2026
Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
ICML 2026poster
Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evidence of robustness. We show that this assumption can fail: Predictions and explanations can be adversarially decoupled, enabling targeted misclassifica…