  With more people publishing their personal data online, unauthorized data
usage has become a serious concern. The unlearnable strategies have been
introduced to prevent third parties from training on the data without
permission. They add perturbations to the users' data before publishing, which
aims to make the models trained on the perturbed published dataset invalidated.
These perturbations have been generated for a specific training setting and a
target dataset. However, their unlearnable effects significantly decrease when
used in other training settings and datasets. To tackle this issue, we propose
a novel unlearnable strategy based on Classwise Separability Discriminant
(CSD), which aims to better transfer the unlearnable effects to other training
settings and datasets by enhancing the linear separability. Extensive
experiments demonstrate the transferability of the proposed unlearnable
examples across training settings and datasets.
