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http://dx.doi.org/10.13089/JKIISC.2015.25.5.1011

Breaking character and natural image based CAPTCHA using feature classification  

Kim, Jaehwan (Center for Information Security Technologies(CIST), Korea University)
Kim, Suah (Center for Information Security Technologies(CIST), Korea University)
Kim, Hyoung Joong (Center for Information Security Technologies(CIST), Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.25, no.5, 2015 , pp. 1011-1019 More about this Journal
Abstract
CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most recent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from the background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHAs were correctly identified, it was demonstrated that the current CAPTCHA is not safe.
Keywords
CAPTCHA; Breaking CAPTCHA; SVM; CNN; HSV color space;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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