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%2F68407700%3A21240%2F17%3A00324710" target="_blank" >RIV/68407700:21240/17:00324710 - 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
—
Continuities
S - Specificky vyzkum na vysokych skolach
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
ITAT 2017: Information Technologies – Applications and Theory
ISBN
—
ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
7
Pages from-to
93-99
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Martinské hole, Malá Fatra
Event date
Sep 22, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—