CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions

The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and data are strong defense systems against malicious bots. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication process to confirm that the user is a human hence, access is granted. This paper provides an in-depth survey on CAPTCHAs and focuses on two main things: (1) a detailed discussion on various CAPTCHA types along with their advantages, disadvantages, and design recommendations, and (2) an in-depth analysis of different CAPTCHA breaking techniques. The survey is based on over two hundred studies on the subject matter conducted since 2003 to date. The analysis reinforces the need to design more attack-resistant CAPTCHAs while keeping their usability intact. The paper also highlights the design challenges and open issues related to CAPTCHAs. Furthermore, it also provides useful recommendations for breaking CAPTCHAs

Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model

To enhance the security of text CAPTCHAs, various methods have been employed, such as adding the interference lines on the text, randomly distorting the characters, and overlapping multiple characters. These methods partly increase the difficulty of automated segmentation and recognition attacks. However, facing the rapid development of the end-to-end breaking algorithms, their security has been greatly weakened. The diffusion model is a novel image generation model that can generate the text images with deep fusion of characters and background images. In this paper, an image-click CAPTCHA scheme called Diff-CAPTCHA is proposed based on denoising diffusion models. The background image and characters of the CAPTCHA are treated as a whole to guide the generation process of a diffusion model, thus weakening the character features available for machine learning, enhancing the diversity of character features in the CAPTCHA, and increasing the difficulty of breaking algorithms. To evaluate the security of Diff-CAPTCHA, this paper develops several attack methods, including end-to-end attacks based on Faster R-CNN and two-stage attacks, and Diff-CAPTCHA is compared with three baseline schemes, including commercial CAPTCHA scheme and security-enhanced CAPTCHA scheme based on style transfer. The experimental results show that diffusion models can effectively enhance CAPTCHA security while maintaining good usability in human testing

Using Generative Adversarial Networks to Break and Protect Text Captchas

Text-based CAPTCHAs remains a popular scheme for distinguishing between a legitimate human user and an automated program. This article presents a novel genetic text captcha solver based on the generative adversarial network. As a departure from prior text captcha solvers that require a labor-intensive and time-consuming process to construct, our scheme needs significantly fewer real captchas but yields better performance in solving captchas. Our approach works by first learning a synthesizer to automatically generate synthetic captchas to construct a base solver. It then improves and fine-tunes the base solver using a small number of labeled real captchas. As a result, our attack requires only a small set of manually labeled captchas, which reduces the cost of launching an attack on a captcha scheme. We evaluate our scheme by applying it to 33 captcha schemes, of which 11 are currently used by 32 of the top-50 popular websites. Experimental results demonstrate that our scheme significantly outperforms four prior captcha solvers and can solve captcha schemes where others fail. As a countermeasure, we propose to add imperceptible perturbations onto a captcha image. We demonstrate that our countermeasure can greatly reduce the success rate of the attack

Mothers\u27 Adaptation to Caring for a New Baby

To date, most research on parents\u27 adjustment after adding a new baby to their family unit has focused on mothers\u27 initial transition to parenthood. This past research has examined changes in mothers\u27 marital satisfaction and perceived well-being across the transition, and has compared their prenatal expectations to their postnatal experiences. This project assessed first-time and experienced mothers\u27 stress and satisfaction associated with parenting, their adjustment to competing demands, and their perceived well-being longitudinally before and after the birth of a baby. Additionally, how maternal and child-related variables influenced the trajectory of mothers\u27 postnatal adaptation was assessed. These variables included mothers\u27 age, their education level, their prenatal expectations and postnatal experiences concerning shared infant care, their satisfaction with the division of infant caregiving, and their perceptions of their infant\u27s temperament. Mothers (N = 136) completed an online survey during their third trimester and additional online surveys when their baby was approximately 2, 4, 6, and 8 weeks old.;First-time mothers prenatally expected a more equal division of infant caregiving between themselves and their partners than did experienced mothers. Both first-time and experienced mothers reported less assistance from their partners than they had prenatally expected. Additionally, they experienced almost twice as many violated expectations than met expectations. Growth curve modeling revealed that a cubic function of time best fit the trajectory of mothers\u27 postnatal parenting satisfaction. Mothers reported less parenting satisfaction at 4 weeks, compared to 2 and 6 weeks, and reported stability in their satisfaction between 6 and 8 weeks. A quadratic function of time best fit the trajectories of mothers\u27 postnatal parenting stress and adjustment to the demands of their baby. Mothers reported more stress and difficulty adjusting to their baby\u27s demands at 4 and 6 weeks, compared to 2 and 8 weeks. A linear function of time best fit the trajectories of mothers\u27 adjustment to home demands, generalized state anxiety, and depressive symptoms. Mothers reported less difficulty meeting home demands, less generalized anxiety, and fewer depressive symptoms across the postnatal period. Mothers\u27 violated expectations were associated with level differences in all aspects of mothers\u27 postnatal adaptation except their adjustment to home demands. Specifically, more violated expectations, in number or in magnitude, were associated with poorer postnatal adaptation. Mothers\u27 violated expectations were not associated with the slope of mothers\u27 postnatal adaptation trajectories. Exploratory models revealed that other maternal and child-related variables also impacted the level and slope of mothers\u27 postnatal adaptation.;Overall, first-time and experienced mothers were more similar than different in regards to their postnatal adaptation. This study suggests that prior findings concerning adults\u27 initial transition to parenthood may also apply to adults during each addition of a new baby into the family unit. Additionally, mothers who reported less of a mismatch between their expectations and experiences concerning shared infant care had fewer issues adapting the postnatal period. Thus, methods to increase the assistance mothers receive from their partner should be sought. Limitations of this study and suggestions for future research are also discussed

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks

A Framework for Devanagari Script-based Captcha

Human Interactive Proofs (HIPs) are automatic reverse Turing tests designed to distinguish between various groups of users. Completely Automatic Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a HIP system that distinguish between humans and malicious computer programs. Many CAPTCHAs have been proposed in the literature that text-graphical based, audio-based, puzzle-based and mathematical questions-based. The design and implementation of CAPTCHAs fall in the realm of Artificial Intelligence. We aim to utilize CAPTCHAs as a tool to improve the security of Internet based applications. In this paper we present a framework for a text-based CAPTCHA based on Devanagari script which can exploit the difference in the reading proficiency between humans and computer programs. Our selection of Devanagari script-based CAPTCHA is based on the fact that it is used by a large number of Indian languages including Hindi which is the third most spoken language. There is potential for an exponential rise in the applications that are likely to be developed in that script thereby making it easy to secure Indian language based applications.Comment: 10 pages, 8 Figures, CCSEA 2011 - First International Conference, Chennai, July 15-17, 201

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.Web of Science20221art. no.

Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas