  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.
