  State-of-the-art convolutional neural networks excel in machine learning
tasks such as face recognition, and object classification but suffer
significantly when adversarial attacks are present. It is crucial that machine
critical systems, where machine learning models are deployed, utilize robust
models to handle a wide range of variability in the real world and malicious
actors that may use adversarial attacks. In this study, we investigate eye
closedness detection to prevent vehicle accidents related to driver
disengagements and driver drowsiness. Specifically, we focus on adversarial
attacks in this application domain, but emphasize that the methodology can be
applied to many other domains. We develop two models to detect eye closedness:
first model on eye images and a second model on face images. We adversarially
attack the models with Projected Gradient Descent, Fast Gradient Sign and
DeepFool methods and report adversarial success rate. We also study the effect
of training data augmentation. Finally, we adversarially train the same models
on perturbed images and report the success rate for the defense against these
attacks. We hope our study sets up the work to prevent potential vehicle
accidents by capturing drivers' face images and alerting them in case driver's
eyes are closed due to drowsiness.
