DiffDeid: High-Quality Face De-identification and Recovery via Diffusion Inversion

Nowadays, personal privacy protection is extremely emphasised. Face de-identification is considered as an effective way to protect the visual privacy through disguising or replacing identity attributes. Existing methods compromise either high fidelity or reversibility. To address these issues, this paper proposes DiffDeid, the first diffusion-based face de-identification and recovery method. Leveraging recent diffusion inversion and control technicques, DiffDeid achieves both high quality imperceptible de-identification and exact recovery with passwords. DiffDeid has three attractions: (1) It can generate de-identified faces with the superior fidelity while maintaining other non-identity attributes for visual tasks. (2) The correct password is powerful enough to restore facial images with extreme details. Meanwhile, incorrect passwords can lead to vastly different decryption results. (3) DiffDeid demands minimal computing resources and instant training time compared to others. We conducted experiments on various face datasets to showcase the superiority of our proposed method. Additional experiments show that DiffDeid is powerful with diverse text prompts and control instructions even beyond human faces. Codes are available at project page.