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Entropy 2018, 20(1), 60;

k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification

Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia
Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia
This paper is an extended version of our paper published in Meden B.; Emeršiˇc Ž.; Štruc V.; Peer P. k-Same-Net: Neural-Network-Based Face Deidentification. In the Proceedings of the International Conference and Workshop on Bioinspired Intelligence (IWOBI), Funchal Madeira, Portugal, 10–12 July 2017.
Author to whom correspondence should be addressed.
Received: 1 December 2017 / Revised: 31 December 2017 / Accepted: 9 January 2018 / Published: 13 January 2018
(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)
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Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure 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 after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control 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 k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area. View Full-Text
Keywords: face deidentification; generative neural networks; k-Same algorithm face deidentification; generative neural networks; k-Same algorithm

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Meden, B.; Emeršič, Ž.; Štruc, V.; Peer, P. k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification. Entropy 2018, 20, 60.

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