EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees

TL;DR

EnSolver is an ensemble-based CAPTCHA solver that detects out-of-distribution samples using uncertainty measures, providing theoretical guarantees and improving robustness against unseen CAPTCHA types.

Contribution

It introduces a novel ensemble uncertainty approach for CAPTCHA solving with theoretical bounds, enhancing detection of out-of-distribution samples.

Findings

Effective in cracking diverse CAPTCHA datasets

Detects and skips out-of-distribution CAPTCHAs

Provides theoretical guarantees on performance

Abstract

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with high accuracy. However, these solvers often perform poorly on out-of-distribution samples that contain visual features different from those in the training set. Furthermore, they lack the ability to detect and avoid such samples, making them susceptible to being locked out by defense systems after a certain number of failed attempts. In this paper, we propose EnSolver, a family of CAPTCHA solvers that use deep ensemble uncertainty to detect and skip out-of-distribution CAPTCHAs, making it harder to be detected. We prove novel theoretical bounds on the effectiveness of our solvers and demonstrate their use with state-of-the-art CAPTCHA solvers. Our experiments show that the proposed approaches perform well when cracking CAPTCHA datasets that contain both in-distribution and out-of-distribution samples.