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

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

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/"

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

python evaluation_texture.py --method TCA --load_path pretrained/TCA.npy --prepare_data

python evaluation_texture.py --method EGA --load_path pretrained/EGA.pkl --prepare_data

python evaluation_texture.py --method TCEGA --load_path pretrained/EGA.pkl --load_path_z pretrained/TCEGA_z.npy --prepare_data

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:

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/".