  Recently it's been shown that neural networks can use images of human faces
to accurately predict Body Mass Index (BMI), a widely used health indicator. In
this paper we demonstrate that a neural network performing BMI inference is
indeed vulnerable to test-time adversarial attacks. This extends test-time
adversarial attacks from classification tasks to regression. The application we
highlight is BMI inference in the insurance industry, where such adversarial
attacks imply a danger of insurance fraud.
