Backdoor Attacks on Time Series: A Generative Approach

@article{Jiang2022BackdoorAO,
  title={Backdoor Attacks on Time Series: A Generative Approach},
  author={Yujing Jiang and Xingjun Ma and Sarah Monazam Erfani and James Bailey},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.07915}
}
—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… 
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