3 citations · 5 across the 29 of their papers we have counts for
9 papers · 1 filter
Machine Unlearning in Low-Dimensional Feature Subspace
Kun Fang, Qinghua Tao, Junxu Liu +5
Machine Unlearning (MU) aims at removing the influence of specific data from a pretrained model while preserving performance on the remaining data. In this work, a novel perspectiv…
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Tairan Huang, Yulin Jin, Junxu Liu +2
Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existi…
Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Zi Liang, Zhiyao Wu, Haoyang Shang +5
Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties an…
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Yaxin Xiao, Qingqing Ye, Li Hu +5
Machine unlearning enables the removal of specific data from ML models to uphold the right to be forgotten. While approximate unlearning algorithms offer efficient alternatives to…
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
Zi Liang, Haibo Hu, Qingqing Ye +2
Low rank adaptation (LoRA) has emerged as a prominent technique for fine-tuning large language models (LLMs) thanks to its superb efficiency gains over previous methods. While exte…
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation
Kun Fang, Qinghua Tao, Mingzhen He +6
Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disparities between OoD and In-Dis…
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
Haoyang Li, Li Bai, Qingqing Ye +4
Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to re…
New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class
Yanyun Wang, Li Liu, Zi Liang +4
Adversarial Training (AT) is one of the most effective methods to enhance the robustness of Deep Neural Networks (DNNs). However, existing AT methods suffer from an inherent accura…
TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
Yanyun Wang, Dehui Du, Haibo Hu +2
Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC). However, neural networks (NNs) are vulnerable to adversarial s…