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Exploring Effective Data for Surrogate Training Towards Black-box Attack (CVPR'22)

This repo contains the code for our CVPR 2022 paper "Exploring Effective Data for Surrogate Training Towards Black-Box Attack" by Xuxiang Sun, Gong Cheng, Hongda Li, Lei Pei, and Junwei Han.

Abstract: Without access to the training data where a black-box victim model is deployed, training a surrogate model for black-box adversarial attack is still a struggle. In terms of data, we mainly identify three key measures for effective surrogate training in this paper. First, we show that leveraging the loss introduced in this paper to enlarge the inter-class similarity makes more sense than enlarging the inter-class diversity like existing methods. Next, unlike the approaches that expand the intra-class diversity in an implicit model-agnostic fashion, we propose a loss function specific to the surrogate model for our generator to enhance the intra-class diversity. Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training. To this end, we propose a triple-player framework by introducing a discriminator into the traditional data-free framework. In this way, our method can be competitive when there are few semantic overlaps between the scarce proxy data (with the size between 1k and 5k) and the training data. We evaluate our method on a range of victim models and datasets. The extensive results witness the effectiveness of our method.

<p align="middle"> <img src="img/framework.png"> </p>

:sparkles: News

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:blue_book: Citation

If you think this repository may be helpful to you, please consider giving a star :star: and citation. Thanks for your consideration.

@inproceedings{sun2022exploring,
  title={Exploring effective data for surrogate training towards black-box attack},
  author={Sun, Xuxiang and Cheng, Gong and Li, Ke and Guo, Lei and Han, Junwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15355--15364},
  year={2022}
}

:page_facing_up: System Environment

:bookmark_tabs: Quick Start

1. Preparing the required packages

To install the python environment, please run:

conda env create -f ./stdata.yaml

Also, we provide the packages installed via pip. Please refer to ./stdata_pip.txt.

:point_right: Tips After installing the environment directly, "advertorch" could report an error because of the following code:

from torch.autograd.gradcheck import zero_gradients

To fix this, simply point to the "advertorch/attacks/fast_adaptive_boundary.py" and comment it out

from torch.autograd.gradcheck import zero_gradients

Then, search the full text of the file for "zero_gradients" and replace "zero_gradients(x)" with" x.grad.zero_ ()"

2. Preparing datasets

The datasets should be downloaded and formatted in ./datasets as that in ./datasets_dict/tree.txt.

3. Training your victims

we provide the codes regarding training your victims. To this end, please run the following command:

CUDA_VISIBLE_DEVICES=<gpuid> python ./tools/train_backbone.py --dataroot ./datasets/<data> --model backbone --backbone <net> --niter <e> --batch_size <b> --lr 0.1 --name backbone/<data>_<net>

Here, --name is to determine where the trained weight of the victim <net> is saved. You should use the above format to set the value of --name, or the pre-trained victim could not be found by our code.

After that, the trained weights will be saved at ./checkpoints/backbone/<data>_<net>/latest_net_backbone.pth, which will be called by the following step.

:gift: Acknowledgement

This repo is mainly based on the framework of pytorch-CycleGAN-and-pix2pix. Thanks for their great work.