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

Related tags

Deep LearningHierarchical-Bayesian-Defense
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
GitHub Repository
Neural Scene Flow Prior (NeurIPS 2021 spotlight)

Neural Scene Flow Prior Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey Will appear on Thirty-fifth Conference on Neural Information Processing Syste

Lilac Lee 85 Jan 03, 2023
Genshin-assets - 👧 Public documentation & static assets for Genshin Impact data.

genshin-assets This repo provides easy access to the Genshin Impact assets, primarily for use on static sites. Sources Genshin Optimizer - An Artifact

Zerite Development 5 Nov 22, 2022
Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

Youngkyu 17 Jan 01, 2023
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.

Intermdiate layer matters - SSL The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper. Downl

Aakash Kaku 35 Sep 19, 2022
[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation

Self-Supervised Learning of Image Scale and Orientation Estimation (BMVC 2021) This is the official implementation of the paper "Self-Supervised Learn

Jongmin Lee 17 Nov 10, 2022
Binary classification for arrythmia detection with ECG datasets.

HEART DISEASE AI DATATHON 2021 [Eng] / [Kor] #English This is an AI diagnosis modeling contest that uses the heart disease echocardiography and electr

HY_Kim 3 Jul 14, 2022
Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

Machine_Learning Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning This project is based on 2 case-studies:

Avnika Mehta 1 Jan 27, 2022
Pipeline code for Sequential-GAM(Genome Architecture Mapping).

Sequential-GAM Pipeline code for Sequential-GAM(Genome Architecture Mapping). mapping whole_preprocess.sh include the whole processing of mapping. usa

3 Nov 03, 2022
Image Captioning using CNN and Transformers

Image-Captioning Keras/Tensorflow Image Captioning application using CNN and Transformer as encoder/decoder. In particulary, the architecture consists

24 Dec 28, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.

Streamlit - Drawable Canvas Streamlit component which provides a sketching canvas using Fabric.js. Features Draw freely, lines, circles, boxes and pol

Fanilo Andrianasolo 325 Dec 28, 2022
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis

OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis Overview OpenABC-D is a large-scale labeled dataset generate

NYU Machine-Learning guided Design Automation (MLDA) 31 Nov 22, 2022
PyTorch implementation of Deformable Convolution

PyTorch implementation of Deformable Convolution !!!Warning: There is some issues in this implementation and this repo is not maintained any more, ple

Wei Ouyang 893 Dec 18, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

This is the official implementation of our paper: Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal. "CaTGrasp: Learning Category-Level Task-R

Bowen Wen 199 Jan 04, 2023
Reinforcement Learning with Q-Learning Algorithm on gym's frozen lake environment implemented in python

Reinforcement Learning with Q Learning Algorithm Q learning algorithm is trained on the gym's frozen lake environment. Libraries Used gym Numpy tqdm P

1 Nov 10, 2021
An Active Automata Learning Library Written in Python

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

TU Graz - SAL Dependable Embedded Systems Lab (DES Lab) 78 Dec 30, 2022
A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.

OpenCDA OpenCDA is a SIMULATION tool integrated with a prototype cooperative driving automation (CDA; see SAE J3216) pipeline as well as regular autom

UCLA Mobility Lab 726 Dec 29, 2022