AI RESEARCH
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models
arXiv CS.LG
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ArXi:2505.12167v2 Announce Type: replace Deep learning-based weather forecasting (DLWF) models have recently nstrated significant performance gains over gold-standard physics-based simulation tools. However, these models are potentially vulnerable to adversarial attacks, which raises concerns about their trustworthiness. In this paper, we investigate the feasibility and challenges of applying existing adversarial attack methods to DLWF models and propose a novel framework called FABLE (Forecast Alteration By Localized targeted advErsarial attack) to address them.