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On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense
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Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient. It is important to evaluate how vulnerable different time series models are to attacks and how well they recover using defense. The sensitivity to various attacks and the robustness using the defense of several time series models are investigated in this study. Experiments are run on seven-time series models with three adversarial attacks and one adversarial defense. According to the findings, all models, particularly GRU and RNN, appear to be vulnerable. LSTM and GRU also have better defense recovery. FGSM exceeds the competitors in terms of attacks. PGD attacks are more difficult to recover from than other sorts of attacks.
Forward citations
Cited by 2 Pith papers
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ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification
ReLATE+ detects adversarial attacks in time-series data, classifies attack family, and selects a resilient model via dataset similarity, reporting near-Oracle accuracy with roughly 78% lower overhead.
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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.
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