Smooth Perturbations for Time Series Adversarial Attacks-Reference-Cited by-同舟云学术

Smooth Perturbations for Time Series Adversarial Attacks

Published:2022 Issue: Volume: Page:485-496
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
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Author:

Pialla Gautier,Fawaz Hassan Ismail,Devanne Maxime,Weber Jonathan,Idoumghar Lhassane,Muller Pierre-Alain,Bergmeir Christoph,Schmidt Daniel,Webb Geoffrey,Forestier Germain

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-05933-9_38

Reference23 articles.

1. Bhambri, S., Muku, S., Tulasi, A., Buduru, A.B.: A survey of black-box adversarial attacks on computer vision models (2020)

2. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39–57. IEEE (2017)

3. Dau, H.A., et al.: The UCR time series archive (2019)

4. Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185–9193 (2018)

5. Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1625–1634 (2018)

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1. SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series;2023 IEEE International Conference on Knowledge Graph (ICKG);2023-12-01

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