Machine Unlearning is rising as a new field, driven by the pressing necessity
of ensuring privacy in modern artificial intelligence models. This technique
primarily aims to eradicate any residual influence of a specific subset of data
from the knowledge acquired by a neural model during its training. This work
introduces a novel unlearning algorithm, denoted as Distance-based Unlearning
via Centroid Kinematics (DUCK), which employs metric learning to guide the
removal of samples matching the nearest incorrect centroid in the embedding
space. Evaluation of the algorithm's performance is conducted across various
benchmark datasets in two distinct scenarios, class removal, and homogeneous
sampling removal, obtaining state-of-the-art performance. We introduce a novel
metric, called Adaptive Unlearning Score (AUS), encompassing not only the
efficacy of the unlearning process in forgetting target data but also
quantifying the performance loss relative to the original model. Moreover, we
propose a novel membership inference attack to assess the algorithm's capacity
to erase previously acquired knowledge, designed to be adaptable to future
methodologies.

机器去学习是一个新兴领域，通过对现代人工智能模型的隐私保护需求的迫切性推动。这项技术主要旨在从神经模型的训练中消除特定数据子集的任何残留影响。该研究介绍了一种新颖的去学习算法，称为基于距离的质心运动的去学习（DUCK），它采用度量学习来引导嵌入空间中与最近的错误质心相匹配的样本的移除。通过在两个不同的场景下对多个基准数据集进行算法性能评估，包括类别移除和同质采样移除，获得了最先进的性能。我们引入了一种称为自适应去学习分数（AUS）的新指标，不仅涵盖了对目标数据遗忘的去学习过程的功效，还量化了相对于原始模型的性能损失。此外，我们提出了一种新颖的成员推理攻击方法，以评估该算法清除先前获取的知识的能力，并设计成适应未来的方法论。