Počet záznamů: 1
Breaking CAPTCHAs with Convolutional Neural Networks
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SYSNO ASEP 0478627 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Breaking CAPTCHAs with Convolutional Neural Networks Tvůrce(i) Kopp, M. (CZ)
Nikl, M. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2017: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2017 / Hlaváčová J. - ISSN 1613-0073 - ISBN 978-1974274741 Rozsah stran s. 93-99 Poč.str. 7 s. Forma vydání Online - E Akce ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./ Datum konání 22.09.2017 - 26.09.2017 Místo konání Martinské hole Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova CAPTCHA ; convolutional neural network ; network security ; optical character recognition Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85045733178 Anotace This paper studies reverse Turing tests to distinguish humans and computers, called CAPTCHA. Contrary to classical Turing tests, in this case the judge is not a human but a computer. The main purpose of such tests is securing user logins against the dictionary or brute force password guessing, avoiding automated usage of various services, preventing bots from spamming on forums and many others. Typical approaches to solving text-based CAPTCHA automatically are based on a scheme specific pipeline containing hand-designed pre-processing, denoising, segmentation, post processing and optical character recognition. Only the last part, optical character recognition, is usually based on some machine learning algorithm. We present an approach using neural networks and a simple clustering algorithm that consists of only two steps, character localisation and recognition. We tested our approach on 11 different schemes selected to present very diverse security features. We experimentally show that using convolutional neural networks is superior to multi-layered perceptrons. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2018 Elektronická adresa http://ceur-ws.org/Vol-1885/93.pdf
Počet záznamů: 1