ScrubRef-Rep: Representation-aware class-level machine unlearning for enhanced privacy

Data privacy mandates necessitate efficient machine unlearning protocols capable of excising specific training data without the prohibitive cost of full retraining. We introduce ScrubRef-Rep , a fast, data-free class unlearning framework. Moving beyond superficial logit manipulation, our approach jointly updates a localized deep-feature tail and the classifier head. It synthesizes proxy distributions for the forgotten class via generative gradient ascent, combining this with masked knowledge distillation and a novel representation-aware objective. Evaluated on CIFAR-10, CIFAR-100, and ImageWoof-320 against state-of-the-art baselines (UNSIR, Bad Teacher, SCRUB, SSD), ScrubRef-Rep consistently achieves an absolute 0.00% forgotten-class accuracy while maintaining robust retained-data utility (89.18%, 70.70%, and 92.30%). Furthermore, it ensures strong empirical privacy preservation , driving membership inference attack (MIA) ROC-AUCs to near-random chance (0.505, 0.508, and 0.527) with up to a 2 2 . 7 × speed-up over full retraining. Crucially, representation audits confirm that our framework achieves near-complete erasure of the target concept in the latent space, with only negligible structural traces remaining . This explicit delineation between robust empirical privacy and theoretical absolute erasure establishes ScrubRef-Rep as a highly practical, transparent, and representation-aware unlearning solution.