A Survey of Adversarial CAPTCHAs on its History, Classification and Generation
This is a survey paper that provides a systematic review for researchers in cybersecurity and AI, focusing on incremental advancements in adversarial CAPTCHA methods.
The paper tackles the problem of balancing security and usability in CAPTCHAs by proposing adversarial CAPTCHAs, which integrate adversarial examples to fool deep models, and it extends definitions, classifies methods, and reviews generation and defense techniques.
Completely Automated Public Turing test to tell Computers and Humans Apart, short for CAPTCHA, is an essential and relatively easy way to defend against malicious attacks implemented by bots. The security and usability trade-off limits the use of massive geometric transformations to interfere deep model recognition and deep models even outperformed humans in complex CAPTCHAs. The discovery of adversarial examples provides an ideal solution to the security and usability trade-off by integrating adversarial examples and CAPTCHAs to generate adversarial CAPTCHAs that can fool the deep models. In this paper, we extend the definition of adversarial CAPTCHAs and propose a classification method for adversarial CAPTCHAs. Then we systematically review some commonly used methods to generate adversarial examples and methods that are successfully used to generate adversarial CAPTCHAs. Also, we analyze some defense methods that can be used to defend adversarial CAPTCHAs, indicating potential threats to adversarial CAPTCHAs. Finally, we discuss some possible future research directions for adversarial CAPTCHAs at the end of this paper.