  We provide a complete characterisation of the phenomenon of adversarial
examples - inputs intentionally crafted to fool machine learning models. We aim
to cover all the important concerns in this field of study: (1) the conjectures
on the existence of adversarial examples, (2) the security, safety and
robustness implications, (3) the methods used to generate and (4) protect
against adversarial examples and (5) the ability of adversarial examples to
transfer between different machine learning models. We provide ample background
information in an effort to make this document self-contained. Therefore, this
document can be used as survey, tutorial or as a catalog of attacks and
defences using adversarial examples.
