This is the implementation of "Query-based Black-box Attack against Medical Image Segmentation Model", which has been accpeted by FGCS, 2022.
With the extensive deployment of deep learning, the research on adversarial example receives more concern than ever before. By modifying a small fraction of the original image, an adversary can lead a well-trained model to make a wrong prediction. However, existing works about adversarial attack and defense mainly focus on image classification but pay little attention to more practical tasks like segmentation. In this work, we propose a query-based black-box attack that could alter the classes of foreground pixels within a limited query budget. The proposed method improves the Adaptive Square Attack by employing a more accurate gradient estimation of loss and replacing the fixed variance of adaptive distribution with a learnable one. We also adopt a novel loss function proposed for attacking medical image segmentation models. Experiments on a widely-used dataset and well-known models demonstrate the effectiveness and efficiency of the proposed method in attacking medical image segmentation models.
Our code is based on the following dependencies
After fill the config file in cfg/, you can run the attack as follows
python attack.py cfg/examples.cfgYou can also train your own models by PyMIC and conduct an attack on it.