A MULTI-FACTOR COMBINATIONS ENHANCED REVERSIBLE PRIVACY PROTECTION SYSTEM FOR FACIAL IMAGES
Journal
Proceedings - IEEE International Conference on Multimedia and Expo
Date Issued
2021
Author(s)
Abstract
With the abuse of deepfake and other deep learning technologies, anonymization and de-anonymization 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 decoder 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 background 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. © 2021 IEEE
Subjects
de/re-identification; Generative Adversarial Network; Privacy protection
SDGs
Other Subjects
Deep learning; Face recognition; Image enhancement; Anonymization; De/re-identification; Face images; Facial images; Image-based; Learning technology; Multi-factor; Privacy protection; Protection systems; Re identifications; Generative adversarial networks
Type
conference paper