Abstract
Recent advances in deep learning and big data have greatly promoted the development of image recognition technology. In the meantime, however, it also makes it more challenging to protect human identify information. In this paper, we propose a novel framework called Embedded AutoEncoders to address face de-identification problem in deep learning. The structure of our framework contains two parts: a Privacy Removal Network and a Feature Selection Network. The main objective of our framework is to ensure that the Privacy Removal Network is capable of discarding information involving privacy and retaining desired information for certain image recognition applications. In order to achieve this goal, the design of the Privacy Removal Network is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. We evaluate the proposed framework through extensive experiments, which show that the Embedded AutoEncoders framework can not only effectively retain data utility, but also protect personal identity information.
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
Boyle, M., Edwards, C., Greenberg, S.: The effects of filtered video on awareness and privacy. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, pp. 1–10, December 2000
Ribaric, S., Pavesic, N.: An overview of face de-identification in still images and videos. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 4, pp. 1–6. IEEE, May 2015
Newton, E.M., Sweeney, L., Malin, B.: Preserving privacy by de-identifying face images. IEEE Trans. Knowl. Data Eng. 17(2), 232–243 (2005)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)
Gross, R., Sweeney, L., De la Torre, F., Baker, S.: Model-based face de-identification. In: 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), p. 161. IEEE, June 2006
Gross, R., Airoldi, E., Malin, B., Sweeney, L.: Integrating utility into face de-identification. In: Danezis, G., Martin, D. (eds.) PET 2005. LNCS, vol. 3856, pp. 227–242. Springer, Heidelberg (2006). https://doi.org/10.1007/11767831_15
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Osia, S.A., et al.: A hybrid deep learning architecture for privacy-preserving mobile analytics. IEEE Internet Things J. 7, 4505–4518 (2020)
Chi, H., Hu, Y.H.: Face de-identification using facial identity preserving features. In: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 586–590. IEEE, December 2015
Malekzadeh, M., Clegg, R.G., Haddadi, H.: Replacement autoencoder: a privacy-preserving algorithm for sensory data analysis. In: 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 165–176. IEEE (2018)
Mirjalili, V., Raschka, S., Namboodiri, A., Ross, A.: Semi-adversarial networks: convolutional autoencoders for imparting privacy to face images. In: 2018 International Conference on Biometrics (ICB), pp. 82–89. IEEE, February 2018
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103, July 2008
Wu, Y., Yang, F., Ling, H.: Privacy-protective-GAN for face de-identification. arXiv preprint arXiv:1806.08906 (2018)
Siwek, K., Osowski, S.: Autoencoder versus PCA in face recognition. In: 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), pp. 1–4. IEEE, September 2017
Almotiri, J., Elleithy, K., Elleithy, A.: Comparison of autoencoder and Principal Component Analysis followed by neural network for e-learning using handwritten recognition. In: 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–5. IEEE, May 2017
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC, vol. 1, no. 3, p. 6 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, J., Liu, J., Li, P., Kuang, Z. (2020). Embedded AutoEncoders: A Novel Framework for Face De-identification. In: Shen, J., Chang, YC., Su, YS., Ogata, H. (eds) Cognitive Cities. IC3 2019. Communications in Computer and Information Science, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6113-9_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-6113-9_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6112-2
Online ISBN: 978-981-15-6113-9
eBook Packages: Computer ScienceComputer Science (R0)