Trustworthy Artificial Intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, Federated Learning (FL) …

Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

The widespread deployment of Deep Neural Networks (DNNs) for 3D point cloud
processing contrasts sharply with their vulnerability to security breaches, particularly …

As ML models become increasingly complex and integral to high-stakes domains such as
finance and healthcare, they also become more susceptible to sophisticated adversarial …

Deep neural networks face persistent challenges in defending against backdoor attacks,
leading to an ongoing battle between attacks and defenses. While existing backdoor …

Backdoor attacks have attracted wide attention from academia and industry due to their
great security threat to deep neural networks (DNN). Most of the existing methods propose to …

Backdoor attacks change a small portion of training data by introducing hand-crafted triggers
and rewiring the corresponding labels towards a desired target class. Training on such data …

Current backdoor defense methods are evaluated against a single attack at a time. This is
unrealistic, as powerful machine learning systems are trained on large datasets scraped …

Graph Neural Networks (GNNs) have significantly advanced various downstream graph-
relevant tasks, encompassing recommender systems, molecular structure prediction, social …

Machine learning (ML) models trained on data from potentially untrusted sources are
vulnerable to poisoning. A small, maliciously crafted subset of the training inputs can cause …