  Voter fraud in the United States is rare and the vote-counting system is
robust against tampering, but there remains widespread distrust in the security
of election infrastructure among the public. We consider statistical means of
detecting anomalous election results that would be indicative of large-scale
fraud, focusing on scenarios in which votes are modified in in a localized
setting. The technique we develop, based on standard regression analysis, makes
use of the fact that vote share is correlated with demographics. We apply our
method to the results of the 2020 US presidential election as a
proof-of-concept, resulting in uncertainties at the few-percent level. We are
able to readily detect an artificial signal of such fraud in some cases, ruling
out some scenarios of localized fraud and placing constraints on other
scenarios.
