  Completely Automated Public Turing Test To Tell Computers and Humans Apart
(CAPTCHA) is a type of challenge-response test widely used in authentication
systems. A well-known challenge it faces is the CAPTCHA farm, where workers are
hired to solve CAPTCHAs manually. In this work, we propose to tackle this
challenge from a novel perspective, converting CAPTCHA farm detection to
identity inconsistency detection, which essentially becomes an authentication
process. Specifically, we develop a novel embedding model, which measures the
similarity between mouse trajectories collected during the session and when
registering/solving CAPTCHA, to authenticate and detect identity inconsistency.
Moreover, unlike most existing works that employ a separate mouse movement
classifier for each individual user, which brings in considerable costs when
serving a large number of users, our model performs detection tasks using only
one classifier for all users, significantly reducing the cost. Experiment
results validate the superiority of our method over the state-of-the-art time
series classification methods, achieving 94.3% and 97.7% of AUC in identity and
authentication inconsistency detection, respectively.
