  Turing test was originally proposed to examine whether machine's behavior is
indistinguishable from a human. The most popular and practical Turing test is
CAPTCHA, which is to discriminate algorithm from human by offering
recognition-alike questions. The recent development of deep learning has
significantly advanced the capability of algorithm in solving CAPTCHA
questions, forcing CAPTCHA designers to increase question complexity. Instead
of designing questions difficult for both algorithm and human, this study
attempts to employ the limitations of algorithm to design robust CAPTCHA
questions easily solvable to human. Specifically, our data analysis observes
that human and algorithm demonstrates different vulnerability to visual
distortions: adversarial perturbation is significantly annoying to algorithm
yet friendly to human. We are motivated to employ adversarially perturbed
images for robust CAPTCHA design in the context of character-based questions.
Three modules of multi-target attack, ensemble adversarial training, and image
preprocessing differentiable approximation are proposed to address the
characteristics of character-based CAPTCHA cracking. Qualitative and
quantitative experimental results demonstrate the effectiveness of the proposed
solution. We hope this study can lead to the discussions around adversarial
attack/defense in CAPTCHA design and also inspire the future attempts in
employing algorithm limitation for practical usage.
