Title: Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: unlearnable examples, data protection, linear model, shortcut, linearity

Abstract: Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this issue, unlearnable examples introduce imperceptible perturbations into data, preventing models from learning effectively. However, existing methods typically rely on deep neural networks as surrogate models for perturbation generation, resulting in significant computational costs. In this work, we propose Perturbation-Induced Linearization (PIL), a computationally efficient yet effective method that generates perturbations using only linear surrogate models. PIL achieves comparable or better performance than existing surrogate-based methods while reducing computational time dramatically. We further reveal a key mechanism underlying unlearnable examples: inducing linearization to deep models, which explains why PIL can achieve competitive results in a very short training time. Beyond this, we provide an analysis about the limitation of unlearnable examples under percentage-based partial perturbation. Our work not only provides a practical approach for data protection but also offers insights into what makes unlearnable examples effective.

TL;DR: We provide a practical approach for data protection, and offer novel insights into what makes an unlearnable example effective.