Vulnerability analysis of captcha using Deep learning

TL;DR

This paper investigates the vulnerabilities of text-based CAPTCHAs against deep learning models, demonstrating that CNNs like CapNet can effectively predict CAPTCHA text, highlighting the need for more resilient CAPTCHA designs.

Contribution

The study introduces CapNet, a CNN-based platform capable of evaluating the security of numerical and alphanumerical CAPTCHAs, revealing weaknesses in current CAPTCHA systems.

Findings

CapNet successfully predicts CAPTCHA text with high accuracy.

Deep learning models can compromise traditional CAPTCHA security.

Highlights the need for more robust CAPTCHA generation methods.

Abstract

Several websites improve their security and avoid dangerous Internet attacks by implementing CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), a type of verification to identify whether the end-user is human or a robot. The most prevalent type of CAPTCHA is text-based, designed to be easily recognized by humans while being unsolvable towards machines or robots. However, as deep learning technology progresses, development of convolutional neural network (CNN) models that predict text-based CAPTCHAs becomes easier. The purpose of this research is to investigate the flaws and vulnerabilities in the CAPTCHA generating systems in order to design more resilient CAPTCHAs. To achieve this, we created CapNet, a Convolutional Neural Network. The proposed platform can evaluate both numerical and alphanumerical CAPTCHAs