DDSR: An Identity Preservation Framework for Facial Privacy Protection

With the increasing risk of facial privacy leakage during image transmission, achieving both recognizability and privacy protection remains a major chal-lenge. This paper proposes a novel diffusion-based framework for facial im-age privacy, termed Diffusion-Driven Steganography and Recovery DDSR . DDSR utilizes a prompt-guided dual-phase diffusion strategy: during the ste-ganography phase, identity prompts guide latent perturbation, and reverse diffusion under unrelated prompts generates semantically irrelevant stego images during the recovery phase, the model re-encodes the stego image and reconstructs the original face under the guidance of the identity prompt. To enhance semantic alignment, we introduce a lightweight Prompt Consistency Regularization PCR , which aligns recovered images and prompts in CLIP semantic space during training. This regularization improves prompt control-lability without adding inference overhead. DDSR is compatible with low-resolution data and small-scale training, and does not rely on high-resolution inputs or large datasets. Extensive experiments demonstrate that DDSR achieves up to 98 face recognition accuracy on recovered images and out-performs prior methods by over 30 in resisting recognition attacks. Fur-thermore, DDSR provides improved robustness under image degradation while maintaining high visual quality and identity fidelity.