1 research outputs found
Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment
CAPTCHA is a human-centred test to distinguish a human operator from bots,
attacking programs, or other computerised agents that tries to imitate human
intelligence. In this research, we investigate a way to crack visual CAPTCHA
tests by an automated deep learning based solution. The goal of this research
is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator
systems; hence, developing more robust CAPTCHAs, without taking the risks of
manual try and fail efforts. We develop a Convolutional Neural Network called
Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate
both numerical and alphanumerical CAPTCHAs. To train and develop an efficient
model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In
this paper, we present our customised deep neural network model, we review the
research gaps, the existing challenges, and the solutions to cope with the
issues. Our network's cracking accuracy leads to a high rate of 98.94% and
98.31% for the numerical and the alpha-numerical test datasets, respectively.
That means more works is required to develop robust CAPTCHAs, to be
non-crackable against automated artificial agents. As the outcome of this
research, we identify some efficient techniques to improve the security of the
CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA
model.Comment: Version 2.