activity
20192022
most citedAdversarial attacks hidden in plain sight

12 citations · 20 across the 7 of their papers we have counts for

collaborators

9 papers

cs.LG2022★ 4 cited

Explainable Artificial Intelligence for Improved Modeling of Processes

Riza Velioglu, Jan Philip Göpfert, André Artelt +1

In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowled…

cs.HC2020

Intuitiveness in Active Teaching

Jan Philip Göpfert, Ulrike Kuhl, Lukas Hindemith +2

While Machine learning gives rise to astonishing results in automated systems, it is usually at the cost of large data requirements. This makes many successful algorithms from mach…

cs.RO2020★ 2 cited

Why robots should be technical: Correcting mental models through technical architecture concepts

Lukas Hindemith, Anna-Lisa Vollmer, Jan Phillip Göpfert +2

Research in social robotics is commonly focused on designing robots that imitate human behavior. While this might increase a user's satisfaction and acceptance of robots at first g…

cs.LG2020

Interpretable Locally Adaptive Nearest Neighbors

Jan Philip Göpfert, Heiko Wersing, Barbara Hammer

When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this…

cs.LG2020

Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation

Dominik Stallmann, Jan P. Göpfert, Julian Schmitz +2

Motivation: Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing n…

cs.LG2020

How to compare adversarial robustness of classifiers from a global perspective

Niklas Risse, Christina Göpfert, Jan Philip Göpfert

Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine lear…

cs.LG2019★ 2 cited

Recovering Localized Adversarial Attacks

Jan Philip Göpfert, Heiko Wersing, Barbara Hammer

Deep convolutional neural networks have achieved great successes over recent years, particularly in the domain of computer vision. They are fast, convenient, and -- thanks to matur…

cs.LG2019

Adversarial Robustness Curves

Christina Göpfert, Jan Philip Göpfert, Barbara Hammer

The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems. This uncertainty…

cs.LG2019★ 12 cited

Adversarial attacks hidden in plain sight

Jan Philip Göpfert, André Artelt, Heiko Wersing +1

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples…