  In this work, we examine the impact of Treadmill Assisted Gait Spoofing
(TAGS) on Wearable Sensor-based Gait Authentication (WSGait). We consider more
realistic implementation and deployment scenarios than the previous study,
which focused only on the accelerometer sensor and a fixed set of features.
Specifically, we consider the situations in which the implementation of WSGait
could be using one or more sensors embedded into modern smartphones. Besides,
it could be using different sets of features or different classification
algorithms, or both. Despite the use of a variety of sensors, feature sets
(ranked by mutual information), and six different classification algorithms,
TAGS was able to increase the average False Accept Rate (FAR) from 4% to 26%.
Such a considerable increase in the average FAR, especially under the stringent
implementation and deployment scenarios considered in this study, calls for a
further investigation into the design of evaluations of WSGait before its
deployment for public use.
