Deep neural networks are vulnerable to adversarial examples, which are well-designed
examples aiming to cause models to produce wrong outputs with high confidence. Although …

Convolutional neural networks (CNNs) have achieved beyond human-level accuracy in the
image classification task and are widely deployed in real-world environments. However …

Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks.
Adversarial training (AT) is, so far, the only method that can guarantee the robustness of …

Adversarial robustness of machine learning models has attracted considerable attention
over recent years. Adversarial attacks undermine the reliability of and trust in machine …

Outlier detection means to characterize the distribution of inliers exactly enough such that
outliers stand out. A natural and interpretable model is an enclosing ball that includes non …

Outlier detection means to characterize the distribution of inliers exactly enough such that
outliers stand out. A natural and inter-pretable model is an enclosing ball that includes non …

Anomaly detection within manufacturing processes is critical for ensuring product quality
and customer satisfaction. In this study, we explore the possibility of employing an efficient …

Robustness of neural networks has recently attracted a great amount of interest. The many
investigations in this area lack a precise common foundation of robustness concepts …