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

Research on CAPTCHA recognition technology based on deep learning

Abstract. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), a security technique widely used on the Internet, can be utilized to distinguish human users from automated programs. The rapid development of deep learning technology has led to the emergence of graph-based recognition techniques that demonstrate excellent performance, which has prompted the investigation of CAPTCHA recognition based on deep learning as a research area of significant interest. This paper addresses the challenging problem of CAPTCHA recognition based on deep learning techniques, reviews the development and classification of CAPTCHA, examines traditional CAPTCHA recognition methods, and delves into the application of deep learning in CAPTCHA recognition. Therefore, a CAPTCHA recognition system is designed and its effectiveness is verified through experiments. This paper makes a significant contribution to the field of CAPTCHA recognition by proposing a deep learning-based approach, which not only enhances the accuracy and efficiency of CAPTCHA recognition, but also provides new ideas and methods for the further development. In the future, further research will be conducted in the field of CAPTCHA recognition to explore additional deep learning models and techniques with the aim of boosting the security and user experience of CAPTCHA.

Boosting the transferability of adversarial CAPTCHAs

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a test to distinguish humans and computers. Since attackers can achieve high accuracy in recognizing the CAPTCHAs using deep learning models, geometric transformations are added to the CAPTCHAs to disturb deep learning model recognition. However, excessive geometric transformations might also affect humans’ recognition of the CAPTCHA. Adversarial CAPTCHAs are special CAPTCHAs that can disrupt deep learning models without affecting humans. Previous works of adversarial CAPTCHAs mainly focus on defending the filtering attack. In real-world scenarios, the attackers’ models are inaccessible when generating adversarial CAPTCHAs, and the attackers may use models with different architectures, thus it is crucial to improve the transferability of the adversarial CAPTCHAs. We propose CFA, a method to generate more transferable adversarial CAPTCHAs focusing on altering content features in the original CAPTCHA. We use the attack success rate as our metric to evaluate the effectiveness of our method when attacking various models. A higher attack success rate means a higher level of preventing models from recognizing the CAPTCHAs. The experiment shows that our method can effectively attack various models, even when facing possible defense methods that the attacker might use. Our method outperforms other feature space attacks and provides a more secure version of adversarial CAPTCHAs.

ImageVeriBypasser: An image verification code recognition approach based on Convolutional Neural Network

AbstractThe recent period has witnessed automated crawlers designed to automatically crack passwords, which greatly risks various aspects of our lives. To prevent passwords from being cracked, image verification codes have been implemented to accomplish the human–machine verification. It is important to note, however, that the most widely‐used image verification codes, especially the visual reasoning Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), are still susceptible to attacks by artificial intelligence. Taking the visual reasoning CAPTCHAs representing the image verification codes, this study introduces an enhanced approach for generating image verification codes and proposes an improved Convolutional Neural Network (CNN)‐based recognition system. After we add a fully connected layer and briefly solve the edge of stability issue, the accuracy of the improved CNN model can smoothly approach 98.40% within 50 epochs on the image verification codes with four digits using a large initial learning rate of 0.01. Compared with the baseline model, it is approximately 37.82% better in accuracy without obvious curve oscillation. The improved CNN model can also smoothly reach the accuracy of 99.00% within 7500 epochs on the image verification codes with six characters, including digits, upper‐case alphabets, lower‐case alphabets, and symbols. A detailed comparison between our proposed approach and the baseline one is presented. The relationship between the time consumption and the length of the seeds is compared theoretically. Subsequently, we figure out the threat assignments on the visual reasoning CAPTCHAs with different lengths based on four machine learning models. Based on the threat assignments, the Kaplan‐Meier (KM) curves are computed.

Enhancing Web Security: Implementing CAPTCHA for Government Websites

In the digital era, government websites serve as critical platforms for citizens to access essential services, information, and resources. However, with the increasing threat of cyber attacks and data breaches, ensuring the security of these websites is paramount. This research paper explores the implementation of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) as a mechanism to enhance web security on government websites. CAPTCHA is a widely adopted security measure designed to distinguish between human users and automated bots, thereby mitigating various forms of cyber threats such as brute force attacks, credential stuffing, and unauthorized access. This paper examines the effectiveness of CAPTCHA in safeguarding government websites, including its impact on user experience, accessibility, and usability. Furthermore, it explores various implementation strategies, best practices, and potential challenges associated with deploying CAPTCHA on government platforms. Through a comprehensive analysis of existing literature, case studies, and empirical data, this paper provides insights into the role of CAPTCHA in bolstering web security for government websites and offers recommendations for policymakers, web developers, and security professionals.

Efficient CAPTCHA Image Recognition Using Convolutional Neural Networks and Long Short-Term Memory Networks

This research paper presents a novel approach to CAPTCHA image recognition using a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems are widely employed to differentiate between human users and bots in online applications, ensuring security and preventing automated attacks. The purpose of this study is to develop an efficient and accurate CAPTCHA recognition system capable of handling complex and distorted CAPTCHA images commonly used on websites. The methodology involves preprocessing CAPTCHA images, including cropping and resizing, followed by feature extraction using CNNs to capture spatial patterns and structures. The extracted features are then fed into LSTMs to model temporal dependencies and sequence information, enabling the model to recognize characters in the CAPTCHA image. The proposed model is trained and evaluated using a dataset consisting of various CAPTCHA images sourced from online platforms. The main results demonstrate the effectiveness of the CNN-LSTM hybrid approach in accurately recognizing characters within CAPTCHA images. The model achieves high accuracy rates in deciphering distorted and noisy CAPTCHA images, outperforming baseline models and existing state-of-the-art methods. Additionally, the model exhibits robustness to variations in CAPTCHA designs and backgrounds, making it suitable for real-world applications requiring robust CAPTCHA recognition. Keywords: CAPTCHA recognition, Deep learning, Convolutional Neural Networks, Long Short-Term Memory Networks, Image processing.

A Comparative Analysis of CNN, RCNN & Faster RCNN Object Detection Algorithm for CAPTCHA Breaking

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.

Improving the Security of Audio CAPTCHAs With Adversarial Examples

CAPTCHAs (completely automated public Turing tests to tell computers and humans apart) have been the main protection against malicious attacks on public systems for many years. Audio CAPTCHAs, as one of the most important CAPTCHA forms, provide an effective test for visually impaired users. However, in recent years, most of the existing audio CAPTCHAs have been successfully attacked by machine learning-based audio recognition algorithms, showing their insecurity. In this paper, a generative adversarial network (GAN)-based method is proposed to generate adversarial audio CAPTCHAs. This method is implemented by using a generator to synthesize noise, a discriminator to make it similar to the target and a threshold function to limit the size of the perturbation; then, the synthetic perturbation is combined with the original audio to generate the adversarial audio CAPTCHA. The experimental results demonstrate that the addition of adversarial examples can greatly reduce the recognition accuracy of automatic models and improve the robustness of different types of audio CAPTCHAs. We also explore ensemble learning strategies to improve the transferability of the proposed adversarial audio CAPTCHA methods. To investigate the effect of adversarial CAPTCHAs on human users, a user study is also conducted.

Implementation of Captcha Mechanisms using Deep Learning to Prevent Automated Bot Attacks

Online platforms' integrity and security are seriously threatened by the growth of automated bot attacks, necessitating the development of effective methods for telling harmful bots apart from legitimate users. In order to successfully combat automated bot attacks, this project investigates the creation and application of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) methods utilising deep learning techniques. In this study, we employ deep learning to create and apply complex CAPTCHA-problems that help us distinguish between real users and automated bots. Convolutional neural networks and recurrent neural networks are employed to build dynamic, adjustable CAPTCHAs in order to keep up with the bots' evolving approaches. Our research focuses on creating CAPTCHAs that are simple to use for humans but difficult to understand by robots, which creates a favourable user experience for those who are actually human. Character skewing, background noise injection, and image obfuscation are some of the methods we use to safeguard our CAPTCHAs while maintaining their usability. Furthermore, we carry out exhaustive trials in the real world to assess the effectiveness of our methods based on in-depth CAPTCHA learning. We evaluate their resistance to a variety of attack methods, such as counterattacks and machine learning-based bot attacks, in order to confirm that they are resilient. The findings of our study show that utilising deep learning in CAPTCHA methods to thwart automated bot attacks is both feasible and effective. Our method makes the internet environment safer and more user-friendly while also enhancing security and reducing user annoyance. This work is an important step in the fight against the growing menace of automated bot attacks in the digital sphere.

VeriBypasser: An automatic image verification code recognition system based on CNN

The rapid development of network technology has brought great convenience to people’s daily life in recent years. The application of network technology in finance, economy, and education is flourishing, dramatically improving people’s quality of life and promoting society’s development. However, the development of network technology brought some new technical problems. Authenticating and controlling access to shared resources has become a significant problem, which limits the development of smart cities. Nowadays, most websites use accounts and passwords for authentication. In order to prevent violent scanning, image-based human–machine inspection solutions are widely used on major platforms. Nonetheless, some human–computer verification schemes can be bypassed or attacked by artificial intelligence. This paper proposes an improved generation method of image verification code and an automatic recognition system based on Cellular Neural Network (CNN), which can achieve an accuracy of 85.20% for a mix of the visual reasoning Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) containing 4-bit case-insensitive letters and digits, the slider CAPTCHA containing one slide, and inference puzzle CAPTCHA containing a 4 × 2 grid. A brief evaluation of system threat assignment with such verification approaches based on four machine learning methods is also proposed.

A Clinic Appointment System with Authentications for Klinik Sejahtera

The clinic is a health facility for outpatients in indigenous communities. Traditionally, patients need to walk into the clinic or telephone the clinic if they want to make a medical appointment. For this project, Klinik Sejahtera has been chosen as the case study. Klinik Sejahtera is located at Parit Raja, Johor. Klinik Sejahtera was using “first come, first serve” to their patients that came on that day. Usually, the appointment will be taken at the counter during the patient's come to the clinic. The responsible employee at the counter will ask the patient for their identity card and search for the record. They also offer an appointment by contacting the clinic. The problem arises when staff need to record manually and need to remind the patient about the appointment. There are also problems with duplication because of too many physical reports. Therefore, this project proposed a clinic appointment system with authentications which is password-based and Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA). The system will be web-based. The web-based will be used by the patients, nurses, and doctor. Object-oriented system development is used for the methodology of the proposed system.

Assessing the Relationship Between Digital Trail Making Test Performance and IT Task Performance: Empirical Study.

Cognitive functional ability affects the accessibility of IT and is thus something that should be controlled for in user experience (UX) research. However, many cognitive function assessment batteries are long and complex, making them impractical for use in conventional experimental time frames. Therefore, there is a need for a short and reliable cognitive assessment that has discriminant validity for cognitive functions needed for general IT tasks. One potential candidate is the Trail Making Test (TMT). This study investigated the usefulness of a digital TMT as a cognitive profiling tool in IT-related UX research by assessing its predictive validity on general IT task performance and exploring its discriminant validity according to discrete cognitive functions required to perform the IT task. A digital TMT (parts A and B) named Axon was administered to 27 healthy participants, followed by administration of 5 IT tasks in the form of CAPTCHAs (Completely Automated Public Turing tests to Tell Computers and Humans Apart). The discrete cognitive functions required to perform each CAPTCHA were rated by trained evaluators. To further explain and cross-validate our results, the original TMT and 2 psychological assessments of visuomotor and short-term memory function were administered. Axon A and B were administrable in less than 5 minutes, and overall performance was significantly predictive of general IT task performance (F5,19=6.352; P=.001; Λ=0.374). This result was driven by performance on Axon B (F5,19=3.382; P=.02; Λ=0.529), particularly for IT tasks involving the combination of executive processing with visual object and pattern recognition. Furthermore, Axon was cross-validated with the original TMT (Pcorr=.001 and Pcorr=.017 for A and B, respectively) and visuomotor and short-term memory tasks. The results demonstrate that variance in IT task performance among an age-homogenous neurotypical population can be related to intersubject variance in cognitive function as assessed by Axon. Although Axon's predictive validity seemed stronger for tasks involving the combination of executive function with visual object and pattern recognition, these cognitive functions are arguably relevant to the majority of IT interfaces. Considering its short administration time and remote implementability, the Axon digital TMT demonstrates the potential to be a useful cognitive profiling tool for IT-based UX research.

Web Authentication Biometric 3D Animated CAPTCHA System Using Artificial Intelligence and Machine Learning Approach

The internet and web security are integral aspects of our daily lives. Many commercial firms provide clients with internet services. For web access, it is assumed that only the genuine user, who is a human, will register. Yet automated hacking programs can also do registrations with fake data that consume a lot of bandwidth, slowing down or occasionally even shutting down websites, leading to Distributed denial-of-service (DDOS) attacks. Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is the solution. Complex CAPTCHA is challenging for humans to recognize, but simple CAPTCHA is simple for AI to decipher. With the developments in neural networks and machine learning bots are mimicking humans and it is becoming difficult to distinguish humans and bots apart. This generated a need to think of some more innovative and novel CAPTCHA. Now, utilizing the same AIML approach to increase the efficacy of CAPTCHA and make it stronger against the bot attack. Biometric 3D Animated (B3DA) Algorithm proposed in this research is a novel approach based on the Face Detection AI algorithm along with handwritten 3D animated characters selected randomly to create a string which makes CAPTCHA simple that humans can identify but very difficult for bots. The test results have proven this to be a very robust CAPTCHA. The machine learning algorithm employed will keep on increasing the efficacy of this CAPTCHA each time the bot tries to break this.

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep learning techniques. To prevent this ability from being utilised, adversarial machine learning has recently been proposed by perturbing CAPTCHA images. As a result of the introduction of various removal methods, this perturbation mechanism can be removed. This paper, thus, presents the first comprehensive survey on adversarial perturbations and attacks on CAPTCHAs. In particular, the art of utilizing deep learning techniques with the aim of breaking CAPTCHAs are reviewed, and the effectiveness of adversarial CAPTCHAs is discussed. Drawing on the reviewed literature, several observations are provided as part of a broader outlook of this research direction. To emphasise adversarial CAPTCHAs as a potential solution for current attacks, a set of perturbation techniques have been suggested for application in adversarial CAPTCHAs.

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.

CAPTCHA RECOGNITION USING DCNN

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for websites to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA technology By using the concept of deep learning and computer vision, the very purpose of the CAPTCHAs can be defeated. This test can be passed automatically with the help of Convolutional Neural networks(CNN). A CNN is an algorithm of deep learning which takes an image as input and then assigns some value to various features in the image which further helps to differentiate one feature from the other. Its main purpose is to transform the images into a form which is much easier to process, without losing features which are essential for getting an optimized prediction. The proposed system for this project is to expand this CAPTCHA recognition system for larger and more noisy CAPTCHA containing all the symbols possible, a method based on the Deep Convolutional Neural Network (DCNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion. The future scope of this project lies in technologies where more noisier images can be processed such as license plates, handwriting recognition etc.

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.

Four-Character Captcha Recognition using Deep Learning Techniques

bstract: The Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a crucial humanmachine distinguishing technique for websites to fend against automated attacks from malicious software. Studies on CAPTCHA recognition have the potential to uncover security flaws, improve CAPTCHA technology, and even advance handwriting and license plate recognition. However, the whole objective of the CAPTCHAs may be bypassed using the principles of deep learning and computer vision. Convolutional neural networks (CNN) may be used to automatically pass this exam. A CNN is a deep learning algorithm that uses an image as input before giving distinct characteristics in the picture a value that further aids in differentiating one feature from another. Its major objective is to convert the pictures into a more simple form while retaining elements that are crucial for obtaining an optimum forecast.

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.

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.

Accessibility Challenges with CAPTCHA Services in Digital Libraries

In the paper are discussed security problems related to online digital libraries and portals. Special attention is placed to one the methods to distinguish usage of online digital libraries from non-human users. The approaches known under the abbreviation CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) are considered in the context of their accessibility to the users with different types of disabilities.