Cover Image

Understanding Model Ensemble in Transferable Adversarial Attack

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yao, Wei, Zhang, Zeliang, Tang, Huayi, Liu, Yong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Harmonizing Intra-coherence and Inter-divergence in Ensemble Attacks for Adversarial Transferability
by: Ma, Zhaoyang, et al.
Published: (2025)

Perfect Alignment May be Poisonous to Graph Contrastive Learning
by: Liu, Jingyu, et al.
Published: (2023)

Auto-Prompt Ensemble for LLM Judge
by: Li, Jiajie, et al.
Published: (2025)

Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)

Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
by: Lukyanov, Kirill, et al.
Published: (2024)

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
by: Rossolini, Giulio, et al.
Published: (2025)

Effective Frontiers: A Unification of Neural Scaling Laws
by: Zou, Jiaxuan, et al.
Published: (2026)

Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
by: Lu, Ning, et al.
Published: (2023)

Understanding Fairness Surrogate Functions in Algorithmic Fairness
by: Yao, Wei, et al.
Published: (2023)

Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning
by: Liu, Jinshan, et al.
Published: (2026)

AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models
by: Zhang, Jiaming, et al.
Published: (2024)

TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
by: He, Zhipeng, et al.
Published: (2025)

Bag of Tricks to Boost Adversarial Transferability
by: Zhang, Zeliang, et al.
Published: (2024)

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

Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective
by: Duan, Tianyang, et al.
Published: (2025)

ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks
by: Yu, Xiaodong, et al.
Published: (2023)

CEAR: Certified Ensemble Adversarial Robustness in DNNs
by: Sadig, Daniel, et al.
Published: (2026)

On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications
by: Jo, Hyeonsoo, et al.
Published: (2025)

Adversarial Attacks on Hyperbolic Networks
by: van Spengler, Max, et al.
Published: (2024)

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

Understanding Expressivity of GNN in Rule Learning
by: Qiu, Haiquan, et al.
Published: (2023)

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
by: Yang, Hunmin, et al.
Published: (2024)

Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
by: Lin, Fudong, et al.
Published: (2025)

X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
by: Huang, Hanxun, et al.
Published: (2025)

Fine-Grained Iterative Adversarial Attacks with Limited Computation Budget
by: Hou, Zhichao, et al.
Published: (2025)

Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles
by: Lao, Dong, et al.
Published: (2025)

Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
by: Zhang, Yuxiang, et al.
Published: (2024)

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
by: Wu, Jiaxi, et al.
Published: (2025)

Revisiting Character-level Adversarial Attacks for Language Models
by: Rocamora, Elias Abad, et al.
Published: (2024)

How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)

Foundation Models for Demand Forecasting via Dual-Strategy Ensembling
by: Yang, Wei, et al.
Published: (2025)

CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
by: Hu, Ruofan, et al.
Published: (2025)

Enhancing Adversarial Transferability via Information Bottleneck Constraints
by: Qi, Biqing, et al.
Published: (2024)

Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing
by: Zhong, Xuyang, et al.
Published: (2025)

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Improvement of AMPs Identification with Generative Adversarial Network and Ensemble Classification
by: Keshavarzpour, Reyhaneh, et al.
Published: (2025)

Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation
by: Zhao, Ke, et al.
Published: (2024)

Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks
by: Jin, Zhibo, et al.
Published: (2024)

How Do Diffusion Models Improve Adversarial Robustness?
by: Yuezhang, Liu, et al.
Published: (2025)

Enhancing Adversarial Transferability through Block Stretch and Shrink
by: Liu, Quan, et al.
Published: (2025)