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SmoothInv

Official PyTorch implementation of our CVPR 2023 paper:

Single Image Backdoor Inversion via Robust Smoothed Classifiers
Mingjie Sun, J. Zico Kolter
Carnegie Mellon University, Bosch Center for AI

For more details, please check out our <ins>paper</ins>.

We propose SmoothInv, a backdoor inversion method that reconstruct faithful backdoors from a single test image.

Setup

Create an new conda virtual environment

conda create -n smoothinv python=3.8 -y
conda activate smoothinv

Install Pytorch>=1.8.0, torchvision>=0.9.0 following official instructions. For example:

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Clone this repo and install required packages:

git clone 
pip install scikit-image

Backdoored Classifiers

Create a directory weights in the main repo and download backdoored classifiers listed below into this directory.

Also to use SmoothInv w diffusion, download the ImageNet unconditional diffusion model from guided-diffusion into the weights directory.

Evaluation

For visualization, use:

CUDA_VISIBLE_DEVICES=[GPU IDs] python main_vis.py --sigma [noise level] --backdoor_clf [trojai/htba/blind-p/blind-s/blind-g] --imagenet_dir [path to ImageNet] --eps 10 --no_diffusion

To evaluate the ASR of reversed backdoors, use:

CUDA_VISIBLE_DEVICES=[GPU IDs] python main_eval.py --sigma [noise level] --backdoor_clf [trojai/htba/blind-p/blind-s/blind-g] --imagenet_dir [path to ImageNet] --no_diffusion

Turn off --no_diffusion if you want to use SmoothInv w diffusion.

Acknowledgement

This repository is built using the smoothadv, blind-backdoors library and diffusion-denoised-smoothing repositories.

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

If you find this repository helpful, please consider citing:

@Article{sun2023smoothinv,
  author  = {Sun, Mingjie and Kolter, Zico},
  title   = {Single Image Backdoor Inversion via Robust Smoothed Classifiers},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2023},
}