Search Results for author:
Found 8 papers, 8 papers with code
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Errors in test sets are numerous and widespread: we estimate an average of at least 3. 3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set.
On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult.
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples.
Black-box Adversarial Attacks with Limited Queries and Information
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model.
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Neural networks are known to be vulnerable to adversarial examples.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.
Query-Efficient Black-box Adversarial Examples (superceded)
Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes.
Synthesizing Robust Adversarial Examples
We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations.
