Fooling Computer Vision into Inferring the Wrong Body Mass Index
This work addresses a security vulnerability in BMI inference systems, particularly for the insurance industry, but is incremental as it applies known adversarial attack methods to a new regression context.
The paper demonstrates that neural networks predicting Body Mass Index (BMI) from facial images are vulnerable to test-time adversarial attacks, extending such attacks from classification to regression tasks, with potential applications in insurance fraud.
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.