  Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, a threat to these systems arises that
adversarial attacks make CNNs vulnerable. Inaccurate diagnosis results make a
negative influence on human healthcare. There is a need to investigate
potential adversarial attacks to robustify deep medical diagnosis systems. On
the other side, there are several modalities of medical images (e.g., CT,
fundus, and endoscopic image) of which each type is significantly different
from others. It is more challenging to generate adversarial perturbations for
different types of medical images. In this paper, we propose an image-based
medical adversarial attack method to consistently produce adversarial
perturbations on medical images. The objective function of our method consists
of a loss deviation term and a loss stabilization term. The loss deviation term
increases the divergence between the CNN prediction of an adversarial example
and its ground truth label. Meanwhile, the loss stabilization term ensures
similar CNN predictions of this example and its smoothed input. From the
perspective of the whole iterations for perturbation generation, the proposed
loss stabilization term exhaustively searches the perturbation space to smooth
the single spot for local optimum escape. We further analyze the KL-divergence
of the proposed loss function and find that the loss stabilization term makes
the perturbations updated towards a fixed objective spot while deviating from
the ground truth. This stabilization ensures the proposed medical attack
effective for different types of medical images while producing perturbations
in small variance. Experiments on several medical image analysis benchmarks
including the recent COVID-19 dataset show the stability of the proposed
method.

Computer Vision and Pattern Recognition;Cryptography and Security;Machine Learning;Image and Video Processing