  Fingerprint traits are widely recognized for their unique qualities and
security benefits. Despite their extensive use, fingerprint features can be
vulnerable to puppet attacks, where attackers manipulate a reluctant but
genuine user into completing the authentication process. Defending against such
attacks is challenging due to the coexistence of a legitimate identity and an
illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to
guard against puppet attacks. This method is based on user behavioral patterns,
specifically, the user needs to press the capture device twice successively
with different fingers during the authentication process. PUPGUARD leverages
both the image features of fingerprints and the timing characteristics of the
pressing intervals to establish two-factor authentication. More specifically,
after extracting image features and timing characteristics, and performing
feature selection on the image features, PUPGUARD fuses these two features into
a one-dimensional feature vector, and feeds it into a one-class classifier to
obtain the classification result. This two-factor authentication method
emphasizes dynamic behavioral patterns during the authentication process,
thereby enhancing security against puppet attacks. To assess PUPGUARD's
effectiveness, we conducted experiments on datasets collected from 31 subjects,
including image features and timing characteristics. Our experimental results
demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a
remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted
comparative experiments to validate the superiority of combining image features
and timing characteristics within PUPGUARD for enhancing resistance against
puppet attacks.

Computer Vision and Pattern Recognition