FADO: A Deterministic Detection/Learning Algorithm

TL;DR

This paper introduces FADO, a deterministic online learning algorithm for adversarial detection that offers theoretical guarantees and is suitable for high-dimensional, non-stochastic, or small-sample scenarios.

Contribution

It presents a novel deterministic detection method based on online learning theory with proven performance bounds and practical case studies.

Findings

FADO provides theoretical detection guarantees.

The margin influences detection performance.

Experimental results validate the approach.

Abstract

This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can be because the studied phenomenon does not follow a stochastic sampling scheme, samples are high-dimensional and subsequent multiple-testing corrections render results overly conservative, sample sizes are too low for asymptotic results (as e.g. the central limit theorem) to kick in, or one cannot allow for the small probability of failure inherent to stochastic approaches. This paper instead designs a method based on insights from machine learning and online learning theory: this detection algorithm - named Online FAult Detection (FADO) - comes with theoretical guarantees of its detection capabilities. A version of the margin is found to regulate the detection performance of FADO. A precise expression is derived for bounding the performance, and experimental results are presented assessing the influence of involved quantities. A case study of scene detection is used to illustrate the approach. The technology is closely related to the linear perceptron rule, inherits its computational attractiveness and flexibility towards various extensions.