Improving the Robustness of Time Series Neural Networks from Adversarial Attacks Using Time Warping

Yoh Yamashita, Brian Kenji Iwana

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Time series neural networks have been shown to be weak against adversarial attacks. This study aims to enhance the robustness of time series neural networks in order to defend against such attacks. To do so, we introduce a new defense method called a Random Warping Self-Ensemble (RWSE). The RWSE has two main components. First, a novel random time warping layer to add randomness to trained models in order to disrupt the adversarial attack. Second, the use of self-ensembling increases robustness and maintains the accuracy of the network. The proposed RWSE does not require any special or extra training, can be used with most time series neural networks, including already trained ones, and does not require any extra trainable parameters. We demonstrate that the RWSE is effective in helping reduce the effects of four gradient-based adversarial attacks on five time series datasets.

本文言語英語
ホスト出版物のタイトルPattern Recognition - 27th International Conference, ICPR 2024, Proceedings
編集者Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal
出版社Springer Science and Business Media Deutschland GmbH
ページ15-30
ページ数16
ISBN(印刷版)9783031783401
DOI
出版ステータス出版済み - 2025
イベント27th International Conference on Pattern Recognition, ICPR 2024 - Kolkata, インド
継続期間: 12月 1 202412月 5 2024

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
15314 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議27th International Conference on Pattern Recognition, ICPR 2024
国/地域インド
CityKolkata
Period12/1/2412/5/24

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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