Voice Recognition Systems (VRSs) employ deep learning for speech recognition and
speaker recognition. They have been widely deployed in various real-world applications …

Federated graph learning (FedGL) is an emerging federated learning (FL) framework that
extends FL to learn graph data from diverse sources without accessing the data. FL for non …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Vertical split learning is a new paradigm of federated learning for participants with vertically
partitioned data. In this paper, we make the first attempt to explore the possibility of backdoor …

Recent works have revealed the vulnerability of deep neural networks to backdoor attacks,
where a backdoored model orchestrates targeted or untargeted misclassification when …

Speech recognition systems driven by Deep Neural Networks (DNNs) have revolutionized
human-computer interaction through voice interfaces, which significantly facilitate our daily …

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where a backdoored
model behaves normally with clean inputs but exhibits attacker-specified behaviors upon the …

Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for image pre-
training. In contrast to previous dominating self-supervised methods, ie, contrastive learning …

Backdoor attacks have been widely studied for image classification tasks, but rarely
investigated for video recognition tasks. In this paper, we explore the possibility of physically …

Despite the tremendous success of deep neural networks (DNNs) across various fields, their
susceptibility to potential backdoor attacks seriously threatens their application security …