# Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks

arXiv: https://arxiv.org/abs/2010.16204 · HTML: https://mlsanity.com/paper/2010.16204/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Robust Text CAPTCHAs Using Adversarial Examples](https://mlsanity.com/paper/2101.02483/) (2021)
- [FedComm: Federated Learning as a Medium for Covert Communication](https://mlsanity.com/paper/2201.08786/) (2022)
- [CAPTCHA Solving for Native GUI Agents: Automated Reasoning-Action Data Generation and Self-Corrective Training](https://mlsanity.com/paper/2603.23559/) (2026)
- [Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle](https://mlsanity.com/paper/2608.04314/) (2026)
- [Minerva: A File-Based Ransomware Detector](https://mlsanity.com/paper/2301.11050/) (2023)
- [HLL: Can Agents Cross Humanity's Last Line of Verification?](https://mlsanity.com/paper/2606.02449/) (2026)
- [A Survey of Adversarial CAPTCHAs on its History, Classification and Generation](https://mlsanity.com/paper/2311.13233/) (2023)
- [CaptchaAgent: Verifiable Visual Reasoning for CAPTCHA Solving](https://mlsanity.com/paper/2610.12866/) (2026)

## Abstract (arXiv)

To this date, CAPTCHAs have served as the first line of defense preventing unauthorized access by (malicious) bots to web-based services, while at the same time maintaining a trouble-free experience for human visitors. However, recent work in the literature has provided evidence of sophisticated bots that make use of advancements in machine learning (ML) to easily bypass existing CAPTCHA-based defenses. In this work, we take the first step to address this problem. We introduce CAPTURE, a novel CAPTCHA scheme based on adversarial examples. While typically adversarial examples are used to lead an ML model astray, with CAPTURE, we attempt to make a "good use" of such mechanisms. Our empirical evaluations show that CAPTURE can produce CAPTCHAs that are easy to solve by humans while at the same time, effectively thwarting ML-based bot solvers.