Imagen de Portada

LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wu, Tao, Luo, Tie, Wunsch, Donald C.
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Cryptography and Security
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Enabling Heterogeneous Adversarial Transferability via Feature Permutation Attacks
por: Wu, Tao, et al.
Publicado: (2025)

Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation
por: Zheng, Meixi, et al.
Publicado: (2025)

Passive Inference Attacks on Split Learning via Adversarial Regularization
por: Zhu, Xiaochen, et al.
Publicado: (2023)

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
por: Nasir, Hira, et al.
Publicado: (2026)

FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
por: Abacha, Fatima Z., et al.
Publicado: (2026)

Transferability Ranking of Adversarial Examples
por: Levy, Mosh, et al.
Publicado: (2022)

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization
por: Luo, Xiaoyu, et al.
Publicado: (2024)

Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
por: Roy, Samrendra, et al.
Publicado: (2026)

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
por: Yuan, Leitao, et al.
Publicado: (2026)

Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers
por: Wu, Kunze, et al.
Publicado: (2024)

The Relationship Between Network Similarity and Transferability of Adversarial Attacks
por: Klause, Gerrit, et al.
Publicado: (2025)

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
por: Jin, Zhibo, et al.
Publicado: (2024)

Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective
por: Roshan, Khushnaseeb, et al.
Publicado: (2024)

IDEA: Invariant Defense for Graph Adversarial Robustness
por: Tao, Shuchang, et al.
Publicado: (2023)

MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
por: Zhang, Jiaming, et al.
Publicado: (2023)

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
por: Nasr, Milad, et al.
Publicado: (2025)

Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
por: Han, Qiao, et al.
Publicado: (2024)

Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
por: Bouras, Stavros, et al.
Publicado: (2026)

Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
por: Takahashi, Tsubasa, et al.
Publicado: (2025)

Understanding and Enhancing the Transferability of Jailbreaking Attacks
por: Lin, Runqi, et al.
Publicado: (2025)

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
por: Schneider, Thomas, et al.
Publicado: (2024)

Interpreting Adversarial Attacks and Defences using Architectures with Enhanced Interpretability
por: Rao, Akshay G, et al.
Publicado: (2025)

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP
por: Xu, Binyan, et al.
Publicado: (2025)

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
por: Lu, Bingguang, et al.
Publicado: (2025)

DP-SGD Without Clipping: The Lipschitz Neural Network Way
por: Bethune, Louis, et al.
Publicado: (2023)

DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection
por: Du, Xia, et al.
Publicado: (2025)

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
por: Yang, Kun, et al.
Publicado: (2025)

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
por: Ribeiro, Antônio H., et al.
Publicado: (2025)

Position: Towards Resilience Against Adversarial Examples
por: Dai, Sihui, et al.
Publicado: (2024)

Debiased Graph Poisoning Attack via Contrastive Surrogate Objective
por: Yoon, Kanghoon, et al.
Publicado: (2024)

HGAttack: Transferable Heterogeneous Graph Adversarial Attack
por: Zhao, He, et al.
Publicado: (2024)

One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
por: Zhang, Jiacheng, et al.
Publicado: (2025)

Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction
por: Deng, Yue, et al.
Publicado: (2025)

On the Adversarial Robustness of Graph Neural Networks with Graph Reduction
por: Wu, Kerui, et al.
Publicado: (2024)

FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models
por: Deng, Yue, et al.
Publicado: (2025)

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
por: Pulido-Cortázar, Daniel, et al.
Publicado: (2025)

Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels
por: Shukla, Shubhi, et al.
Publicado: (2024)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
por: Yao, Duanyi, et al.
Publicado: (2024)

How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?
por: Liu, Meiqi, et al.
Publicado: (2025)

PEAS: A Strategy for Crafting Transferable Adversarial Examples
por: Avraham, Bar, et al.
Publicado: (2024)