# Adversarial Attacks on Time Series

arXiv: https://arxiv.org/abs/1902.10755 · HTML: https://mlsanity.com/paper/1902.10755/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 30 of 40)

- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [Are socially-aware trajectory prediction models really socially-aware?](https://mlsanity.com/paper/2108.10879/) (2021)
- [BASAR:Black-box Attack on Skeletal Action Recognition](https://mlsanity.com/paper/2103.05266/) (2021)
- [Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack](https://mlsanity.com/paper/2103.05347/) (2021)
- [Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis](https://mlsanity.com/paper/2408.07314/) (2024)
- [Robust Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2202.11910/) (2022)
- [Defending Black-box Skeleton-based Human Activity Classifiers](https://mlsanity.com/paper/2203.04713/) (2022)
- [TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack](https://mlsanity.com/paper/2209.06388/) (2022)
- [Targeted Adversarial Attacks on Wind Power Forecasts](https://mlsanity.com/paper/2303.16633/) (2023)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [Targeted Attacks on Timeseries Forecasting](https://mlsanity.com/paper/2301.11544/) (2023)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack](https://mlsanity.com/paper/2211.11312/) (2022)
- [SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series](https://mlsanity.com/paper/2309.02752/) (2023)
- [Dynamic Time Warping based Adversarial Framework for Time-Series Domain](https://mlsanity.com/paper/2207.04308/) (2022)
- [Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability](https://mlsanity.com/paper/2208.11264/) (2022)
- [Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining](https://mlsanity.com/paper/2501.15070/) (2025)
- [Spatially Focused Attack against Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2109.04608/) (2021)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)
- [Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures](https://mlsanity.com/paper/2408.14875/) (2024)
- [Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains](https://mlsanity.com/paper/2505.19397/) (2025)
- [Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis](https://mlsanity.com/paper/2207.04305/) (2022)
- [Post-train Black-box Defense via Bayesian Boundary Correction](https://mlsanity.com/paper/2306.16979/) (2023)
- [Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks](https://mlsanity.com/paper/2308.06467/) (2023)
- [Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting](https://mlsanity.com/paper/2311.09790/) (2023)
- [Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble](https://mlsanity.com/paper/2409.02802/) (2024)
- [Concealed Adversarial attacks on neural networks for sequential data](https://mlsanity.com/paper/2502.20948/) (2025)
- [Mini-Batch Robustness Verification of Deep Neural Networks](https://mlsanity.com/paper/2508.15454/) (2025)
- [Wasserstein Adversarial Examples on Univariant Time Series Data](https://mlsanity.com/paper/2303.12357/) (2023)

## Abstract (arXiv)

Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN ) DTW, a Fully Connected Network and a Fully Convolutional Network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. To the best of our knowledge, such an attack on time series classification models has never been done before. Finally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples and to consider model robustness as an evaluative metric.