挑战遗忘：揭示机器遗忘中的最坏情况遗忘集
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
AI总结：
该研究从对抗视角提出机器遗忘的新评估框架，通过双层优化定位最坏情况遗忘集，在多数据集与多模型上的实验揭示了现有遗忘策略的优缺点，为更鲁棒的遗忘算法研发提供指引。
摘要：
可信机器学习（ML）领域日益认识到，模型需要具备在训练后选择性“遗忘”数据点的关键能力。这催生了机器遗忘（MU）问题，其目标是消除选定数据点对模型性能的影响，同时保持模型在遗忘后的效用。尽管已有多种用于擦除数据影响的MU方法，但相关评估大多聚焦于随机数据遗忘，忽略了一个关键问题：应选择哪类数据子集才能真正衡量遗忘性能的真实性。为解决这一问题，我们从对抗性视角提出了一种全新的MU评估角度。我们提出识别对影响擦除构成最大挑战的数据子集，即定位最坏情况遗忘集。该方法基于双层优化原理：在上层优化中放大遗忘难度以模拟最坏情况，同时在下层执行标准训练与遗忘流程，实现数据影响擦除与模型效用之间的平衡。我们的方案为MU的鲁棒性与有效性提供了最坏情况层面的评估。通过在多个数据集（包括CIFAR-10、CIFAR-100、CelebA、Tiny ImageNet和ImageNet）及多种模型（涵盖图像分类器与生成模型）上开展的大量实验，我们揭示了现有（近似）遗忘策略的关键优缺点。研究结果阐明了MU在实际应用中面临的复杂挑战，为未来开发更准确、更鲁棒的遗忘算法提供了指导。相关代码可在https://github.com/OPTML-Group/Unlearn-WorstCase获取。
英文摘要：
The trustworthy machine learning (ML) community is increasingly recognizing the crucial need for models capable of selectively 'unlearning' data points after training. This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on random data forgetting, ignoring the vital inquiry into which subset should be chosen to truly gauge the authenticity of unlearning performance. To tackle this issue, we introduce a new evaluative angle for MU from an adversarial viewpoint. We propose identifying the data subset that presents the most significant challenge for influence erasure, i.e., pinpointing the worst-case forget set. Utilizing a bi-level optimization principle, we amplify unlearning challenges at the upper optimization level to emulate worst-case scenarios, while simultaneously engaging in standard training and unlearning at the lower level, achieving a balance between data influence erasure and model utility. Our proposal offers a worst-case evaluation of MU's resilience and effectiveness. Through extensive experiments across different datasets (including CIFAR-10, 100, CelebA, Tiny ImageNet, and ImageNet) and models (including both image classifiers and generative models), we expose critical pros and cons in existing (approximate) unlearning strategies. Our results illuminate the complex challenges of MU in practice, guiding the future development of more accurate and robust unlearning algorithms. The code is available at https://github.com/OPTML-Group/Unlearn-WorstCase.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
