计算机工程2026，Vol.52Issue(8)：114-125,12.DOI:10.19678/j.issn.1000-3428.0260116
基于平滑优化的鲁棒ID遗忘
Robust ID Unlearning via Smoothness Optimization
摘要
Abstract
Recently,generative models have advanced rapidly and demonstrated remarkable capabilities in image synthesis,artistic creation,and other fields.However,although these models improve generation performance,they introduce significant privacy risks:the private information contained in the training data may be leaked through the generated content.To address this issue,machine unlearning techniques have been proposed to weaken the memorization of specific data by the model,thereby preventing the leakage of private information during generation tasks.Among these,Identity(ID)unlearning methods for face generation aim to prevent a model from generating images with specific identities,thereby protecting personal privacy.However,the existing ID unlearning methods suffer from insufficient robustness.Attackers can exploit a small amount of data from the forgotten identity to fine-tune the model in a limited number of steps,thereby recovering the forgotten identity,a threat known as the relearning attack.Although traditional ID unlearning methods can effectively reduce the similarity between the target identity and generated images,the unlearning effect is easily reversed after a relearning attack,compromising their robustness.To address this problem,a relearning attack method is constructed to expose the robustness deficiency of existing ID unlearning methods.On this basis,a robust ID unlearning method is proposed that enables the model to maintain a stable and effective unlearning performance even under relearning attacks.First,a relearning attack method targeting Generative Adversarial Networks(GAN)is proposed.Building on an already unlearned model,this method performs a limited number of gradient updates using a small amount of data from the forgotten identity,driving the generated images back toward the forgotten identity representation,thereby evaluating the robustness of the ID unlearning methods.Second,a Robust ID Unlearning(RIDU)method based on smoothness optimization is proposed.During the unlearning training process,this method applies random perturbations to the model's weight parameters;therefore,the optimization not only pursues the unlearning effect at the current parameter point but also considers the overall stability within the parameter neighborhood,thus forming a smooth and stable unlearning region.Thus,the unlearning effect no longer relies on a single fragile local optimum,making it difficult for relearning attacks to recover forgotten identities.Moreover,the method employs appropriate loss functions to ensure a balance between the unlearning objective and the generation quality of the model.Experimental results on the CelebAHQ dataset demonstrate that under nonattack conditions,RIDU significantly reduces the similarity between the target identity and generated images,outperforming traditional methods.Under relearning attacks,although the unlearning effect of traditional methods is easily reversed,RIDU maintains strong unlearning capability and effectively suppresses the recovery of the target identity.This method enables the model to forget specific identities while preserving its generation quality with minimal degradation.关键词
机器遗忘/ID遗忘/生成模型/鲁棒性/平滑优化Key words
machine unlearning/ID unlearning/generative models/robustness/smoothness optimization分类
信息技术与安全科学引用本文复制引用
任知非,张家铭,陈超超,吴天星..基于平滑优化的鲁棒ID遗忘[J].计算机工程,2026,52(8):114-125,12.基金项目
国家自然科学基金青年科学基金项目(B类)(62522217). （B类）
