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03/26/2021
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
We algorithmically identify label errors in the test sets of 10 of the m...
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02/18/2019
On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to...
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07/26/2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
We evaluate the robustness of Adversarial Logit Pairing, a recently prop...
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04/23/2018
Black-box Adversarial Attacks with Limited Queries and Information
Current neural network-based classifiers are susceptible to adversarial ...
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04/10/2018
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Neural networks are known to be vulnerable to adversarial examples. In t...
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02/01/2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
We identify obfuscated gradients as a phenomenon that leads to a false s...
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12/19/2017
Query-Efficient Black-box Adversarial Examples
Current neural network-based image classifiers are susceptible to advers...
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07/24/2017