Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods based on backdooring techniques. However, these methods typically require participation in the model's initial training phase to backdoor the model for later verification, which is inefficient and impractical. In this paper, we propose an efficient verification of erasure method (EVE) for verifying machine unlearning without requiring involvement in the model's initial training process. The core idea is to perturb the unlearning data to ensure the model prediction of the specified samples will change before and after unlearning with perturbed data. The unlearning users can leverage the observation of the changes as a verification signal. Specifically, the perturbations are designed with two key objectives: ensuring the unlearning effect and altering the unlearned model's prediction of target samples. We formalize the perturbation generation as an adversarial optimization problem, solving it by aligning the unlearning gradient with the gradient of boundary change for target samples. We conducted extensive experiments, and the results show that EVE can verify machine unlearning without involving the model's initial training process, unlike backdoor-based methods. Moreover, EVE significantly outperforms state-of-the-art unlearning verification methods, offering significant speedup in efficiency while enhancing verification accuracy. The source code of EVE is released at \uline{https://anonymous.4open.science/r/EVE-C143}, providing a novel tool for verification of machine unlearning.

翻译：验证机器学习遗忘过程是否被正确执行至关重要，但相关研究仍显不足。现有的一些方法基于后门技术提出了遗忘验证方案。然而，这些方法通常需要参与模型的初始训练阶段，以便为后续验证植入后门，这种做法既低效又不切实际。本文提出一种高效擦除验证方法（EVE），用于验证机器学习遗忘过程，且无需介入模型的初始训练阶段。其核心思想是通过扰动待遗忘数据，确保模型对指定样本的预测在基于扰动数据的遗忘前后发生变化。遗忘用户可利用观测到的变化作为验证信号。具体而言，扰动设计需实现两个关键目标：确保遗忘效果，并改变遗忘模型对目标样本的预测。我们将扰动生成形式化为一个对抗性优化问题，通过将遗忘梯度与目标样本边界变化的梯度对齐来求解。我们进行了大量实验，结果表明：与基于后门的方法不同，EVE能够在无需参与模型初始训练过程的情况下验证机器学习遗忘。此外，EVE在验证准确率显著提升的同时，其效率也大幅超越现有最先进的遗忘验证方法，实现了验证速度的显著加快。EVE的源代码已发布于 \uline{https://anonymous.4open.science/r/EVE-C143}，为机器学习遗忘验证提供了一种新颖的工具。