mlsanityData to 14 Oct 2026

Cloud Adversarial Example Generation for Remote Sensing Image Classification

arXiv 2409.14240 ↗First posted 21 Sep 2024cs.CVcited by 2

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Abstract (arXiv)

Most existing adversarial attack methods for remote sensing images merely add adversarial perturbations or patches, resulting in unnatural modifications. Clouds are common atmospheric effects in remote sensing images. Generating clouds on these images can produce adversarial examples better aligning with human perception. In this paper, we propose a Perlin noise based cloud generation attack method. Common Perlin noise based cloud generation is a random, non-optimizable process, which cannot be directly used to attack the target models. We design a Perlin Gradient Generator Network (PGGN), which takes a gradient parameter vector as input and outputs the grids of Perlin noise gradient vectors at different scales. After a series of computations based on the gradient vectors, cloud masks at corresponding scales can be produced. These cloud masks are then weighted and summed depending on a mixing coefficient vector and a scaling factor to produce the final cloud masks. The gradient vector, coefficient vector and scaling factor are collectively represented as a cloud parameter vector, transforming the cloud generation into a black-box optimization problem. The Differential Evolution (DE) algorithm is employed to solve for the optimal solution of the cloud parameter vector, achieving a query-based black-box attack. Detailed experiments confirm that this method has strong attack capabilities and achieves high query efficiency. Additionally, we analyze the transferability of the generated adversarial examples and their robustness in adversarial defense scenarios.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 37 of its 68 references that are in the map. First posted on arXiv 21 Sep 2024.

Builds on

37 of 68 references.

References · 37

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25 more
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  20. 2024Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warpingcited by 18
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  22. 2023Threatening Patch Attacks on Object Detection in Optical Remote Sensing Imagescited by 8
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  2. 2025O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentationcited by 0