24 citations · 59 across the 12 of their papers we have counts for
4 papers · 1 filter
Adversarial Vulnerability Bounds for Gaussian Process Classification
Michael Thomas Smith, Kathrin Grosse, Michael Backes +1
Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threa…
The Limitations of Model Uncertainty in Adversarial Settings
Kathrin Grosse, David Pfaff, Michael Thomas Smith +1
Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification. While an obvious security thr…
Killing four birds with one Gaussian process: the relation between different test-time attacks
Kathrin Grosse, Michael T. Smith, Michael Backes
In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship b…
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models
Kathrin Grosse, David Pfaff, Michael Thomas Smith +1
Machine learning models are vulnerable to Adversarial Examples: minor perturbations to input samples intended to deliberately cause misclassification. Current defenses against adve…