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.

翻译：随着隐私保护和法规遵从问题的日益关注，机器遗忘概念逐渐兴起，其目标是有选择地从训练模型中遗忘或擦除特定学习信息。为应对这一关键需求，我们提出了一种名为“攻击与重置遗忘学习”（ARU）的新方法。该算法利用精心设计的对抗噪声生成参数掩码，有效重置部分参数使其不可学习。ARU在两个人脸机器遗忘基准数据集MUFAC和MUCAC上超越了当前最先进的结果。具体而言，我们展示了攻击与掩码步骤，策略性地过滤并重初始化偏向遗忘集的网络参数。我们的工作通过利用对抗噪声构建掩码，在通过参数重初始化使数据对深度学习模型不可利用方面取得了重要进展。