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IoUattack

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[CVPR2021] IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

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# IoUattack

:herb: **[IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking](https://arxiv.org/pdf/2103.14938.pdf)**

Shuai Jia, Yibing Song, Chao Ma and Xiaokang Yang

*IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.*

## Introduction

We observe that the increase of noise level positively correlates to the decrease of IoU scores, but their directions are not exactly the same.
- Our IoU attack seeks to inject the lowest amount of noisy perturbations at the same contour line of IoU score for each iteration.
- We choose three representative trackers with different structures, **SiamRPN++**, **DiMP** and **LTMU**, respectively.

## Results

#### Result for SiamRPN++ on multiple datasets
| | VOT2019 A / R / EAO | VOT2018 A / R / EAO | VOT2016 A / R / EAO | VOT2018lt F-score| OTB2015 OP / DP| NFS30 OP / DP| 
| ------------------| :--------------------: | :----: |:----: |:----: |:----: |:----: |
| SiamRPN++ | 0.596 / 0.472 / 0.287 | 0.602 / 0.239 / 0.413 |0.643 / 0.200 / 0.461| 0.625 | 0.695 / 0.905 | 0.509 / 0.601 |
| SiamRPN++(Random) | 0.591 / 0.727 / 0.220 | 0.587 / 0.365 / 0.301 |0.632 / 0.340 / 0.331| 0.553 | 0.631 / 0.818 | 0.466 / 0.550 |
| SiamRPN++(Attack) | 0.575 / 1.575 / 0.124 | 0.568 / 1.171 / 0.129 |0.605 / 0.802 / 0.183| 0.453 | 0.499 / 0.644 | 0.394 / 0.446 |

#### Result for DiMP on multiple datasets
| | VOT2019 A / R / EAO | VOT2018 A / R / EAO | VOT2016 A / R / EAO | VOT2018lt F-score| OTB2015 OP / DP| NFS30 OP / DP| 
| ------------------| :--------------------: | :----: |:----: |:----: |:----: |:----: |
| DiMP | 0.568 / 0.277 / 0.332 | 0.574 / 0.145 / 0.427 |0.599 / 0.140 / 0.449| 0.609 | 0.671 / 0.869 | 0.614 / 0.729 |
| DiMP(Random) | 0.567 / 0.373 / 0.284 | 0.560 / 0.202 / 0.363 |0.592 / 0.168 / 0.404| 0.555 | 0.659 / 0.860 | 0.591 / 0.710 |
| DiMP(Attack) | 0.474 / 0.641 / 0.195 | 0.507 / 0.400 / 0.248 |0.536 / 0.374 / 0.256| 0.443 | 0.592 / 0.791 | 0.545 / 0.658 |

#### Result for LTMU on multiple datasets
| | VOT2019 A / R / EAO | VOT2018 A / R / EAO | VOT2016 A / R / EAO | VOT2018ltT F-score| OTB2015 OP / DP| NFS30 OP / DP| 
| ------------------| :--------------------: | :----: |:----: |:----: |:----: |:----: |
| LTMU | 0.625 / 0.913 / 0.201 | 0.624 / 0.702 / 0.195 |0.661 / 0.522 / 0.236| 0.691 | 0.672 / 0.872 | 0.631 / 0.764 |
| LTMU(Random) | 0.623 / 1.073 / 0.175 | 0.622 / 0.805 / 0.178 |0.646 / 0.592 / 0.233| 0.657 | 0.622 / 0.815 | 0.579 / 0.699 |
| LTMU(Attack) | 0.576 / 1.470 / 0.150 | 0.590 / 1.320 / 0.120 |0.604 / 0.904 / 0.170| 0.589 | 0.517 / 0.712 | 0.462 / 0.559 |

:herb: **All raw results are available.**  [[Google_drive]](https://drive.google.com/drive/folders/1WjYJzsLEJZkB1dw-17ZLJNYZ9THK-jL4?usp=sharing)

## Code
:herb: **The code of IoU attack for SiamRPN++ is released!!**
- You should put the datasets into ```pysot/testing_dataset``` folder.
- Please download the pretrained model and set the environments of SiamPRN++.
- See [SiamRPN++](https://github.com/STVIR/pysot) for more details.

Test the original performance on VOT2018 dataset, please use the following command.
```
cd pysot/experiments/siamrpn_r50_l234_dwxcorr
python -u ../../tools/test_original.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file
```
Test IoU attack on VOT2018 dataset, please use the following command.
```
cd pysot/experiments/siamrpn_r50_l234_dwxcorr
python -u ../../tools/test_IoU_attack.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file
```

For the adversarial attack of other datasets, you should change the dataset name as mentioned above.

## Demo

## Citation
If any part of our paper and code is helpful to your work, please generously citing: 
```
@inproceedings{jia-cvpr21-iouattack,
  title={IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking},
  author={Jia, Shuai and Song, Yibing and Ma, Chao and Yang, Xiaokang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2021}
}
```

Thank you :)

## Reference
We choose three representative trackers, SiamRPN++, DiMP and LTMU. 
The original code of these trackers are list as follows:
- SiamRPN++: https://github.com/STVIR/pysot
- DiMP: https://github.com/visionml/pytracking
- LTMU: https://github.com/Daikenan/LTMU

We also refer to the code of Boundary Attack for IoU attack.
- Boundary Attack: https://github.com/greentfrapp/boundary-attack

Thanks for their wonderful works!

## License
Licensed under an MIT license.

IoUattack

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