To augment or not to augment? Data augmentation in user identification based on motion sensors

TL;DR

This study investigates the effectiveness of various data augmentation techniques for user identification using motion sensor data, revealing that augmentation may not always improve accuracy due to the sensitivity of signal patterns.

Contribution

The paper evaluates multiple data augmentation methods for motion sensor-based user identification and provides insights into when augmentation is beneficial or not.

Findings

Data augmentation often does not improve accuracy in motion sensor user identification.

Certain transformations can distort signal patterns critical for user discrimination.

Augmentation's effectiveness depends on the nature of motion sensor data and the transformations applied.

Abstract

Nowadays, commonly-used authentication systems for mobile device users, e.g. password checking, face recognition or fingerprint scanning, are susceptible to various kinds of attacks. In order to prevent some of the possible attacks, these explicit authentication systems can be enhanced by considering a two-factor authentication scheme, in which the second factor is an implicit authentication system based on analyzing motion sensor data captured by accelerometers or gyroscopes. In order to avoid any additional burdens to the user, the registration process of the implicit authentication system must be performed quickly, i.e. the number of data samples collected from the user is typically small. In the context of designing a machine learning model for implicit user authentication based on motion signals, data augmentation can play an important role. In this paper, we study several data augmentation techniques in the quest of finding useful augmentation methods for motion sensor data. We propose a set of four research questions related to data augmentation in the context of few-shot user identification based on motion sensor signals. We conduct experiments on a benchmark data set, using two deep learning architectures, convolutional neural networks and Long Short-Term Memory networks, showing which and when data augmentation methods bring accuracy improvements. Interestingly, we find that data augmentation is not very helpful, most likely because the signal patterns useful to discriminate users are too sensitive to the transformations brought by certain data augmentation techniques. This result is somewhat contradictory to the common belief that data augmentation is expected to increase the accuracy of machine learning models.