Cover Image

Towards Interpretable Adversarial Examples via Sparse Adversarial Attack

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Fudong, Lou, Jiadong, Wang, Hao, Jalaian, Brian, Yuan, Xu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Towards a Novel Perspective on Adversarial Examples Driven by Frequency
by: Zhang, Zhun, et al.
Published: (2024)

MemLoss: Enhancing Adversarial Training with Recycling Adversarial Examples
by: Mahdi, Soroush, et al.
Published: (2025)

Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
by: Pal, Ambar, et al.
Published: (2023)

A New Type of Adversarial Examples
by: Nie, Xingyang, et al.
Published: (2025)

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

Towards Robust Vision Transformer via Masked Adaptive Ensemble
by: Lin, Fudong, et al.
Published: (2024)

Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment
by: Sander, Jacob, et al.
Published: (2026)

Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models
by: Winninger, Thomas, et al.
Published: (2025)

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
by: Devabhakthini, Prathyusha, et al.
Published: (2023)

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

Generative Adversarial Networks for Imputing Sparse Learning Performance
by: Zhang, Liang, et al.
Published: (2024)

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

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

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

Explanation-Guided Adversarial Training for Robust and Interpretable Models
by: Chen, Chao, et al.
Published: (2026)

Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs
by: Wang, Hao, et al.
Published: (2026)

Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
by: Kim, Taejin, et al.
Published: (2023)

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

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
by: Cui, Canyixing, et al.
Published: (2026)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
by: Yuan, Jinsheng, et al.
Published: (2025)

Certified Robustness against Sparse Adversarial Perturbations via Data Localization
by: Pal, Ambar, et al.
Published: (2024)

Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation
by: Wu, Wenyuan, et al.
Published: (2025)

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
by: Stevens, Alexander, et al.
Published: (2024)

GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization
by: Yang, Xiaodong, et al.
Published: (2024)

Understanding Model Ensemble in Transferable Adversarial Attack
by: Yao, Wei, et al.
Published: (2024)

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

Adversarial Examples in the Physical World: A Survey
by: Wang, Jiakai, et al.
Published: (2023)

Untargeted Adversarial Attack on Knowledge Graph Embeddings
by: Zhao, Tianzhe, et al.
Published: (2024)

Fast-Slow Co-advancing Optimizer: Toward Harmonious Adversarial Training of GAN
by: Wang, Lin, et al.
Published: (2025)

Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
by: Luo, Jiayi, et al.
Published: (2025)

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

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

An Adversarial Example for Direct Logit Attribution: Memory Management in GELU-4L
by: Janiak, Jett, et al.
Published: (2023)

Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
by: Nguyen, Thanh, et al.
Published: (2024)

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

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

Adversarial Training for Defense Against Label Poisoning Attacks
by: Bal, Melis Ilayda, et al.
Published: (2025)

Adversarial Attacks in Multimodal Systems: A Practitioner's Survey
by: Kapoor, Shashank, et al.
Published: (2025)

Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
by: He, Zhipeng, et al.
Published: (2025)