k-Same-Net : k-Anonymity with generative deep neural networks for face deidentification

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

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.

• Language: English
• Keywords: face deidentification, generative neural networks, k-Same algorithm
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2018
• Number of pages: 24 str.
• Numbering: Vol. 20, iss. 1, art. 60
• PID: 20.500.12556/RUL-131874
• UDC: 004.93\'1
• ISSN on article: 1099-4300
• DOI: 10.3390/e20010060
• COBISS.SI-ID: 1537688771
• Publication date in RUL: 05.10.2021

Record is a part of a journal

• Title: Entropy
• Shortened title: Entropy
• Publisher: MDPI
• ISSN: 1099-4300
• COBISS.SI-ID: 515806233

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 13.01.2018

Secondary language

• Language: Slovenian
• Keywords: deidentifikacija obrazov, generativne nevronske mreže, algoritem k-Same

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0250
• Name: Metrologija in biometrični sistemi

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0214
• Name: Računalniški vid