March 14, 2024, 4:43 a.m. | Yanyun Wang, Dehui Du, Haibo Hu, Zi Liang, Yuanhao Liu

cs.LG updates on

arXiv:2209.06388v3 Announce Type: replace
Abstract: Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC). However, neural networks (NNs) are vulnerable to adversarial samples, which cause real-life adversarial attacks that undermine the robustness of AI models. To date, most existing attacks target at feed-forward NNs and image recognition tasks, but they cannot perform well on RNN-based TSC. This is due to the cyclical computation of RNN, which prevents direct model differentiation. In addition, …

abstract adversarial adversarial attacks ai models arxiv attacks classification cs.lg however life multi-objective network networks neural network neural networks nns recurrent neural network rnn robustness samples series success through time series type undermine vulnerable

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