Toward Robust Detection of Puppet Attacks via Characterizing Fingertip-Touch Behaviors

Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namely puppet attack, in which an attacker places an unwilling victim's finger on the fingerprint sensor. In this article, we propose FinAuth, an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks using fingertip-touch characteristics. FinAuth characterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates. FinAuth only utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness of FinAuth, we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show that FinAuth can achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate that FinAuth is resilient against possible attacks. In addition, we report the usability analysis results of FinAuth, including user authentication delay and overhead.