Related papers: EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees

EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees

URL: http://arxiv.org/abs/2307.15180v2
Date: Fri, 28 Jun 2024 16:40:39 GMT
Title: EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees
Authors: Duc C. Hoang, Behzad Ousat, Amin Kharraz, Cuong V. Nguyen,
Abstract summary: We propose Enr, a family of solvers that use deep ensemble uncertainty to detect and skip out-of-distribution CAPTCHAs. We prove novel theoretical bounds on the effectiveness of our solvers and demonstrate their use with state-of-the-art CAPTCHA solvers.
Score: 1.9649272351760065
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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