  We introduce a Noise-based prior Learning (NoL) approach for training neural
networks that are intrinsically robust to adversarial attacks. We find that the
implicit generative modeling of random noise with the same loss function used
during posterior maximization, improves a model's understanding of the data
manifold furthering adversarial robustness. We evaluate our approach's efficacy
and provide a simplistic visualization tool for understanding adversarial data,
using Principal Component Analysis. Our analysis reveals that adversarial
robustness, in general, manifests in models with higher variance along the
high-ranked principal components. We show that models learnt with our approach
perform remarkably well against a wide-range of attacks. Furthermore, combining
NoL with state-of-the-art adversarial training extends the robustness of a
model, even beyond what it is adversarially trained for, in both white-box and
black-box attack scenarios.
