  We propose a novel architecture which is able to automatically anonymize
faces in images while retaining the original data distribution. We ensure total
anonymization of all faces in an image by generating images exclusively on
privacy-safe information. Our model is based on a conditional generative
adversarial network, generating images considering the original pose and image
background. The conditional information enables us to generate highly realistic
faces with a seamless transition between the generated face and the existing
background. Furthermore, we introduce a diverse dataset of human faces,
including unconventional poses, occluded faces, and a vast variability in
backgrounds. Finally, we present experimental results reflecting the capability
of our model to anonymize images while preserving the data distribution, making
the data suitable for further training of deep learning models. As far as we
know, no other solution has been proposed that guarantees the anonymization of
faces while generating realistic images.
