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20152021
most citedObfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1.1k citations · 1.9k across the 11 of their papers we have counts for

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Showing 2016Show all

2 papers · 1 filter

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.