2 citations · 2 across the 2 of their papers we have counts for
8 papers · 1 filter
Towards more Practical Threat Models in Artificial Intelligence Security
Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold +1
Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and securi…
Adversarial Examples and Metrics
Nico Döttling, Kathrin Grosse, Michael Backes +1
Adversarial examples are a type of attack on machine learning (ML) systems which cause misclassification of inputs. Achieving robustness against adversarial examples is crucial to…
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…
On the security relevance of weights in deep learning
Kathrin Grosse, Thomas A. Trost, Marius Mosbach +2
Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed. We show that the threat is broader: A task-independent permutation on the ini…
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…