Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei Jin†, Yaxin Li†, Han Xu†, Yiqi Wang† …

Deep neural networks are known to be extremely vulnerable to adversarial examples under
white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) …

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …

Skip connections are an essential component of current state-of-the-art deep neural
networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their …

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 …

Deep neural networks have achieved great success in many important remote sensing
tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In …

Achieving transferability of targeted attacks is reputed to be remarkably difficult. The current
state of the art has resorted to resource-intensive solutions that necessitate training model …

Graph neural networks (GNNs) are widely used in many applications. However, their
robustness against adversarial attacks is criticized. Prior studies show that using …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

Adversarial training is an effective defense method to protect classification models against
adversarial attacks. However, one limitation of this approach is that it can require orders of …