  Machine Learning models face increased concerns regarding the storage of
personal user data and adverse impacts of corrupted data like backdoors or
systematic bias. Machine Unlearning can address these by allowing post-hoc
deletion of affected training data from a learned model. Achieving this task
exactly is computationally expensive; consequently, recent works have proposed
inexact unlearning algorithms to solve this approximately as well as evaluation
methods to test the effectiveness of these algorithms.
  In this work, we first outline some necessary criteria for evaluation methods
and show no existing evaluation satisfies them all. Then, we design a stronger
black-box evaluation method called the Interclass Confusion (IC) test which
adversarially manipulates data during training to detect the insufficiency of
unlearning procedures. We also propose two analytically motivated baseline
methods~(EU-k and CF-k) which outperform several popular inexact unlearning
methods. Overall, we demonstrate how adversarial evaluation strategies can help
in analyzing various unlearning phenomena which can guide the development of
stronger unlearning algorithms.
