MIA: A Framework for Certified Robustness of Time-Series Classification and Forecasting Against Temporally-Localized Perturbations

Ruoxin Chen, Ruizhe Zhong, Jiawei Sun, Jie LI, Chentao Wu, Junchi Yan

Keywords: Certified robustness, time series forecasting, time series classification

Abstract: Recent literature demonstrates that times-series forecasting/classification are sensitive to input perturbations. However, the defenses for time-series models are relatively under-explored. In this paper, we propose \textbf{M}asking \textbf{I}mputing \textbf{A}ggregation (MIA), a plug-and-play framework to provide an arbitrary deterministic time-series model with certified robustness against temporally-localized perturbations (also known as $\ell_0$-norm localized perturbations), which is to our knowledge the first $\ell_0$-norm defense for time-series models. Our main insight is to let an occluding mask move across the input series, guaranteeing that, for an arbitrary localized perturbation there must exist at least a mask that completely remove out the perturbation, so that our prediction on this masked series is uninfluenced. Remarkably, MIA is high-availability as it still works even if we only have query access to the pretrained model. Furthermore, as there is no dedicated defense against $\ell_0$-norm perturbations for time-series models, we specifically adapt two matrix-based defenses to time-series models for comparison. Extensive experiments show that MIA yields stronger robustness as well as practicality.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: General Machine Learning (ie none of the above)