源模型泄露了不应泄露的信息：通过对抗优化在域自适应中遗忘零样本迁移
$\oslash$ Source Models Leak What They Shouldn't $\nrightarrow$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization
提出在无源域自适应中通过对抗优化遗忘源域独有类的方法，可复用于视觉模型跨域部署时的隐私保护与知识擦除。
原文依据 We propose a new unlearning method, where an adversarially generated forget class sample is unlearned by the model during the domain adaptation process using a novel rescaled labeling strategy and adversarial optimization.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉模型在卫星图像和医学扫描等不同域之间的日益广泛的自适应，引发了一个新兴的隐私风险：模型可能在目标域中无意保留并泄露敏感的源域特定信息。这为机器遗忘创造了一个有说服力的应用场景，以保护敏感源域数据的隐私。在自适应技术中，无源域自适应（SFDA）迫切需要进行机器遗忘（MU），其中源数据本身受到保护，但自适应过程中暴露的源模型编码了其影响。我们的实验表明，现有的SFDA方法在目标域中对源域独有类别表现出很强的零样本性能，表明它们无意中将关于这些类别的知识泄露到目标域中，即使这些类别并未在目标数据中出现。我们通过提出一种名为SCADA-UL的MU设置来识别并解决这一风险：在域自适应中遗忘源域独有类别。现有的MU方法无法解决这一设置，因为它们并非为处理数据分布偏移而设计。我们提出了一种新的遗忘方法，其中对抗性生成的遗忘类样本在域自适应过程中通过一种新颖的重新缩放标签策略和对抗优化被模型遗忘。我们还将研究扩展到两个变体：该问题设置的持续版本，以及待遗忘的特定源类别可能未知的情况。除了理论解释外，我们全面的实证结果表明，我们的方法在所提出的设置中始终优于基线，同时在基准数据集上实现了重训练级别的遗忘性能。代码可在 https://github.com/D-Arnav/SCADA 获取。
英文原文摘要 1790 字符
The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may inadvertently retain and leak sensitive source-domain specific information in the target domain. This creates a compelling use case for machine unlearning to protect the privacy of sensitive source-domain data. Among adaptation techniques, source-free domain adaptation (SFDA) calls for an urgent need for machine unlearning (MU), where the source data itself is protected, yet the source model exposed during adaptation encodes its influence. Our experiments reveal that existing SFDA methods exhibit strong zero-shot performance on source-exclusive classes in the target domain, indicating they inadvertently leak knowledge of these classes into the target domain, even when they are not represented in the target data. We identify and address this risk by proposing an MU setting called SCADA-UL: Unlearning Source-exclusive ClAsses in Domain Adaptation. Existing MU methods do not address this setting as they are not designed to handle data distribution shifts. We propose a new unlearning method, where an adversarially generated forget class sample is unlearned by the model during the domain adaptation process using a novel rescaled labeling strategy and adversarial optimization.We also extend our study to two variants: a continual version of this problem setting and to one where the specific source classes to be forgotten may be unknown.Alongside theoretical interpretations, our comprehensive empirical results show that our method consistently outperforms baselines in the proposed setting while achieving retraining-level unlearning performance on benchmark datasets. Code is available at https://github.com/D-Arnav/SCADA