Copertina

Empirical evaluation of the Frank-Wolfe methods for constructing white-box adversarial attacks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Korotkova, Kristina, Katrutsa, Aleksandr
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
di: Matveeva, Tatyana, et al.
Pubblicazione: (2025)

DFWLayer: Differentiable Frank-Wolfe Optimization Layer
di: Liu, Zixuan, et al.
Pubblicazione: (2023)

Functional multi-armed bandit and the best function identification problems
di: Dorn, Yuriy, et al.
Pubblicazione: (2025)

Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble
di: Xue, Yunzhe, et al.
Pubblicazione: (2024)

Fixed-point graph convolutional networks against adversarial attacks
di: Khan, Shakib, et al.
Pubblicazione: (2025)

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization
di: Chen, Hao Mark, et al.
Pubblicazione: (2025)

Thundernna: a white box adversarial attack
di: Ye, Linfeng, et al.
Pubblicazione: (2021)

Deep generative models as an adversarial attack strategy for tabular machine learning
di: Dyrmishi, Salijona, et al.
Pubblicazione: (2024)

How adversarial attacks can disrupt seemingly stable accurate classifiers
di: Sutton, Oliver J., et al.
Pubblicazione: (2023)

Properties that allow or prohibit transferability of adversarial attacks among quantized networks
di: Shrestha, Abhishek, et al.
Pubblicazione: (2024)

Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning
di: García-Márquez, Mario, et al.
Pubblicazione: (2025)

FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection
di: Lunghi, Daniele, et al.
Pubblicazione: (2025)

Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation
di: Sun, Peiran
Pubblicazione: (2025)

Enhancing generalization in high energy physics using white-box adversarial attacks
di: Rothen, Franck, et al.
Pubblicazione: (2024)

RAB$^2$-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients
di: Rodríguez-Barroso, Nuria, et al.
Pubblicazione: (2024)

RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
di: Song, Jialei, et al.
Pubblicazione: (2025)

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection
di: Li, Xiao, et al.
Pubblicazione: (2025)

Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe
di: Thekumparampil, Kiran Koshy, et al.
Pubblicazione: (2024)

Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
di: Chicchi, Lorenzo, et al.
Pubblicazione: (2024)

Quantifying construct validity in large language model evaluations
di: Kearns, Ryan Othniel
Pubblicazione: (2026)

Can Go AIs be adversarially robust?
di: Tseng, Tom, et al.
Pubblicazione: (2024)

NODE-AdvGAN: Improving the transferability and perceptual similarity of adversarial examples by dynamic-system-driven adversarial generative model
di: Xie, Xinheng, et al.
Pubblicazione: (2024)

Deep MMD Gradient Flow without adversarial training
di: Galashov, Alexandre, et al.
Pubblicazione: (2024)

Missing value imputation with adversarial random forests -- MissARF
di: Golchian, Pegah, et al.
Pubblicazione: (2025)

Robust NAS under adversarial training: benchmark, theory, and beyond
di: Wu, Yongtao, et al.
Pubblicazione: (2024)

From Black-box to Causal-box: Towards Building More Interpretable Models
di: Hwang, Inwoo, et al.
Pubblicazione: (2025)

Enhancing web traffic attacks identification through ensemble methods and feature selection
di: Urda, Daniel, et al.
Pubblicazione: (2024)

Towards more transferable adversarial attack in black-box manner
di: Lei, Chun Tong, et al.
Pubblicazione: (2025)

Concealed Adversarial attacks on neural networks for sequential data
di: Sokerin, Petr, et al.
Pubblicazione: (2025)

Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
di: Mohammadi, Hadi, et al.
Pubblicazione: (2025)

Enhancing robustness of data-driven SHM models: adversarial training with circle loss
di: Yang, Xiangli, et al.
Pubblicazione: (2024)

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents
di: Leong, Jun Wen
Pubblicazione: (2026)

Seismic full-waveform inversion based on a physics-driven generative adversarial network
di: Zhang, Xinyi, et al.
Pubblicazione: (2026)

Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection
di: Wu, Yinting, et al.
Pubblicazione: (2024)

HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
di: Wang, Jiacheng, et al.
Pubblicazione: (2026)

Saliency strikes back: How filtering out high frequencies improves white-box explanations
di: Muzellec, Sabine, et al.
Pubblicazione: (2023)

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
di: Beznosikov, Aleksandr, et al.
Pubblicazione: (2023)

Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning
di: Black, James R. M., et al.
Pubblicazione: (2025)

Simulating realistic short tandem repeat capillary electrophoretic signal using a generative adversarial network
di: Taylor, Duncan, et al.
Pubblicazione: (2024)

NNTile: a machine learning framework capable of training extremely large GPT language models on a single node
di: Mikhalev, Aleksandr, et al.
Pubblicazione: (2025)