  In this paper, we present a novel attack against authorship attribution of
source code. We exploit that recent attribution methods rest on machine
learning and thus can be deceived by adversarial examples of source code. Our
attack performs a series of semantics-preserving code transformations that
mislead learning-based attribution but appear plausible to a developer. The
attack is guided by Monte-Carlo tree search that enables us to operate in the
discrete domain of source code. In an empirical evaluation with source code
from 204 programmers, we demonstrate that our attack has a substantial effect
on two recent attribution methods, whose accuracy drops from over 88% to 1%
under attack. Furthermore, we show that our attack can imitate the coding style
of developers with high accuracy and thereby induce false attributions. We
conclude that current approaches for authorship attribution are inappropriate
for practical application and there is a need for resilient analysis
techniques.
