Detection and Defense of Unlearnable Examples
Yifan Zhu Lijia Yu Xiao-Shan Gao
Privacy preserving has become increasingly critical with the emergence of
social media. Unlearnable examples have been proposed to avoid leaking personal
information on the Internet by degrading generalization abilities of deep
learning models. However, our study reveals that unlearnable examples are
easily detectable. We provide theoretical results on linear separability of
certain unlearnable poisoned dataset and simple network based detection methods
that can identify all existing unlearnable examples, as demonstrated by
extensive experiments. Detectability of unlearnable examples with simple
networks motivates us to design a novel defense method. We propose using
stronger data augmentations coupled with adversarial noises generated by simple
networks, to degrade the detectability and thus provide effective defense
against unlearnable examples with a lower cost. Adversarial training with large
budgets is a widely-used defense method on unlearnable examples. We establish
quantitative criteria between the poison and adversarial budgets which
determine the existence of robust unlearnable examples or the failure of the
adversarial defense.