Spectral and Gradient Alignment for Time-Series Adversarial Training

Deep neural networks (DNNs) are vulnerable to small adversarial perturbations, which is concerning in high-stakes domains such as healthcare. Adversarial training (AT) is a standard defense, but it often degrades performance on clean test data. For time-series, unconstrained perturbations can introduce frequency content that is inconsistent with the acquisition process and sensor bandwidth, yielding a poor training prior. We propose an adaptive filtering method that generates adversarial perturbations regularized toward each input’s spectral distribution without requiring application-specific frequency cutoffs. Across nine datasets spanning five health-related applications, our method improves clean-test performance over baselines while retaining competitive robustness.