Code for ICLR2021 Spotlight Paper "Unlearnable Examples: Making Personal Data Unexploitable " by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang.
In the notebook, you can find the minimal implementation for generating sample-wise unlearnable examples on CIFAR-10. Please remove mlconfig from models/__init__.py if you are only using the notebook and copy-paste the model to the notebook.
Check scripts folder for *.sh for each corresponding experiments.
python3 perturbation.py --config_path configs/cifar10 \ --exp_name path/to/your/experiment/folder \ --version resnet18 \ --train_data_type CIFAR10 \ --noise_shape 50000 3 32 32 \ --epsilon 8 \ --num_steps 20 \ --step_size 0.8 \ --attack_type min-min \ --perturb_type samplewise \ --universal_stop_error 0.01python3 -u main.py --version resnet18 \ --exp_name path/to/your/experiment/folder \ --config_path configs/cifar10 \ --train_data_type PoisonCIFAR10 \ --poison_rate 1.0 \ --perturb_type samplewise \ --perturb_tensor_filepath path/to/your/experiment/folder/perturbation.pt \ --trainpython3 perturbation.py --config_path configs/cifar10 \ --exp_name path/to/your/experiment/folder \ --version resnet18 \ --train_data_type CIFAR10 \ --noise_shape 10 3 32 32 \ --epsilon 8 \ --num_steps 1 \ --step_size 0.8 \ --attack_type min-min \ --perturb_type classwise \ --universal_train_target 'train_subset' \ --universal_stop_error 0.1 \ --use_subsetpython3 -u main.py --version resnet18 \ --exp_name path/to/your/experiment/folder \ --config_path configs/cifar10 \ --train_data_type PoisonCIFAR10 \ --poison_rate 1.0 \ --perturb_type classwise \ --perturb_tensor_filepath path/to/your/experiment/folder/perturbation.pt \ --train@inproceedings{huang2021unlearnable, title={Unlearnable Examples: Making Personal Data Unexploitable}, author={Hanxun Huang and Xingjun Ma and Sarah Monazam Erfani and James Bailey and Yisen Wang}, booktitle={ICLR}, year={2021} } 
