Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Ao, Li, Wenshan, Li, Beibei, Ma, Wengang, Li, Tao, Zhou, Pan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
by: Liu, Ao, et al.
Published: (2023)
by: Liu, Ao, et al.
Published: (2023)
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
by: Li, Jiate, et al.
Published: (2025)
by: Li, Jiate, et al.
Published: (2025)
Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?
by: Ma, Yuhang, et al.
Published: (2026)
by: Ma, Yuhang, et al.
Published: (2026)
PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
by: Yan, Hanshu, et al.
Published: (2024)
by: Yan, Hanshu, et al.
Published: (2024)
ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks
by: Chen, Zhaorun, et al.
Published: (2025)
by: Chen, Zhaorun, et al.
Published: (2025)
Defending against Backdoor Attack on Deep Neural Networks
by: Cheng, Hao, et al.
Published: (2020)
by: Cheng, Hao, et al.
Published: (2020)
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning
by: De Gaspari, Fabio, et al.
Published: (2024)
by: De Gaspari, Fabio, et al.
Published: (2024)
MEA-Defender: A Robust Watermark against Model Extraction Attack
by: Lv, Peizhuo, et al.
Published: (2024)
by: Lv, Peizhuo, et al.
Published: (2024)
PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation
by: Wu, Xuhang, et al.
Published: (2026)
by: Wu, Xuhang, et al.
Published: (2026)
Rapid Plug-in Defenders
by: Wu, Kai, et al.
Published: (2023)
by: Wu, Kai, et al.
Published: (2023)
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks
by: Li, Tao, et al.
Published: (2024)
by: Li, Tao, et al.
Published: (2024)
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
by: Liu, Xin, et al.
Published: (2024)
by: Liu, Xin, et al.
Published: (2024)
Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
by: Zhang, Yuxiang, et al.
Published: (2026)
by: Zhang, Yuxiang, et al.
Published: (2026)
Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks
by: Razmi, Fereshteh, et al.
Published: (2021)
by: Razmi, Fereshteh, et al.
Published: (2021)
Practical Poisoning Attacks against Retrieval-Augmented Generation
by: Zhang, Baolei, et al.
Published: (2025)
by: Zhang, Baolei, et al.
Published: (2025)
ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization
by: Jiang, RuiZhe, et al.
Published: (2024)
by: Jiang, RuiZhe, et al.
Published: (2024)
Benchmarking Poisoning Attacks against Retrieval-Augmented Generation
by: Zhang, Baolei, et al.
Published: (2025)
by: Zhang, Baolei, et al.
Published: (2025)
Defending Against Knowledge Poisoning Attacks During Retrieval-Augmented Generation
by: Edemacu, Kennedy, et al.
Published: (2025)
by: Edemacu, Kennedy, et al.
Published: (2025)
Your Agent Can Defend Itself against Backdoor Attacks
by: Changjiang, Li, et al.
Published: (2025)
by: Changjiang, Li, et al.
Published: (2025)
Defending Deep Regression Models against Backdoor Attacks
by: Du, Lingyu, et al.
Published: (2024)
by: Du, Lingyu, et al.
Published: (2024)
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
by: Hertrich, Johannes, et al.
Published: (2020)
by: Hertrich, Johannes, et al.
Published: (2020)
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024)
by: Gosch, Lukas, et al.
Published: (2024)
Indiscriminate Data Poisoning Attacks on Neural Networks
by: Lu, Yiwei, et al.
Published: (2022)
by: Lu, Yiwei, et al.
Published: (2022)
Defending Against Poisoning Attacks in Federated Learning with Blockchain
by: Dong, Nanqing, et al.
Published: (2023)
by: Dong, Nanqing, et al.
Published: (2023)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)
by: Wang, Yujing, et al.
Published: (2024)
Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers
by: Agah, Nora, et al.
Published: (2025)
by: Agah, Nora, et al.
Published: (2025)
Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)
by: Wang, Xiaoyang, et al.
Published: (2022)
Backdoor or Manipulation? Graph Mixture of Experts Can Defend Against Various Graph Adversarial Attacks
by: Feng, Yuyuan, et al.
Published: (2025)
by: Feng, Yuyuan, et al.
Published: (2025)
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation
by: Liu, Yinuo, et al.
Published: (2025)
by: Liu, Yinuo, et al.
Published: (2025)
APEX: Probing Neural Networks via Activation Perturbation
by: Ren, Tao, et al.
Published: (2026)
by: Ren, Tao, et al.
Published: (2026)
Plug-and-Play Controllable Generation for Discrete Masked Models
by: Guo, Wei, et al.
Published: (2024)
by: Guo, Wei, et al.
Published: (2024)
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
by: Andrews, Emma, et al.
Published: (2026)
by: Andrews, Emma, et al.
Published: (2026)
Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
by: Assumpcao, Nicolas Riccieri Gardin, et al.
Published: (2025)
by: Assumpcao, Nicolas Riccieri Gardin, et al.
Published: (2025)
Adversarial Attacks on Fairness of Graph Neural Networks
by: Zhang, Binchi, et al.
Published: (2023)
by: Zhang, Binchi, et al.
Published: (2023)
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
by: He, Longzhu, et al.
Published: (2025)
by: He, Longzhu, et al.
Published: (2025)
Safeguarding Graph Neural Networks against Topology Inference Attacks
by: Fu, Jie, et al.
Published: (2025)
by: Fu, Jie, et al.
Published: (2025)
CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
by: Lai, Pin-Hua, et al.
Published: (2022)
by: Lai, Pin-Hua, et al.
Published: (2022)
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
by: Liu, Shijie, et al.
Published: (2023)
by: Liu, Shijie, et al.
Published: (2023)
MPAT: Building Robust Deep Neural Networks against Textual Adversarial Attacks
by: Zhang, Fangyuan, et al.
Published: (2024)
by: Zhang, Fangyuan, et al.
Published: (2024)
Similar Items
-
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
by: Liu, Ao, et al.
Published: (2023) -
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
by: Li, Jiate, et al.
Published: (2025) -
Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?
by: Ma, Yuhang, et al.
Published: (2026) -
PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator
by: Yan, Hanshu, et al.
Published: (2024) -
ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks
by: Chen, Zhaorun, et al.
Published: (2025)