Understanding Measures of Uncertainty for Adversarial Example Detection

Understand

Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence.

• But many measures of uncertainty exist, including predictive en- tropy and mutual information, each capturing different types of uncertainty.
• We study these measures, and shed light on why mutual information seems to be effective at the task of adversarial example detection.
• We highlight failure modes for MC dropout, a widely used approach for estimating uncertainty in deep models.