Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks

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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.