Hierarchical Randomized Smoothing

Reference implementation of the robustness certificate proposed in the paper:

Hierarchical Randomized Smoothing
Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Conference on Neural Information Processing Systems (NeurIPS), 2023
[ Project page | PDF | Talk | Slides | Poster ]

Examples

We provide two demo notebooks with examples of how to compute hierarchical smoothing certificates for images (demo-images.ipynb) and graphs (demo-graphs.ipynb).

We also provide pretrained models for efficient demonstration. If you want to use the pretrained ResNet50 model, execute the following:

wget --show-progress -qO data/precomputed/pretrained_ResNet50_CIFAR10.pt https://www.dropbox.com/scl/fi/el5onf6nha1wb16ro7e8h/pretrained_ResNet50_CIFAR10.pt?rlkey=xac8by8b7blz88st1a7h7e1nz

You can also manually download the file here.

Install

Instructions to install required dependencies:

python -m venv hs 
source hs/bin/activate
pip install -r requirements.txt
pip install .

The code was tested with Python 3.10.8, Pip 23.3.2, PyTorch 1.13.1+cu116, PyTorch-Geometric 2.2.0, and CUDA 11.6 on NVIDIA GeForce GTX 1080 Ti.

Usage

To compute robustness certificates systematically on a cluster we provide seml scripts:

For images:

export CUBLAS_WORKSPACE_CONFIG=:4096:8 # for reproducibility
seml hs add configs/images/*.yaml
seml hs start

For graphs:

export CUBLAS_WORKSPACE_CONFIG=:4096:8 # for reproducibility
seml hs add configs/graphs/*.yaml
seml hs start

We assume the following folder structure:

hierarchical_smoothing
├── configs
│   ├── graphs
│   │   ├── ablation.yaml
│   │   ├── hierarchical.yaml
│   │   ├── sparse.yaml
│   │   └── unprotected.yaml
│   └── images
│       ├── ablation.yaml
│       ├── gaussian.yaml
│       ├── hierarchical.yaml
│       └── unprotected.yaml
├── data
│   ├── graphs
│   │   └── cora_ml.npz
│   ├── images
│   └── seeds.pt
├── hierarchical_smoothing
├── results
│   ├── graphs
│   └── images

The computed robustness certificates will be stored in the results folder.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{scholten2023hierarchical,
title={Hierarchical Randomized Smoothing},
author={Yan Scholten and Jan Schuchardt and Aleksandar Bojchevski and Stephan G{\"u}nnemann},
booktitle={Advances in Neural Information Processing Systems, {NeurIPS}},
year={2023}
}

Contact

For questions and feedback please contact:

Yan Scholten, Technical University of Munich
Jan Schuchardt, Technical University of Munich
Aleksandar Bojchevski, University of Cologne, Germany
Stephan Günnemann, Technical University of Munich

License

The code by Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann is licensed under MIT license.