A Multi-Factor Combinations Enhanced Reversible Privacy Protection System for Facial Images

With the abuse of deepfake and other deep learning technologies, anonymization and deanonymization for face images have become one of the essential tasks for privacy protection. Thus, we propose a novel reversible privacy protection framework for facial images based on conditional encoder and de-coder framework. For the purpose to increase the diversity and controllability over the anonymized faces, we also introduce facial attributes and a style vector from a reference back-ground face dataset and pretrained face recognition model and thus name the proposed framework as the Multi-factor Modifier (MfM) to achieve multi-factor facial de/re-identification. Specifically, with the correct password, our method produces near-original reconstructed images. Otherwise, it can generate photo-realistic and diverse anonymized images. With extensive experiments, it shows that the proposed approach can successfully anonymize face images in high fidelity according to the given conditions as compared with other methods and deanonymize without altering the facial data distributions.