  Since its discovery in 2013, the phenomenon of adversarial examples has
attracted a growing amount of attention from the machine learning community. A
deeper understanding of the problem could lead to a better comprehension of how
information is processed and encoded in neural networks and, more in general,
could help to solve the issue of interpretability in machine learning. Our idea
to increase adversarial resilience starts with the observation that artificial
neurons can be divided in two broad categories: AND-like neurons and OR-like
neurons. Intuitively, the former are characterised by a relatively low number
of combinations of input values which trigger neuron activation, while for the
latter the opposite is true. Our hypothesis is that the presence in a network
of a sufficiently high number of OR-like neurons could lead to classification
"brittleness" and increase the network's susceptibility to adversarial attacks.
After constructing an operational definition of a neuron AND-like behaviour, we
proceed to introduce several measures to increase the proportion of AND-like
neurons in the network: L1 norm weight normalisation; application of an input
filter; comparison between the neuron output's distribution obtained when the
network is fed with the actual data set and the distribution obtained when the
network is fed with a randomised version of the former called "scrambled data
set". Tests performed on the MNIST data set hint that the proposed measures
could represent an interesting direction to explore.
