You Don't Need Robust Machine Learning to Manage Adversarial Attack
  Risks
Edward Raff Michel Benaroch Andrew L. Farris
The robustness of modern machine learning (ML) models has become an
increasing concern within the community. The ability to subvert a model into
making errant predictions using seemingly inconsequential changes to input is
startling, as is our lack of success in building models robust to this concern.
Existing research shows progress, but current mitigations come with a high cost
and simultaneously reduce the model's accuracy. However, such trade-offs may
not be necessary when other design choices could subvert the risk. In this
survey we review the current literature on attacks and their real-world
occurrences, or limited evidence thereof, to critically evaluate the real-world
risks of adversarial machine learning (AML) for the average entity. This is
done with an eye toward how one would then mitigate these attacks in practice,
the risks for production deployment, and how those risks could be managed. In
doing so we elucidate that many AML threats do not warrant the cost and
trade-offs of robustness due to a low likelihood of attack or availability of
superior non-ML mitigations. Our analysis also recommends cases where an actor
should be concerned about AML to the degree where robust ML models are
necessary for a complete deployment.