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https://github.com/RyanHangZhou/tensorflow-LG-GAN
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
https://github.com/RyanHangZhou/tensorflow-LG-GAN
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LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
- Host: GitHub
- URL: https://github.com/RyanHangZhou/tensorflow-LG-GAN
- Owner: RyanHangZhou
- License: mit
- Created: 2020-07-04T01:37:44.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-08-28T22:46:17.000Z (over 3 years ago)
- Last Synced: 2024-07-30T06:03:01.965Z (5 months ago)
- Language: Python
- Size: 129 KB
- Stars: 19
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LG-GAN
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
Created by [Hang Zhou](http://www.sfu.ca/~hza162/), [Dongdong Chen](http://www.dongdongchen.bid/), [Jing Liao](https://liaojing.github.io/html/), [Weiming Zhang](http://staff.ustc.edu.cn/~zhangwm/index.html), [Kejiang Chen](http://home.ustc.edu.cn/~chenkj/), [Xiaoyi Dong](https://scholar.google.com/citations?user=FscToE0AAAAJ&hl=en), [Kunlin Liu](https://scholar.google.com/citations?user=TaSC9y8AAAAJ&hl=en&oi=ao), [Gang Hua](https://www.ganghua.org/), [Nenghai Yu](http://staff.ustc.edu.cn/~ynh/).
Introduction
--
This repository is for our CVPR 2020 paper [LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks](https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.html).
Installation
--
This repository is based on Python 3.6, TensorFlow 1.8.0, CUDA 9.0 and cuDNN 7 on Ubuntu 18.04.
1. Set up a virtual environments using conda for the Anaconda Python distribution.
```shell
conda create -n LGGAN python=3.6 anaconda
```
2. Install tensorflow-gpu.
```shell
pip install tensorflow-gpu==1.8.0
```
3. While `nvcc` from CUDA needs to compile TF operators, install CUDA from CUDA Source Packages.
After downloading, implement
```shell
bash cuda_9.0.176_384.81_linux.run --tmpdir=/tmp --override
```
Note that the installation directory is set to `/xxx/cuda-9.0`
4. For compiling TF operators, please check `tf_xxx_compile.sh` under each op subfolder in `tf_ops` folder. Note that you need to update `nvcc`, `python` and `tensoflow` to include library if necessary.
5. Install other packages.
```shell
pip install h5py
pip install Pillow
pip install matplotlib
```
6. Point clouds of the ModelNet40 data in HDF5 files are downloaded and unzipped to the `data` folder.
```shell
wget -c https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip
unzip modelnet40_ply_hdf5_2048.zip
```
7. Download the pretrained PointNet model from [GoogleDrive](https://drive.google.com/drive/folders/11c6v_umZmSHiq-1TLKpSyPQK0E9fDkMU), extract it and put it in folder `checkpoints/pointnet/`.
8. Download [neural_toolbox](https://github.com/GuessWhatGame/neural_toolbox), extract it and put it in folder `LG-GAN`.
Usage
--
1. Activate LG-GAN environment.
```shell
source activate LGGAN
```
2. Set LD_LIBRARY_PATH.
```shell
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/xxx/cuda-9.0/lib64
```
3. Run:
```shell
python -u lggan.py --adv_path LGGAN --checkpoints_path LGGAN --log_path LGGAN --tau 1e2
python -u lggan_single.py --adv_path LGGAN_s --checkpoints_path LGGAN_s --log_path LGGAN_s --tau 1e2
python -u lg.py --adv_path LG --checkpoints_path LG --log_path LG --tau 1e2
```
Reference
--
If you find our LG-GAN is useful for your research, please consider citing:
@inproceedings{zhou2020lg,
title={LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks},
author={Zhou, Hang and Chen, Dongdong and Liao, Jing and Zhang, Weiming and Chen, Kejiang and Dong, Xiaoyi and Liu, Kunlin and Hua, Gang and Yu, Nenghai},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10356--10365},
year={2020}
}