EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees
Duc C. Hoang, Behzad Ousat, Amin Kharraz, Cuong V. Nguyen

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…
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Taxonomy
TopicsUser Authentication and Security Systems · Spam and Phishing Detection · Misinformation and Its Impacts
