Hunting CAPTCHA-solving bots

openToday, smart phones have become an integral part of modern human life. By increasing CPU power and energy efficiency of these types of equipment, almost all daily routines and even personal activities of people have become dependent on these devices. By knowing the importance of these equipment in today's human life and crucial role of them to protect personal sensitive information, security and authorized access to these data are indispensable requirement in any new methods in this field of study. Today, CAPTCHAs are used to protect smart phones and computers from robot access, however most of which are broken and hacked by robots and machine learning based method. Therefore, it is necessary to provide more accurate and comprehensive algorithm in order to identify robots and prevent them from entering mobile phones

Automation of complex text CAPTCHA recognition using conditional generative adversarial networks

With the rapid development of Internet technologies, the problems of network security continue to worsen. So, one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (fully automated public Turing test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessary to perform a simple task, such as entering a word displayed in an image, solving a basic arithmetic equation, etc. However, the most widely used type of CAPTCHA is still the text type. In the recent years, the development of computer vision and, in particular, neural networks has contributed to a decrease in the resistance to hacking of text CAPTCHA. However, the security and resistance to recognition of complex CAPTCHA containing a lot of noise and distortion is still insufficiently studied. This study examines CAPTCHA, the distinctive feature of which is the use of a large number of different distortions, and each individual image uses its own different set of distortions, that is why even the human eye cannot always recognize what is depicted in the photo. The purpose of this work is to assess the security of sites using the CAPTCHA text type by testing their resistance to an automated solution. This testing will be used for the subsequent development of recommendations for improving the effectiveness of protection mechanisms. The result of the work is an implemented synthetic generator and discriminator of the CGAN architecture, as well as a decoder program, which is a trained convolutional neural network that solves this type of CAPTCHA. The recognition accuracy of the model constructed in the article was 63 % on an initially very limited data set, which shows the information security risks that sites using a similar type of CAPTCHA can carry

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.Web of Science234art. no. 233

Democracy Enhancing Technologies: Toward deployable and incoercible E2E elections

End-to-end verifiable election systems (E2E systems) provide a provably correct tally while maintaining the secrecy of each voter's ballot, even if the voter is complicit in demonstrating how they voted. Providing voter incoercibility is one of the main challenges of designing E2E systems, particularly in the case of internet voting. A second challenge is building deployable, human-voteable E2E systems that conform to election laws and conventions. This dissertation examines deployability, coercion-resistance, and their intersection in election systems. In the course of this study, we introduce three new election systems, (Scantegrity, Eperio, and Selections), report on two real-world elections using E2E systems (Punchscan and Scantegrity), and study incoercibility issues in one deployed system (Punchscan). In addition, we propose and study new practical primitives for random beacons, secret printing, and panic passwords. These are tools that can be used in an election to, respectively, generate publicly verifiable random numbers, distribute the printing of secrets between non-colluding printers, and to covertly signal duress during authentication. While developed to solve specific problems in deployable and incoercible E2E systems, these techniques may be of independent interest

Mixing Methods: Practical Insights from the Humanities in the Digital Age

The digital transformation is accompanied by two simultaneous processes: digital humanities challenging the humanities, their theories, methodologies and disciplinary identities, and pushing computer science to get involved in new fields. But how can qualitative and quantitative methods be usefully combined in one research project? What are the theoretical and methodological principles across all disciplinary digital approaches? This volume focusses on driving innovation and conceptualising the humanities in the 21st century. Building on the results of 10 research projects, it serves as a useful tool for designing cutting-edge research that goes beyond conventional strategies