  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.
