Abstract. 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 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.