Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Yi Yu, Wenhan Yang, Yap-Peng Tan, Alex C. Kot
In CVPR'22
We offer the test set of Rain100H ./data/testand RainCityscapes ./rain_cityscapes/test.
For the full dataset, please refer to Rain100H and RainCityscapes.
- GPU with memory size >= 24 GB
- pytorch==1.1 or higher version
- lpips
cd ./code
# LMSE attack with perturbation bound {1,2,4,8}
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_1 --n_GPUs 1 --attack_iters 20 --robust_epsilon 1 --robust_alpha 0.25
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_2 --n_GPUs 1 --attack_iters 20 --robust_epsilon 2 --robust_alpha 0.5
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_4 --n_GPUs 1 --attack_iters 20 --robust_epsilon 4 --robust_alpha 1
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_8 --n_GPUs 1 --attack_iters 20 --robust_epsilon 8 --robust_alpha 2
# LPIPS attack with perturbation bound {1,2,4,8}
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_1_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 1 --robust_alpha 0.25 --attack_loss lpips
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_2_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 2 --robust_alpha 0.5 --attack_loss lpips
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_4_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 4 --robust_alpha 1 --attack_loss lpips
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_Rain100H_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_Rain100H_e4_8_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 8 --robust_alpha 2 --attack_loss lpips
cd ./code
# LMSE attack with perturbation bound {1,2,4,8}
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_1 --n_GPUs 1 --attack_iters 20 --robust_epsilon 1 --robust_alpha 0.25 --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_2 --n_GPUs 1 --attack_iters 20 --robust_epsilon 2 --robust_alpha 0.5 --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_4 --n_GPUs 1 --attack_iters 20 --robust_epsilon 4 --robust_alpha 1 --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_8 --n_GPUs 1 --attack_iters 20 --robust_epsilon 8 --robust_alpha 2 --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
# LPIPS attack with perturbation bound {1,2,4,8}
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_1_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 1 --robust_alpha 0.25 --attack_loss lpips --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_2_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 2 --robust_alpha 0.5 --attack_loss lpips --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_4_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 4 --robust_alpha 1 --attack_loss lpips --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
python robust.py --data_test RainHeavyTest --ext img --pre_train ../experiment/MPRNet_R_SEADD_MB_robust_pgd_RainCityscapes100mm_half_e4/model/model_latest.pt --model MPRNet_R_SEADD_MB --test_only --save_results --save_gt --save_attack --save MPRNet_R_SEADD_MB_robust_pgd_test_RainCityscapes100mm_half_e4_8_lpips --n_GPUs 1 --attack_iters 20 --robust_epsilon 8 --robust_alpha 2 --attack_loss lpips --branch_reduction 4 --dir_data ../rain_cityscapes --apath ../rain_cityscapes/test/small/ --dir_hr ../rain_cityscapes/test/small/norain --dir_lr ../rain_cityscapes/test/small/rain100mm
The generated results are in the folder: ./experiments, and you can evaluate the results by PSNR or SSIM. Images with suffix 'SR' are the clean outputs, images with suffix 'SR_attack' are the attacked outputs, images with suffix 'LR' are the clean inputs, images with suffix 'LR_attack' are the perturbed inputs, and images with suffix 'HR' are the groundtruth. For downstream tasks, please refer to the code of SSeg and Pedestron.
If you find our work useful for your research, please consider citing this paper:
@article{yu2022towards,
title={Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond},
author={Yu, Yi and Yang, Wenhan and Tan, Yap-Peng and Kot, Alex C},
journal={arXiv preprint arXiv:2203.16931},
year={2022}
}
If you have any questions, please feel free to contact us via [email protected].
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent