  Deep neural networks learn fragile "shortcut" features, rendering them
difficult to interpret (black box) and vulnerable to adversarial attacks. This
paper proposes semantic features as a general architectural solution to this
problem. The main idea is to make features locality-sensitive in the adequate
semantic topology of the domain, thus introducing a strong regularization. The
proof of concept network is lightweight, inherently interpretable and achieves
almost human-level adversarial test metrics - with no adversarial training!
These results and the general nature of the approach warrant further research
on semantic features. The code is available at
https://github.com/314-Foundation/white-box-nn
