Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness

Understand

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.