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This repository contains the official PyTorch implementation of our paper: "CDMA: Query-Efficient Black-box Attack to Deep Neural Networks with Conditional Diffusion Models".

If you think this work or our codes are useful for your research, please cite our paper via:

@article{liu2024boosting,
  title={Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models},
  author={Liu, Renyang and Zhou, Wei and Zhang, Tianwei and Chen, Kangjie and Zhao, Jun and Lam, Kwok-Yan},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2024},
  volume={19},
  pages={5207--5219},
  publisher={IEEE}
}

To run this code, ensure you have the following packages installed:

• torch>=1.7.0
• torchvision>=0.8.1
• tqdm>=4.31.1
• pillow>=7.0.0
• matplotlib>=3.2.2
• numpy>=1.18.1

Installation of these packages can be achieved through pip: bash pip install torch torchvision tqdm pillow matplotlib numpy

Download the pre-trained weights for the victim models and CDMA from GoogleDrive. After downloading, unzip the file into the root directory of this project.

bash scripts/attack.sh

bash scripts/attack_T.sh

This project is licensed under the MIT License - see the LICENSE file for details.

The implementation extends the functionality of the Palette-Image-to-Image-Diffusion-Models GitHub repository. Thanks for their excellent works!