  One-pixel attack is a curious way of deceiving neural network classifier by
changing only one pixel in the input image. The full potential and boundaries
of this attack method are not yet fully understood. In this research, the
successful and unsuccessful attacks are studied in more detail to illustrate
the working mechanisms of a one-pixel attack created using differential
evolution. The data comes from our earlier studies where we applied the attack
against medical imaging. We used a real breast cancer tissue dataset and a real
classifier as the attack target. This research presents ways to analyze
chromatic and spatial distributions of one-pixel attacks. In addition, we
present one-pixel attack confidence maps to illustrate the behavior of the
target classifier. We show that the more effective attacks change the color of
the pixel more, and that the successful attacks are situated at the center of
the images. This kind of analysis is not only useful for understanding the
behavior of the attack but also the qualities of the classifying neural
network.
