Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
Fuente:
arXiv
Saved in:
| Main Authors: | Xin, Yuan, Chen, Dingfan, Backes, Michael, Zhang, Xiao |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
by: Hong, Hanbin, et al.
Published: (2023)
by: Hong, Hanbin, et al.
Published: (2023)
Detecting Adversarial Data via Provable Adversarial Noise Amplification
by: Mumcu, Furkan, et al.
Published: (2026)
by: Mumcu, Furkan, et al.
Published: (2026)
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost
by: Yeh, Cheng-Han, et al.
Published: (2024)
by: Yeh, Cheng-Han, et al.
Published: (2024)
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
by: Lyu, Saiyue, et al.
Published: (2024)
by: Lyu, Saiyue, et al.
Published: (2024)
Transferable Availability Poisoning Attacks
by: Liu, Yiyong, et al.
Published: (2023)
by: Liu, Yiyong, et al.
Published: (2023)
FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
by: Yan, Xinhai, et al.
Published: (2025)
by: Yan, Xinhai, et al.
Published: (2025)
Towards Strong Certified Defense with Universal Asymmetric Randomization
by: Hong, Hanbin, et al.
Published: (2025)
by: Hong, Hanbin, et al.
Published: (2025)
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
by: Hase, Ryo, et al.
Published: (2024)
by: Hase, Ryo, et al.
Published: (2024)
Towards Biologically Plausible and Private Gene Expression Data Generation
by: Chen, Dingfan, et al.
Published: (2024)
by: Chen, Dingfan, et al.
Published: (2024)
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)
by: Feng, Chen, et al.
Published: (2024)
IDEA: Invariant Defense for Graph Adversarial Robustness
by: Tao, Shuchang, et al.
Published: (2023)
by: Tao, Shuchang, et al.
Published: (2023)
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026)
by: elShehaby, Mohamed, et al.
Published: (2026)
Adversarial Suffix Filtering: a Defense Pipeline for LLMs
by: Khachaturov, David, et al.
Published: (2025)
by: Khachaturov, David, et al.
Published: (2025)
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
by: Han, Qiao, et al.
Published: (2024)
by: Han, Qiao, et al.
Published: (2024)
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
by: Wen, Rui, et al.
Published: (2024)
by: Wen, Rui, et al.
Published: (2024)
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race?
by: Xin, Yuan, et al.
Published: (2025)
by: Xin, Yuan, et al.
Published: (2025)
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
by: Zhang, Jiacheng, et al.
Published: (2025)
by: Zhang, Jiacheng, et al.
Published: (2025)
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
by: Thakkar, Janvi, et al.
Published: (2023)
by: Thakkar, Janvi, et al.
Published: (2023)
Pruning Graphs by Adversarial Robustness Evaluation to Strengthen GNN Defenses
by: Wang, Yongyu
Published: (2025)
by: Wang, Yongyu
Published: (2025)
Rethinking Randomized Smoothing from the Perspective of Scalability
by: Kumari, Anupriya, et al.
Published: (2023)
by: Kumari, Anupriya, et al.
Published: (2023)
Provable Watermarking for Data Poisoning Attacks
by: Zhu, Yifan, et al.
Published: (2025)
by: Zhu, Yifan, et al.
Published: (2025)
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
by: Schuchardt, Jan, et al.
Published: (2023)
by: Schuchardt, Jan, et al.
Published: (2023)
Vera Verto: Multimodal Hijacking Attack
by: Zhang, Minxing, et al.
Published: (2024)
by: Zhang, Minxing, et al.
Published: (2024)
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
by: Tafreshian, Benyamin, et al.
Published: (2025)
by: Tafreshian, Benyamin, et al.
Published: (2025)
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
by: Jin, Zhibo, et al.
Published: (2024)
by: Jin, Zhibo, et al.
Published: (2024)
Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction
by: Chaudhary, Ayush, et al.
Published: (2025)
by: Chaudhary, Ayush, et al.
Published: (2025)
MGTBench: Benchmarking Machine-Generated Text Detection
by: He, Xinlei, et al.
Published: (2023)
by: He, Xinlei, et al.
Published: (2023)
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
by: Shen, Xinyue, et al.
Published: (2023)
by: Shen, Xinyue, et al.
Published: (2023)
Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations
by: Liu, Jun, et al.
Published: (2026)
by: Liu, Jun, et al.
Published: (2026)
Prompt Stealing Attacks Against Text-to-Image Generation Models
by: Shen, Xinyue, et al.
Published: (2023)
by: Shen, Xinyue, et al.
Published: (2023)
Voice Jailbreak Attacks Against GPT-4o
by: Shen, Xinyue, et al.
Published: (2024)
by: Shen, Xinyue, et al.
Published: (2024)
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark
by: Wen, Rui, et al.
Published: (2025)
by: Wen, Rui, et al.
Published: (2025)
When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
by: Shen, Xinyue, et al.
Published: (2025)
by: Shen, Xinyue, et al.
Published: (2025)
Image-Perfect Imperfections: Safety, Bias, and Authenticity in the Shadow of Text-To-Image Model Evolution
by: Wu, Yixin, et al.
Published: (2024)
by: Wu, Yixin, et al.
Published: (2024)
Differentially Private Selection using Smooth Sensitivity
by: Chaves, Iago, et al.
Published: (2025)
by: Chaves, Iago, et al.
Published: (2025)
Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
by: Hayder, Yasmine
Published: (2026)
by: Hayder, Yasmine
Published: (2026)
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing
by: Fu, Chengyan, et al.
Published: (2024)
by: Fu, Chengyan, et al.
Published: (2024)
Early Approaches to Adversarial Fine-Tuning for Prompt Injection Defense: A 2022 Study of GPT-3 and Contemporary Models
by: Sandoval, Gustavo, et al.
Published: (2025)
by: Sandoval, Gustavo, et al.
Published: (2025)
Evaluations of Machine Learning Privacy Defenses are Misleading
by: Aerni, Michael, et al.
Published: (2024)
by: Aerni, Michael, et al.
Published: (2024)
Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
by: Brown, Hannah, et al.
Published: (2024)
by: Brown, Hannah, et al.
Published: (2024)
Similar Items
-
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
by: Hong, Hanbin, et al.
Published: (2023) -
Detecting Adversarial Data via Provable Adversarial Noise Amplification
by: Mumcu, Furkan, et al.
Published: (2026) -
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost
by: Yeh, Cheng-Han, et al.
Published: (2024) -
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
by: Lyu, Saiyue, et al.
Published: (2024) -
Transferable Availability Poisoning Attacks
by: Liu, Yiyong, et al.
Published: (2023)