11 citations · 35 across the 19 of their papers we have counts for
7 papers · 1 filter
BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection
Yihan Wang, Yiwei Lu, Xiao-Shan Gao +2
Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models fr…
Machine Unlearning Fails to Remove Data Poisoning Attacks
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu +3
We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion request…
MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
Yihan Wang, Yiwei Lu, Guojun Zhang +4
Machine unlearning offers effective solutions for revoking the influence of specific training data on pre-trained model parameters. While existing approaches address unlearning for…
Disguised Copyright Infringement of Latent Diffusion Models
Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu +2
Copyright infringement may occur when a generative model produces samples substantially similar to some copyrighted data that it had access to during the training phase. The notion…
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
Yiwei Lu, Yaoliang Yu, Xinlin Li +1
In neural network binarization, BinaryConnect (BC) and its variants are considered the standard. These methods apply the sign function in their forward pass and their respective gr…
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors
Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath +1
Machine learning models have achieved great success in supervised learning tasks for end-to-end training, which requires a large amount of labeled data that is not always feasible.…