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

Last update: Dec 02, 2022

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.

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

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

Related tags

Overview

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

Citation

Hierarchical-Bayeisan-Defense

Dataset

Network

Attack (by torchattack)

Defense methods

How to Train

1. Adversarial training

2. Adversarial training with BNN

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

How to Test

Testing adversarial robustness

How to check the learning parameters and KL divergence

How to check uncertainty by predictive entropy

Owner

LBK

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PIXIE: Collaborative Regression of Expressive Bodies

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

Related tags

Overview

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

Citation

Hierarchical-Bayeisan-Defense

Dataset

Network

Attack (by torchattack)

Defense methods

How to Train

1. Adversarial training

2. Adversarial training with BNN

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

How to Test

Testing adversarial robustness

How to check the learning parameters and KL divergence

How to check uncertainty by predictive entropy

Owner

LBK

Scrutinizing XAI with linear ground-truth data

The challenge for Quantum Coalition Hackathon 2021

On-device wake word detection powered by deep learning.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

A collection of educational notebooks on multi-view geometry and computer vision.

Official implementation of Long-Short Transformer in PyTorch.

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning

Code and Data for the paper: Molecular Contrastive Learning with Chemical Element Knowledge Graph [AAAI 2022]

PIXIE: Collaborative Regression of Expressive Bodies

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

Code accompanying "Learning What To Do by Simulating the Past", ICLR 2021.

SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Differentiable molecular simulation of proteins with a coarse-grained potential

Source code for Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Towards Debiasing NLU Models from Unknown Biases

Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

This is the Pytorch implementation of Progressive Attentional Manifold Alignment.