κ-Same-Net: Neural-Network-Based Face Deidentification

An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data deidentification. In this work, we propose a novel approach towards face deidentification, called κ-Same-Net, which combines recent generative neural networks (GNNs) with the well-known κ-anonymity mechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for idedentification by seamlessly combining features of identities used to train the GNN mode. furthermore, it allows us to guide the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of κ-Same-Net in comparative experiments with competing techniques on the XM2VTS dataset and discuss the main characteristics of our approach.

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