  Machine learning models are vulnerable to adversarial inputs that induce
seemingly unjustifiable errors. As automated classifiers are increasingly used
in industrial control systems and machinery, these adversarial errors could
grow to be a serious problem. Despite numerous studies over the past few years,
the field of adversarial ML is still considered alchemy, with no practical
unbroken defenses demonstrated to date, leaving PHM practitioners with few
meaningful ways of addressing the problem. We introduce turbidity detection as
a practical superset of the adversarial input detection problem, coping with
adversarial campaigns rather than statistically invisible one-offs. This
perspective is coupled with ROC-theoretic design guidance that prescribes an
inexpensive domain adaptation layer at the output of a deep learning model
during an attack campaign. The result aims to approximate the Bayes optimal
mitigation that ameliorates the detection model's degraded health. A
proactively reactive type of prognostics is achieved via Monte Carlo simulation
of various adversarial campaign scenarios, by sampling from the model's own
turbidity distribution to quickly deploy the correct mitigation during a
real-world campaign.
