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Completely Automated Public Turing Test To Tell Computers and Humans Apart(CAPTCHA) is a type of challenge-response test widely used in authenticationsystems. A well-known challenge it faces is the CAPTCHA farm, where workers arehired to solve CAPTCHAs manually. In this work, we propose to tackle thischallenge from a novel perspective, converting CAPTCHA farm detection toidentity inconsistency detection, which essentially becomes an authenticationprocess. Specifically, we develop a novel embedding model, which measures thesimilarity between mouse trajectories collected during the session and whenregistering/solving CAPTCHA, to authenticate and detect identity inconsistency.Moreover, unlike most existing works that employ a separate mouse movementclassifier for each individual user, which brings in considerable costs whenserving a large number of users, our model performs detection tasks using onlyone classifier for all users, significantly reducing the cost. Experimentresults validate the superiority of our method over the state-of-the-art timeseries classification methods, achieving 94.3% and 97.7% of AUC in identity andauthentication inconsistency detection, respectively.

User Authentication and Identity Inconsistency Detection via Mouse-trajectory Similarity Measurement