Adversarial training has been widely acknowledged as the most effective method to improve
the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs) …

Abstract Adversarial Robustness Distillation (ARD) aims to transfer the robustness of large
teacher models to small student models, facilitating the attainment of robust performance on …

Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial
transferability: AEs generated for the source model can mislead another (target) model's …

Machine learning-based forecasting models are commonly used in Intelligent Transportation
Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the …

There is a soaring interest in the news recommendation research scenario due to the
information overload. To accurately capture users' interests, we propose to model multi …

This paper provides a unified view to explain different adversarial attacks and defense
methods, ie the view of multi-order interactions between input variables of DNNs. Based on …