Evaluating a Bimodal User Verification Robustness Against Synthetic Data Attacks

Smartphones balance security and convenience by offering both knowledge-based (PINs, patterns) and biometric (facial, fingerprint) verification methods. However, studies have reported that PINs and patterns can be readily circumvented, while synthetically manipulated face data can easily deceive smartphone facial verification mechanisms. In this paper, we design a bimodal user verification mechanism that combines behavioral (pickup gesture) and biological (face) biometrics for user verification on smartphones. This work establishes a baseline for single-user verification scenarios on smartphones using a one-class verification model. The evaluation is performed in two stages: first, performance is assessed in both unimodal and bimodal settings using publicly available datasets; second, the robustness of the employed biological and behavioral traits is examined against four diverse attacks. Our findings emphasize the necessity of investigating diverse attack vectors, particularly fully synthetic data, to design robust user verification mechanisms.