1.1k citations · 1.8k across the 5 of their papers we have counts for
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cs.CR2019★ 5 cited
Stateful Detection of Black-Box Adversarial Attacks
Steven Chen, Nicholas Carlini, David Wagner
The problem of adversarial examples, evasion attacks on machine learning classifiers, has proven extremely difficult to solve. This is true even when, as is the case in many practi…
cs.CR2016
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini, David Wagner
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input and any…
cs.CR2016★ 238 cited
Defensive Distillation is Not Robust to Adversarial Examples
Nicholas Carlini, David Wagner
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.