Instability of computer vision models is a necessary result of the task
  itself
Oliver Turnbull George Cevora
Adversarial examples resulting from instability of current computer vision
models are an extremely important topic due to their potential to compromise
any application. In this paper we demonstrate that instability is inevitable
due to a) symmetries (translational invariance) of the data, b) the categorical
nature of the classification task, and c) the fundamental discrepancy of
classifying images as objects themselves. The issue is further exacerbated by
non-exhaustive labelling of the training data. Therefore we conclude that
instability is a necessary result of how the problem of computer vision is
currently formulated. While the problem cannot be eliminated, through the
analysis of the causes, we have arrived at ways how it can be partially
alleviated. These include i) increasing the resolution of images, ii) providing
contextual information for the image, iii) exhaustive labelling of training
data, and iv) preventing attackers from frequent access to the computer vision
system.