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

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to
language models have been proposed and successfully implemented. However, this raises …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Diffusion Models are vulnerable to backdoor attacks, where malicious attackers inject
backdoors by poisoning some parts of the training samples during the training stage. This …

We propose a Universal Defence against backdoor attacks based on Clustering and
Centroids Analysis (CCA-UD). The goal of the defence is to reveal whether a Deep Neural …

A backdoored deep hashing model is expected to behave normally on original query
images and return the images with the target label when a specific trigger pattern presents …

Untrusted data used to train a model might have been manipulated to endow the learned
model with hidden properties that the data contributor might later exploit. Data purification …

Recently, backdoor attacks have become an emerging threat to the security of machine
learning models. From the adversary's perspective, the implanted backdoors should be …

Data-poisoning backdoor attacks are serious security threats to machine learning models,
where an adversary can manipulate the training dataset to inject backdoors into models. In …

Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various
backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain …