This is the official Tensorflow implementation of the universal physical camouflage (UPC) method proposed in Universal Physical Camouflage Attacks on Object Detectors. The project page (including demo & dataset) is here.
python 2.7
scipy 1.2.2
opencv-python 4.1.2
tensorflow 1.12rc1
easydict 1.6
Faster-RCNN our code uses the repository of tf-faster-rcnn as an example to attack.
(1) please download the repository from tf-faster-rcnn and compile the code according to its README.md.
(2) run the tools/demo.py to test the installation.
(3) copy the folder lib from tf-faster-rcnn into the root directory of UPC-tf except lib/nets/network.py file, which is uploaded and modified for feeding the tensor in our pipeline.
Download the training data from this link.
For changing the parameters, please refer to the settings in flags.py.
For runing the training code, please execute bash run_main.sh.
Requirements: Autodesk 3dsMax 2018 and V-ray 3.6.
(1) train the camouflage patterns
(2) download the dataset AttackScenes and release the human.zip and scenes.zip to get human models and scenes files.
(3) use Autodesk 3dsMax software to paste (i.e. uv mapping function) the trained patterns on human models.
(4) export the human model as (.fbx) format from 3dsMax software and import it into each virtual scene (.3max).
(5) install the V-ray 3.6 plug-ins (for 3dsMax), and render the images from pre-defined cameras.
(6) run the faster-rcnn demo tools/demo.py to test the rendered images.
We also provide the rendered images, please click here.
We collect the first standardized dataset, named AttackScenes, for fairly evaluating the performace of physical attacks under a controllable and reproducible environment.
Environments AttackScenes includes different virtual scenes under various physical conditions.
Cameras For each virtual scene, 18 cameras are placed for capturing images from different viewpoints.
Illuminations The illuminations are accessible at 3 levels, and can be adjusted by controlling the strength of light sources.
If you find this project is useful for your research, please consider citing:
@inproceedings{Huang2020UPC,
title={Universal Physical Camouflage Attacks on Object Detectors},
author={Lifeng Huang and Chengying Gao and Yuyin Zhou and Cihang Xie and Alan L. Yuille and Changqing Zou and Ning Liu},
booktitle={CVPR},
year={2020}
}