20.500.12556/RUL-131874 k-Same-Net k-Anonymity with generative deep neural networks for face deidentification 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-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 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. face deidentification generative neural networks k-Same algorithm deidentifikacija obrazov generativne nevronske mreže algoritem k-Same true false true Angleški jezik Slovenski jezik Članek v reviji 2021-10-05 11:54:09 2021-10-05 11:54:09 2022-09-10 04:14:18 0000-00-00 00:00:00 2018 0 0 24 str. iss. 1, art. 60 Vol. 20 2018 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93\'1 1099-4300 10.3390/e20010060 1537688771 RAZ_Meden_Blaz_2018.pdf RAZ_Meden_Blaz_2018.pdf 1 E6710497C8C730409F16BC14D0FDE9DC eaa1ac8966171dcd027a9f8e31dcab526f85a40e28cdb5254278ee9f7dfe94d8 5af1e1ae-25c2-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149309 http://www.mdpi.com/1099-4300/20/1/60 1 e5198151-25c1-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149307 Fakulteta za računalništvo in informatiko Fakulteta za elektrotehniko 0 0 0