Abstract
Existing methods for face de-identification often cause inevitable damage to the utility of facial information. The anonymized facial images can hardly be applied in practical applications. In this work, we propose a cluster-based generative model that conceals the identity of images while preserving the utility of facial images. We extract facial features in the first stage, then classify images into several clusters. Four naive protection methods, blindfold, mosaic, cartoon and mosaic, are adopted to form facial image inputs without private information. Along with the four de-identified images, a random facial image in the same cluster is also chosen as another input for better preservation of facial features. We train a novel model with multiple inputs, called Multi-stage Utility Maintenance-Variational AutoEncoder (MsUM-VAE), generating a facial image using the mentioned multi-inputs. The output of the model retains a large portion of the facial characteristics, but cannot be distinguished from the original image dataset, avoiding the disclosure of privacy. We perform numerous evaluations on the CelebA dataset to showcase the effectiveness of our model, and the findings indicate that the model surpasses conventional techniques for obscuring identity and maintaining the utility of images.
The authors extend their appreciation to National Key Research and Development Program of China (International Technology Cooperation Project No.2021YFE014400) and National Science Foundation of China (No.42175194) for funding this work.
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Yang, Y. et al. (2023). A Cluster-Based Facial Image Anonymization Method Using Variational Autoencoder. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_45
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