  With growing concerns surrounding privacy and regulatory compliance, the
concept of machine unlearning has gained prominence, aiming to selectively
forget or erase specific learned information from a trained model. In response
to this critical need, we introduce a novel approach called Attack-and-Reset
for Unlearning (ARU). This algorithm leverages meticulously crafted adversarial
noise to generate a parameter mask, effectively resetting certain parameters
and rendering them unlearnable. ARU outperforms current state-of-the-art
results on two facial machine-unlearning benchmark datasets, MUFAC and MUCAC.
In particular, we present the steps involved in attacking and masking that
strategically filter and re-initialize network parameters biased towards the
forget set. Our work represents a significant advancement in rendering data
unexploitable to deep learning models through parameter re-initialization,
achieved by harnessing adversarial noise to craft a mask.
