  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 novel CAPTCHA solver that utilizes deep
ensemble uncertainty estimation to detect and skip out-of-distribution
CAPTCHAs, making it harder to be detected. We demonstrate the use of our solver
with object detection models and show empirically that it performs well on both
in-distribution and out-of-distribution data, achieving up to 98.1% accuracy
when detecting out-of-distribution data and up to 93% success rate when solving
in-distribution CAPTCHAs.

Computer Vision and Pattern Recognition;Cryptography and Security