  The nature of deep neural networks has given rise to a variety of attacks,
but little work has been done to address the effect of adversarial attacks on
segmentation models trained on MRI datasets. In light of the grave consequences
that such attacks could cause, we explore four models from the U-Net family and
examine their responses to the Fast Gradient Sign Method (FGSM) attack. We
conduct FGSM attacks on each of them and experiment with various schemes to
conduct the attacks. In this paper, we find that medical imaging segmentation
models are indeed vulnerable to adversarial attacks and that there is a
negligible correlation between parameter size and adversarial attack success.
Furthermore, we show that using a different loss function than the one used for
training yields higher adversarial attack success, contrary to what the FGSM
authors suggested. In future efforts, we will conduct the experiments detailed
in this paper with more segmentation models and different attacks. We will also
attempt to find ways to counteract the attacks by using model ensembles or
special data augmentations. Our code is available at
https://github.com/ZhongxuanWang/adv_attk
