Secure and Reversible Face De-Identification With Format-Preserving Encryption

a reversible face de-identification method using format-preserving encryption (FPE). Our method integrates symmetric-key FPE into two deep neural network (DNN)-based face swap models (FaceShifter and SimSwap) that separate identity and attribute information. This approach enables the generation of de-identified facial images and allows only authorized users to restore the original data. Experiments on the LFW, FFHQ, VGGFace2-HQ, and CelebA-HQ datasets showed that, on average, the FaceShifter model achieved a 98.64% de-identification success rate and 96.86% restoration rate, while SimSwap recorded 99.58% and 99.39%, respectively. Image quality was evaluated using SSIM, FID, LPIPS, PSNR, and BRISQUE, confirming that restored images closely resemble the originals. In conclusion, the proposed method provides a robust privacy-preserving solution for facial data in digital environments, balancing security and utility while supporting lawful restoration.