Defensive Distillation is Not Robust to Adversarial Examples

Abstract

We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.

Cited by in corpus (2)

• Assessing Threat of Adversarial Examples on Deep Neural Networks
• Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks