NullFace: Training-Free Localized Face Anonymization
Authors: name: Han-Wei Kung, name: Tuomas Varanka, name: Terence Sim, name: Nicu Sebe

Privacy concerns around ever increasing number of cameras are increasing in
today's digital age. Although existing anonymization methods are able to
obscure identity information, they often struggle to preserve the utility of
the images. In this work, we introduce a training-free method for face
anonymization that preserves key non-identity-related attributes. Our approach
utilizes a pre-trained text-to-image diffusion model without requiring
optimization or training. It begins by inverting the input image to recover its
initial noise. The noise is then denoised through an identity-conditioned
diffusion process, where modified identity embeddings ensure the anonymized
face is distinct from the original identity. Our approach also supports
localized anonymization, giving users control over which facial regions are
anonymized or kept intact. Comprehensive evaluations against state-of-the-art
methods show our approach excels in anonymization, attribute preservation, and
image quality. Its flexibility, robustness, and practicality make it
well-suited for real-world applications. Code and data can be found at
https://github.com/hanweikung/nullface .