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Towards Evaluating and Training Verifiably Robust Neural Networks

This repo intends to release code for our work:

Zhaoyang Lyu, Minghao Guo, Tong Wu, Guodong Xu, Kehuan Zhang, Dahua Lin, "Towards Evaluating and Training Verifiably Robust Neural Networks", CVPR 2021 (Oral).

The appendix of our paper is in the file _appendix.pdf.

Updates

  • Jun 26, 2021: Initial release. Release the codes for experiments on MNIST dataset.

Setup

The code is tested with python 3.8.5, Pytorch 1.6.0 and CUDA 10.1. Run the following conda command to create a environment with all the requirements:

conda create --name verify --file conda_spec.txt
conda activate verify 

Train Verifiably Robust Neural Networks

To train verifiably robust neural networks, just call the file train.py with a config file. For example, if you want to use CROWN-IBP to train a DM-large network with ParamRamp activation on MNIST dataset, run the following command:

python train.py --config exp_configs/mnist/crown-ibp/mnist_dm-large_crown-ibp_ParamRamp(0.01~0)_kappa_1-0.5.json

ParamRamp(0.01~0) in the file name means that we adopt a deceasing schedule of the neg-slope of the ParamRamp activation, i.e., the neg-slope starts with 0.01 and gradually decrease to 0 in the training process. Similarly, kappa_1-0.5 means that we adopt a deceasing schedule of the hyper parameter kappa described in the paper.

All experiments reported in our paper has a corresponding config file in the folder exp_configs. You should easily find the corresponding config file for a specific experiment based on their names.

For each experiment, the results will be saved to the path specified by models_path in the corresponding config file. For MNIST dataset, the model is trained at epsilon=0.4 and tested at epsilon=0.2, 0.3, 0.4 respectively. We save the model with the lowest IBP verified errors in the training process.

The experiments are run on GPUs by default. You can change the device in training_params in the config file to set the index of the GPU where you want to run the experiments. You could also set multi_gpu to true and specify the device_ids if you want to use multiple GPUs for training.

Evaluate Trained Networks

We empirically evaluted the trained networks by 200-step PGD attacks with 10 random starts. We also compute IBP, CROWN-IBP, LBP, and CROWN-LBP verified errors for the trained networks. To evaluate the above trained network at epsilon=0.2, run the following command:

python eval_models.py --epsilon 0.2 --config exp_configs/mnist/crown-ibp/mnist_dm-large_crown-ibp_ParamRamp(0.01~0)_kappa_1-0.5.json 

This command will automatically load the trained network and evaluate it using PGD attacks and compute IBP, CROWN-IBP, LBP, and CROWN-LBP verified errors for the network. You can also change some arguments for evaluation specified in the argparser in the file eval_models.py.

Acknowledgements

This repo is adapted from the Repo https://github.com/huanzhang12/CROWN-IBP.

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