Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Deep neural network (DNN) classifiers are potent instruments that can be used in various
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …

Abstract Model quantization is widely used to compress and accelerate deep neural
networks. However recent studies have revealed the feasibility of weaponizing model …

The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural Networks (DNNs)
has become a significant concern in recent years. In such attacks, the adversaries …

Adversarial machine learning (AML) studies the adversarial phenomenon of machine
learning, which may make inconsistent or unexpected predictions with humans. Some …

Federated Learning (FL) is commonly used to collaboratively train models with privacy
preservation. In this paper, we found out that the popular diffusion models have introduced a …

As an emerging approach to explore the vulnerability of deep neural networks (DNNs),
backdoor learning has attracted increasing interest in recent years, and many seminal …

Recently, with the development and widespread application of deep neural networks
(DNNs), backdoor attacks have posed new security threats to the training process of DNNs …

Federated Learning (FL) is becoming a popular paradigm for leveraging distributed data
and preserving data privacy. However, due to the distributed characteristic, FL systems are …

With the emergence of advanced backdoor defense methods, the success rate of backdoor
attacks in Deep Neural Networks (DNNs) has dramatically decreased. This situation may …