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Fingerprint traits are widely recognized for their unique qualities andsecurity benefits. Despite their extensive use, fingerprint features can bevulnerable to puppet attacks, where attackers manipulate a reluctant butgenuine user into completing the authentication process. Defending against suchattacks is challenging due to the coexistence of a legitimate identity and anillegitimate intent. In this paper, we propose PUPGUARD, a solution designed toguard against puppet attacks. This method is based on user behavioral patterns,specifically, the user needs to press the capture device twice successivelywith different fingers during the authentication process. PUPGUARD leveragesboth the image features of fingerprints and the timing characteristics of thepressing intervals to establish two-factor authentication. More specifically,after extracting image features and timing characteristics, and performingfeature selection on the image features, PUPGUARD fuses these two features intoa one-dimensional feature vector, and feeds it into a one-class classifier toobtain the classification result. This two-factor authentication methodemphasizes dynamic behavioral patterns during the authentication process,thereby enhancing security against puppet attacks. To assess PUPGUARD'seffectiveness, we conducted experiments on datasets collected from 31 subjects,including image features and timing characteristics. Our experimental resultsdemonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and aremarkably low false positive rate (FPR) of 1.89%. Furthermore, we conductedcomparative experiments to validate the superiority of combining image featuresand timing characteristics within PUPGUARD for enhancing resistance againstpuppet attacks.

Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks