Unified Neural Backdoor Removal with Only Few Clean Samples through Unlearning and Relearning
Fuente:
arXiv
Saved in:
| Main Authors: | Min, Nay Myat, Pham, Long H., Sun, Jun |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
by: Min, Nay Myat, et al.
Published: (2026)
by: Min, Nay Myat, et al.
Published: (2026)
CORVUS: Red-Teaming Hallucination Detectors via Internal Signal Camouflage in Large Language Models
by: Min, Nay Myat, et al.
Published: (2026)
by: Min, Nay Myat, et al.
Published: (2026)
AutoBackdoor: Automating Backdoor Attacks via LLM Agents
by: Li, Yige, et al.
Published: (2025)
by: Li, Yige, et al.
Published: (2025)
Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
by: Li, Yige, et al.
Published: (2026)
by: Li, Yige, et al.
Published: (2026)
Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models
by: Shin, Jeongjin, et al.
Published: (2024)
by: Shin, Jeongjin, et al.
Published: (2024)
SSCL-BW: Sample-Specific Clean-Label Backdoor Watermarking for Dataset Ownership Verification
by: Wang, Yingjia, et al.
Published: (2025)
by: Wang, Yingjia, et al.
Published: (2025)
FFCBA: Feature-based Full-target Clean-label Backdoor Attacks
by: Yin, Yangxu, et al.
Published: (2025)
by: Yin, Yangxu, et al.
Published: (2025)
A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
by: Wu, Zhixiao, et al.
Published: (2025)
by: Wu, Zhixiao, et al.
Published: (2025)
Clean-Label Physical Backdoor Attacks with Data Distillation
by: Dao, Thinh, et al.
Published: (2024)
by: Dao, Thinh, et al.
Published: (2024)
Strategic Sample Selection for Improved Clean-Label Backdoor Attacks in Text Classification
by: Kirci, Onur Alp, et al.
Published: (2025)
by: Kirci, Onur Alp, et al.
Published: (2025)
Selection-Based Vulnerabilities: Clean-Label Backdoor Attacks in Active Learning
by: Zhi, Yuhan, et al.
Published: (2025)
by: Zhi, Yuhan, et al.
Published: (2025)
CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models
by: Li, Yuetai, et al.
Published: (2024)
by: Li, Yuetai, et al.
Published: (2024)
Injection, Attack and Erasure: Revocable Backdoor Attacks via Machine Unlearning
by: Song, Baogang, et al.
Published: (2025)
by: Song, Baogang, et al.
Published: (2025)
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks
by: Dai, Jiazhu, et al.
Published: (2025)
by: Dai, Jiazhu, et al.
Published: (2025)
BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models
by: Zeng, Yi, et al.
Published: (2024)
by: Zeng, Yi, et al.
Published: (2024)
Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models
by: Jiang, Peihai, et al.
Published: (2025)
by: Jiang, Peihai, et al.
Published: (2025)
Does Few-shot Learning Suffer from Backdoor Attacks?
by: Liu, Xinwei, et al.
Published: (2023)
by: Liu, Xinwei, et al.
Published: (2023)
Poison Once, Control Anywhere: Clean-Text Visual Backdoors in VLM-based Mobile Agents
by: Wang, Xuan, et al.
Published: (2025)
by: Wang, Xuan, et al.
Published: (2025)
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack
by: Ma, Binhao, et al.
Published: (2023)
by: Ma, Binhao, et al.
Published: (2023)
BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting
by: Qiu, Huming, et al.
Published: (2023)
by: Qiu, Huming, et al.
Published: (2023)
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2025)
by: Wang, Cheng-Long, et al.
Published: (2025)
ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
by: Alam, Manaar, et al.
Published: (2025)
by: Alam, Manaar, et al.
Published: (2025)
Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward
by: Guo, Weiyang, et al.
Published: (2026)
by: Guo, Weiyang, et al.
Published: (2026)
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
by: Braun, Tobias, et al.
Published: (2026)
by: Braun, Tobias, et al.
Published: (2026)
Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference
by: Chen, Depeng, et al.
Published: (2024)
by: Chen, Depeng, et al.
Published: (2024)
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation
by: Zhao, Shuai, et al.
Published: (2024)
by: Zhao, Shuai, et al.
Published: (2024)
DUP: Detection-guided Unlearning for Backdoor Purification in Language Models
by: Hu, Man, et al.
Published: (2025)
by: Hu, Man, et al.
Published: (2025)
Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Reference
by: Li, Jianwei, et al.
Published: (2026)
by: Li, Jianwei, et al.
Published: (2026)
In-Context Unlearning: Language Models as Few Shot Unlearners
by: Pawelczyk, Martin, et al.
Published: (2023)
by: Pawelczyk, Martin, et al.
Published: (2023)
Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
by: Wu, Zongru, et al.
Published: (2024)
by: Wu, Zongru, et al.
Published: (2024)
Robustifying Safety-Aligned Large Language Models through Clean Data Curation
by: Liu, Xiaoqun, et al.
Published: (2024)
by: Liu, Xiaoqun, et al.
Published: (2024)
CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks
by: Wu, Yinghao, et al.
Published: (2025)
by: Wu, Yinghao, et al.
Published: (2025)
Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models
by: Peng, Zuquan, et al.
Published: (2025)
by: Peng, Zuquan, et al.
Published: (2025)
MADE: Graph Backdoor Defense with Masked Unlearning
by: Lin, Xiao, et al.
Published: (2024)
by: Lin, Xiao, et al.
Published: (2024)
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
by: Yu, Haiyang, et al.
Published: (2024)
by: Yu, Haiyang, et al.
Published: (2024)
Backdoor Attribution: Elucidating and Controlling Backdoor in Language Models
by: Yu, Miao, et al.
Published: (2025)
by: Yu, Miao, et al.
Published: (2025)
When Forgetting Triggers Backdoors: A Clean Unlearning Attack
by: Arazzi, Marco, et al.
Published: (2025)
by: Arazzi, Marco, et al.
Published: (2025)
CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization
by: Min, Nay Myat, et al.
Published: (2024)
by: Min, Nay Myat, et al.
Published: (2024)
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
by: Zhu, Mingyan, et al.
Published: (2023)
by: Zhu, Mingyan, et al.
Published: (2023)
Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks
by: Fu, Anmin, et al.
Published: (2025)
by: Fu, Anmin, et al.
Published: (2025)
Similar Items
-
Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
by: Min, Nay Myat, et al.
Published: (2026) -
CORVUS: Red-Teaming Hallucination Detectors via Internal Signal Camouflage in Large Language Models
by: Min, Nay Myat, et al.
Published: (2026) -
AutoBackdoor: Automating Backdoor Attacks via LLM Agents
by: Li, Yige, et al.
Published: (2025) -
Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
by: Li, Yige, et al.
Published: (2026) -
Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models
by: Shin, Jeongjin, et al.
Published: (2024)