FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.
A comprehen- sive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking
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Near-field mmWave imaging is highly vulnerable to waveform-domain attacks that conceal or alter targets with moderate power, with deep-learning algorithms demonstrating higher robustness than classical methods.
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Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.