Deep neural networks are increasingly being used as controllers for safety-critical systems.
Because neural networks are opaque, certifying their correctness is a significant challenge …

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 …

Neural networks provide quick approximations to complex functions, and have been
increasingly used in perception as well as control tasks. For use in mission-critical and …

Neural networks have had discernible achievements in a wide range of applications. The
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …

We propose a novel verification method for high-dimensional feed-forward neural networks
governed by ReLU, Sigmoid and Tanh activation functions. We show that the method is …

The use of neural networks in safety-critical computer vision systems calls for their
robustness certification against natural geometric transformations (eg, rotation, scaling) …

Robustness verification that aims to formally certify the prediction behavior of neural
networks has become an important tool for understanding model behavior and obtaining …

With the rapid growth of machine learning, deep neural networks (DNNs) are now being
used in numerous domains. Unfortunately, DNNs are “black-boxes”, and cannot be …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the
safety and reliability of these systems, extensive testings are being conducted before their …