Averaging the predictions of several data-augmented models defends time series classifiers against gradient attacks more cheaply than adversarial training.
IEEE/CAA Journal of Automatica Sinica6(6), 1293–1305 (2019)
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Lightweight Defense Against Adversarial Attacks in Time Series Classification
Averaging the predictions of several data-augmented models defends time series classifiers against gradient attacks more cheaply than adversarial training.