# Benchmarking adversarial attacks and defenses for time-series data

arXiv: https://arxiv.org/abs/2008.13261 · HTML: https://mlsanity.com/paper/2008.13261/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/2505.21027/) (2025)
- [On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense](https://mlsanity.com/paper/2301.03703/) (2023)
- [Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains](https://mlsanity.com/paper/2505.19397/) (2025)
- [Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble](https://mlsanity.com/paper/2409.02802/) (2024)
- [ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks](https://mlsanity.com/paper/2503.07882/) (2025)
- [The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security](https://mlsanity.com/paper/2306.14609/) (2023)
- [Correlation Analysis of Adversarial Attack in Time Series Classification](https://mlsanity.com/paper/2408.11264/) (2024)
- [MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense](https://mlsanity.com/paper/2606.17435/) (2026)
- [LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2609.38381/) (2026)

## Abstract (arXiv)

The adversarial vulnerability of deep networks has spurred the interest of researchers worldwide. Unsurprisingly, like images, adversarial examples also translate to time-series data as they are an inherent weakness of the model itself rather than the modality. Several attempts have been made to defend against these adversarial attacks, particularly for the visual modality. In this paper, we perform detailed benchmarking of well-proven adversarial defense methodologies on time-series data. We restrict ourselves to the L∞ threat model. We also explore the trade-off between smoothness and clean accuracy for regularization-based defenses to better understand the trade-offs that they offer. Our analysis shows that the explored adversarial defenses offer robustness against both strong white-box as well as black-box attacks. This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.