Imagen de Portada

Adversarial Attacks on Hyperbolic Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	van Spengler, Max, Zahálka, Jan, Mettes, Pascal
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space
por: van Spengler, Max, et al.
Publicado: (2025)

Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectors
por: Kasarla, Tejaswi, et al.
Publicado: (2025)

Compositional Entailment Learning for Hyperbolic Vision-Language Models
por: Pal, Avik, et al.
Publicado: (2024)

Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring
por: Ayoughi, Melika, et al.
Publicado: (2025)

Hyperbolic Random Forests
por: Doorenbos, Lars, et al.
Publicado: (2023)

Continual Hyperbolic Learning of Instances and Classes
por: Ayoughi, Melika, et al.
Publicado: (2025)

Fast and Geometrically Grounded Lorentz Neural Networks
por: van der Klis, Robert, et al.
Publicado: (2026)

Geometry-Aware Backdoor Attacks: Leveraging Curvature in Hyperbolic Embeddings
por: Baheri, Ali
Publicado: (2025)

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

Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing
por: Zhang, Milin, et al.
Publicado: (2023)

On the Robustness of Bayesian Neural Networks to Adversarial Attacks
por: Bortolussi, Luca, et al.
Publicado: (2022)

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

Hyperbolic Concept Bottleneck Models
por: Uyterlinde, Daniel, et al.
Publicado: (2026)

A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
por: Derner, Erik, et al.
Publicado: (2023)

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

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

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

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

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

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

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

Efficient Adversarial Attacks on High-dimensional Offline Bandits
por: Hosseini, Seyed Mohammad Hadi, et al.
Publicado: (2026)

CAMA: Exploring Collusive Adversarial Attacks in c-MARL
por: Niu, Men, et al.
Publicado: (2026)

Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization
por: Meng, Dekang, et al.
Publicado: (2025)

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

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

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

Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
por: Saday, Artun, et al.
Publicado: (2025)

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

Learning to Attack: A Bandit Approach to Adversarial Context Poisoning
por: Telikani, Ray, et al.
Publicado: (2026)

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

Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network
por: Park, Jongmin, et al.
Publicado: (2024)

Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks
por: Yamamura, Keiichiro, et al.
Publicado: (2024)

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

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

Exploring Sparsity and Smoothness of Arbitrary $\ell_p$ Norms in Adversarial Attacks
por: Duhme, Christof, et al.
Publicado: (2026)

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

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

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

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