  Unlearning techniques are proposed to prevent third parties from exploiting
unauthorized data, which generate unlearnable samples by adding imperceptible
perturbations to data for public publishing. These unlearnable samples
effectively misguide model training to learn perturbation features but ignore
image semantic features. We make the in-depth analysis and observe that models
can learn both image features and perturbation features of unlearnable samples
at an early stage, but rapidly go to the overfitting stage since the shallow
layers tend to overfit on perturbation features and make models fall into
overfitting quickly. Based on the observations, we propose Progressive Staged
Training to effectively prevent models from overfitting in learning
perturbation features. We evaluated our method on multiple model architectures
over diverse datasets, e.g., CIFAR-10, CIFAR-100, and ImageNet-mini. Our method
circumvents the unlearnability of all state-of-the-art methods in the
literature and provides a reliable baseline for further evaluation of
unlearnable techniques.
