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CUDA: Convolution-based Unlearnable Datasets (CVPR 2023)

Authors: Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi

Paper: https://arxiv.org/abs/2303.04278

Requirements

Python 3.8.5 (GCC 7.3.0)

NVIDIA GPU with CUDA 11.0

Python requirements in requirements.txt

Directory tree

The readme file is in the current directory "."

Make folder "../datasets/" where datasets will be downloaded

Make folder "results/" where results will be saved

Codes

{densenet, resnet, vgg}.py contain networks from https://github.com/fshp971/robust-unlearnable-examples/tree/main/models

util.py contains progress bar utils from https://github.com/HanxunH/Unlearnable-Examples

final_filter_unlearnable.py contains code for executing CUDA dataset training.

final_muladv.py contains code for executing Deconvolution-based Adversarial Training (DAT) on CUDA CIFAR-10 dataset with ResNet-18.

To Run

For executing final_filter_unlearnable.py goto "." and run

python final_filter_unlearnable.py --arch='resnet18' --dataset='cifar10' --train-type='adv' \
--blur-parameter=0.3 --seed=0 --pgd-norm='linf' --pgd-steps=10 --pgd-radius=0.015 --mix=1.0 \
--name='results/resnet18_cifar10_adv_bp=0.3_linf_eps=4_steps=10_seed0_mix=1.0.pkl'

Above code will perform L_{\infty} adversarial training with CUDA CIFAR-10 dataset using ResNet-18.

For executing DAT, goto "." and run

python final_muladv.py

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