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arXiv · 1905.06916

Fooling Computer Vision into Inferring the Wrong Body Mass Index

Owen Levin·Zihang Meng·Vikas Singh·Xiaojin Zhu
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Abstract

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.

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Owen Levin, Zihang Meng, Vikas Singh, Xiaojin Zhu. 2019-05-16. Fooling Computer Vision into Inferring the Wrong Body Mass Index. https://arxiv.org/abs/1905.06916

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