PrivacyGAN: robust generative image privacy
Mariia Zameshina Marlene Careil Olivier Teytaud Laurent Najman
Classical techniques for protecting facial image privacy typically fall into
two categories: data-poisoning methods, exemplified by Fawkes, which introduce
subtle perturbations to images, or anonymization methods that generate images
resembling the original only in several characteristics, such as gender,
ethnicity, or facial expression.In this study, we introduce a novel approach,
PrivacyGAN, that uses the power of image generation techniques, such as VQGAN
and StyleGAN, to safeguard privacy while maintaining image usability,
particularly for social media applications. Drawing inspiration from Fawkes,
our method entails shifting the original image within the embedding space
towards a decoy image.We evaluate our approach using privacy metrics on
traditional and novel facial image datasets. Additionally, we propose new
criteria for evaluating the robustness of privacy-protection methods against
unknown image recognition techniques, and we demonstrate that our approach is
effective even in unknown embedding transfer scenarios. We also provide a human
evaluation that further proves that the modified image preserves its utility as
it remains recognisable as an image of the same person by friends and family.