112 citations
- Technical University of MunichDE41 papers
- Blekinge Institute of TechnologySE26 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
- Siemens (Germany)DE7 papers
- Munich University of Applied SciencesDE4 papers
- Universidade Federal da ParaíbaBR4 papers
- Universität HamburgDE4 papers
- VerimagFR4 papers
- LMU KlinikumDE3 papers
- Microsoft Research (United Kingdom)GB3 papers
- Microsoft (United States)US3 papers
- University of StuttgartDE3 papers
7 papers · 1 filter
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
Thomas Brunner, Frederik Diehl, Michael Truong Le +1
We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most…
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Chih-Hong Cheng, Chung-Hao Huang, Georg Nührenberg
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolb…
A general proof certification framework for modal logic
Tomer Libal, Marco Volpe
One of the main issues in proof certification is that different theorem provers, even when designed for the same logic, tend to use different proof formalisms and produce outputs i…
Runtime Monitoring Neuron Activation Patterns
Chih-Hong Cheng, Georg Nührenberg, Hirotoshi Yasuoka
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runt…
ML + FV = ? A Survey on the Application of Machine Learning to Formal Verification
Moussa Amrani, Levi Lúcio, Adrien Bibal
Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on dis…
Towards Dependability Metrics for Neural Networks
Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang +2
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for…