  An ever-growing body of work has demonstrated the rich information content
available in eye movements for user modelling, e.g. for predicting users'
activities, cognitive processes, or even personality traits. We show that
state-of-the-art classifiers for eye-based user modelling are highly vulnerable
to adversarial examples: small artificial perturbations in gaze input that can
dramatically change a classifier's predictions. We generate these adversarial
examples using the Fast Gradient Sign Method (FGSM) that linearises the
gradient to find suitable perturbations. On the sample task of eye-based
document type recognition we study the success of different adversarial attack
scenarios: with and without knowledge about classifier gradients (white-box vs.
black-box) as well as with and without targeting the attack to a specific
class, In addition, we demonstrate the feasibility of defending against
adversarial attacks by adding adversarial examples to a classifier's training
data.
