# untargeted settings with ODS:
python blackbox_simbaODS.py --num_sample 5 --ODS 
# targeted settings with ODS:
python blackbox_simbaODS.py --num_sample 5 --num_step 30000 --ODS --targeted

# untargeted settings with ODS:
python blackbox_boundaryODS.py --num_sample 5 --ODS 
# targeted settings with ODS:
python blackbox_boundaryODS.py --num_sample 5 --ODS --targeted
# untargeted settings with random sampling:
python blackbox_boundaryODS.py --num_sample 5 
# targeted settings with random sampling:
python blackbox_boundaryODS.py --num_sample 5 --targeted

python whitebox_train_cifar10.py --model-dir [PATH_TO_SAVE_FOLDER] --data-dir [PATH_TO_DATA_FOLDER]

# Evaluate PGD attack with ODI:
python whitebox_pgd_attack_cifar10_ODI.py --ODI-num-steps 2 --model-path [PATH_TO_THE_MODEL] --data-dir [PATH_TO_DATA_FOLDER] 
# Evaluate PGD attack with naive random initialization (sampled from a uniform distribution):
python whitebox_pgd_attack_cifar10_ODI.py --ODI-num-steps 0 --model-path [PATH_TO_THE_MODEL] --data-dir [PATH_TO_DATA_FOLDER]

@inproceedings{tashiro2020ods,
  title={Diversity can be Transferred: Output Diversification for White- and Black-box Attacks},
  author={Tashiro, Yusuke and Song, Yang and Ermon, Stefano},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}