# Adversarial_T

**Repository Path**: wang_yang123/adversarial_-t

## Basic Information

- **Project Name**: Adversarial_T
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-01-02
- **Last Updated**: 2024-01-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Adversarial Texture for Fooling Person Detectors in the Physical World

This is the official repository for the paper [Adversarial Texture for Fooling Person Detectors in the Physical World](https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html).

#### 1. Installation
### Requirements
All the codes are tested in the following environment:
* Linux (Ubuntu 18.04.4)
* Python 3.6
* PyTorch 1.7.1
* CUDA 11.0
* TensorboardX 2.2
* EasyDict 1.9

#### 2. Preparation
You need to download the yolov2 weights by
```
wget -P ./data/models/ https://pjreddie.com/media/files/yolov2.weights
```
and prepare the Inria Dataset
```
curl ftp://ftp.inrialpes.fr/pub/lear/douze/data/INRIAPerson.tar -o inria.tar
tar xf inria.tar
mv INRIAPerson ./data
```
#### 3. Evaluation
We provide the pre-trained parameters of 4 methods for implementing Adversarial Textures (AdvTexture) to attack YOLOv2. Use the following commands to output average precision (AP) for single run. Each command will additionally output a numpy data file and a png format image which are stored in directory "test_results/"
##### RCA
When the side length of the cloth is two times that of the patch
```
python evaluation_texture.py --method RCA --load_path pretrained/RCA2.npy --suffix yolov2_RCA2 --prepare_data
```
When the side length of the cloth is six times that of the patch
```
python evaluation_texture.py --method RCA --load_path pretrained/RCA6.npy --suffix yolov2_RCA6 --prepare_data
```
##### TCA
```
python evaluation_texture.py --method TCA --load_path pretrained/TCA.npy --prepare_data
```
##### EGA
```
python evaluation_texture.py --method EGA --load_path pretrained/EGA.pkl --prepare_data
```
##### TC-EGA
```
python evaluation_texture.py --method TCEGA --load_path pretrained/EGA.pkl --load_path_z pretrained/TCEGA_z.npy --prepare_data
```
##### Plot multiple results together
In addition, we provide a command to plot all the results together. For example, if one run all five evaluations above, then run:
```
python evaluation_texture.py --npz_dir ./test_results
```
It will output a precision v.s. recall curve located at "test_result/PR-curve.png". 

An instance:
<p align="center">
  <img src="test_results/PR-curve.png" width="70%">
</p>
 
#### 4. Train
We provide the command to train for each method.
```
 python training_texture.py --method $METHOD_NAME
```
$METHOD_NAME can be replaced to "RCA", "TCA", EGA" or "TCEGA" when one use different methods. The patterns and checkpoints will be saved in directory "results/".