Unrestricted adversarial attacks typically manipulate the semantic content of an image (eg,
color or texture) to create adversarial examples that are both effective and photorealistic …

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of
defense methods have been proposed to train adversarially robust DNNs, among which …

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …

Graph deep learning models, such as graph convolutional networks (GCN) achieve
remarkable performance for tasks on graph data. Similar to other types of deep models …

Adversarial examples are perturbed inputs designed to fool machine learning models.
Adversarial training injects such examples into training data to increase robustness. To …

Deep learning (DL) defines a new data-driven programming paradigm that constructs the
internal system logic of a crafted neuron network through a set of training data. We have …

A recent study (Rice et al., 2020) revealed overfitting to be a dominant phenomenon in
adversarially robust training of deep networks, and that appropriate early-stopping of …

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial
examples, there is an urgent need for an effective adversarial attack to identify the …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs)
based vision systems eg, for classification, segmentation and object detection. The …