Unified Adversarial Patch for Cross-modal Attacks in the Physical World

This is an official implementation and we aim to implement an effective rgb-infrared multi-modal attack in the physical world.

Usage

Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/Aries-iai/Cross-modal_Patch_Attack
$ cd Cross-modal_Patch_Attack
$ pip install -r requirements.txt

Data Convention

The data is organized as follows:

dataset  
|-- attack_infrared
    |-- 000.png        # images in the infrared modality
    |-- 001.png
    ...
|-- attack_visible
    |-- 000.png        # images in the visible modality
    |-- 001.png
    ...

Here, we should ensure the consistency of infrared images and visible images' names.

Running

python spline_DE_attack.py

Notes

1. When you prepare dataset, you need to change directory path in spline_DE_attack.py and DE.py.

2. If you want to attack other detection models, you only change yolov3 folder to the model folder you want to attack and return targets' detection confidence scores to DE.

Citation

Cite as below if you find this repository is helpful to your project:

@misc{wei2023unified,
      title={Unified Adversarial Patch for Cross-modal Attacks in the Physical World}, 
      author={Xingxing Wei and Yao Huang and Yitong Sun and Jie Yu},
      year={2023},
      eprint={2307.07859},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

Dataset is made from LLVIP: A Visible-infrared Paired Dataset for Low-light Vision. YOLOv3 code is the version of ultralytics-yolov3. Thanks for these great projects.