Evaluating Robustness of Learning-Enabled Medical Cyber-Physical Systems with Naturally Adversarial Datasets

Medical cyber-physical systems (MCPS) are increasingly adopting learning-enabled components (LECs) to enhance their decision-making capabilities. Due to the safety-critical nature of MCPS, these systems must maintain high performance on both expected and unexpected input data. Therefore, ensuring the robustness of LE-MCPS is crucial for their successful deployment. Existing research predominantly focuses on robustness to synthetic adversarial examples , crafted by adding imperceptible perturbations to clean input data. However, these synthetic adversarial examples do not accurately reflect the most challenging real-world scenarios, especially in the context of healthcare data. Consequently, robustness to synthetic adversarial examples may not necessarily translate to robustness against naturally occurring adversarial examples . We propose a method to evaluate the robustness of LE-MCPS to natural adversarial examples. The method curates naturally adversarial datasets leveraging probabilistic labels obtained from automated weakly supervised labeling which combines noisy and cheap-to-obtain labeling heuristics. Based on these labels, the method adversarially orders the input data and uses this ordering to construct a sequence of increasingly adversarial datasets for assessing robustness. Our evaluation on six MCPS case studies and two non-medical case studies demonstrates (1) the efficacy and statistical validity of our approach to generating naturally adversarial datasets and (2) the utility of our robustness evaluation in classifying robust and non-robust LE-MCPS.