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<div align="center"> <h1>Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond</h1> </div>

arxiv link

Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond <br>Yi Yu, Wenhan Yang, Yap-Peng Tan, Alex C. Kot<br> In CVPR'22

Datasets

We offer the test set of Rain100H ./data/testand RainCityscapes ./rain_cityscapes/test.

For the full dataset, flease refer to Rain100H and RainCityscapes.

Requirement

GPU with memory size >= 24 GB
pytorch==1.1 or higher version
lpips

Evaluate the robustness of our model on Rain100H

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

Evaluate the robustness of our model on RainCityscape

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

Evaluate the result

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.

Citation

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}
}

Contact

If you have any questions, please feel free to contact us via yuyi0010@e.ntu.edu.sg.