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Black-Box-Sparse-Adversarial-Attack-via-Multi-Objective-Optimisation

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Code for our CVPR 2023 paper: Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation

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# Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation

### This repository contains the code for our 2023 CVPR paper "Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation"

## Installation
<code>
1. Download repository <br>
2. pip install -r requirements.txt
</code>

## Attacking models
<code>
- The method relies on a taking a callable function that returns
the loss off of an adversarial image i.e. f(<strong>x_adv}</strong>). The method assumes the task is minimization problem. <br>
- View main.py for an example of how to run the method. We provide the suggested parameters there.
</code>

## Citation
<pre>
@inproceedings{williams2023black,
  title={Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation},
  author={Williams, Phoenix Neale and Li, Ke},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12291--12301},
  year={2023}
}
</pre>

## Paper
<code>
You can access the paper <a href="./CVPR_paper.pdf" download>[here]</a>.
</code>

Black-Box-Sparse-Adversarial-Attack-via-Multi-Objective-Optimisation

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