activity
20212026
most citedRevisiting Differential Verification: Equivalence Verification with Confidence

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.SE2026

CANcept: Model-based CAN Traffic Generation and Manipulation

Lino Wertz, Junes Sheikhi, Florian Fehrle +5

Testing timing-related safety properties of Controller Area Network (CAN)-based software requires precise control over transmitted data and communication timing. Existing open-sour…

cs.LG2026

Shifting-based Optimizable Linear Relaxations for General Activation Functions

Philipp Kern, László Antal, Erika Ábráham +1

The use of neural networks (NNs) is rapidly increasing, including in safety- and security-critical domains. To provide formal guarantees about NN behavior, many verification method…

cs.CR2026

Encrypted Neural Networks without Overflows

Philipp Kern, Lorenzo Rovida, Samuel Teuber +3

The popular Cheon-Kim-Kim-Song (CKKS) scheme enables efficient private inference in neural networks by evaluating them on encrypted data. Since CKKS only supports addition, multipl…

cs.LG2024★ 2 cited

Revisiting Differential Verification: Equivalence Verification with Confidence

Samuel Teuber, Philipp Kern, Marvin Janzen +1

When validated neural networks (NNs) are pruned (and retrained) before deployment, it is desirable to prove that the new NN behaves equivalently to the (original) reference NN. To…

cs.LG2022

Optimized Symbolic Interval Propagation for Neural Network Verification

Philipp Kern, Marko Kleine Büning, Carsten Sinz

Neural networks are increasingly applied in safety critical domains, their verification thus is gaining importance. A large class of recent algorithms for proving input-output rela…

cs.LG2021

Geometric Path Enumeration for Equivalence Verification of Neural Networks

Samuel Teuber, Marko Kleine Büning, Philipp Kern +1

As neural networks (NNs) are increasingly introduced into safety-critical domains, there is a growing need to formally verify NNs before deployment. In this work we focus on the fo…