With growing demands for privacy protection, security, and legal compliance (e.g., GDPR), machine unlearning has emerged as a critical technique for ensuring the controllability and regulatory alignment of machine learning models. However, a fundamental challenge in this field lies in effectively verifying whether unlearning operations have been successfully and thoroughly executed. Despite a growing body of work on unlearning techniques, verification methodologies remain comparatively underexplored and often fragmented. Existing approaches lack a unified taxonomy and a systematic framework for evaluation. To bridge this gap, this paper presents the first structured survey of machine unlearning verification methods. We propose a taxonomy that organizes current techniques into two principal categories -- behavioral verification and parametric verification -- based on the type of evidence used to assess unlearning fidelity. We examine representative methods within each category, analyze their underlying assumptions, strengths, and limitations, and identify potential vulnerabilities in practical deployment. In closing, we articulate a set of open problems in current verification research, aiming to provide a foundation for developing more robust, efficient, and theoretically grounded unlearning verification mechanisms.
Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation
School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；College of Computer Science & Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学网络空间学院）
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China（交通物联网湖北重点实验室，计算机科学与人工智能学院，武汉理工大学，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）
HKUST（香港科技大学）；University of Cambridge（剑桥大学）；Central South University（中南大学）；Shanghai Jiaotong University（上海交通大学）；SenseTime Research（商汤科技研究院）；Tsinghua University（清华大学）
Decoupling the Class Label and the Target Concept in Machine Unlearning
Hong Kong Baptist University（香港浸会大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能中心）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Tokyo（东京大学）
FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning
University of Science and Technology of China(USTC)（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州研究院）；City University of Hong Kong（香港城市大学）；Tongji University（同济大学）
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols
Department of Artificial Intelligence, Korea University, South Korea（韩国大学人工智能系）；Computer Science Dept. at Courant Institute of Mathemtical Sciences, New York University, United States（纽约大学Courant数学科学研究所计算机科学系）
SAU: Sparsity-Aware Unlearning for LLMs via Gradient Masking and Importance Redistribution
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；University of California San Diego（加州大学圣地亚哥分校）
CYBERCAT (Center for Cybersecurity Research of Catalonia)（加泰罗尼亚网络安全研究中心）；ComSCIAM-Center for Computational Science and Applied Mathematics（计算科学与应用数学中心）；Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili（罗维拉-维尔吉利大学计算机工程与数学系）
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
NewraLab；Beihang University（北京航空航天大学）；GoerTek Alpha Lab（歌尔Alpha实验室）；Skyline University Nigeria（尼日利亚天际线大学）；UESTC（电子科技大学）；Imperial College London（帝国理工学院）
FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs
Digital Trust Centre, Nanyang Technological University, Singapore（南洋理工大学数字信任中心）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）
Efficient Machine Unlearning via Influence Approximation
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（人工智能与数据科学学院，中国科学技术大学）；Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China（安徽省大数据分析与应用重点实验室，计算机科学与技术学院，中国科学技术大学）
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality
School of Computer Science and Technology, Xi’an Jiaotong University, China（西安交通大学计算机科学与技术学院）；MOE KLINNS Lab, Xi’an Jiaotong University, China（西安交通大学MOE KLINNS实验室）；Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, China（陕西省大数据知识工程重点实验室）
Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Manchester（曼彻斯特大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlea…
