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

Abstract

To date, CAPTCHAs have served as the first line of defense to prevent 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 shown that sophisticated bots using advancements in Machine Learning (ML) can easily bypass existing CAPTCHA-based defenses. This work introduces CAPTURE, a novel CAPTCHA scheme based on adversarial examples. Typically adversarial examples are used to lead an ML model astray. With CAPTURE, we attempt to make a “good use” of such mechanisms in order to increase the robustness and security of existing CAPTCHA schemes. Our empirical evaluations show that CAPTURE can produce CAPTCHA challenges that are easy for humans to solve, while at the same time, CAPTURE can effectively thwart sophisticated ML-based bot solvers.