Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving such an attack arises in practice when only a subset of the poisoned data can be identified. This necessitates the development of methods to remove, i.e. unlearn, poison triggers from already trained models with only a subset of the poison data available. The requirements for this task significantly deviate from privacy-focused unlearning where all of the data to be forgotten by the model is known. Previous work has shown that the undiscovered poisoned samples lead to a failure of established unlearning methods, with only one method, Selective Synaptic Dampening (SSD), showing limited success. Even full retraining, after the removal of the identified poison, cannot address this challenge as the undiscovered poison samples lead to a reintroduction of the poison trigger in the model. Our work addresses two key challenges to advance the state of the art in poison unlearning. First, we introduce a novel outlier-resistant method, based on SSD, that significantly improves model protection and unlearning performance. Second, we introduce Poison Trigger Neutralisation (PTN) search, a fast, parallelisable, hyperparameter search that utilises the characteristic "unlearning versus model protection" trade-off to find suitable hyperparameters in settings where the forget set size is unknown and the retain set is contaminated. We benchmark our contributions using ResNet-9 on CIFAR10 and WideResNet-28x10 on CIFAR100. Experimental results show that our method heals 93.72% of poison compared to SSD with 83.41% and full retraining with 40.68%. We achieve this while also lowering the average model accuracy drop caused by unlearning from 5.68% (SSD) to 1.41% (ours).
Poisoning Attacks and Defenses to Federated Unlearning
ShanghaiTech University（上海科技大学）；Xidian University（西安电子科技大学）；North Carolina State University（北卡罗来纳州立大学）；University of North Texas（北德克萨斯大学）；University of Louisville（路易斯维尔大学）
Delta-Influence: Unlearning Poisons via Influence Functions
ShanghaiTech University（上海科技大学）；Tsinghua University（清华大学）；University of Oxford（牛津大学）；Tübingen AI Center, University of Tübingen（图宾根人工智能中心，图宾根大学）；University of Copenhagen（哥本哈根大学）
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chengdu University of Information Technology（成都信息工程大学）；Chongqing Normal University（重庆师范大学）
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
$f$-FUM: Federated Unlearning via min--max and $f$-divergence
Department of Statistics, Universite de Geneve, Geneva, Switzerland（日内瓦大学统计系）；University of Cambridge, Cambridge, United Kingdom（剑桥大学）；The Alan Turing Institute, London, UK（艾伦·图灵研究所）
Machine Unlearning of Traffic State Estimation and Prediction
Department of Civil and Environmental Engineering, University of Washington（土木与环境工程系，华盛顿大学）；Department of Mathematics, University of Washington（数学系，华盛顿大学）；(Jeff) Ban（Ban）
University of Technology Sydney（新南威尔士大学）；City University of Macau（澳门城市大学）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61部门）；Torrens University Australia（澳大利亚托伦斯大学）
Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；Hanoi University of Science and Technology（河内科技大学）；Griffith University（格里菲斯大学）；Humboldt-Universitat zu Berlin（柏林洪堡大学）
