TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
arXiv preprint arXiv:2412.01528 , year=
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Introduces the first passive source attribution benchmark for 22 generative 3D models and a Transformer achieving 97.22% accuracy under full supervision and 77.17% with 1% training data.
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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
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Who Generated This 3D Asset? Learning Source Attribution for Generative 3D Models
Introduces the first passive source attribution benchmark for 22 generative 3D models and a Transformer achieving 97.22% accuracy under full supervision and 77.17% with 1% training data.