With the rapid development of Iiitemet teclmologies. the problems of network security continue to worsen. So. one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (ftilly automated public Tuiiiig test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessaiy to perfbnn a simple task, such as entering a word displayed in an image, solving a basic aritlunetic 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, neinal netvirorks 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 camiot always recognize what is depicted in the photo. The puipose of this work is to assess the security of sites using the CAPTCHA text type by testing theiiresistance 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 discriniiiiator of the CGAN architectiu e. 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 constnicted in the article was 63 % on an initially veiy limited data set. which shows the infbnnation security risks that sites using a similar type of CAPTCHA can cany. © Задорожный А.С., Корепанова А.А., Абрамов М.В., Сабреков А.А., 2024.
Переведенное названиеAutomation of complex text CAPTCHA recognition using conditional generative adversarial networks
Язык оригиналарусский
Страницы (с-по)90-100
Число страниц11
Номер выпуска1
СостояниеОпубликовано - 1 фев 2024

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  • CGAN, CNN, conditional generative adversarial network, deep learning, information security, text-based CAPTCHAs

ID: 117487934