README.md

metadata

license: mit
datasets:
  - imagenet-1k
metrics:
  - accuracy
tags:
  - adversarial machine learning

RobArch: Designing Robust Architectures against Adversarial Attacks

ShengYun Peng, Weilin Xu, Cory Cornelius, Kevin Li, Rahul Duggal, Duen Horng Chau, Jason Martin

Check https://github.com/ShengYun-Peng/RobArch for the complete code.

Abstract

Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the adversarial training process, there are few investigations into how architecture components affect robustness, and they rarely constrain model capacity. Thus, it is unclear where robustness precisely comes from. In this work, we present the first large-scale systematic study on the robustness of DNN architecture components under fixed parameter budgets. Through our investigation, we distill 18 actionable robust network design guidelines that empower model developers to gain deep insights. We demonstrate these guidelines' effectiveness by introducing the novel Robust Architecture (RobArch) model that instantiates the guidelines to build a family of top-performing models across parameter capacities against strong adversarial attacks. RobArch achieves the new state-of-the-art AutoAttack accuracy on the RobustBench ImageNet leaderboard.

Prerequisites

1. Register Weights & Biases account
2. Prepare ImageNet via Fast AT - Installation step 3 & 4

   Run step 4 only if you want to use Fast-AT.

3. Set up venv:

make .venv_done

Training

Fast-AT is much faster than standard PGD AT. For RobArch-S, Fast-AT takes ~1.5 days on 2 Nvidia A100s, but ~5 days on 4 Nvidia A100s.

Torchvision models - Fast AT (e.g., ResNet-50)

make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/Torch_ResNet50/.done_test_pgd

If you want to test other off-the-shelf models in torchvision, add the model name in MODEL.mk, and create a new make target by following other ResNets/WideResNets in Makefile.

RobArch - Fast AT (e.g., RobArch-S)

make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/RobArch_S/.done_test_pgd

RobArch - Standard PGD AT (e.g., RobArch-S)

# Training
make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_train

# Evaluation on PGD
make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_pgd

# Evaluation on AutoAttack
make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_aa

# Pretrained models evaluated on AutoAttack
make BASE=<imagenet root dir> WANDB_ACCOUNT=<name> experiments/PGDAT_RobArch_S/.done_test_pretrained

Pretrained models

• ImageNet $\ell_\infty$-norm

Architecture	#Param	Natural	AutoAttack	PGD10-4	PGD50-4	PGD100-4	PGD100-2	PGD100-8
RobArch-S	26M	70.17%	44.14%	48.19%	47.78%	47.77%	60.06%	21.77%
RobArch-M	46M	71.88%	46.26%	49.84%	49.32%	49.30%	61.89%	23.01%
RobArch-L	104M	73.44%	48.94%	51.72%	51.04%	51.03%	63.49%	25.31%

Citation

@misc{peng2023robarch,
      title={RobArch: Designing Robust Architectures against Adversarial Attacks}, 
      author={ShengYun Peng and Weilin Xu and Cory Cornelius and Kevin Li and Rahul Duggal and Duen Horng Chau and Jason Martin},
      year={2023},
      eprint={2301.03110},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}