Cameras are prevalent in our daily lives, and enable many useful systems
built upon computer vision technologies such as smart cameras and home robots
for service applications. However, there is also an increasing societal concern
as the captured images/videos may contain privacy-sensitive information (e.g.,
face identity). We propose a novel face identity transformer which enables
automated photo-realistic password-based anonymization as well as
deanonymization of human faces appearing in visual data. Our face identity
transformer is trained to (1) remove face identity information after
anonymization, (2) make the recovery of the original face possible when given
the correct password, and (3) return a wrong--but photo-realistic--face given a
wrong password. Extensive experiments show that our approach enables multimodal
password-conditioned face anonymizations and deanonymizations, without
sacrificing privacy compared to existing anonymization approaches.Comment: ECCV 202