k-Same-Net

k-Anonymity with generative deep neural networks for face deidentification

Blaž Meden (Author), Žiga Emeršič (Author), Vitomir Štruc (Author), Peter Peer (Author)

Abstract

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.

Keywords

deidentifikacija obrazov;generativne nevronske mreže;algoritem k-Same;face deidentification;generative neural networks;k-Same algorithm;

Data

Language:	English
Year of publishing:	2018
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004.93'1
COBISS:	1537688771
ISSN:	1099-4300
Views:	176
Downloads:	72
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	deidentifikacija obrazov;generativne nevronske mreže;algoritem k-Same;
Type (COBISS):	Article
Pages:	str. 1-24
Volume:	ǂVol. ǂ20
Issue:	ǂno. ǂ1
Chronology:	2018
DOI:	10.3390/e20010060
ID:	13646701