Adversarial CAPTCHAs

doi:10.1109/TCYB.2021.3071395

Adversarial CAPTCHAs

2022-07-01

22

PRE

AI

DOI：10.1109/TCYB.2021.3071395

Original

Original request for help

Save

Abstract

En 中文

Following the principle of to set one's own spear against one's own shield, we study how to design adversarial completely automated public turing test to tell computers and humans apart (CAPTCHA) in this article. We first identify the similarity and difference between adversarial CAPTCHA generation and existing hot adversarial example (image) generation research. Then, we propose a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques. Finally, we design and implement an adversarial CAPTCHA generation and evaluation system, called aCAPTCHA, which integrates 12 image preprocessing techniques, nine CAPTCHA attacks, four baseline adversarial CAPTCHA generation methods, and eight new adversarial CAPTCHA generation methods. To examine the performance of aCAPTCHA, extensive security and usability evaluations are conducted. The results demonstrate that the generated adversarial CAPTCHAs can significantly improve the security of normal CAPTCHAs while maintaining similar usability. To facilitate the CAPTCHA security research, we also open source the aCAPTCHA system, including the source code, trained models, datasets, and the usability evaluation interfaces.

Keywords:

CAPTCHAs

Security

Usability

Image synthesis

Electronic mail

Computers

Resilience

Adversarial image

completely automated public turing test to tell computers and humans apart (CAPTCHA)

deep learning

usable security

Adversarial CAPTCHAs

Abstract

AI Summary

Journal

Organization