  There is an increasing concern in computer vision devices invading users'
privacy by recording unwanted videos. On the one hand, we want the camera
systems to recognize important events and assist human daily lives by
understanding its videos, but on the other hand we want to ensure that they do
not intrude people's privacy. In this paper, we propose a new principled
approach for learning a video \emph{face anonymizer}. We use an adversarial
training setting in which two competing systems fight: (1) a video anonymizer
that modifies the original video to remove privacy-sensitive information while
still trying to maximize spatial action detection performance, and (2) a
discriminator that tries to extract privacy-sensitive information from the
anonymized videos. The end result is a video anonymizer that performs
pixel-level modifications to anonymize each person's face, with minimal effect
on action detection performance. We experimentally confirm the benefits of our
approach compared to conventional hand-crafted anonymization methods including
masking, blurring, and noise adding. Code, demo, and more results can be found
on our project page https://jason718.github.io/project/privacy/main.html.
