The Dimpled Manifold Model of Adversarial Examples in Machine Learning. (arXiv:2106.10151v2 [cs.LG] UPDATED)

The extreme fragility of deep neural networks, when presented with tiny
perturbations in their inputs, was independently discovered by several research
groups in 2013. However, despite enormous effort, these adversarial examples
remained a counterintuitive phenomenon with no simple testable explanation. In
this paper, we introduce a new conceptual framework for how the decision
boundary between classes evolves during training, which we call the {\em
Dimpled Manifold Model}. In particular, we demonstrate that training is divided
into two distinct phases. The …

adversarial lg machine machine learning manifold