  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 series classifiers. Backdoor
attacks have two main goals: high stealthiness and high attack success rate. We
find that, compared to images, it can be more challenging to achieve the two
goals on time series. This is because time series have fewer input dimensions
and lower degrees of freedom, making it hard to achieve a high attack success
rate without compromising stealthiness. Our generative approach addresses this
challenge by generating trigger patterns that are as realistic as real-time
series patterns while achieving a high attack success rate without causing a
significant drop in clean accuracy. We also show that our proposed attack is
resistant to potential backdoor defenses. Furthermore, we propose a novel
universal generator that can poison any type of time series with a single
generator that allows universal attacks without the need to fine-tune the
generative model for new time series datasets.
