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Backdoor Attacks on Time Series: A Generative Approach. (arXiv:2211.07915v1 [cs.LG])
Nov. 16, 2022, 2:20 a.m. | Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James Bailey
cs.CR updates on arXiv.org arxiv.org
Backdoor attacks have emerged as one of the major security threats to deep
learning models as they can easily control the model's test-time predictions by
pre-injecting a backdoor trigger into the model at training time. While
backdoor attacks have been extensively studied on images, few works have
investigated the threat of backdoor attacks on time series data. To fill this
gap, in this paper we present a novel generative approach for time series
backdoor attacks against deep learning based time …
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