AdvMissing-TSC: Adversarial Missingness and Mask-Aware Defense for Time Series Classification

Time-series classifiers can perform well under random missingness yet misclassify samples when class-discriminative observations are missing. We introduce AdvMissing-TSC to evaluate this vulnerability by selectively masking a fixed fraction of observations in each sample while leaving the remaining values unchanged. Its target-guided sparse missingness attack (TG-SMA) combines Gumbel relaxation with top-k projection to optimize masks using classifier feedback, either to reduce classification accuracy or to approach a specified target accuracy. Temporal regularization encourages contiguous missing blocks. Using masks optimized under mean or zero imputation, we evaluate the missingness–imputation–classification pipeline, examining how missingness structure affects classification accuracy and comparing imputation methods under fixed masks. We further propose MID, a mask-aware consistency defense that uses learned gating to fuse numerical and missingness features. MID penalizes prediction discrepancies between complete inputs and their masked-and-imputed counterparts more strongly at higher missing rates. Experiments across multiple datasets show that adversarial missingness yields lower accuracy than the corresponding random-missingness conditions for all evaluated classifiers. Among the four evaluated combinations of missingness patterns and imputation methods, MID raises accuracy in the most severely degraded setting to a level comparable to or slightly higher than that of the least degraded setting without defense.