)with a 62.1%success rate.This indicates that the text-based CAPTCHA is more effective in terms of usability tests while image-based CAPTCHA is more efficient in terms of system responsiveness and recommended for potential users.

The Recognition of CAPTCHA

CAPTCHA is a completely automated program designed to distinguish whether the user is a computer or human. As the problems of Internet security are worsening, it is of great significance to do research on CAPTCHA. This article starts from the recognition of CAPTCHAs, then analyses the weaknesses in its design and gives corresponding recognition proposals according to various weaknesses, finally offers suggestions related to the improvement of CAPTCHAs. Firstly, this article briefly introduces the basic steps during the decoding process and their principles. And during each step we choose methods which are better adapted to the features of different CAPTCHA images. Methods chosen are as followings: bimodal method in binarization, improved corrosion algorithm in denoising, projection segmentation method in denoised image processing and SVM in recognition. Then, we demonstrate detailed process through the samples taken from the online registration system of ICBC, show the recognition effect and correct the results according to the statistical data in the process. This article decodes CAPTCHAS from three other large banks in the same way but just provides the recognition results. Finally, this article offers targeted suggestions to the four banks based on the recognition effect and analysis process stated above.

Usability and Security of Arabic Text-based CAPTCHA Using Visual Cryptography

Recently,with the spread of online services involving websites,attack-ers have the opportunity to expose these services to malicious actions.To protect these services,A Completely Automated Public Turing Test to Tell Computers and Humans Apart(CAPTCHA)is a proposed technique.Since many Arabic countries have developed their online services in Arabic,Arabic text-based CAPTCHA has been introduced to improve the usability for their users.More-over,there exist a visual cryptography(VC)technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each other.However,as yet,the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried out.Therefore,this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC technique.To evaluate this scheme,experi-mental studies are conducted,and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.

基于CAPTCHA的中文安全机制的研究

随着越来越多的“网络机器人”在Internet上活动,网站的安全性问题显得越来越严峻。全自动人机识别系统(CAPT-CHA),一个让人类能够通过测试,而当前的计算机不能通过的程序出现了。它的原理建立在未解决的人工智能问题领域。通过对几种在实际安全应用中的不同CAPTCHA结构的考察,描述了它们的原理、模型和优缺点,并结合我国情况,提出了基于中文文字识别的CAPTCHA模型,详细描述了在中文CAPTCHA编程设计中的实现策略。

拼图形式CAPTCHA的构建及性能评价

为了提高目前多数在线系统的验证码的安全性,提出了一种以拼图形式构建全自动区分计算机和人类的图灵测试(completely automated public Turing test to tell computers and humans apart,CAPTCHA)的方案,该方案能有效抵御光学字符识别技术和外部轮廓识别技术的攻击又不失易用性。基于该方案,研究其实现的途径及方法,接着分别用4种方式对其进行开发实现,最后应用浏览器兼容性等标准对各种实现方式的性能进行综合测试及评价,测试结果表明,以大图拼接结合Cookie方式实现拼图形式CAPTCHA是最佳方法。

PHP生成CAPTCHA的改进方法在平台建设中的应用

建设社会各方资源的协同学习型平台在开放大学办学中有非常重要的意义。验证码技术可以为平台构建保驾护航。PHP作为一种web编程语言,可以创建和输出多种格式的图像。结合PHP程序特点,生成验证码,输出干扰曲线、背景以及干扰字符。对PHP生成验证码进行类实例化,在视图中调用,通过异步方式完成对输入验证码表单的验证,有效防止非法用户对平台注册用户的跨站攻击和暴力破解。

基于LSTM型RNN的CAPTCHA识别方法

全自动区分计算机和人的图灵测试(CAPTCHA)是一种基于人工智能难题的网络安全机制.研究CAPTCHA的识别能够使其变得更加安全,并能促进一些人工智能难题的求解.文中首先对现有的CAPTCHA识别方法进行总结和分析,然后提出一种基于长短时记忆(LSTM)型递归神经网络(RNN)进行识别的方法,并对CAPTCHA识别中的特征提取问题进行研究.最后,为进一步提高RNN的识别率,提出一种解码算法.实验结果表明,文中方法是有效的,灰度值对于RNN是一种较好的特征,提出的解码算法能够取得较高的识别率,又有较低的时间复杂度.

Towards Understanding the Security of Modern Image Captchas and Underground Captcha-Solving Services

Image captchas have recently become very popular and are widely deployed across the Internet to defend against abusive programs. However, the ever-advancing capabilities of computer vision have gradually diminished the security of image captchas and made them vulnerable to attack. In this paper, we first classify the currently popular image captchas into three categories: selection-based captchas, slide-based captchas, and click-based captchas. Second, we propose simple yet powerful attack frameworks against each of these categories of image captchas. Third, we systematically evaluate our attack frameworks against 10 popular real-world image captchas,including captchas from tencent.com, google.com, and 12306.cn. Fourth, we compare our attacks against nine online image recognition services and against human labors from eight underground captcha-solving services. Our evaluation results show that(1) each of the popular image captchas that we study is vulnerable to our attacks;(2) our attacks yield the highest captcha-breaking success rate compared with state-of-the-art methods in almost all scenarios; and(3) our attacks achieve almost as high a success rate as human labor while being much faster.Based on our evaluation, we identify some design flaws in these popular schemes, along with some best practices and design principles for more secure captchas. We also examine the underground market for captcha-solving services, identifying 152 such services. We then seek to measure this underground market with data from these services. Our findings shed light on understanding the scale, impact, and commercial landscape of the underground market for captcha solving.

基于改进Xception网络的验证码识别

验证码是一种公共自动化程序,用于区分用户和计算机.为了从网站大量获取信息,机器必须自动识别网站的验证码.为了自动识别验证码,研究基于深度学习的验证码识别,提出基于Xception网络和MLP的验证码识别方法.先利用Xception提取验证码特征,再经MLP标定不同权重,最终得到网络的最优权重分布.这种端到端的深度学习具有从输入到输出的预测,可以省去预处理、字符分割等步骤.经对不同验证码数据集的测试,该算法识别正确率在95%以上.

小数据集复杂文本验证码识别

针对小数据集复杂文本验证码识别,使用传统的机器学习方法存在去噪难和分割难的问题,使用深度学习方法需要大量数据增加泛化性,数据不足容易导致过拟合。根据验证码的特点,提出了基于字符分割的小数据集复杂文本验证码识别方法,使用pix2pix做文本去噪,使用目标检测网络YOLOv4分割单字符,使用KNN算法进行分类。实验结果表明,该方法的单字符识别准确率达98.7%,优于开源模型的识别结果,验证了该方法在破解小数据集复杂文本验证码上的可行性和有效性。