Clone the repository:
https://github.com/tdchaitanya/nas-robustness.git
- Python 3.6
- Pytorch 1.2.0
- CUDA 10.1
For a hassle-free environment setup, use the environment.yml file included in the repository.
For easy reproduction of the result shown in the paper, this repository is organized dataset-wise, and all the pre-trained models can be downloaded from here
All the commands in this section should be executed in the cifar directory.
All the files corresponding to this dataset are included in cifar-10/100 directories. Download cifar weigths from the shared drive link and place them in nas-robustness/cifar-10/cifar10_models/state_dicts directory.
For running all the four attacks on Resnet-50 (shown in Table 1) run the following command.
python handcrafted.py --arch resnet50
Change the architecture parameter to run attacks on other models. Only resnet-18, resnet-50, densenet-121, densenet-169, vgg-16 are supported for now. For other models, you may have to train them from scratch before running these attacks.
For training the models on CIFAR-100 we have used fastai library. Download cifar-100 weigths from the shared drive link and place them in nas-robustness/cifar/c100-weights directory.
Additionally, you'll also have to download the CIFAR-100 dataset from here and place it in the data directory (we'll not be using this anywhere, this is just needed to initialize the fastai model).
python handcrafted_c100.py --arch resnet50
Download DARTS CIFAR-10/100 weights from the drive and place it nas-robustness/darts/pretrained
For running all the four attacks on DARTS run the following command:
python darts-nas.py
Add --cifar100 to run the experiments on cifar-100
Download P-DARTS CIFAR-10/100 weights from the drive and place it nas-robustness/pdarts/pretrained
For running all the four attacks on P-DARTS run the following command:
python pdarts-nas.py
Add --cifar100 to run the experiments on CIFAR-100
Download NSGA-Net CIFAR-10/100 weights from the drive and place it nas-robustness/nsga_net/pretrained
For running all the four attacks on P-DARTS run the following command:
python nsganet-nas.py
Add --cifar100 to run the experiments on CIFAR-100
Download PC-DARTS CIFAR-10/100 weights from the drive and place it nas-robustness/pcdarts/pretrained
For running all the four attacks on PC-DARTS run the following command:
python pcdarts-nas.py
Add --cifar100 to run the experiments on CIFAR-100
All the commands in this section should be executed in ImageNet directory.
All the files corresponding to this dataset are included in imagenet directory. We use the default pre-trained weights provided by PyTorch for all attacks.
For running all the four attacks on Resnet-50 run the following command:
python handcrafted.py --arch resnet50
For DARTS, P-DARTS, PC-DARTS follow the same instructions as mentioned above for CIFAR-10/100, just change the working directory to ImageNet
Download DenseNAS ImageNet weights from the drive (these are same as the weights provided in thier official repo) and place it nas-robustness/densenas/pretrained
For running all the four attacks on DenseNAS-R3 run the following command:
python dense-nas.py --model DenseNAS-R3
@InProceedings{Devaguptapu_2021_ICCV,
author = {Devaguptapu, Chaitanya and Agarwal, Devansh and Mittal, Gaurav and Gopalani, Pulkit and Balasubramanian, Vineeth N},
title = {On Adversarial Robustness: A Neural Architecture Search Perspective},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {October},
year = {2021},
pages = {152-161}
}Some of the code and weights provided in this library are borrowed from the libraries mentioned below: