:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhao, Mengnan, Zhang, Lihe, Yin, Baocai
Format:	Preprint
Published:	2026
Subjects:	Cryptography and Security
Online Access:	https://arxiv.org/abs/2606.01658
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Catastrophic Overfitting: A Potential Blessing in Disguise
by: Zhao, Mengnan, et al.
Published: (2024)

Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
by: Zhang, Yimeng, et al.
Published: (2024)

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
by: Zhao, Mengnan, et al.
Published: (2026)

Rethinking Robust Adversarial Concept Erasure in Diffusion Models
by: Yin, Qinghong, et al.
Published: (2025)

Injection, Attack and Erasure: Revocable Backdoor Attacks via Machine Unlearning
by: Song, Baogang, et al.
Published: (2025)

Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts
by: Gao, Hongcheng, et al.
Published: (2024)

Separable Multi-Concept Erasure from Diffusion Models
by: Zhao, Mengnan, et al.
Published: (2024)

Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
by: Huang, Yangsibo, et al.
Published: (2024)

AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors
by: Zhao, Mengnan, et al.
Published: (2024)

Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
by: Hu, Hongsheng, et al.
Published: (2024)

VideoEraser: Concept Erasure in Text-to-Video Diffusion Models
by: Xu, Naen, et al.
Published: (2025)

Neighbor-Aware Localized Concept Erasure in Text-to-Image Diffusion Models
by: Shi, Zhuan, et al.
Published: (2026)

Don't Forget Too Much: Towards Machine Unlearning on Feature Level
by: Xu, Heng, et al.
Published: (2024)

Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
by: Xu, Heng, et al.
Published: (2024)

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
by: Suriyakumar, Vinith M., et al.
Published: (2024)

Rethinking the Vulnerability of Concept Erasure and a New Method
by: Richardson, Alex D., et al.
Published: (2025)

DUP: Detection-guided Unlearning for Backdoor Purification in Language Models
by: Hu, Man, et al.
Published: (2025)

Split Unlearning
by: Yu, Guangsheng, et al.
Published: (2023)

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
by: Wang, Houzhe, et al.
Published: (2026)

Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
by: Xu, Heng, et al.
Published: (2024)

Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)

Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
by: Li, Jun, et al.
Published: (2026)

Machine Unlearning in Large Language Models
by: Chen, Kongyang, et al.
Published: (2024)

Rethinking Machine Unlearning in Image Generation Models
by: Liu, Renyang, et al.
Published: (2025)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

IMU: Influence-guided Machine Unlearning
by: Fan, Xindi, et al.
Published: (2025)

Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)

Mitigating Error Amplification in Fast Adversarial Training
by: Zhao, Mengnan, et al.
Published: (2026)

Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)

Unlearning Backdoor Attacks through Gradient-Based Model Pruning
by: Dunnett, Kealan, et al.
Published: (2024)

KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models
by: Fang, Ce, et al.
Published: (2026)

UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
by: Xue, Lulu, et al.
Published: (2026)

Unlink to Unlearn: Simplifying Edge Unlearning in GNNs
by: Tan, Jiajun, et al.
Published: (2024)

Verification of Machine Unlearning is Fragile
by: Zhang, Binchi, et al.
Published: (2024)

Label Inference Attacks against Federated Unlearning
by: Wang, Wei, et al.
Published: (2025)

BURN: Backdoor Unlearning via Adversarial Boundary Analysis
by: Su, Yanghao, et al.
Published: (2025)

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
by: Chen, Jian, et al.
Published: (2025)

QUEEN: Query Unlearning against Model Extraction
by: Chen, Huajie, et al.
Published: (2024)

On Large Language Model Continual Unlearning
by: Gao, Chongyang, et al.
Published: (2024)