Facial Identity Editing: Towards Effective De-Identification

We introduce a new method for face de-identification using a frozen diffusion model. In contrast to previous methods that carefully design and train a generative model, we reformulate face de-identification as an identity editing task and employ a pre-trained unconditional diffusion model. Also, unlike previous facial image editing approaches that try to preserve the identity and change only the demanded attributes, we aim to shift the identity while preserving the rest. This approach is significantly efficient because there is no need to construct or train any part of the diffusion model for identity shift. To the best of our knowledge, this is the first work to perform face de-identification with image editing. Ultimately, our findings, supported by both qualitative and quantitative results, show that image editing can effectively achieve de-identification.