To analyse local robustness properties of deep neural networks (DNNs), we present a practical
framework from a model learning perspective. Based on black-box model learning with …

Deep neural networks (DNNs) have been applied in safety-critical domains such as self driving
cars, aircraft collision avoidance systems, malware detection, etc. In such scenarios, it is …

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 …

Transfer adversarial attacks raise critical security concerns in real-world, black-box
scenarios. However, the actual progress of this field is difficult to assess due to two common …

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 …

Transferable adversarial examples raise critical security concerns in real-world, black-box
attack scenarios. However, in this work, we identify two main problems in common evaluation …

In recent years, the study of adversarial robustness in object detection systems, particularly
those based on deep neural networks (DNNs), has become a pivotal area of research. …

Stochastic discrete-time systems, ie, discrete-time dynamic systems subject to stochastic
disturbances, are an essential modelling tool for many engineering systems, and reach-avoid …

3D digitizing technology has a variety of applications including reverse engineering, quality
control, virtual reality and digital heritage. Recently, great development in 3D digitizing …

Three-dimensional (3D) model is a major form of cultural heritage documentation. In most
cases, the properties of digital artefacts (eg readability, coverage) are affected by the …