# adaptive-segmentation-mask-attack

**Repository Path**: cuge1995/adaptive-segmentation-mask-attack

## Basic Information

- **Project Name**: adaptive-segmentation-mask-attack
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-12
- **Last Updated**: 2022-02-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Adaptive Segmentation Mask Attack

This repository contains the implementation of the _Adaptive Segmentation Mask Attack (ASMA)_, a targeted adversarial example generation method for deep learning segmentation models. This attack was proposed in the paper "_Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation._" published in the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-2019. ([Link to the paper](https://arxiv.org/abs/1907.13124))

<img src="https://raw.githubusercontent.com/utkuozbulak/adaptive-segmentation-mask-attack/master/media/asma.png">

## General Information
This repository is organized as follows:
* **Code** - *src/* folder contains necessary python files to perform the attack and calculate various stats (i.e., correctness and modification)

* **Data** - *data/* folder contains a couple of examples for testing purposes. The data we used in this study can be taken from [1].
  
* **Model** - Example model used in this repository can be downloaded from https://www.dropbox.com/s/6ziz7s070kkaexp/eye_pretrained_model.pt . _helper_functions.py_ contains a function to load this file and _main.py_ contains an exaple that uses this model.

## Frequently Asked Questions (FAQ)

* How can I run the demo? 

  **1-** Download the model from https://www.dropbox.com/s/6ziz7s070kkaexp/eye_pretrained_model.pt
  
  **2-** Create a folder called _model_ on the same level as _data_ and _src_, put the model under this (_model_) folder.
  
  **3-** Run _main.py_.

* Would this attack work in multi-class segmentation models?

  Yes, given that you provide a proper target mask, model etc.
  
* Does the code require any modifications in order to make it work for multi-class segmentation models?

  No (probably, depending on your model/input). At least the attack itself (adaptive_attack.py) should not require major modifications on its logic.
 
## Citation
If you find the code in this repository useful for your research, consider citing our paper. Also, feel free to use any visuals available here.

    @inproceedings{ozbulak2019impact,
        title={Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation},
        author={Ozbulak, Utku and Van Messem, Arnout and De Neve, Wesley},
        booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
        pages={300--308},
        year={2019},
        organization={Springer}
    }


## Requirements
```
python > 3.5
torch >= 0.4.0
torchvision >= 0.1.9
numpy >= 1.13.0
PIL >= 1.1.7
```

## References
[1]  Pena-Betancor C., Gonzalez-Hernandez M., Fumero-Batista F., Sigut J., Medina-Mesa E., Alayon S., Gonzalez M. _Estimation of the relative amount of hemoglobin in the cup and neuroretinal rim using stereoscopic color fundus images._

[2] Ronneberger, O., Fischer, P., Brox, T. _U-Net: Convolutional networks for biomedical image segmentation._