19 citations · 21 across the 6 of their papers we have counts for
7 papers
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
Georg Siedel, Weijia Shao, Silvia Vock +1
Robustness is a fundamental property of machine learning classifiers required to achieve safety and reliability. In the field of adversarial robustness of image classifiers, robust…
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
Georg Siedel, Silvia Vock, Andrey Morozov +1
Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore esse…
Model-based Fault Injection Experiments for the Safety Analysis of Exoskeleton System
Tagir Fabarisov, Ilshat Mamaev, Andrey Morozov +1
Model-based fault injection methods are widely used for the evaluation of fault tolerance in safety-critical control systems. In this paper, we introduce a new model-based fault in…
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs
Michael Beyer, Andrey Morozov, Emil Valiev +4
Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification tec…
Iterative Variable Reordering: Taming Huge System Families
Clemens Dubslaff, Andrey Morozov, Christel Baier +1
For the verification of systems using model-checking techniques, symbolic representations based on binary decision diagrams (BDDs) often help to tackle the well-known state-space e…
Reduction Methods on Probabilistic Control-flow Programs for Reliability Analysis
Clemens Dubslaff, Andrey Morozov, Christel Baier +1
Modern safety-critical systems are heterogeneous, complex, and highly dynamic. They require reliability evaluation methods that go beyond the classical static methods such as fault…