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.

翻译：人工智能的快速发展，加上其前所未有的公众普及程度以及大型神经网络记忆训练数据的倾向性，引发了重大的数据隐私问题。为应对这些挑战，机器遗忘技术应运而生，成为选择性移除已训练模型中特定训练数据点影响的关键手段。在本文中，我们从持续学习的视角研究机器遗忘问题。针对一个已训练模型和需被遗忘的训练数据子集（即"遗忘集"），我们提出一个名为CovarNav的三步流程以促进遗忘过程。首先，利用模型反演攻击推导模型训练数据的代理表示；其次，通过选择偏离真实标注的最大概率类别对遗忘集进行错误标注；最后，采用梯度投影方法最小化修改后遗忘集上的交叉熵损失（即学习该集合的错误标签），同时防止对反演样本的遗忘。我们在CIFAR-10和Vggface2数据集上对CovarNav进行了严格评估，将结果与该领域最新基准方法对比，验证了所提方法的有效性。