  As more and more personal photos are shared and tagged in social media,
avoiding privacy risks such as unintended recognition becomes increasingly
challenging. We propose a new hybrid approach to obfuscate identities in photos
by head replacement. Our approach combines state of the art parametric face
synthesis with latest advances in Generative Adversarial Networks (GAN) for
data-driven image synthesis. On the one hand, the parametric part of our method
gives us control over the facial parameters and allows for explicit
manipulation of the identity. On the other hand, the data-driven aspects allow
for adding fine details and overall realism as well as seamless blending into
the scene context. In our experiments, we show highly realistic output of our
system that improves over the previous state of the art in obfuscation rate
while preserving a higher similarity to the original image content.
