This paper presents a summary and meta-analysis of the first three iterations of the annual
International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021 …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

This report summarizes the second International Verification of Neural Networks
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …

This report summarizes the 3rd International Verification of Neural Networks Competition
(VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled …

This manuscript presents the updated version of the Neural Network Verification (NNV) tool.
NNV is a formal verification software tool for deep learning models and cyber-physical …

Fairness of machine learning (ML) software has become a major concern in the recent past.
Although recent research on testing and improving fairness have demonstrated impact on …

Recent research has proposed various methods to formally verify neural networks against
minimal input perturbations; this verification task is also known as local robustness …

Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-
world problems. However, like human-written software, DNNs can have bugs and can be …

Recent research has proposed various methods to formally verify neural networks against
minimal input perturbations. This type of verification is referred to as local robustness …

We present CAISAR, an open-source platform under active development for the
characterization of AI systems' robustness and safety. CAISAR provides a unified entry point …