Robust Unlearnable Examples: Protecting Data Against Adversarial Learning

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.

Requirements

• Python 3.8
• PyTorch 1.8.1
• Torchvision 0.9.1
• OpenCV 4.5.5

Install dependencies using pip

shell pip install -r requirements.txt

Install dependencies using Anaconda

It is recommended to create your experiment environment with Anaconda3.

shell conda install pytorch=1.8.1 torchvision=0.9.1 cudatoolkit=10.2 -c pytorch conda install -c conda-forge opencv=4.5.5

Quick Start

We give an example of creating robust unlearnable examples from CIFAR-10 dataset. More experiment examples can be found in ./scripts.

Generate robust error-minimizing noise for CIFAR-10 dataset

bash 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 rem

Perform adversarial training on robust unlearnable examples

bash python 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

Citation

@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} }

Acknowledgment

• 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