This repository is the official implementation of ACES for the paper Robust and Accurate - Compositional Architectures for Randomized Smoothing (ICLR 2022 Workshop on Socially Responsible Machine Learning).

The key idea of ACES is to utilize a smoothed selection-model to certifiable decide on a per-sample basis whether to use a robust smoothed certification-model with guarantees or a highly accurate core-model without guarantees. By doing so, it achieves significantly more attractive trade-offs between robustness and accuracy than any current method on ImageNet and CIFAR10.

This codebase implements ACES with entropy-based selection (smooth_ace.py and certify_ace.py), as well as an arbitrary binary selection mechanism (smooth_selection.py and certify_selection.py). In addition to the code, the scripts folder contains scripts that were used to create data for the paper. The resulting data is stored in the data folder. Finally, this data was processed by analyze_utils.py and the resulting LaTex tables are stored in the analysis folder.

First, please install the required environment as follows:

conda create --name aces_env python=3.8
conda activate aces_env
pip install -r requirements.txt

To install pytorch 1.8.0 and torchvision 0.9.0 you can use the following command (depending on your installed CUDA version (can be checked e.g., by running nvidia-smi)):

# CUDA 10.2
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch

# CUDA 11.1
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

If you are not using the most recent GPU drivers, please see here for a compatible cudatoolkit version.

We provide trained models on CIFAR10 and ImageNet we use here (2.3 GB). Please move the folder aces-models into the root directory of this codebase.

We provide an example of the whole ACES pipeline with an entropy-based selection model on CIFAR10 to show how to use our codebase. For this, first, please create a folder output_dir where the outputs of the example will be saved, e.g. via mkdir output_dir.

We can certify the certification and corresponding entropy-based selection model with the certify_ace.py code as follows:

python certify_ace.py \
    cifar10 \
    ./aces-models/cifar10/consistency/noise_0.25/checkpoint.pth.tar \
    0.25 \
    ./output_dir/output_file_smoothed

The first argument is the dataset (cifar10 or imagenet), the second is the path to the (pre-trained) classifier, followed by the certification noise and the path where the results should be saved.

We can compute results for the core model via predict_core.py as follows:

python predict_core.py \
    cifar10 \
    ./output_dir/output_file_core \
    --core_classifier ./aces-models/cifar10/gaussian/noise_0.00/checkpoint.pth.tar

The first argument is the dataset (either cifar10 or imagenet), the second one is the path where the output should be saved. The path to the classifier can be added with --core_classifier.

We remark, that for some core models, we used their some publicly available code bases and just adapted them such that they created the outputs in a similar format as predict_core.py:

• LaNet for CIFAR10:
• EfficientNet for ImageNet:

Finally, to compute the ACES outputs, we combine the results of the constituting models via analyze_utils.py:

python analyze_utils.py \
    output_dir/output_file_smoothed \
    output_dir/output_file_core \
    output_dir/aces_table \
    output_dir/aces_table_selection;

The output_dir/output_file_smoothed is the path to the outputs of the selection and certification model from from step 1, the output_dir/output_file_core is the path to the output from step 2. The final LaTex table with the certified radii is then stored in output_dir/aces_table while the corresponding certified selection radii is stored in output_dir/aces_table_selection.

• Miklós Z. Horváth
• Mark Niklas Müller
• Marc Fischer
• Martin Vechev

If you find this work useful for your research, please cite it as:

@misc{
    horvath2022robust,
    title={Robust and Accurate - Compositional Architectures for Randomized Smoothing},
    author={Mikl{\'o}s Z. Horv{\'a}th and Mark Niklas M{\"u}ller and Marc Fischer and Martin Vechev},
    booktitle={ICLR 2022 Workshop on Socially Responsible Machine Learning},
    year={2022},
    url={https://www.sri.inf.ethz.ch/publications/horvath2022robust}
}