IFBA: Frequency-Aware Backdoor Attacks on Multivariate Time Series Forecasting via Input-Aware Triggers and Frequency-Guided Scheduling

Summary

IFBA is a frequency-aware backdoor attack on multivariate time series forecasting models. It generates input-aware triggers from the local pre-trigger context and chooses poisoning variables and timestamps with a low-frequency spectral-consistency score. The authors report stronger attack effectiveness than prior forecasting backdoors on five public benchmarks and three architectures, with comparable clean accuracy.

Topics

• Adversarial machine learning

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