364 citations · 942 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse +4
With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query i…
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…