Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine
  Unlearning
Chongyu Fan Jiancheng Liu Alfred Hero Sijia Liu
The trustworthy machine learning (ML) community is increasingly recognizing
the crucial need for models capable of selectively 'unlearning' data points
after training. This leads to the problem of machine unlearning (MU), aiming to
eliminate the influence of chosen data points on model performance, while still
maintaining the model's utility post-unlearning. Despite various MU methods for
data influence erasure, evaluations have largely focused on random data
forgetting, ignoring the vital inquiry into which subset should be chosen to
truly gauge the authenticity of unlearning performance. To tackle this issue,
we introduce a new evaluative angle for MU from an adversarial viewpoint. We
propose identifying the data subset that presents the most significant
challenge for influence erasure, i.e., pinpointing the worst-case forget set.
Utilizing a bi-level optimization principle, we amplify unlearning challenges
at the upper optimization level to emulate worst-case scenarios, while
simultaneously engaging in standard training and unlearning at the lower level,
achieving a balance between data influence erasure and model utility. Our
proposal offers a worst-case evaluation of MU's resilience and effectiveness.
Through extensive experiments across different datasets (including CIFAR-10,
100, CelebA, Tiny ImageNet, and ImageNet) and models (including both image
classifiers and generative models), we expose critical pros and cons in
existing (approximate) unlearning strategies. Our results illuminate the
complex challenges of MU in practice, guiding the future development of more
accurate and robust unlearning algorithms. The code is available at
https://github.com/OPTML-Group/Unlearn-WorstCase.