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 …

Recent research findings suggest that machine learning models are highly susceptible to
backdoor poisoning attacks. Backdoor poisoning attacks can be easily executed and …

Studies on backdoor attacks in recent years suggest that an adversary can compromise the
integrity of a deep neural network (DNN) by manipulating a small set of training samples …

Multimodal Large Language Models (MLLMs) have emerged as a central focus in both
industry and academia, but often suffer from biases introduced by visual and language …

Deep learning, as an important branch of machine learning, has been widely applied in
computer vision, natural language processing, speech recognition, and more. However …

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 …

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 …

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 …