  Many deep learning methods have successfully solved complex tasks in computer
vision and speech recognition applications. Nonetheless, the robustness of
these models has been found to be vulnerable to perturbed inputs or adversarial
examples, which are imperceptible to the human eye, but lead the model to
erroneous output decisions. In this study, we adapt and introduce two geometric
metrics, density and coverage, and evaluate their use in detecting adversarial
samples in batches of unseen data. We empirically study these metrics using
MNIST and two real-world biomedical datasets from MedMNIST, subjected to two
different adversarial attacks. Our experiments show promising results for both
metrics to detect adversarial examples. We believe that his work can lay the
ground for further study on these metrics' use in deployed machine learning
systems to monitor for possible attacks by adversarial examples or related
pathologies such as dataset shift.
