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2008.13261
Cited By

Benchmarking adversarial attacks and defenses for time-series data

Benchmarking adversarial attacks and defenses for time-series data

International Conference on Neural Information Processing (ICONIP), 2020

30 August 2020

Shoaib Ahmed Siddiqui

ArXiv (abs)PDF HTML

Papers citing "Benchmarking adversarial attacks and defenses for time-series data"

6 / 6 papers shown

ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification

ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification

Cagla Ipek Kocal

161

0

0

26 Aug 2025

Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains

Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains

407

1

0

26 May 2025

ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial AttacksComputer Science Symposium in Russia (CSR), 2025

Cagla Ipek Kocal

240

1

0

10 Mar 2025

Correlation Analysis of Adversarial Attack in Time Series Classification

Correlation Analysis of Adversarial Attack in Time Series ClassificationInternational Conference on Advanced Data Mining and Applications (ADMA), 2024

Zhengyang Li

Chang Dong

167

0

0

21 Aug 2024

The race to robustness: exploiting fragile models for urban camouflage
and the imperative for machine learning security

The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security

Matthew A. Garratt

142

1

0

26 Jun 2023

On the Susceptibility and Robustness of Time Series Models through
Adversarial Attack and Defense

On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense

Asadullah Hill Galib

73

5

0

09 Jan 2023