Universal Adversarial Perturbations in Epileptic Seizure Detection
Amir Aminifar · 2020
Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical and health applications and focus on the epileptic seizure detection problem. We formulate an optimization problem for computing universal adversarial perturbations and show that such universal perturbations may be used to declare the majority of seizure samples as non-seizure, i.e., to fool the classification algorithm, while being imperceptible to the medical expert eye.