Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
The unauthorized use of personal data in model training has emerged as a growing privacy threat. Unlearnable examples (UEs) address this issue by embedding imperceptible perturbations into benign examples to obstruct feature learning. However, existing studies mainly evaluate UEs under from-scratch training settings, leaving their behavior under the widely adopted pretraining-finetuning (PF) paradigm largely unexplored. In this work, we provide the first systematic investigation of unlearnable examples across diverse training paradigms. Our analysis reveals that loading and freezing pretrained weights significantly weakens the effectiveness of existing UEs methods. We further explain thes...
| Authors | Bo Wang, Jia Ni, Mengnan Zhao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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