  Face deidentification is an active topic amongst privacy and security
researchers. Early deidentification methods relying on image blurring or
pixelization were replaced in recent years with techniques based on formal
anonymity models that provide privacy guaranties and at the same time aim at
retaining certain characteristics of the data even after deidentification. The
latter aspect is particularly important, as it allows to exploit the
deidentified data in applications for which identity information is irrelevant.
In this work we present a novel face deidentification pipeline, which ensures
anonymity by synthesizing artificial surrogate faces using generative neural
networks (GNNs). The generated faces are used to deidentify subjects in images
or video, while preserving non-identity-related aspects of the data and
consequently enabling data utilization. Since generative networks are very
adaptive and can utilize a diverse set of parameters (pertaining to the
appearance of the generated output in terms of facial expressions, gender,
race, etc.), they represent a natural choice for the problem of face
deidentification. To demonstrate the feasibility of our approach, we perform
experiments using automated recognition tools and human annotators. Our results
show that the recognition performance on deidentified images is close to
chance, suggesting that the deidentification process based on GNNs is highly
effective.
