Multilingual Highlighting CAPTCHA

Nowadays, many daily human activities such as education, commerce, talks, etc. are carried out through the Internet. In cases such as the registering in Web sites, some hackers write programs to make automatic false enrolments which waste the resources of the Web site while this may even stop the entire Web site from working. Therefore, it is necessary to tell apart human users from computer programs which is known as CAPTCHA (completely automated public Turing test to tell computers and human apart). CAPTCHA methods are mainly based on the weak points of OCR (optical character recognition) systems while using them are undesirable to human users and esp. difficult in tools such as PDA's or mobile phones that lack a big keyboard. On the other hand, the use of Internet services has also expanded on such devices. This paper has attempted to provide a method to tell apart human users from computer programs by drawing lines with the PDA light pens. In this method, numerous dots are drawn on a screen with noisy background, some of which are diverse from the others, which the user has to connect them to each other. Considering the weakness of computers in identifying these dots, the human user can be recognized from the computer program. The proposed method was implemented by the Java programming language

Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding Website in order to enroll Web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the Website by reducing the performance and efficiency of servers, even stop the entire Web service. It is crucial for the Websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely automated public Turing test to tell computers and human apart (CAPTCHA) is such a defense system against optical character recognition (OCR) software. OCR can be defined as a software which work for defeating CAPTCHA images and make countless number of registration on the Websites. This study focuses on a new method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. In this paper, the algorithm of our method is introduced in detail.

In the area of image processing and Optical Character Recognition, Segmentation is one of the steps which plays an important role in dealing with offline and online text images. Character segmentation means breaking an image with word into a sequence of characters. A broad perspective of segmentation lies in segmenting the characters in CAPTCHA (Completely Automated Public Turing Tests to Tell Computers and Humans Apart). It is a test that authenticated users have to pass to gain access to their respective mail accounts. Malicious programs like bots, attack the accounts and are a threat to the data integrity, privacy and confidentiality. To avoid this, CAPTCHA was introduced. Segmentation of characters acts as the basis of analyzing the strength of CAPTCHA. Stronger the CAPTCHA, more difficult it is to break. The proposed work is about a CAPTCHA segmenter. It segments the CAPTCHA image with the help of a CAPTCHA Trainer. It is a user created set of pre-processing operations on images which can simply be re-used for segmenting the similar types of images, thus saving time. The operations that can be performed on any image are, gray scale conversion, dot removal, line removal, slant correction, color extraction, thinning.

As the prevalence of online services and platforms continues to grow, so too does the threat posed by automated attacks seeking to circumvent security measures such as CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) mechanisms. This research paper delves into the various attack vectors and vulnerabilities inherent in CAPTCHA systems, aiming to provide a comprehensive understanding of the methodologies employed by attackers to bypass these safeguards. Through an investigation into machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial attacks, this study sheds light on the evolving landscape of CAPTCHA circumvention strategies. By analyzing the strengths and limitations of each attack vector, as well as the potential countermeasures available, this research contributes to the ongoing efforts to enhance the resilience of CAPTCHA systems against automated threats. Ultimately, the findings of this study serve to inform the development of more robust and adaptive CAPTCHA designs, thereby bolstering cyber security in the digital realm. This research paper focuses on investigating attack vectors and vulnerabilities in CAPTCHA mechanisms, including machine learning-based algorithms, OCR techniques, and adversarial attacks. It emphasizes the importance of understanding these methodologies to inform the development of more resilient CAPTCHA designs that can secure online platforms from various attacks.

The internet these days plays an important role in our daily lives, as the number of web services are increasing such as search engines and email, they are threatened by different types of attacks from computer programs such as robots. In order to solve this problem, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) was implemented to be used as a base to distinguish between human users and computer bots. Although this technique limits the automatic registration to web services to provide security but, still some CAPTCHAs have several weaknesses which allow the computer bots to infiltrate the mechanism of the CAPTCHA. The aim of this paper is to search for the problems in the existing types of CAPTCHA systems and to provide solutions or alternative approaches to solve these problem.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems serve as a crucial defense mechanism against automated attacks by distinguishing between human users and bots. However, advancements in deep learning have posed significant challenges to the security of conventional CAPTCHA systems. In this research paper, we present a comparative analysis of two prominent object detection algorithms, Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN), for breaking CAPTCHAs. The study evaluates the performance of CNN and RCNN algorithms in accurately identifying and deciphering characters within CAPTCHA images. Utilizing a diverse dataset of CAPTCHA samples, experiments are conducted to assess the effectiveness of both algorithms in handling variations in CAPTCHA styles, languages, and complexities. Through extensive experimentation and evaluation, we analyze the strengths and limitations of CNN and RCNN in the context of CAPTCHA breaking. Key metrics such as accuracy, precision, recall, and computational efficiency are compared to provide insights into the relative performance of each algorithm. The findings of this research contribute to the understanding of object detection techniques for CAPTCHA breaking and provide valuable insights for enhancing the security of CAPTCHA systems against emerging threats posed by deep learning-based attacks.

Cloud computing is simply a metaphor for the internet. User does not requiredknowledge, control, and ownership in the computer infrastructure.User simply access or rent the software and paying only for what they use. Advantage of cloud computing is huge like Broad network access, Cost effectiveness, Rapid elasticity, Measured services, On -Demand service, Resource pooling , Location independence, Reliability, Energy saving and so on. But its global phenomenon that everything in this world has advantage as well as disadvantage, cloud computing also suffering from some drawback like security & privacy, Internet Dependency, Availability, And Current Enterprise Applications Can't Be Migrated Easily. I conclude that security is biggest hurdle in wide acceptance of cloud computing. U ser of cloud services are in fear of data loss, security and a vailability issues. At virtual level DDOS (Distributed Denial of Service Attack)is biggest threat of availability in cloud computing. In Denial of service attack an attacker prevent legitimate users of service from using the desired resources by flood a network or by consuming bandwidth .So authentication is need to distinguish legitimated clients from malicious clien ts, which can be performed through strong cryptographic verification (for a private server) or graphical Turing tests (for a public server). Where the authentication is performed by Graphical Turing Tests, which is widely used to distinguish human users from robots through their reaction. On the other hand, CAPTCHA (Completely Automated Public Turing Tests to Tell Computers and Humans Apart) is used for Graphical Turing Test. There are many OCR or Non -OCR based CAPTCHA’s are used widely but they are vulnerable to many attacks likePixel-Count Attack, Recognition by using OCR, Dictionary Attack, and Vertical Segmentation. This paper introduces a new CAPTCHA method called Two-Tier CAPTCHA.In this method CLAD node need to generate two things,first a alphanumeric CAPTCHA code with image. Second Query related to that CAPTCHA code. E.g. enter only Digit’s .We can increase the rate of its difficulty in order to improve its resistance against the attacks by ad ding more and more query and combination in database.The algorithm of this method makes it hard for bot programs which mean that it is more secure.This project has been implemented byASP.NET and PHP Language.

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) recognition is one of the most important branches in CAPTCHA research. The existing CAPTCHA recognition methods based on Deep Convolutional Neural Network (DCNN) have low recognition accuracy in confusion class. To solve this problem, a novel method of selective learning confusion class for text-based CAPTCHA recognition is presented. First, a frame with two-stage DCNN is proposed, which integrates all-class DCNN and confusion-class DCNN. Second, a confusion relation matrix is constructed to show confusion relations among classes, which can be used to analyze the output of all-class DCNN. Third, a set partition algorithm is presented, which can be used to divide a confusion class set into multiple subsets, each one corresponding to a new confusion-class DCNN. Fourth, with a view on improving the recognition accuracy of the confusing characters in confusion-class DCNN, training and validating interactive learning algorithm is proposed. Lastly, the outputs of the two stages were combined as the final recognition result. The experimental results based on real CAPTCHA data sets demonstrate that, compared with the four state-of-the-art attacks, the proposed method could effectively improve text-based CAPTCHA recognition accuracy by 1.4%-39.4%.

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), is the wide-spread concept of systems suited to secure the web services from automated SPAM scripts. The most common CAPTCHA systems benefit from imperfections of Optical Character Recognition algorithms. This paper presents our ongoing work focused on the development of a new CAPTCHA scheme based on a human perception. The goal of this work is to evaluate the usability of randomly generated fonts used in Bubble Captcha scheme with both humans and OCR classifiers.

A so-called completely automated public Turing test to tell computers and humans apart (CAPTCHA) represents a challenge-response test that is widely used on the Internet to distinguish human users from fraudulent computer programs, often referred to as bots. To enable access for visually impaired users, most Web sites utilize audio CAPTCHAs in addition to a conventional image-based scheme. Recent research has shown that most currently available audio CAPTCHAs are insecure, as they can be broken by means of machine learning at relatively low costs. Moreover, most audio CAPTCHAs suffer from low human success rates that arise from severe signal distortions. This article proposes two different audio CAPTCHA schemes that systematically exploit differences between humans and computers in terms of auditory perception and language understanding, yielding a better trade-off between usability and security as compared to currently available schemes. Furthermore, we provide an elaborate analysis of Google’s prominent reCAPTCHA that serves as a baseline setting when evaluating our proposed CAPTCHA designs.

This study was an attempt to control and monitor Computerized Numerical Controller (CNC) machines anywhere and anytime for the development of a ubiquitous machine (u-machine). With a Personal Digital Assistant (PDA) phone, the machine status and machining data of CNC machines can be monitored in wired and wireless environments, including the environments of IMT2000 and Wireless LAN. Moreover, CNC machines can be controlled anywhere and anytime. The concept of the anywhere-anytime controlling and monitoring of a manufacturing system was implemented in this study for the purpose of u-manufacturing and u-machines. In this concept, the communication between the CNC controller and the PDA phone was successfully performed anywhere and anytime for the real-time monitoring and control of CNC machines. In addition, the interface between the CNC controller and the developed application module was implemented by Object linking and embedding for Process Control (OPC) and shared CNC memory. For communication, the design of a server contents module within the target CNC was based on a TCP/IP. Furthermore, the client contents module within the PDA phone was designed with the aid of embedded c++ programming for mobile communication. For the interface, the monitoring data, such as the machine status, the machine running state, the name of the Numerical Control (NC) program, the alarm and the position of the stage axes, were acquired in real time from real machines with the aid of the OPC method and by sharing the CNC memory. The control data, such as the start, hold, emergency stop, reserved start and reserved stop, were also applied to the CNC domain of the real machine. CNC machines can therefore be controlled and monitored in real time, anywhere and anytime. Moreover, prompt notification from CNC machines to mobile phones, including cellular phones and PDA phones, can be automatically realized in emergencies.

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.

To date, most optical character recognition (OCR) systems process binary document images, and the quality of the input image strongly affects their performance. Since a binarization process is inherently lossy, different algorithms typically produce different binary images from the same gray scale image. The objective of this research is to study effects of global binarization algorithms on the performance of OCR systems. Several binarization methods were examined: the best fixed threshold value for the data set, the ideal histogram method, and Otsu's algorithm. Four contemporary OCR systems and 50 hard copy pages containing 91,649 characters were used in the experiments. These pages were digitized at 300 dpi and 8 bits/pixel, and 36 different threshold values (ranging from 59 to 199 in increments of 4) were used. The resulting 1,800 binary images were processed by all four OCR systems. All systems made approximately 40% more errors from images generated by Otsu's method than those of the ideal histogram method. Two of the systems made approximately the same number of errors from images generated by the best fixed threshold value and Otsu's method.

We examine the use of character image analysis coupled with contextual information in complex data gathering forms to identify and correct optical character recognition (OCR) system rejection and substitution errors. Segmented characters from a complex data gathering form are initially classified using an OCR engine based on a combination of Karhunen-Loeve transforms and a back-propagation neural network. Systems of equations are derived from the data gathering form to determine the values of characters rejected by the OCR engine and to verify the consistency of the data captured. If the OCR results for a single form are determined to be inconsistent with respect to the form's data relationships, a set of decision algorithms which incorporates a second neural network and uses additional character features is used to tag characters according to their likelihood of substitution error. Potential substitution errors are incrementally added to the set of OCR reject errors and are processed through dynamically selected systems of equations and search techniques which correct both error classes. We provide experimental results and determine the extent to which errors can be detected and corrected for various OCR error rates.

In this paper we present an end-to-end trainable optical character recognition (OCR) system for recognizing machine-printed text in Thai documents. The end-to-end OCR system is based on a script-independent methodology using hidden Markov models. Our system provides an integrated workflow beginning with annotation and transcription of training images to performing OCR on new images with models trained on transcribed training images. The efficacy of our end-to-end OCR system is demonstrated by rapidly configuring our OCR engine for the Thai script. We present experimental results on Thai documents to highlight the specific challenges posed by the Thai script and analyze the recognition performance as a function of amount of training data.