  Computer vision and machine learning can be used to automate various tasks in
cancer diagnostic and detection. If an attacker can manipulate the automated
processing, the results can be devastating and in the worst case lead to wrong
diagnosis and treatment. In this research, the goal is to demonstrate the use
of one-pixel attacks in a real-life scenario with a real pathology dataset,
TUPAC16, which consists of digitized whole-slide images. We attack against the
IBM CODAIT's MAX breast cancer detector using adversarial images. These
adversarial examples are found using differential evolution to perform the
one-pixel modification to the images in the dataset. The results indicate that
a minor one-pixel modification of a whole slide image under analysis can affect
the diagnosis by reversing the automatic diagnosis result. The attack poses a
threat from the cyber security perspective: the one-pixel method can be used as
an attack vector by a motivated attacker.
