This repository contains code for reproducing our NeurIPS 2020 Oral paper "DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles".
Create the conda environment called dverge containing all the dependencies by running
conda env create -f environment.yml
We were using PyTorch 1.4.0 for all the experiments. You may want to install other versions of PyTorch according to the cuda version of your computer/server. The code is run and tested on a single TITAN Xp GPU. Running on multiple GPUs with parallelism may need adjustments.
The pre-trained models and generated black-box transfer adversarial examples can be accessed via this link. Specifically, the pre-trained models are stored in the folder named checkpoints. Download and put checkpoints under this repo.
The black-box transfer adversarial examples (refer to the paper for more details) are stored in transfer_adv_examples.zip. Make a folder named data under this repo. Download the zip file, unzip it, and put the extracted folder transfer_adv_examples/ under data/. Then one can evaluate the black-box transfer robustness of ensembles.
Examples of training and evaluation scripts can be found in scripts/training.sh and scripts/evaluation.sh.
Note that for now we extract models' intermediate features in a very naive way which may only support the ResNet20 architecture. One can implement a more robust feature extraction with the help of forward hook of Pytorch.
Also, you may observe a high variation in results when training DVERGE, which we suspect is due to the random layer sampling for distillation. Please refer to Appendix C.5 of the paper for a discussion on the layer effects.
We have been receiving many questions regarding the decision region plot in Figure 1. To understand how it works, a neat working example can be found in the "What is happening with these robust models?" section in this fantastic tutorial. Our code is adapted from that example, and the only difference is that while they plot the loss, we plot the model's decision/predicted class. Our code can be found here. It is pretty messy, yet the essential part starts from line 177. When plotting Figure 1, we use args.steps=1000 and args.vmax=0.1, which means that we are perturbing along each direction by a maximum of distance of 0.1, and along each direction we sample 1000 perturbations and record the model's decision on each of the corresponding perturbed sample. So totally we sample 1000*1000 data points to make each of the plot in Figure 1.
If you find our paper/this repo useful for your research, please consider citing our work.
@article{yang2020dverge,
title={DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles},
author={Yang, Huanrui and Zhang, Jingyang and Dong, Hongliang and Inkawhich, Nathan and Gardner, Andrew and Touchet, Andrew and Wilkes, Wesley and Berry, Heath and Li, Hai},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
The training code of ADP (Adaptive Diversity Promoting Regularizer) is adapted from the official repo, which is originally written in TensorFlow and we turned it into Pytorch here.