FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models

TL;DR

This paper introduces FABLE, a novel adversarial attack framework for weather forecasting models that manipulates inputs while maintaining realism, revealing vulnerabilities in deep learning-based weather prediction systems.

Contribution

FABLE is the first targeted, localized adversarial attack framework tailored for 3D weather data, utilizing wavelet decomposition to craft effective adversarial inputs.

Findings

FABLE successfully alters weather forecasts with minimal input perturbations.

Experimental results show FABLE outperforms baseline attack methods.

FABLE demonstrates the vulnerability of DLWF models to targeted adversarial attacks.

Abstract

Deep learning-based weather forecasting (DLWF) models have recently demonstrated 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. FABLE performs a 3D discrete wavelet decomposition to disentangle the spatial and temporal components of the data. By regulating the magnitude of adversarial perturbations across different components, FABLE produces adversarial inputs that remain closely aligned with the original inputs while steering the DLWF models toward generating the targeted forecast outcomes. Experimental results on real-world weather datasets demonstrate the effectiveness of FABLE over baseline methods across various metrics.