Vision transformers (ViTs) have been successfully deployed in a variety of computer vision
tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local …

Deep neural networks have achieved unprecedented success on diverse vision tasks.
However, they are vulnerable to adversarial noise that is imperceptible to humans. This …

Graph neural networks (GNNs) have recently achieved remarkable success on a variety of
graph-related tasks, while such success relies heavily on a given graph structure that may …

Graph neural networks (GNNs) have been playing important roles in various graph-related
tasks. However, most existing GNNs are based on the assumption of homophily, so they …

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct
graphs, aiming to learn distinctive news embeddings for classification. Since the …

Recent studies have shown that Deep Neural Networks (DNNs) are easily deceived by
adversarial examples, revealing their serious vulnerability. Due to the transferability …

Deep Graph Learning (DGL) has emerged as a crucial technique across various domains.
However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility …

Recommender systems are vulnerable to injective attacks, which inject limited fake users
into the platforms to manipulate the exposure of target items to all users. In this work, we …

Abstract Language models are known to be vulnerable to textual adversarial attacks, which
add humanimperceptible perturbations to the input to mislead DNNs. It is thus imperative to …

As the study of graph neural networks becomes more intensive and comprehensive, their
robustness and security have received great research interest. The existing global attack …