Adversarial Machine Learning Phases of Matter

Understand

We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios.

• We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate examples will cause the classifiers to make incorrect predictions at a notably high confidence level.
• Through the lens of activation maps, we find that some important underlying physical principles and symmetries remain to be adequately captured for classifiers with even near-perfect performance.
• This explains why adversarial perturbations exist for fooling these classifiers.