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Semi-supervised deep learning approach to break common CAPTCHAs

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140411" target="_blank" >RIV/00216305:26220/21:PU140411 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007%2Fs00521-021-05957-0" target="_blank" >https://link.springer.com/article/10.1007%2Fs00521-021-05957-0</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00521-021-05957-0" target="_blank" >10.1007/s00521-021-05957-0</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Semi-supervised deep learning approach to break common CAPTCHAs

  • Original language description

    Manual data annotation is a time consuming activity. A novel strategy for automatic training of the CAPTCHA breaking system with no manual dataset creation is presented in this paper. We demonstrate the feasibility of the attack against a text-based CAPTCHA scheme utilizing similar network infrastructure used for Denial of Service attacks. The main goal of our research is to present a possible vulnerability in CAPTCHA systems when combining the brute-force attack with transfer learning. The classification step utilizes a simple convolutional neural network with 15 layers. Training stage uses automatically prepared dataset created without any human intervention and transfer learning for fine-tuning the deep neural network classifier. The designed system for breaking text-based CAPTCHAs achieved 80% classification accuracy after 6 fine-tuning steps for a 5 digit text-based CAPTCHA system. The results presented in this paper suggest, that even the simple attack with a large number of attacking computers can be an effective alternative to current CAPTCHA breaking systems.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    NEURAL COMPUTING & APPLICATIONS

  • ISSN

    0941-0643

  • e-ISSN

    1433-3058

  • Volume of the periodical

    33

  • Issue of the periodical within the volume

    20

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    11

  • Pages from-to

    13333-13343

  • UT code for WoS article

    000639371700001

  • EID of the result in the Scopus database

    2-s2.0-85104497839