Abstract
This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake images, with the objective of anonymizing patients information in the health sector. This is intended to create valuable images that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN’s state of the art and available databases has been developed. The outcome of the research is a GAN system prototype adapted to generate personal images that imitates provided samples. The performance of this prototype has been checked and satisfactory results have been obtained. Moreover, a novel research pathway has been opened so further research can be developed.
This research has been partially supported by the EDITH Research Project (PGC2018-102145-B-C22 (AEI/FEDER, UE)), funded by the Spanish Ministry of Science, Innovation and Universities – State Research Agency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bae, H., Jung, D., Yoon, S.: AnomiGAN: generative adversarial networks for anonymizing private medical data. arXiv (2019). https://arxiv.org/abs/1901.11313
Barnett, S.A.: Convergence problems with generative adversarial networks (GANs) (2018). https://arxiv.org/abs/1806.11382
Feutry, C., Piantanida, P., Bengio, Y., Duhamel, P.: Learning anonymized representations with adversarial neural networks. arXiv (2018). https://arxiv.org/abs/1802.09386
Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc., New york (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Lakhey, M.: Generative adversarial networks demystified. Medium, Data Driven Investor (2019). https://medium.com/datadriveninvestor/gans-demystified-f057f5e32fc9
Li, S.C.X., Jiang, B., Marlin, B.: MisGan: learning from incomplete data with generative adversarial networks. arXiv https://arxiv.org/abs/1902.09599 (2019)
Wang, P., Shao, S., Yan, R.: Generative adversarial networks for data augmentation in machine fault diagnosis. Comput. Ind. 106, 85–93 (2019). https://doi.org/10.1016/j.compind.2019.01.001
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Piacentino, E., Angulo, C. (2020). Anonymizing Personal Images Using Generative Adversarial Networks. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)