Diff-Privacy: Diffusion-based Face Privacy Protection

Privacy protection has become a top priority due to the widespread collection and misuse of personal data. Anonymization and visual identity information hiding are two crucial tasks in face privacy protection, both striving to alter identifying characteristics from face images to prevent privacy information leakage. However, the goals of the two are not entirely the same. Consequently, training a model to simultaneously perform both tasks proves challenging. In this paper, we propose Diff-Privacy, a novel face privacy protection method based on diffusion models that unifies the task of anonymization and visual identity information hiding. Specifically, we present a Multi-Scale image Inversion module (MSI) that, through training, generates a set of Stable Diffusion (SD) format conditional embeddings for the original image. With these conditional embeddings, we design corresponding embedding scheduling strategies and formulate distinct energy functions during the inference process to achieve anonymization and visual identity information hiding, respectively. Extensive experiments demonstrate the effectiveness of the proposed method in protecting face privacy.