Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

FogFool is a novel adversarial framework designed to generate physically plausible, fog-based perturbations for remote sensing (RS) image classification models. Unlike traditional pixel-wise attacks, FogFool optimizes atmospheric patterns using Perlin noise to create visually consistent and deceptive adversarial examples. This method leverages the spatial coherence and mid-to-low-frequency characteristics of atmospheric phenomena, embedding adversarial information into structural features that transfer across diverse deep learning architectures. Extensive experiments on two benchmark RS datasets demonstrate FogFool's superior performance, achieving 83.74% Targeted Attack Success Rate (TASR) in black-box settings and robustness against defenses like JPEG compression and filtering. Analyses show these atmospheric-driven perturbations universally shift model attention, indicating FogFool as a practical, stealthy, and persistent threat to RS classification systems.

Key takeaway

For AI Security Engineers evaluating remote sensing classification systems, FogFool highlights a critical vulnerability: physically plausible atmospheric perturbations. You should prioritize testing your models against environmental adversarial attacks that mimic natural phenomena, rather than solely relying on pixel-wise perturbations. This approach will reveal more realistic and persistent threats, guiding the development of robust defenses against stealthy, transferable attacks.

Key insights

FogFool generates physically plausible fog-based adversarial perturbations for robust remote sensing image classification attacks.

Principles

• Atmospheric phenomena enhance adversarial transferability.
• Spatial coherence improves attack persistence.
• Mid-to-low frequencies enable cross-architecture deception.

Method

FogFool iteratively optimizes Perlin noise-based atmospheric patterns to generate fog-based adversarial perturbations, embedding information into structural features for enhanced transferability and robustness.

In practice

• Evaluate RS models against physically plausible attacks.
• Develop defenses robust to atmospheric perturbations.
• Analyze model attention shifts from environmental factors.

Topics

• FogFool Framework
• Remote Sensing Image Classification
• Atmospheric Adversarial Perturbations
• Black-box Transferability
• Model Robustness

Best for: Research Scientist, CTO, AI Architect, AI Scientist, AI Security Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.