Copertina

Towards Strong Certified Defense with Universal Asymmetric Randomization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hong, Hanbin, Kundu, Ashish, Payani, Ali, Wang, Binghui, Hong, Yuan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
di: Hong, Hanbin, et al.
Pubblicazione: (2023)

Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
di: Feng, Shuya, et al.
Pubblicazione: (2024)

Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
di: Zhang, Xinyu, et al.
Pubblicazione: (2023)

LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
di: Yang, Qin, et al.
Pubblicazione: (2024)

Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
di: Noorbakhsh, Sayedeh Leila, et al.
Pubblicazione: (2024)

Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
di: Yang, Yuxin, et al.
Pubblicazione: (2024)

Learning Robust and Privacy-Preserving Representations via Information Theory
di: Zhang, Binghui, et al.
Pubblicazione: (2024)

Backdoor Attacks on Discrete Graph Diffusion Models
di: Wang, Jiawen, et al.
Pubblicazione: (2025)

PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
di: Yang, Qin, et al.
Pubblicazione: (2025)

Certified Defense on the Fairness of Graph Neural Networks
di: Dong, Yushun, et al.
Pubblicazione: (2023)

Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness
di: Deng, Jieren, et al.
Pubblicazione: (2024)

Certified Causal Defense with Generalizable Robustness
di: Qiao, Yiran, et al.
Pubblicazione: (2024)

PECAN: A Deterministic Certified Defense Against Backdoor Attacks
di: Zhang, Yuhao, et al.
Pubblicazione: (2023)

FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
di: Yang, Yuxin, et al.
Pubblicazione: (2024)

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
di: Naderloui, Nima, et al.
Pubblicazione: (2025)

Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing
di: Fu, Chengyan, et al.
Pubblicazione: (2024)

Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
di: Xin, Yuan, et al.
Pubblicazione: (2023)

Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
di: Kundu, Satwik, et al.
Pubblicazione: (2024)

Using Retriever Augmented Large Language Models for Attack Graph Generation
di: Prapty, Renascence Tarafder, et al.
Pubblicazione: (2024)

Cross-Input Certified Training for Universal Perturbations
di: Xu, Changming, et al.
Pubblicazione: (2024)

SWaRL: Safeguard Code Watermarking via Reinforcement Learning
di: Javidnia, Neusha, et al.
Pubblicazione: (2026)

Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
di: Lyu, Saiyue, et al.
Pubblicazione: (2024)

Towards Generalized Certified Robustness with Multi-Norm Training
di: Jiang, Enyi, et al.
Pubblicazione: (2024)

Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method
di: Li, Jiate, et al.
Pubblicazione: (2024)

Transfer Learning for Security: Challenges and Future Directions
di: Li, Adrian Shuai, et al.
Pubblicazione: (2024)

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
di: Jiang, Yu, et al.
Pubblicazione: (2024)

Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
di: Giri, Nandakrishna, et al.
Pubblicazione: (2026)

Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
di: Pan, Mengting, et al.
Pubblicazione: (2026)

LMN: A Tool for Generating Machine Enforceable Policies from Natural Language Access Control Rules using LLMs
di: Sonune, Pratik, et al.
Pubblicazione: (2025)

Collective Certified Robustness against Graph Injection Attacks
di: Lai, Yuni, et al.
Pubblicazione: (2024)

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
di: Li, Jiate, et al.
Pubblicazione: (2025)

PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
di: Zou, Wei, et al.
Pubblicazione: (2024)

Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
di: Ghosh, Archisman, et al.
Pubblicazione: (2025)

Automated Consistency Analysis of LLMs
di: Patwardhan, Aditya, et al.
Pubblicazione: (2025)

Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
di: Kim, Joon, et al.
Pubblicazione: (2024)

Certified Unlearning for Neural Networks
di: Koloskova, Anastasia, et al.
Pubblicazione: (2025)

Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks
di: Bakos, Steve, et al.
Pubblicazione: (2024)

Graph Neural Network Explanations are Fragile
di: Li, Jiate, et al.
Pubblicazione: (2024)

Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
di: Wang, Yujing, et al.
Pubblicazione: (2024)

A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
di: Yang, Yuxin, et al.
Pubblicazione: (2024)