This is the official repository for ICLR 2022 paper "Robust Unlearnable Examples: Protecting Data Against Adversarial Learning" by Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen and Dacheng Tao.
- Python 3.8
- PyTorch 1.8.1
- Torchvision 0.9.1
- OpenCV 4.5.5
pip install -r requirements.txtIt is recommended to create your experiment environment with Anaconda3.
conda install pytorch=1.8.1 torchvision=0.9.1 cudatoolkit=10.2 -c pytorch
conda install -c conda-forge opencv=4.5.5We give an example of creating robust unlearnable examples from CIFAR-10 dataset. More experiment examples can be found in ./scripts.
python generate_robust_em.py \
--arch resnet18 \
--dataset cifar10 \
--train-steps 5000 \
--batch-size 128 \
--optim sgd \
--lr 0.1 \
--lr-decay-rate 0.1 \
--lr-decay-freq 2000 \
--weight-decay 5e-4 \
--momentum 0.9 \
--pgd-radius 8 \
--pgd-steps 10 \
--pgd-step-size 1.6 \
--pgd-random-start \
--atk-pgd-radius 4 \
--atk-pgd-steps 10 \
--atk-pgd-step-size 0.8 \
--atk-pgd-random-start \
--samp-num 5 \
--report-freq 1000 \
--save-freq 1000 \
--data-dir ./data \
--save-dir ./exp_data/cifar10/noise/rem8-4 \
--save-name rempython train.py \
--arch resnet18 \
--dataset cifar10 \
--train-steps 40000 \
--batch-size 128 \
--optim sgd \
--lr 0.1 \
--lr-decay-rate 0.1 \
--lr-decay-freq 16000 \
--weight-decay 5e-4 \
--momentum 0.9 \
--pgd-radius 4 \
--pgd-steps 10 \
--pgd-step-size 0.8 \
--pgd-random-start \
--report-freq 1000 \
--save-freq 100000 \
--noise-path ./exp_data/cifar10/noise/rem8-4/rem-fin-def-noise.pkl \
--data-dir ./data \
--save-dir ./exp_data/cifar10/train/rem8-4/r4 \
--save-name train@inproceedings{fu2022robust,
title={Robust Unlearnable Examples: Protecting Data Against Adversarial Learning},
author={Shaopeng Fu and Fengxiang He and Yang Liu and Li Shen and Dacheng Tao},
booktitle={International Conference on Learning Representations},
year={2022}
}
- Unlearnable examples: https://github.com/HanxunH/Unlearnable-Examples
- Adversarial poisons: https://github.com/lhfowl/adversarial_poisons
- Neural tangent generalization attacks: https://github.com/lionelmessi6410/ntga
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