25 citations · 27 across the 4 of their papers we have counts for
4 papers · 1 filter
A Framework for Verification of Wasserstein Adversarial Robustness
Tobias Wegel, Felix Assion, David Mickisch +1
Machine learning image classifiers are susceptible to adversarial and corruption perturbations. Adding imperceptible noise to images can lead to severe misclassifications of the ma…
Risk Assessment for Machine Learning Models
Paul Schwerdtner, Florens Greßner, Nikhil Kapoor +5
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definitio…
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study
David Mickisch, Felix Assion, Florens Greßner +2
Despite achieving remarkable performance on many image classification tasks, state-of-the-art machine learning (ML) classifiers remain vulnerable to small input perturbations. Espe…
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
Felix Assion, Peter Schlicht, Florens Greßner +4
Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challen…