ReFaceX: donor-driven reversible face anonymisation with detached recovery
摘要
Organisations must share facial imagery that remains useful for analysis while protecting identity. Many current methods fail to strike this balance: reconstruction-centred encoder–decoder designs tend to blur salient detail, whereas latent edits in pretrained generators often retain or drift identity cues, undermining privacy and utility. We present ReFaceX, a reversible anonymisation framework that separates what to protect from what to preserve. A donor identity code steers a U-Net anonymiser with Identity Feature Fusion to change identity while retaining non-identity content such as pose, background and expression. A learned steganographic channel carries a compact recovery payload, and reconstruction gradients are blocked at the stego image so the anonymiser is never rewarded for keeping identity. The threat model is stated explicitly and outcomes are audited with strong recognisers. On LFW and CelebA-HQ datasets at