Environment‐Adaptive Stealth Attack Framework for Natural Camouflage Adversarial Attacks in Remote‐Sensing Image

ABSTRACT Deep neural networks (DNNs) have significantly advanced the classification of remote‐sensing images (RSIs), achieving remarkable performance in critical applications such as disaster monitoring, urban planning, and environmental assessment. However, despite their sophistication, these systems remain highly vulnerable to adversarial attacks that can undermine their reliability in real‐world deployments. Current adversarial attack methods suffer from a critical drawback: they produce visually apparent perturbations characterized by artificial geometric patterns and unnatural textures that are easily identified by both human observers and automated detection systems, limiting their practical threat potential. To overcome these limitations, we introduce the environment‐adaptive stealth attack (EASA) framework, which strategically combines stealth patch placement with environment‐conscious perturbation generation to create highly effective adversarial examples with high visual fidelity. The framework consists of two complementary components: the stealth region locator (SRL) performs comprehensive environmental analysis using HSV‐based sky detection, discrete cosine transform (DCT) frequency‐domain texture evaluation, and gradient‐aware region identification. It incorporates probability‐guided sampling to optimize the placement of attacks while preserving spatial naturalness; the Adaptive Environment Perturbation Optimizer (AEPO) generates physically plausible perturbations via multilayer noise synthesis, integrating environmental color schemes through illumination physics modeling, and utilizing momentum‐based optimization with projection constraints to control perturbation intensity. Comprehensive experiments across diverse state‐of‐the‐art deep learning architectures demonstrate that EASA achieves a favorable balance between attack effectiveness and visual naturalness. While maintaining competitive attack success rates, EASA improves visual fidelity and environmental realism compared to existing approaches, enhancing the imperceptibility of adversarial perturbations. This work contributes to a deeper understanding of the trade‐offs between attack stealthiness and effectiveness in remote sensing systems, providing valuable insights for developing more comprehensive defense strategies.