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Breaking CAPTCHAs with Convolutional Neural Networks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478627" target="_blank" >RIV/67985807:_____/17:00478627 - isvavai.cz</a>

  • Result on the web

    <a href="http://ceur-ws.org/Vol-1885/93.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/93.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Breaking CAPTCHAs with Convolutional Neural Networks

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings ITAT 2017: Information Technologies - Applications and Theory

  • ISBN

    978-1974274741

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    93-99

  • Publisher name

    Technical University & CreateSpace Independent Publishing Platform

  • Place of publication

    Aachen & Charleston

  • Event location

    Martinské hole

  • Event date

    Sep 22, 2017

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article