13 citations · 17 across the 5 of their papers we have counts for
5 papers
HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
Martin Nocker, David Drexel, Michael Rader +2
Machine learning (ML) algorithms are increasingly important for the success of products and services, especially considering the growing amount and availability of data. This also…
On the Effect of Adversarial Training Against Invariance-based Adversarial Examples
Roland Rauter, Martin Nocker, Florian Merkle +1
Adversarial examples are carefully crafted attack points that are supposed to fool machine learning classifiers. In the last years, the field of adversarial machine learning, espec…
Less is More: The Influence of Pruning on the Explainability of CNNs
Florian Merkle, David Weber, Pascal Schöttle +2
Over the last century, deep learning models have become the state-of-the-art for solving complex computer vision problems. These modern computer vision models have millions of para…
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Maximilian Samsinger, Florian Merkle, Pascal Schöttle +1
Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly propose…
Pruning in the Face of Adversaries
Florian Merkle, Maximilian Samsinger, Pascal Schöttle
The vulnerability of deep neural networks against adversarial examples - inputs with small imperceptible perturbations - has gained a lot of attention in the research community rec…