Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment
Zahra Noury, Mahdi Rezaei

TL;DR
This paper presents Deep-CAPTCHA, a deep learning model that effectively cracks visual CAPTCHA tests, revealing vulnerabilities and guiding the development of more secure CAPTCHA systems.
Contribution
It introduces a novel convolutional neural network for CAPTCHA cracking and provides a comprehensive analysis of CAPTCHA vulnerabilities using a large dataset.
Findings
Achieved over 98% accuracy in cracking numerical CAPTCHAs.
Achieved over 98% accuracy in cracking alphanumerical CAPTCHAs.
Identified techniques to improve CAPTCHA security based on model performance.
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…
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Taxonomy
TopicsUser Authentication and Security Systems · Advanced Malware Detection Techniques · Information and Cyber Security
MethodsDeep-CAPTCHA
