  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.
