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

With the rapid growth of digital services and transactions, concerns about personal information leakage and security threats have intensified. In particular, facial images stored in surveillance systems, security agency databases, and biometric authentication platforms can be exploited for identity theft, fraud, phishing, illegal marketing, or deepfake-based misinformation. These risks have increased the need for technologies that securely protect stored facial data while enabling authorized restoration when necessary. We propose 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.