This is the code for Boosting Certified $\ell_\infty$-dist Robustness with EMA Method and Ensemble Model. We use the EMA technique and model ensemble method to improve the performance and robustness of our model. We also use $\ell_\infty$-dist neurons to build commonly used CNN architectures. The $\ell_\infty$-dist neurons we use are implemented in $\ell_\infty$-dist Net. We achieve state-of-the-art performance on commonly used datasets: 93.14% certified accuracy on MNIST under eps = 0.3 and 35.42% on CIFAR-10 under eps = 8/255. We also use lightweight network $\ell_\infty$-dist LeNet with very few parameters to achieve 33.42% on CIFAR-10 under eps = 8/255.

• torch 1.8.1
• torchvision 0.9.1
• numpy 1.20.2
• matplotlib 3.4.0
• tensorboard

After cloning this repo into your computer, first run the following command to install the CUDA extension, which can speed up the training procedure considerably.

python setup.py install --user

You can train your $\ell_\infty$-dist nets and test their performance using the command below:

python main.py

Choose --model(MLP, Conv, LeNet, AlexNet, VGGNet) for network architecture, --dataset(MNIST, FashionMNIST, CIFAR10, CIFAR100) for dataset, --predictor-hidden-size for the hidden size of Predictor, --loss(hinge, cross_entropy) for loss function type and --opt(adamw, madam) for optimizer type.

You can also train your ensemble $\ell_\infty$-dist nets and test their performance using the command below:

python main_ensemble.py

In addition to the above options, you can choose --model-num for number of ensemble models.

In this repo, we provide complete training scripts as well. You can run the scripts directly to reproduce the results on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 datasets in our paper. The scripts are in the command folder.

For example, to reproduce the results of MNIST using a single $\ell_\infty$-dist Net+MLP , simply run

bash command/lipnet++_mnist.sh

And to reproduce the results of CIFAR-10 using ensemble $\ell_\infty$-dist LeNet+MLP, simply run

bash command/liplenet++_ensemble_cifar10.sh

We also support multi-GPU training using distributed data parallel. By default the code will use all available GPUs for training. To use a single GPU, add the following parameter --gpu GPU_ID where GPU_ID is the GPU ID. You can also specify --world-size, --rank and --dist-url for advanced multi-GPU training.

The model is automatically saved when the training procedure finishes. Use --checkpoint model_file_name.pth to load a specified model before training. You can use --start-epoch NUM_EPOCHS to skip training and only test the model's performance for a pretrained model, where NUM_EPOCHS is the number of epochs in total.

By default the code will generate three files named train.log, test.log and log.txt which contain all training logs. If you want to further display training curves, you can add the parameter --visualize to show these curves using Tensorboard.

Please contact [email protected] if you have any question on our paper or the codes. Enjoy!