README.md

Code for Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, Aleksander Madry https://arxiv.org/abs/1809.03008 International Conference on Learning Representations (ICLR), 2019

Workflow

See example.ipynb for an interactive version of the following instructions.

Model Training

1. python train.py: trains a model using parameters in config.json.

   Description of the defaults in config.json:

        1. It trains a 3-hidden layer convolutional architecture on MNIST.
        
        2. It uses adversarial training, L1 regularization, and ReLU stability regularization.
        
        3. The model is saved in `trained_models/relu_stable`
   

2. python post_process_model.py --model_dir $MODELDIR --output $MATNAME: apply post-processing, converting the model from $MODELDIR to a .mat file and saving it as `model_mats/$MATNAME.mat`.

   Command-line flags are available to choose post-processing options. Type python post_process_model.py -h to see all options.

Verification

In theory, you can use any verification procedure here; this code repo is set up to use the same verifier as in the paper.

Requires installation of the Julia package of https://github.com/vtjeng/MIPVerify.jl, as well as the Gurobi solver. Details are in the linked github repo.

3. ./verification/verify.sh $MATNAME $EPS ($START_INDEX) ($END_INDEX) to verify robustness of the saved .mat file against L_infinity perturbations with norm-bound $EPS. The script automatically does the following two things:

   A. Runs julia verification/verify_MNIST.jl - outputs a summary.csv to the folder ./verification/results/$MATNAME__linf-norm-bounded-$EPS__Inf__0.0/ for step 4

   B. Writes the console output to the log file ./verification/logs/$MATNAME.log for step 5

   You most likely want to set $EPS to the epsilon specified in config.json during training. Setting $START_INDEX=1 and $END_INDEX=10000 verifies the entire MNIST test set.

4. python parser/parse_csv.py --csv_name $CSVPATH: parses the .csv file to get provable adversarial accuracy and solve times

5. (OPTIONAL) ./parser/convert_log.sh $MATNAME follwed by python parser/parse_log.py --log_name $MATNAME: parse the logs to get exact ReLU stability numbers

Citing this Code

@article{xiao2019training,
  title={Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability},
  author={Xiao, Kai and Tjeng, Vincent and Shafiullah, Nur Muhammad and Madry, Aleksander},
  journal={ICLR},
  year={2019}
}