AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns

TL;DR

This paper introduces AirSignatureDB, a comprehensive dataset for in-air signature biometrics, benchmarks verification methods, and explores privacy concerns by reconstructing signature trajectories from sensor data.

Contribution

It provides the first large-scale, real-world dataset for in-air signatures, benchmarks verification techniques, and demonstrates trajectory reconstruction from inertial sensors, raising privacy issues.

Findings

Reconstructed 3D signature trajectories from sensor data.

Benchmarking shows deep learning improves verification accuracy.

Trajectory reconstruction challenges the assumption of traceless in-air gestures.

Abstract

Behavioral biometrics based on smartphone motion sensors are growing in popularity for authentication purposes. In this study, AirSignatureDB is presented: a new publicly accessible dataset of in-air signatures collected from 108 participants under real-world conditions, using 83 different smartphone models across four sessions. This dataset includes genuine samples and skilled forgeries, enabling a comprehensive evaluation of system robustness against realistic attack scenarios. Traditional and deep learning-based methods for in-air signature verification are benchmarked, while analyzing the influence of sensor modality and enrollment strategies. Beyond verification, a first approach to reconstructing the three-dimensional trajectory of in-air signatures from inertial sensor data alone is introduced. Using on-line handwritten signatures as a reference, we demonstrate that the recovery of accurate trajectories is feasible, challenging the long-held assumption that in-air gestures are inherently traceless. Although this approach enables forensic traceability, it also raises critical questions about the privacy boundaries of behavioral biometrics. Our findings underscore the need for a reevaluation of the privacy assumptions surrounding inertial sensor data, as they can reveal user-specific information that had not previously been considered in the design of in-air signature systems.