87 citations · 125 across the 10 of their papers we have counts for
5 papers · 1 filter
Antibody: Strengthening Defense Against Harmful Fine-Tuning for Large Language Models via Attenuating Harmful Gradient Influence
Quoc Minh Nguyen, Trung Le, Jing Wu +2
Fine-tuning-as-a-service introduces a threat to Large Language Models' safety when service providers fine-tune their models on poisoned user-submitted datasets, a process known as…
IMU: Influence-guided Machine Unlearning
Xindi Fan, Jing Wu, Mingyi Zhou +3
Machine Unlearning (MU) aims to selectively erase the influence of specific data points from pretrained models. However, most existing MU methods rely on the retain set to preserve…
Concealing Sensitive Samples against Gradient Leakage in Federated Learning
Jing Wu, Munawar Hayat, Mingyi Zhou +1
Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server. Despite the s…
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Jing Wu, Mingyi Zhou, Ce Zhu +3
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, eve…
A Survey On Universal Adversarial Attack
Chaoning Zhang, Philipp Benz, Chenguo Lin +3
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of uni…