  Source code attribution approaches have achieved remarkable accuracy thanks
to the rapid advances in deep learning. However, recent studies shed light on
their vulnerability to adversarial attacks. In particular, they can be easily
deceived by adversaries who attempt to either create a forgery of another
author or to mask the original author. To address these emerging issues, we
formulate this security challenge into a general threat model, the
$\textit{relational adversary}$, that allows an arbitrary number of the
semantics-preserving transformations to be applied to an input in any problem
space. Our theoretical investigation shows the conditions for robustness and
the trade-off between robustness and accuracy in depth. Motivated by these
insights, we present a novel learning framework,
$\textit{normalize-and-predict}$ ($\textit{N&P}$), that in theory guarantees
the robustness of any authorship-attribution approach. We conduct an extensive
evaluation of $\textit{N&P}$ in defending two of the latest
authorship-attribution approaches against state-of-the-art attack methods. Our
evaluation demonstrates that $\textit{N&P}$ improves the accuracy on
adversarial inputs by as much as 70% over the vanilla models. More importantly,
$\textit{N&P}$ also increases robust accuracy to 45% higher than adversarial
training while running over 40 times faster.
