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)
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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.
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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.
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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}
}