Facial Attribute Based Text Guided Face Anonymization
To address the privacy-compliance dilemma in facial data collection—where individual consent is mandatory yet high-quality annotated data remains difficult to obtain—this paper proposes a three-stage face anonymization framework based on diffusion models. Methodologically, it introduces BrushNet, the first text-guided regeneration model that jointly incorporates semantic textual prompts (e.g., age, gender, expression) and facial attribute control, enabling image+mask+text co-conditioning without GAN training. Integrated with RetinaNet for precise facial region detection and VGG-Face for perceptual feature evaluation, the framework achieves fine-grained, controllable synthesis. The key contribution is the first systematic application of text-driven diffusion models to face anonymization, achieving strong de-identification guarantees while significantly improving visual naturalness and utility for downstream vision tasks (e.g., detection, recognition), thereby reconciling stringent privacy protection with high-fidelity reconstruction.