The download link of these datasets can be accessed at AAOD-ORSI.
A total of four kinds of detectors are utilized in this paper for the evaluations including Faster R-CNN, RetinaNet, FCOS, and Yolo-v4. For Faster R-CNN, RetinaNet, and FCOS, we use MMDetection(we use mmcv-full=1.4.7=pypi_0, mmdet=2.3.0=dev_0 in this paper) as the main framework. For Yolo-v4, we use the same framework as RPAttack.
The main body of our method is based on the framework of RPAttack. Since I have graduated with a master's degree and started to work, the collation of the code may be delayed for a while. I will do my best to update the code as soon as possible. Below are the training details of the victim detectors leveraged in this paper. Thanks again for browsing this repository and hoping this can help you.
| model | datesets | epoch | learning rate | batch | decay | decay rate | mAP | recall |
|---|---|---|---|---|---|---|---|---|
| Faster R-CNN+Resnet50 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 88.3 | 90.3 |
| Faster R-CNN+Resnet50 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 68.7 | 77.7 |
| Faster R-CNN+Resnet101 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 88.6 | 90.9 |
| Faster R-CNN+Resnet101 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 68.4 | 76.1 |
| FCOS+Resnet50 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 87.3 | 91.3 |
| FCOS+Resnet50 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 65.7 | 79.1 |
| FCOS+Resnet101 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 87.6 | 91.6 |
| FCOS+Resnet101 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 66.8 | 80.0 |
| RetinaNet+Resnet50 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 87.3 | 92.8 |
| RetinaNet+Resnet50 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 62.2 | 79.5 |
| RetinaNet+Resnet101 | DIOR(train+val) | 24 | 0.01 | 4 | [16,22] | 0.1 | 87.3 | 92.8 |
| RetinaNet+Resnet101 | DOTA(train) | 12 | 0.001 | 2 | [8,10] | 0.1 | 64.8 | 81.3 |
| YOLOv4 | DIOR(train+val) | 30000 | 0.001 | 32 | [20000,25000] | 0.1 | 89.5 | 90.0 |
| YOLOv4 | DOTA(train) | 30000 | 0.001 | 32 | [20000,25000] | 0.1 | 69.7 | 76.8 |
Preparing your environment via requirements.txt, Then, to run the attack against the detector, e.g., faster r-cnn, with iteration = 10, you can run
REPO_ROOT$ python attack_faster.py --iters 10 --save_name xxxx
If you want to attack YOLO, please edit the file yolov4/eval_code/models/yolov4.cfg to set the parameters, then run
REPO_ROOT$ python attack_yolo.py --iters 10 --save_name xxxx
If you use these repository, please cite the following:
@article{sun2023threatening,
title={Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images},
author={Sun, Xuxiang and Cheng, Gong and Pei, Lei and Li, Hongda and Han, Junwei},
journal={IEEE Trans. Geosci. Remote Sens.},
volume={61},
pages={1-10},
year={2023}
}
As I have graduated and am working on a new job position, I am currently unable to dedicate time to maintaining this codebase. Therefore, this repository is no longer actively maintained.
I want to extend my gratitude to everyone who has shown interest in this project, and to all colleagues who have contributed to its development. Thank you for your support and understanding!
I wish everyone success in their research endeavors and hope for smooth progress in your work.