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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)

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

General Information

This repository is organized as follows:

Frequently Asked Questions (FAQ)

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.