mlsanityData to 7 Oct 2026

Dynamic Time Warping based Adversarial Framework for Time-Series Domain

arXiv 2207.04308 ↗First posted 9 Jul 2022cs.LGcited by 4

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Abstract (arXiv)

Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as em Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard Euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training. The source code of DTW-AR algorithms is available at https://github.com/tahabelkhouja/DTW-AR

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 9 Jul 2022.

Builds on

22 of 47 references.

References · 22

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  3. 2016Adversarial examples in the physical worldcited by 1,762
  4. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  5. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  6. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
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  8. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  9. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  10. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  11. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
  12. 2017Soft-DTW: a Differentiable Loss Function for Time-Seriescited by 265
10 more
  1. 2018Spatially Transformed Adversarial Examplescited by 195
  2. 2016Improving the Robustness of Deep Neural Networks via Stability Trainingcited by 156
  3. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
  4. 2020Deep Learning for Financial Applications : A Surveycited by 90
  5. 2018Universal Adversarial Trainingcited by 85
  6. 2019Functional Adversarial Attackscited by 82
  7. 2017Towards Crafting Text Adversarial Samplescited by 55
  8. 2019Adversarial Attacks on Deep Neural Networks for Time Series Classificationcited by 53
  9. 2019Adversarial Attacks on Time Seriescited by 40
  10. 2017On the Limitation of Convolutional Neural Networks in Recognizing Negative Imagescited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 2.

4 papers.

  1. 2022TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attackcited by 9
  2. 2026Temporally Unified Adversarial Perturbations for Time Series Forecastingcited by 1
  3. 2026Fortifying Time Series: DTW-Certified Robust Anomaly Detectioncited by 1
  4. 2025Differentiable Adversarial Attacks for Marked Temporal Point Processescited by 0