On the Detection of Anomalous or Out-Of-Distribution Data in Vision
  Models Using Statistical Techniques
Laura O'Mahony David JP O'Sullivan Nikola S. Nikolov
Out-of-distribution data and anomalous inputs are vulnerabilities of machine
learning systems today, often causing systems to make incorrect predictions.
The diverse range of data on which these models are used makes detecting
atypical inputs a difficult and important task. We assess a tool, Benford's
law, as a method used to quantify the difference between real and corrupted
inputs. We believe that in many settings, it could function as a filter for
anomalous data points and for signalling out-of-distribution data. We hope to
open a discussion on these applications and further areas where this technique
is underexplored.