Deep neural networks are widely used for nonlinear function approximation, with
applications ranging from computer vision to control. Although these networks involve the …

We review state-of-the-art formal methods applied to the emerging field of the verification of
machine learning systems. Formal methods can provide rigorous correctness guarantees on …

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 …

The use of counterfactual explanations (CFXs) is an increasingly popular explanation
strategy for machine learning models. However, recent studies have shown that these …

While abstraction is a classic tool of verification to scale it up, it is not used very often for
verifying neural networks. However, it can help with the still open task of scaling existing …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

The paper extends the recent star reachability method to verify the robustness of recurrent
neural networks (RNNs) for use in safety-critical applications. RNNs are a popular machine …

This paper proposes a novel, abstraction-based, certified training method for robust image
classifiers. Via abstraction, all perturbed images are mapped into intervals before feeding …