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.
@InProceedings{sun2018hybrid, title = {A Hybrid Model for Identity Obfuscation by Face Replacement}, author = {Sun, Qianru and Tewari, Ayush and Xu, Weipeng and Fritz, Mario and Theobalt, Christian and Schiele, Bernt}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018} }
