  While generalizing well over natural inputs, neural networks are vulnerable
to adversarial inputs. Existing defenses against adversarial inputs have
largely been detached from the real world. These defenses also come at a cost
to accuracy. Fortunately, there are invariances of an object that are its
salient features; when we break them it will necessarily change the perception
of the object. We find that applying invariants to the classification task
makes robustness and accuracy feasible together. Two questions follow: how to
extract and model these invariances? and how to design a classification
paradigm that leverages these invariances to improve the robustness accuracy
trade-off? The remainder of the paper discusses solutions to the aformenetioned
questions.
