A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

Abstract

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep learning techniques. To prevent this ability from being utilised, adversarial machine learning has recently been proposed by perturbing CAPTCHA images. As a result of the introduction of various removal methods, this perturbation mechanism can be removed. This paper, thus, presents the first comprehensive survey on adversarial perturbations and attacks on CAPTCHAs. In particular, the art of utilizing deep learning techniques with the aim of breaking CAPTCHAs are reviewed, and the effectiveness of adversarial CAPTCHAs is discussed. Drawing on the reviewed literature, several observations are provided as part of a broader outlook of this research direction. To emphasise adversarial CAPTCHAs as a potential solution for current attacks, a set of perturbation techniques have been suggested for application in adversarial CAPTCHAs.