8 citations · 8 across the 1 of their papers we have counts for
3 papers
cs.CV2022★ 8 cited
DeepPrivacy2: Towards Realistic Full-Body Anonymization
Håkon Hukkelås, Frank Lindseth
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymizatio…
cs.CV2020
Image Inpainting with Learnable Feature Imputation
Håkon Hukkelås, Frank Lindseth, Rudolf Mester
A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. Several studies address this issue with f…
cs.CV2019
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Håkon Hukkelås, Rudolf Mester, Frank Lindseth
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…