:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wu, Tao, Luo, Tie, Wunsch, Donald C.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Cryptography and Security
Online Access:	https://arxiv.org/abs/2312.13118
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

Transferability Ranking of Adversarial Examples
by: Levy, Mosh, et al.
Published: (2022)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Boosting Adversarial Transferability via Residual Perturbation Attack
by: Peng, Jinjia, et al.
Published: (2025)

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

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

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

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

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

Medusa: Cross-Modal Transferable Adversarial Attacks on Multimodal Medical Retrieval-Augmented Generation
by: Shang, Yingjia, et al.
Published: (2025)

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

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

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

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

Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
by: Gao, Junqi, et al.
Published: (2024)

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