Deep-CAPTCHA: A Deep Learning Based CAPTCHA Solver for Vulnerability Assessment

9 Pages Posted: 21 Jul 2020

See all articles by Zahra Nouri

Zahra Nouri

Qazvin Azad University

Mahdi Rezaei

University of Leeds, Institute for Transport Studies; The University of Auckland, School of Computer Science

Date Written: June 15, 2020

Abstract

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.

Keywords: CAPTCHA Solver; CAPTCHA Cracker; Deep Learning, Computer Vision, Image and Pattern Recognition, Convolutional Neural Networks

Suggested Citation

Nouri, Zahra and Rezaei, Mahdi, Deep-CAPTCHA: A Deep Learning Based CAPTCHA Solver for Vulnerability Assessment (June 15, 2020). Available at SSRN: https://ssrn.com/abstract=3633354 or http://dx.doi.org/10.2139/ssrn.3633354

Zahra Nouri

Qazvin Azad University ( email )

Nokhbegan Boulevard, Barajin
Qazvin, 10
Iran

Mahdi Rezaei (Contact Author)

University of Leeds, Institute for Transport Studies ( email )

34-40 University Raod, Institute for Transport Stu
Leeds
United Kingdom
01133435342 (Phone)

HOME PAGE: http://https://environment.leeds.ac.uk/transport/staff/9408/dr-mahdi-rezaei

The University of Auckland, School of Computer Science ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

HOME PAGE: http://MahdiRezaei.auckland.ac.nz

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