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Adversarial examples resulting from instability of current computer visionmodels are an extremely important topic due to their potential to compromiseany application. In this paper we demonstrate that instability is inevitabledue to a) symmetries (translational invariance) of the data, b) the categoricalnature of the classification task, and c) the fundamental discrepancy ofclassifying images as objects themselves. The issue is further exacerbated bynon-exhaustive labelling of the training data. Therefore we conclude thatinstability is a necessary result of how the problem of computer vision iscurrently formulated. While the problem cannot be eliminated, through theanalysis of the causes, we have arrived at ways how it can be partiallyalleviated. These include i) increasing the resolution of images, ii) providingcontextual information for the image, iii) exhaustive labelling of trainingdata, and iv) preventing attackers from frequent access to the computer visionsystem.

Instability of computer vision models is a necessary result of the task itself