The project
Source code: [GitHub]
(Under construction...)
The very high recognition accuracy of iris-based biometric systems and the increasing distribution of high-resolution personal images on websites and social media are creating privacy risks that users and the biometric community have not yet addressed properly. Biometric information contained in the iris region can be used to automatically recognize individuals even after several years, potentially enabling pervasive identification, recognition, and tracking of individuals without explicit consent. To address this issue, this paper presents two main contributions. First, we demonstrate, through practical examples, that the risk associated with iris-based identification by means of images collected from public websites and social media is real. Second, we propose an innovative method based on generative adversarial networks (GANs) that can automatically generate novel images with high visual realism, in which all the biometric information associated with an individual in the iris region has been removed and replaced. We tested the proposed method on an image dataset composed of high-resolution portrait images collected from the web. The results show that the generated deidentified images significantly reduce the privacy risks and, in most cases, are indistinguishable from real samples.
The method
Examples
| Examples of faces with irises deidentified using the proposed approach (face image). The proposed method for iris deidentification generates images with high visual realism. |
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| Examples of images with irises deidentified using the proposed approach (only iris region). The proposed method for iris deidentification generates images with high visual realism. |
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Related publication
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M. Barni, R. Donida Labati, A. Genovese, V. Piuri, and F. Scotti, "Iris deidentification with high visual realism for privacy protection on websites and social networks", in IEEE Access, vol. 9, 2021, pp. 131995-132010. ISSN: 2169-3536. [DOI: 10.1109/ACCESS.2021.3114588] [PDF] [BibTex entry]
Citation
@Article {iride21,
author = {M. Barni and R. {Donida Labati} and A. Genovese and V. Piuri and F. Scotti},
title = {Iris deidentification with high visual realism for privacy protection on websites and social networks},
journal = {IEEE Access},
volume = {9},
pages = {131995-132010},
year = {2021},
note = {2169-3536}
}
People
- Mauro Barni, barni AT dii.unisi.it
- Ruggero Donida Labati, ruggero.donida AT unimi.it
- Angelo Genovese (maintainer) angelo.genovese AT unimi.it
- Vincenzo Piuri, vincenzo.piuri AT unimi.it
- Fabio Scotti, fabio.scotti AT unimi.it
Downloads
Source code: [GitHub]
(Under construction...)