  Face recognition models embed a face image into a low-dimensional identity
vector containing abstract encodings of identity-specific facial features that
allow individuals to be distinguished from one another. We tackle the
challenging task of inverting the latent space of pre-trained face recognition
models without full model access (i.e. black-box setting). A variety of methods
have been proposed in literature for this task, but they have serious
shortcomings such as a lack of realistic outputs and strong requirements for
the data set and accessibility of the face recognition model. By analyzing the
black-box inversion problem, we show that the conditional diffusion model loss
naturally emerges and that we can effectively sample from the inverse
distribution even without an identity-specific loss. Our method, named identity
denoising diffusion probabilistic model (ID3PM), leverages the stochastic
nature of the denoising diffusion process to produce high-quality,
identity-preserving face images with various backgrounds, lighting, poses, and
expressions. We demonstrate state-of-the-art performance in terms of identity
preservation and diversity both qualitatively and quantitatively, and our
method is the first black-box face recognition model inversion method that
offers intuitive control over the generation process.
