  Deep neural networks perform exceptionally well on various learning tasks
with state-of-the-art results. While these models are highly expressive and
achieve impressively accurate solutions with excellent generalization
abilities, they are susceptible to minor perturbations. Samples that suffer
such perturbations are known as "adversarial examples". Even though deep
learning is an extensively researched field, many questions about the nature of
deep learning models remain unanswered. In this paper, we introduce a new
conceptual framework attached with a formal description that aims to shed light
on the network's behavior and interpret the behind-the-scenes of the learning
process. Our framework provides an explanation for inherent questions
concerning deep learning. Particularly, we clarify: (1) Why do neural networks
acquire generalization abilities? (2) Why do adversarial examples transfer
between different models?. We provide a comprehensive set of experiments that
support this new framework, as well as its underlying theory.

Machine Learning;Cryptography and Security