@inbook{7477c53084214fc59b668c7c6aa8102f,
title = "On the Detection of Anomalous or Out-of-Distribution Data in Vision Models Using Statistical Techniques",
abstract = "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{\textquoteright}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.",
keywords = "Anomaly detection, Benford{\textquoteright}s law, Computer vision, Image corruption, Out-of-distribution data",
author = "Laura O{\textquoteright}Mahony and O{\textquoteright}Sullivan, {David Jp} and Nikolov, {Nikola S.}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-27762-7_40",
language = "English",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "426--435",
booktitle = "Lecture Notes on Data Engineering and Communications Technologies",
}