• DualAttentionAttack
• • • • Framework
  • Running
  • • before running
    • training
    • testing

DualAttentionAttack

This paper is accepted by CVPR'2021(Oral).

This paper proposed a dual attention supression attack approach, which exploits both the modle attention and human attention. Specifically, we distract the model attention to obtain a better attack ability, and moreover, we evade the human attention to help improving the naturalness.

Framework

Running

before running

you need:

• dataset
  • The dataset can be generated by CARLA, which is a 3D virtual simulated environment and a commonly used open-source simulator for autonomous driving research. Specifically, you can select different conditional parementers to dicide the angles, distances, and so on.
  • The dataset we generated can be accessed in baidu pan (dual) and Google Drive.
  • unzip the masks.zip and phy_attack.zip in the src/data.
• 3d object .obj and texture file .mtl (eg. src/audi_et_te.obj and src/audi_et_te.mtl)
• face id list .txt which need to be trained (eg. src/all_faces.txt)
• seed content texture and edge mask texture

training

python train.py --datapath=[path to dataset] --content=[path to seed content] --canny=[path to edge mask]

results will be stored in src/logs/, include:

• output images
• loss.txt
• texture.npy the trained texture file

testing

python test.py --texture=[path to texture]

results will be stored in src/acc.txt