Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems,
and distributed control systems (DCSs) are fundamental components of critical infrastructure …

Data augmentation is a simple yet effective way to improve the robustness of deep neural
networks (DNNs). Diversity and hardness are two complementary dimensions of data …

The adversarial vulnerability of deep neural networks has attracted significant attention in
machine learning. As causal reasoning has an instinct for modelling distribution change, it is …

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by
slightly perturbing the features of correctly labeled training examples. By formalizing this …

Various approaches have been proposed for out-of-distribution (OOD) detection by
augmenting models, input examples, training set, and optimization objectives. Deviating …

Black-box attacks can generate adversarial examples without accessing the parameters of
target model, largely exacerbating the threats of deployed deep neural networks (DNNs) …

Adversarial detection aims to determine whether a given sample is an adversarial one
based on the discrepancy between natural and adversarial distributions. Unfortunately …

Reweighting adversarial data during training has been recently shown to improve
adversarial robustness, where data closer to the current decision boundaries are regarded …

Recent studies have investigated how to achieve robustness for unsupervised domain
adaptation (UDA). While most efforts focus on adversarial robustness, ie how the model …