Hierarchical-Bayesian-Defense - Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference (Openreview)

Overview

Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference [paper]

Baseline of this code is the official repository for this paper. We just replace the BNN regularizer from ELBO with enhanced Bayesian regularizer based on hierarchical-ELBO.

Alt text


Citation

If you find this work helpful, please cite it as:

@misc{
lee2021towards,
title={Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference},
author={Byung-Kwan Lee and Youngjoon Yu and Yong Man Ro},
year={2021},
url={https://openreview.net/forum?id=Cue2ZEBf12}
}

Hierarchical-Bayeisan-Defense

Dataset

  • CIFAR10
  • STL10
  • CIFAR100
  • Tiny-ImageNet

Network

  • VGG16 (for CIFAR-10/CIFAR-100/Tiny-ImageNet)
  • Aaron (for STL10)
  • WideResNet (for CIFAR-10/100)

Attack (by torchattack)

  • PGD attack
  • EOT-PGD attack

Defense methods

  • adv: Adversarial training
  • adv_vi: Adversarial training with Bayesian neural network
  • adv_hvi: Adversarial training with Enhanced Bayesian neural network based on hierarchical-ELBO

How to Train

1. Adversarial training

Run train_adv.sh

lr=0.01
steps=10
max_norm=0.03
data=tiny # or `cifar10`, `stl10`, `cifar100`
root=./datasets
model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
model_out=./checkpoint/${data}_${model}_${max_norm}_adv
echo "Loading: " ${model_out}
CUDA_VISIBLE_DEVICES=0 python ./main_adv.py \
                        --lr ${lr} \
                        --step ${steps} \
                        --max_norm ${max_norm} \
                        --data ${data} \
                        --model ${model} \
                        --root ${root} \
                        --model_out ${model_out}.pth \

2. Adversarial training with BNN

Run train_adv_vi.sh

lr=0.01
steps=10
max_norm=0.03
sigma_0=0.1
init_s=0.1
data=tiny # or `cifar10`, `stl10`, `cifar100`
root=./datasets
model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
model_out=./checkpoint/${data}_${model}_${max_norm}_adv_vi
echo "Loading: " ${model_out}
CUDA_VISIBLE_DEVICES=0 python3 ./main_adv_vi.py \
                        --lr ${lr} \
                        --step ${steps} \
                        --max_norm ${max_norm} \
                        --sigma_0 ${sigma_0} \
                        --init_s ${init_s} \
                        --data ${data} \
                        --model ${model} \
                        --root ${root} \
                        --model_out ${model_out}.pth \

3. Adversarial training with enhanced Bayesian regularizer based on hierarchical-ELBO

Run train_adv_hvi.sh

lr=0.01
steps=10
max_norm=0.03
sigma_0=0.1
init_s=0.1
data=tiny # or `cifar10`, `stl10`, `cifar100`
root=./datasets
model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
model_out=./checkpoint/${data}_${model}_${max_norm}_adv_hvi
echo "Loading: " ${model_out}
CUDA_VISIBLE_DEVICES=0 python3 ./main_adv_hvi.py \
                        --lr ${lr} \
                        --step ${steps} \
                        --max_norm ${max_norm} \
                        --sigma_0 ${sigma_0} \
                        --init_s ${init_s} \
                        --data ${data} \
                        --model ${model} \
                        --root ${root} \
                        --model_out ${model_out}.pth \

How to Test

Testing adversarial robustness

Run acc_under_attack.sh

model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
defense=adv_hvi # or `adv_vi`, `adv`
data=tiny-imagenet # or `cifar10`, `stl10`, `cifar100`
root=./datasets
n_ensemble=50
step=10
max_norm=0.03
echo "Loading" ./checkpoint/${data}_${model}_${max_norm}_${defense}.pth

CUDA_VISIBLE_DEVICES=0 python3 acc_under_attack.py \
    --model $model \
    --defense $defense \
    --data $data \
    --root $root \
    --n_ensemble $n_ensemble \
    --step $step \
    --max_norm $max_norm

How to check the learning parameters and KL divergence

Run check_parameters.sh

model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
defense=adv_hvi # or `adv_vi`
data=tiny-imagenet # or `cifar10`, `stl10`, `cifar100`
max_norm=0.03
echo "Loading" ./checkpoint/${data}_${model}_${max_norm}_${defense}.pth

CUDA_VISIBLE_DEVICES=0 python3 check_parameters.py \
    --model $model \
    --defense $defense \
    --data $data \
    --max_norm $max_norm \

How to check uncertainty by predictive entropy

Run uncertainty.sh

model=vgg # vgg for `cifar10` `stl10` `cifar100`, aaron for `stl10`, wide for `cifar10` or `cifar100`
defense=adv_hvi # or `adv_vi`
data=tiny-imagenet # or `cifar10`, `stl10`, `cifar100`
root=./datasets
n_ensemble=50
step=10
max_norm=0.03
echo "Loading" ./checkpoint/${data}_${model}_${max_norm}_${defense}.pth

CUDA_VISIBLE_DEVICES=0 python3 uncertainty.py \
    --model $model \
    --defense $defense \
    --data $data \
    --root $root \
    --n_ensemble $n_ensemble \
    --step $step \
    --max_norm $max_norm
Owner
LBK
Ph.D Candidate, KAIST EE
LBK
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