Minimal Adversarial Perturbations in Mobile Health Applications: The Epileptic Brain Activity Case Study

Abstract

Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. 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 demonstrate the power of such adversarial attacks based on a real-world epileptic seizure detection problem. We identify the minimum perturbation required by the adversaries to declare a seizure (ictal) sample as non-seizure (inter-ictal) in emergency situations, i.e., minimal adversarial perturbation to fool the classification algorithm.