Jun-Jie Huang#([email protected]), Ziyue Wang#, Tianrui Liu*, Wenhan Luo, Zihan Chen, Wentao Zhao*, and Meng Wang

IEEE Transactions on Geoscience and Remote Sensing 2024. (#equal contribution, *corresponding author)

• We propose a novel Deployable Multi-Mini-Patch Adversarial Attack (DeMPAA) method for RSI classification. Instead of using a single large adversarial patch, we propose to search and optimize multiple small adversarial patches for robust and deployable adversarial attack.

• In the proposed DeMPAA method, a Feasible and Effective Map Generation (FEMG) module determines a patch location map considering both the feasibility and the effectiveness, and a Patch Generation (PG) module then samples n key patch locations and optimizes the adversarial patches with respect to adversarial classification loss and imperceptible loss.

• From extensive experimental results, the proposed DeMPAA method achieves a significantly higher attacking success rate when compared to other methods, and an imperceptible version of the proposed method i.e., DeMPAA-IP generates even more visually imperceptible adversarial patches to be practically feasible for attacking RSI scenes.

• PyTorch>=1.0
• Python>=3.7
• NVIDIA GPU + CUDA CuDNN

We use the Aerial Image Dataset (AID) and the Remote Sensing Image classification dataset created by Northwestern Polytechnical University (NWPU-RESISC) to evaluate the proposed method. Both the datasets are publicly available.

--patch_size: The total patch size. E.g. 0.01 ~= 1% of image.

--patch_number: The number of patches.

--netClassifier: The target classifier: resnet50/resnet34/resnet101/densenet121.

--save_path: The path to save the result.

--data_path: The data path.

--train_size: Number of training images.

--test_size: Number of test images.

--max_count: The max number of iterations to find adversarial example.

--image_size: The height / width of the input image to network.

We provide two versions of DeMPAA, i.e. DeMPAA and DeMPAA-IP, DeMPAA-IP is the more imperceptible version of DeMPAA.

• To Run DeMPAA:

python train_DeMPAA.py

• To Run DeMPAA-IP:

python train_DeMPAA_IP.py

The visualization results of different adversarial patch attack methods on AID.

@article{huang2024DeMPAA,
  title={DeMPAA: Deployable Multi-Mini-Patch Adversarial Attack for Remote Sensing Image Classification},
  author={Huang, Jun-Jie and Wang, Ziyue and Liu, Tianrui and Luo, Wenhan and Chen, Zihan and Zhao, Wentao and Wang, Meng},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2024},
  volume={62},
  pages={1-13},
  publisher={IEEE}
}