Text-based Completely Automated Public Turing Test To Tell Computers And Humans Apart Research Articles

New Cognitive Deep-Learning CAPTCHA.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.

An Improved Text-Based and Image-Based CAPTCHA Based on Solving and燫esponse 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 text-based and image-based CAPTCHA were 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 text-based CAPTCHA has 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.

Attack-filtered interactive arabic CAPTCHAs

A CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is widely used challenge–response test intended to distinguish between a human user and a malicious bot. This paper deals with text-based interactive CAPTCHA generation. Solving a CAPTCHA is interactive in the sense that the user is asked to solve a challenge from a pool of challenges. The novelty of the proposed method is that we introduce the concept of attack-filtering as a pre-release step for CAPTCHAs. In other words, the distortion level and challenge type for a particular CAPTCHA image are selected in a way that enables the presented CAPTCHA to withstand certain attacks. In addition, attack-filtering enables us to select an appropriate distortion level, present challenges in the proper order, and apply security thresholds. We implemented the presented model in a novel Arabic handwritten text-based interactive CAPTCHA scheme. The experimental results show that, with an interactive challenge pool, the proposed scheme can attain high usability even after filtering the CAPTCHA sample with pre-set bot attacks.

Adversarial CAPTCHAs.

Following the principle of to set one's own spear against one's own shield, we study how to design adversarial completely automated public turing test to tell computers and humans apart (CAPTCHA) in this article. We first identify the similarity and difference between adversarial CAPTCHA generation and existing hot adversarial example (image) generation research. Then, we propose a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques. Finally, we design and implement an adversarial CAPTCHA generation and evaluation system, called aCAPTCHA, which integrates 12 image preprocessing techniques, nine CAPTCHA attacks, four baseline adversarial CAPTCHA generation methods, and eight new adversarial CAPTCHA generation methods. To examine the performance of aCAPTCHA, extensive security and usability evaluations are conducted. The results demonstrate that the generated adversarial CAPTCHAs can significantly improve the security of normal CAPTCHAs while maintaining similar usability. To facilitate the CAPTCHA security research, we also open source the aCAPTCHA system, including the source code, trained models, datasets, and the usability evaluation interfaces.

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks.

A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA’s. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.

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

Recently, with the spread of online services involving websites, attackers 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. Moreover, 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, experimental studies are conducted, and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.

A Parametric Study of Arabic Text-Based CAPTCHA Difficulty for Humans

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed scheme, we ran four filter attacks, which led to a success rate of less than 5%. Thus, we developed a defensive method against adaptive attacks which is a standard for evaluating defenses to adversarial examples. This method is ensemble adversarial training, and it gave an accuracy result of 41.51%. For the usability aspect, we conducted an experimental study, and the results showed that it can be solved by humans in a few seconds with a success rate of 93.10%.

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

A Novel Attack on Monochrome and Greyscale Devanagari CAPTCHAs

The use of computer programs in breaching web site security is common today. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) and human interaction proofs are the cost-effective solution to these kinds of computer attacks on web sites. These CAPTCHAs are available in many forms, such as those based on text, images and audio. A CAPTCHA must be secure enough that it cannot be broken by a computer program, and it must be usable enough that humans can easily understand it. The most popular is the text-based scheme. Most text-based CAPTCHAs are based on the English language and are not usable by the native people of India. Research has proven that native people are more comfortable with native language–based CAPTCHA. Devanagari-based CAPTCHAs are also available, but the security aspect has not been tested. Unfortunately, English language–based CAPTCHAs are successfully broken. Therefore, it is important to test the security of Devanagari script-based CAPTCHAs. We picked five unique monochrome CAPTCHAs and five unique greyscale CAPTCHAs for testing security. We achieved 88.13% to 97.6% segmentation rates on these schemes and generated six types of features for these segmented characters, such as raw pixels, zoning, projection, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented Fast and Rotated BRIEF (ORB). For classification, we used three classifiers for comparative analyses. Using k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Random Forest, we achieved high recognition on monochrome and greyscale schemes. For monochrome Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 64.78% to 82.39%, SVM ranges from 76.46% to 91.34% and Random Forest ranges from 80.34% to 91.28%. For greyscale Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 67.52% to 85.47%, SVM ranges from 76.9% to 91.71% and Random Forest ranges from 83.07% to 92.13%. We achieved a breaking rate for monochrome schemes of 66% to 85% and for greyscale schemes of 73% to 93%.

A Review on CAPTCHAs in Use with Focus on Their Existing Vulnerabilities Concerning Web Security

Over last decades or so, CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart)are used to differentiate between humans and bots. In spite of the fact that numerous choices of CAPTCHAs are presented in later a long time but still text-based CAPTCHAs are most predominant among all other choices on the web. CAPTCHA has been broadly utilized over web to guard against assaults from pernicious bots and robotized assaults.Hackers use bots which perform auto registrations on the websites, often resulting in wastage of resources and compromising the security of the website. Therefore, we need a web security component like CAPTCHA which helps us in telling human clients separated from computerprograms. With progresses in Artificial Intelligence and computer vision, it is currently conceivable to break the current CAPTCHAs.InText-based CAPTCHA,more distortion, different orientation and noise make it difficult for humans also. Text-based Captcha can be cracked by using OCR software.It is suffered by relay, random guess, and dictionary attacks. In images-based captcha, sometimes it very difficult to read and are not compatible with users with disabilities. It is also time-consuming to decipher with technical difficulties with certain internet browsers. It may greatly enhance Artificial Intelligence. In audio based CAPTCHA, performance of human users on this task is highly dependent on their fluency. This implies more youthful users whose language abilities are not all around created, non-local speakers or clients with language or learning incapacities may not perform well. In this research work, various reviews of the existing CAPTCHAs in use, with major focus on their existing vulnerabilities has been performed.

An Efficient and Accurate Depth-Wise Separable Convolutional Neural Network for Cybersecurity Vulnerability Assessment Based on CAPTCHA Breaking

Cybersecurity practitioners generate a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) as a form of security mechanism in website applications, in order to differentiate between human end-users and machine bots. They tend to use standard security to implement CAPTCHAs in order to prevent hackers from writing malicious automated programs to make false website registrations and to restrict them from stealing end-users’ private information. Among the categories of CAPTCHAs, the text-based CAPTCHA is the most widely used. However, with the evolution of deep learning, it has been so dramatic that tasks previously thought not easily addressable by computers and used as CAPTCHA to prevent spam are now possible to break. The workflow of CAPTCHA breaking is a combination of efforts, approaches, and the development of the computation-efficient Convolutional Neural Network (CNN) model that attempts to increase accuracy. In this study, in contrast to breaking the whole CAPTCHA images simultaneously, this study split four-character CAPTCHA images for the individual characters with a 2-pixel margin around the edges of a new training dataset, and then proposed an efficient and accurate Depth-wise Separable Convolutional Neural Network for breaking text-based CAPTCHAs. Most importantly, to the best of our knowledge, this is the first CAPTCHA breaking study to use the Depth-wise Separable Convolution layer to build an efficient CNN model to break text-based CAPTCHAs. We have evaluated and compared the performance of our proposed model to that of fine-tuning other popular CNN image recognition architectures on the generated CAPTCHA image dataset. In real-time, our proposed model used less time to break the text-based CAPTCHAs with an accuracy of more than 99% on the testing dataset. We observed that our proposed CNN model has efficiently improved the CAPTCHA breaking accuracy and streamlined the structure of the CAPTCHA breaking network as compared to other CAPTCHA breaking techniques.

Is image-based CAPTCHA secure against attacks based on machine learning? An experimental study

The completely automated public Turing test to tell computers and humans apart (CAPTCHA) is among the most common methods of authentication used by websites and web services. It is intended to protect online services from automated scripts and malicious programs. Text-based and audio CAPTCHA are two of the earliest such methods, and have been shown to be inadequate at protecting systems and services. Image-based CAPTCHA has been introduced to address the limitations of previous CAPTCHA methods. It uses image recognition tasks to determine whether the user is a human or a malicious program. In light of the sensitivity of protected resources, challenges to their security arising from advances in machine learning algorithms are investigated here. This study examines the strength of image-based CAPTCHA by proposing an image-based CAPTCHA breaking system. The proposed system can automatically answer challenges posed by the recently proposed Google image reCAPTCHA with minimal human intervention. It employs deep learning technologies and machine learning algorithms, including random forest, classification and regression trees (CART), bagging with CART, and Naïve Bayes to automatically answer challenges. The proposed attack mechanism achieved an average accuracy of 85.32% while successfully solving 56.29% of reCAPTCHA challenges posed to it. The results show current image-based CAPTCHAs to deter automated scripts and malicious programs provide a false sense of security.

Selective Learning Confusion Class for Text-Based CAPTCHA Recognition

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%.

Image-based face detection CAPTCHA for improved security

In this paper, we propose a novel image-based completely automated public turing test to tell computers and humans apart (CAPTCHA) that relies on detecting human faces to provide an additional layer of security in web-based services. Face images were selected from the CMU face database and subjected to different types of distortions at different intensity levels to make the automatic face detection very challenging. An extensive experimental study involving 1,100 individuals was undertaken to determine the efficacy of the proposed approach and evaluate the performance of humans compared to computers. We also used two image quality metrics to objectively study the characteristics of the composite CAPTCHA images. Unlike a text-based CAPTCHA, a major benefit of the proposed image-based face detection CAPTCHA is that it does not have any language barriers. In addition, the proposed CAPTCHA can easily be implemented on handheld devices to provide an additional level of security.