  In this paper, we study dependence of the success rate of adversarial attacks
to the Deep Neural Networks on the biomedical image type, control parameters,
and image dataset size. With this work, we are going to contribute towards
accumulation of experimental results on adversarial attacks for the community
dealing with biomedical images. The white-box Projected Gradient Descent
attacks were examined based on 8 classification tasks and 13 image datasets
containing a total of 605,080 chest X-ray and 317,000 histology images of
malignant tumors. We concluded that: (1) An increase of the amplitude of
perturbation in generating malicious adversarial images leads to a growth of
the fraction of successful attacks for the majority of image types examined in
this study. (2) Histology images tend to be less sensitive to the growth of
amplitude of adversarial perturbations. (3) Percentage of successful attacks is
growing with an increase of the number of iterations of the algorithm of
generating adversarial perturbations with an asymptotic stabilization. (4) It
was found that the success of attacks dropping dramatically when the original
confidence of predicting image class exceeds 0.95. (5) The expected dependence
of the percentage of successful attacks on the size of image training set was
not confirmed.
