Overcoming the Paradox of Certified Training with Gaussian Smoothing

arXiv:2403.07095v1 Announce Type: new
Abstract: Training neural networks with high certified accuracy against adversarial examples remains an open problem despite significant efforts. While certification methods can effectively leverage tight convex relaxations for bound computation, in training, these methods perform worse than looser relaxations. Prior work hypothesized that this is caused by the discontinuity and perturbation sensitivity of the loss surface induced by these tighter relaxations. In this work, we show theoretically that Gaussian Loss Smoothing can alleviate both of these …

abstract accuracy adversarial adversarial examples arxiv certification computation cs.lg examples networks neural networks paradox prior training type work