  The rapid progress of AI, combined with its unprecedented public adoption and
the propensity of large neural networks to memorize training data, has given
rise to significant data privacy concerns. To address these concerns, machine
unlearning has emerged as an essential technique to selectively remove the
influence of specific training data points on trained models. In this paper, we
approach the machine unlearning problem through the lens of continual learning.
Given a trained model and a subset of training data designated to be forgotten
(i.e., the "forget set"), we introduce a three-step process, named CovarNav, to
facilitate this forgetting. Firstly, we derive a proxy for the model's training
data using a model inversion attack. Secondly, we mislabel the forget set by
selecting the most probable class that deviates from the actual ground truth.
Lastly, we deploy a gradient projection method to minimize the cross-entropy
loss on the modified forget set (i.e., learn incorrect labels for this set)
while preventing forgetting of the inverted samples. We rigorously evaluate
CovarNav on the CIFAR-10 and Vggface2 datasets, comparing our results with
recent benchmarks in the field and demonstrating the efficacy of our proposed
approach.
