# Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment

arXiv: https://arxiv.org/abs/2006.08296 · HTML: https://mlsanity.com/paper/2006.08296/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents](https://mlsanity.com/paper/2505.24878/) (2025)
- [Breaking reCAPTCHAv2](https://mlsanity.com/paper/2409.08831/) (2024)
- [MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks](https://mlsanity.com/paper/2506.05982/) (2025)
- [Extreme Image Transformations Affect Humans and Machines Differently](https://mlsanity.com/paper/2212.13967/) (2022)
- [Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence](https://mlsanity.com/paper/2201.02799/) (2022)
- [Throttling Web Agents Using Reasoning Gates](https://mlsanity.com/paper/2509.01619/) (2025)
- [HLL: Can Agents Cross Humanity's Last Line of Verification?](https://mlsanity.com/paper/2606.02449/) (2026)
- [EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees](https://mlsanity.com/paper/2307.15180/) (2023)
- [Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification](https://mlsanity.com/paper/2402.05417/) (2024)
- [Benchmarking of Different YOLO Models for CAPTCHAs Detection and Classification](https://mlsanity.com/paper/2502.13740/) (2025)
- [On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting](https://mlsanity.com/paper/2606.30119/) (2026)

## Abstract (arXiv)

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.