Cover Image

Cross-Input Certified Training for Universal Perturbations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Changming, Singh, Gagandeep
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
by: Jiang, Enyi, et al.
Published: (2024)

Bypassing the Safety Training of Open-Source LLMs with Priming Attacks
by: Vega, Jason, et al.
Published: (2023)

Universal Black-Box Reward Poisoning Attack against Offline Reinforcement Learning
by: Xu, Yinglun, et al.
Published: (2024)

How Catastrophic is Your LLM? Certifying Risk in Conversation
by: Wang, Chengxiao, et al.
Published: (2025)

Compression Aware Certified Training
by: Xu, Changming, et al.
Published: (2025)

On Using Certified Training towards Empirical Robustness
by: De Palma, Alessandro, et al.
Published: (2024)

ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
by: Yang, Yuchen, et al.
Published: (2024)

CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models
by: Lou, Qian, et al.
Published: (2024)

Is The Watermarking Of LLM-Generated Code Robust?
by: Suresh, Tarun, et al.
Published: (2024)

Towards Strong Certified Defense with Universal Asymmetric Randomization
by: Hong, Hanbin, et al.
Published: (2025)

Differentially Private Range Queries with Correlated Input Perturbation
by: Dharangutte, Prathamesh, et al.
Published: (2024)

Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025)

Authority Backdoor: A Certifiable Backdoor Mechanism for Authoring DNNs
by: Yang, Han, et al.
Published: (2025)

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

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

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
by: Wu, Jiapeng, et al.
Published: (2023)

Incremental Randomized Smoothing Certification
by: Ugare, Shubham, et al.
Published: (2023)

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

Certified Machine Unlearning via Noisy Stochastic Gradient Descent
by: Chien, Eli, et al.
Published: (2024)

A Certified Unlearning Approach without Access to Source Data
by: Basaran, Umit Yigit, et al.
Published: (2025)

Position: Certified Robustness Does Not (Yet) Imply Model Security
by: Cullen, Andrew C., et al.
Published: (2025)

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

Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation
by: Pagh, Rasmus, et al.
Published: (2024)

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

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

Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors
by: Zhang, Ruihan, et al.
Published: (2024)

A Robust Certified Machine Unlearning Method Under Distribution Shift
by: Guo, Jinduo, et al.
Published: (2026)

CBD: A Certified Backdoor Detector Based on Local Dominant Probability
by: Xiang, Zhen, et al.
Published: (2023)

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

CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
by: Wang, Derui, et al.
Published: (2025)

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

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

Low Rank Adaptation for Adversarial Perturbation
by: Liu, Han, et al.
Published: (2026)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
by: Jiang, Yu, et al.
Published: (2025)

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
by: Liu, Peizhuo
Published: (2025)

Certified Robustness to Data Poisoning in Gradient-Based Training
by: Sosnin, Philip, et al.
Published: (2024)

Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
by: Wang, Bo, et al.
Published: (2026)

Efficient Adversarial Input Generation via Neural Net Patching
by: Khan, Tooba, et al.
Published: (2022)

How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
by: Kalra, Akansha, et al.
Published: (2025)