Paper Info
Title
Automated CAPTCHA Solving: An Empirical Comparison of Selected Techniques
Abstract
CAPTCHAs exploit the gap in the ability between a human and a machine to understand the semantics of specific multimedia content, with vast applications in computer security. In this paper we compare two techniques in automated CAPTCHA solving for text-based CAPTCHA schemes, i.e., Classification based on the Vector Space Model (VSM) versus a popular Optical Character Recognition (OCR) engine. For each technique, we build a CAPTCHA solver and give it specific sets of text-based challenges to break. From our results we draw conclusions whether it is efficient to create a CAPTCHA solver by applying parts of the VSM theory and implementing a Vector Space Image Recognizer (VSIR).
Year
DOI
Venue
2014
10.1109/SMAP.2014.29
Semantic and Social Media Adaptation and Personalization
Keywords
DocType
Citations 
image classification,multimedia systems,optical character recognition,vectors,OCR engine,VSIR,VSM theory,automated CAPTCHA solving,classification,completely automated public turing test to tell computers and humans apart,computer security,multimedia content,optical character recognition engine,text-based CAPTCHA schemes,vector space image recognizer,vector space model,CAPTCHA,Image recognition,OCR,Semantic context extraction,VSM
Conference
4
PageRank 
References 
Authors
0.41
17
3
Name
Order
Citations
PageRank
Michalis Korakakis1212.08
Emmanouil Magkos221724.01
Phivos Mylonas325244.52