Structural Robustness for Deep Learning Architectures
This addresses robustness issues in deep learning for vision tasks, but appears incremental as it builds on existing notions.
The paper tackled the problem of deep networks being susceptible to noise like adversarial attacks by introducing a formal definition of robustness based on a localized Lipschitz constant, and evaluated it on competitive vision datasets.
Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including adversarial attacks and corrupted inputs. In this work we introduce a formal definition of robustness which can be viewed as a localized Lipschitz constant of the network function, quantified in the domain of the data to be classified. We compare this notion of robustness to existing ones, and study its connections with methods in the literature. We evaluate this metric by performing experiments on various competitive vision datasets.