Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints

Code for the paper "Improving $\ell_1$-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints" (https://openreview.net/pdf?id=vPLIRidmYO)

Pats of the code are reused from (Levine 2021) https://github.com/alevine0/smoothingSplittingNoise (and thus from (Yang et al. 2020) available at https://github.com/tonyduan/rs4a), indicated on top of files.

Instructions for installing dependencies are reproduced here:

conda install numpy matplotlib pandas seaborn 
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
pip install torchnet tqdm statsmodels dfply

The train -> certification pipeline is in main.py

For imagenet experiments, set the environment variables $IMAGENET_TRAIN_DIR and $IMAGENET_TEST_DIR.