We propose a spurious region guided refinement approach for robustness verification of
deep neural networks. Our method starts with applying the DeepPoly abstract domain to …

Deep learning has become a promising programming paradigm in software development,
owing to its surprising performance in solving many challenging tasks. Deep neural …

Verifying and explaining the behavior of neural networks is becoming increasingly
important, especially when they are deployed in safety-critical applications. In this paper, we …

Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles.
Although previous works have studied adversarial robustness in the context of trajectory …

Abstract Machine learning has made remarkable advancements, but confidently utilising
learning-enabled components in safety-critical domains still poses challenges. Among the …

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge
devices, quantization is widely regarded as one promising technique. It reduces the …

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 …

Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable
to small perturbations on the inputs. This has led to safety concerns on applying DNNs to …

Autonomous driving has gained much attention from both industry and academia. Currently,
Deep Neural Networks (DNNs) are widely used for perception and control in autonomous …

This book addresses the safety and security perspective of machine learning, focusing on its
vulnerability to environmental noise and various safety and security attacks. Machine …