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DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

The official implement of paper DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation.

Requirements

python==3.11
numpy==1.25.2
torch==2.0.1
torchattacks==3.4.0
torchvision==0.15.2
scipy==1.11.1

Train networks on distilled datasets from scratch

After acquiring distilled datasets with distillation methods, you can train your own models on them.

python train.py --dataset CIFAR10 --model ConvNet --model_num 5 --train_batch 256  --train_epoch 1000 --save_path ./result/convnet_cifar10_dc --optimizer sgd --distilled --distill_method DC --pt_path <path_to_distilled_dataset> [--dsa]

Evaluate trained networks on clean test sets

If you want to evaluate test accuracy of a trained network on clean datasets, you can run scripts below.

python eval.py --dataset CIFAR10 --model ConvNet --data_path ./data/ --pt_path <path_to_weight>

Perturb test images and evaluate robust accuracy

To evaluate robust accuracy of models, you need to prepare two configuration files before running the scripts.

The weights.yaml contains paths to weight files of the trained models. For example:

Path:
 - '../trained_models/dc_cifar10_ipc1.pt'
 - '../trained_models/dc_cifar10_ipc5.pt'
 - '../trained_models/dc_cifar10_ipc10.pt'
 - '../trained_models/dc_cifar10_ipc30.pt'
 - '../trained_models/dc_cifar10_ipc50.pt'

The params.yaml contains parameters for different attacking algorithms. For example:

# eps, alpha, steps
VMIFGSM:
 - [0.0078431373,0.0078431373,10]
 - [0.0078431373,0.0078431373,5]
 
# eps, alpha, steps
PGD:
 - [0.0078431373,0.0078431373,10]
 - [0.0078431373,0.0078431373,5]

Please note that all parameter formats must strictly match the corresponding attacking algorithm.

To perturb test images and evaluate robust accuracy, you can run scripts below.

python robust.py --dataset CIFAR10 --model ConvNet --attacker FGSM --log_path <path_to_save_output> --weights <path_to_weights.yaml> --params <path_to_params.yaml> --repeat 5

Extension

Introduce distillation methods

Our code provides a unified function for loading distilled datasets with different formats. If you want to extend a new distilled dataset format which is different from the existing ones, you can add the data loader into load_distilled_dataset in datasets.py. Specifically, you need to:

  1. Add the name of your method to the name list:

    methods = ['DC','DSA',...,'YOUR_METHOD']

  2. Add a new elif branch and create your customized data loader:

    elif method=='YOUR_METHOD':
    train_images = ...
    train_labels = ...

Introduce attacking algorithms

If you want to add a new attacking algorithm, you can extend it from the predefined class ATTACK in attack_utils.py and rewrite the function perturb. Specifically, you can:

  1. Add the name of your attacking algorithm to the name list:

    ATTACKERS = ['FGSM','PGD',...,'YOUR_ATTACK']

  2. Define your attack:

    class YOUR_ATTACK(ATTACKS):
    def __init__(self, model, params=[<DEFAULT PARAMETERS>], normalize=True, mean=None, std=None):
        super().__init__(model, params, normalize, mean, std)
        assert len(params)==<NUMBER OF PARAMETERS>, 'Parameters for YOUR_ATTACK invalid!'
    
    def perturb(self,images,labels):
        ...
        <ATTACKING ALGORITHM>
        ...
        adv_images = ...
        return adv_images

  3. Add it to get_attacker`:

    elif name=='YOUR_ATTACK':
    return YOUR_ATTACK

Introduce network architecture

If you want to define a new network, you can define it in models.py and instantiate it in get_network. Specifically, you can:

  1. Define your model:

    class YOUR_MODEL(nn.Module):
    ...
    <Define your model>
    ...

  2. Add it to get_network:

    elif model=='YOUR_MODEL':
    net = YOUR_MODEL(<ARGS>)

Acknowledgements

We referred to the code from the following repository:

Citation

@article{wu2024dd,
  title={DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation},
  author={Wu, Yifan and Du, Jiawei and Liu, Ping and Lin, Yuewei and Cheng, Wenqing and Xu, Wei},
  journal={arXiv preprint arXiv:2403.13322},
  year={2024}
}