TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
Researchers have developed TimeGuard, a new defense mechanism against backdoor attacks specifically designed for time series forecasting (TSF). Existing defenses struggle with TSF due to data entanglement and task formulation shifts, which dilute signals and make poisoned data indistinguishable from clean data. TimeGuard addresses these issues by employing channel-wise pool training and a high-confidence pool initialized with time-aware criteria, alongside distance-regularized loss selection to manage training degeneration. Experiments show TimeGuard significantly enhances robustness against TSF backdoor attacks while maintaining clean performance. AI
IMPACT Introduces a novel defense against backdoor attacks in time series forecasting, potentially improving the security of AI systems in critical applications.