Combating Adversarial Inputs Using a Predictive-Estimator Network

Jeff Orchard, Louis Castricato

Abstract: Deep classification networks have shown great accuracy in classifying inputs. However, they fall prey to adversarial inputs, random inputs chosen to yield a classification with a high confidence. But perception is a two-way process, involving the interplay between feedforward sensory input and feedback expectations. In this paper, we construct a predictive estimator (PE) network, incorporating generative (predictive) feedback, and show that the PE network is less susceptible to adversarial inputs. We also demonstrate some other properties of the PE network.