6 research outputs found
CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions
The proliferation of the Internet and mobile devices has resulted in
malicious bots access to genuine resources and data. Bots may instigate
phishing, unauthorized access, denial-of-service, and spoofing attacks to
mention a few. Authentication and testing mechanisms to verify the end-users
and prohibit malicious programs from infiltrating the services and data are
strong defense systems against malicious bots. Completely Automated Public
Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication
process to confirm that the user is a human hence, access is granted. This
paper provides an in-depth survey on CAPTCHAs and focuses on two main things:
(1) a detailed discussion on various CAPTCHA types along with their advantages,
disadvantages, and design recommendations, and (2) an in-depth analysis of
different CAPTCHA breaking techniques. The survey is based on over two hundred
studies on the subject matter conducted since 2003 to date. The analysis
reinforces the need to design more attack-resistant CAPTCHAs while keeping
their usability intact. The paper also highlights the design challenges and
open issues related to CAPTCHAs. Furthermore, it also provides useful
recommendations for breaking CAPTCHAs
An Optimized System to Solve Text-Based Captcha
CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to
protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently
utilize text-based scheme because of most convenience and user-friendly way [1]. Currently, various types
of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different
segmentation ways. Our goal is to defeat the CAPTCHA,thus rstly the CAPTCHAs need to be split into
character by character. There isn't a regular segmentation algorithm to obtain the divided characters in all
kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build
a whole system todefeat the CAPTCHAs as well as achieve state-of-the-art performance.In detail, we
present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both
the existing methods and our own constructed convolutional neural network as an extra classfier. Results
are provided showing how our system work well towards defeating these CAPTCHAs