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

Convolutional neural network that analyzes self-generated images in a variety of languages to find etymological similarities

《Train in Germany, Test in The USA: Making 3D Object Detectors Generalize》(CVPR 2020)

Few-Shot Object Detection via Association and DIscrimination

Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

PyTorch-based framework for Deep Hedging

商品推荐系统

PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

Julia and Matlab codes to simulated all problems in El-Hachem, McCue and Simpson (2021)

Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

Contrastive Learning Inverts the Data Generating Process

Depression Asisstant GDSC Challenge Solution

Inteligência artificial criada para realizar interação social com idosos.

Experimental Python implementation of OpenVINO Inference Engine (very slow, limited functionality). All codes are written in Python. Easy to read and modify.

This is the pytorch re-implementation of the IterNorm

[CVPRW 2022] Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Campsite Reservation Finder

A PyTorch Lightning solution to training OpenAI's CLIP from scratch.

Google-drive-to-sqlite - Create a SQLite database containing metadata from Google Drive

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.