Imagen de Portada

Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yamamura, Keiichiro, Oe, Issa, Ishikura, Hiroki, Fujisawa, Katsuki
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

Superpixel Attack: Enhancing Black-box Adversarial Attack with Image-driven Division Areas
por: Oe, Issa, et al.
Publicado: (2025)

Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features
por: Yamamura, Keiichiro, et al.
Publicado: (2025)

Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning
por: Fujisawa, Yoshihiko, et al.
Publicado: (2026)

Norm Anchors Make Model Edits Last
por: Liu, Mingda, et al.
Publicado: (2026)

More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization
por: Ichikawa, Yuma, et al.
Publicado: (2025)

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
por: Chen, Feng, et al.
Publicado: (2023)

Adjusting the Output of Decision Transformer with Action Gradient
por: Lin, Rui, et al.
Publicado: (2025)

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

Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks
por: Zhang, Xiaomei, et al.
Publicado: (2025)

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
por: Zhang, Jiayu, et al.
Publicado: (2025)

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

Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense
por: Zhan, Haoxi, et al.
Publicado: (2021)

Fooling SHAP with Output Shuffling Attacks
por: Yuan, Jun, et al.
Publicado: (2024)

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)

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

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

Gradient Inversion Attack on Graph Neural Networks
por: Sinha, Divya Anand, et al.
Publicado: (2024)

Contrastive ECOC: Learning Output Codes for Adversarial Defense
por: Chou, Che-Yu, et al.
Publicado: (2025)

Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
por: Xian, Guanmeng, et al.
Publicado: (2026)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
por: Li, Rui, et al.
Publicado: (2024)

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)

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

Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies
por: Fan, Junchao, et al.
Publicado: (2024)

Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies
por: Fan, Junchao, et al.
Publicado: (2025)

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
por: Yu, Yunrui, et al.
Publicado: (2025)

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)

NeuraLSP: An Efficient and Rigorous Neural Left Singular Subspace Preconditioner for Conjugate Gradient Methods
por: Benanti, Alexander, et al.
Publicado: (2026)

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

Randomness and Interpolation Improve Gradient Descent
por: Li, Jiawen, et al.
Publicado: (2025)

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

Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration
por: Kimura, Masanari, et al.
Publicado: (2024)