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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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