Defensive Distillation is Not Robust to Adversarial Examples

This is an incremental result that challenges a proposed defense for machine learning security.

The paper tackled the problem of adversarial robustness in neural networks by evaluating defensive distillation, finding that it offers no more resistance to targeted misclassification attacks than unprotected networks.