AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916892600434688 |
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| author | Robledo-Moreno, Marta Vera-Rodriguez, Ruben Tolosana, Ruben Ortega-Garcia, Javier Huergo, Andres Fierrez, Julian |
| author_facet | Robledo-Moreno, Marta Vera-Rodriguez, Ruben Tolosana, Ruben Ortega-Garcia, Javier Huergo, Andres Fierrez, Julian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08502 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns Robledo-Moreno, Marta Vera-Rodriguez, Ruben Tolosana, Ruben Ortega-Garcia, Javier Huergo, Andres Fierrez, Julian Human-Computer Interaction 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. |
| title | AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.08502 |