# Vulnerability analysis of captcha using Deep learning

arXiv: https://arxiv.org/abs/2302.09389 · HTML: https://mlsanity.com/paper/2302.09389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Benchmarking of Different YOLO Models for CAPTCHAs Detection and Classification](https://mlsanity.com/paper/2502.13740/) (2025)

## Abstract (arXiv)

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