This repository releases the code of our paper "Going Grayscale: The Road to Understanding and Improving Unlearnable Examples".

Overview

We first show that grayscale pre-filtering (S1: Reactive Exploiter) can be used to mitigate original unlearnable examples (Huang et al. 2021). Then, we propose adaptive unlearnable examples (S2: Adaptive Defender) that are resistant to grayscale pre-filtering. We also show that unlearnable examples generated using Multi-Layer Perceptrons (MLPs) are effective in the case of complex Convolutional Neural Network (CNN) classifiers.

This figure shows the clean test accuracies of ResNet-18 on CIFAR-10 in S1 and S2 scenarios.

Our code is mainly based on the official code of the original unlearnable exmaple work (Huang et al. 2021).

How to use the code:

1. Generate ULEs in our S1 and S2 scenarios:

bash ./scripts/cifar10_poison/cifar10_ULEO/resnet18.sh
bash ./scripts/cifar10_poison/cifar10_ULEO_GRAYAUG/resnet18.sh

Train a standard exploiter on ULEOs:

bash ./scripts/cifar10_train/standard_exploiter/cifar10_ULEO/resnet18.sh

Train a standard exploiter on ULEO-GrayAugs:

bash ./scripts/cifar10_train/standard_exploiter/cifar10_ULEO_GRAYAUG/resnet18.sh

Train a grayscale exploiter on ULEOs:

bash ./scripts/cifar10_train/gray_exploiter/cifar10_ULEO/resnet18.sh

Train a grayscale exploiter on ULEO-GrayAugs:

bash ./scripts/cifar10_train/gray_exploiter/cifar10_ULEO_GRAYAUG/resnet18.sh

ULE perturbations can be found in ./experiments/cifar10_poison/. After running the scripts, training results can be found in the folder ./experiments/cifar10_train/.

2. Generate ULEs on MLPs and test transferability:

Generate ULEOs on MLPs:

bash scripts/cifar10_poison/cifar10_ULEO/mlp.sh

Test transferability to three CNNs:

bash ./scripts/cifar10_train/standard_exploiter/cifar10_ULEO/mlp2dense.sh
bash ./scripts/cifar10_train/standard_exploiter/cifar10_ULEO/mlp2resnet.sh
bash ./scripts/cifar10_train/standard_exploiter/cifar10_ULEO/mlp2vgg.sh

After running the scripts, the transferability results can be found in the folder ./experiments/cifar10_cross/mlp2x/.

More examples:

This figure shows unlearnable examples generated by the original approach (left) and ours (right) with the order of original image, perturbations, and perturbed images.

Cite our work:

Please cite our paper if you use this implementation in your research.

@misc{liu2021going,
      title={Going Grayscale: The Road to Understanding and Improving Unlearnable Examples}, 
      author={Zhuoran Liu and Zhengyu Zhao and Alex Kolmus and Tijn Berns and Twan van Laarhoven and Tom Heskes and Martha Larson},
      eprint={2111.13244},
      archivePrefix={arXiv},
      year={2021}
}

Acknowledgement:

@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}
}