Abstract
A large amount of high-quality data are collected through autonomous vehicles, CCTVs, guidance service robots, and web map services (Google Street View). However, the data collected through them include personal information such as peoples’ faces and vehicle license plates. Currently, personal information contained in data is de-identified using methods such as face blur, pixelation, and masking. Consequently, it loses value as data for artificial intelligence (AI) learning. Therefore, in this study, we propose a model to generate a fake face that maintains the basic structure of the human face. There are several methods for generating faces. One is to generate them using a generative adversarial network (GAN) model. The GAN is an AI algorithm used for unsupervised learning and is implemented by a system in which two neural networks compete. However, because GAN operates as an input of a random noise vector, it is difficult to obtain results for the desired face angle and shape. Therefore, pre- and post-processing is required to generate a fake face that maintains the basic structure and angles; however, the calculation is complicated, and it is difficult to generate a natural image. To solve this problem, we propose a method for generating a fake face that maintains the basic structure and angle of the real face by applying a facial landmark. Using the proposed method, it was possible to generate a fake face with a different impression while maintaining the basic structure and angle of the face.
Similar content being viewed by others
References
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:1–13
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106
Fukushima K (1988) Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Netw 1(2):119–130
Ide H, Kurita T (2017) Improvement of learning for CNN with ReLU activation by sparse regularization. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 2684–2691.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Ribaric S, Ariyaeeinia A, Pavesic N (2016) De-identification for privacy protection in multimedia content: a survey. Signal Process Image Commun 47:131–151
Meden B, Rot P, Terhörst P, Damer N, Kuijper A, Scheirer WJ et al (2021) Privacy–enhancing face biometrics: a comprehensive survey. IEEE Trans Inf Forensics Secur 16:4147–4183
Oh SJ, Benenson R, Fritz M, Schiele B (2016) Faceless person recognition: privacy implications in social media. European Conference on Computer Vision. Springer, Cham, pp 19–35
McPherson R, Shokri R, Shmatikov V (2016) Defeating image obfuscation with deep learning. arXiv preprint arXiv:1609.00408
Newton EM, Sweeney L, Malin B (2005) Preserving privacy by de-identifying face images. IEEE Trans Knowl Data Eng 17(2):232–243
Gross R, Sweeney L, De la Torre F, Baker S (2006) Model-based face de-identification. In: 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06). IEEE, pp. 161–161
Gross R, Airoldi E, Malin B, Sweeney L (2005) Integrating utility into face de-identification. International Workshop on Privacy Enhancing Technologies. Springer, Berlin, pp 227–242
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(05):557–570
Cootes T, Edwards G, Taylor C (2001) Robust real-time periodic motion detection, analysis, and applications. IEEE Trans Patt Analy Mach Intell 23(6):681–685
Du L, Yi M, Blasch E, Ling H (2014) GARP-face: Balancing privacy protection and utility preservation in face de-identification. In: IEEE International Joint Conference on Biometrics. IEEE, pp. 1–8
Jourabloo A, Yin X, Liu X (2015) Attribute preserved face de-identification. In: 2015 International Conference on Biometrics (ICB). IEEE, pp. 278–285
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, vol 27, pp 2672–2680
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint at arXiv:1511.06434
Chen J, Konrad J, Ishwar P (2018) Vgan-based image representation learning for privacy-preserving facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1570–1579
Ren Z, Lee YJ, Ryoo MS (2018) Learning to anonymize faces for privacy preserving action detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 620–636
Li T, Lin L (2019) Anonymousnet: natural face de-identification with measurable privacy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Wang HP, Orekondy T, Fritz M (2021) Infoscrub: towards attribute privacy by targeted obfuscation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3281–3289
Hukkelås H, Mester R, Lindseth F (2019) Deepprivacy: a generative adversarial network for face anonymization. International symposium on visual computing. Springer, Cham, pp 565–578
Sun Q, Ma L, Oh SJ, Van Gool L, Schiele B, Fritz M (2018) Natural and effective obfuscation by head inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5050–5059
Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802
Wen Y, Song L, Liu B, Ding M, Xie R (2021) Identitydp: Differential private identification protection for face images. Preprint at arXiv:2103.01745
Cho D, Lee JH, Suh IH (2020) CLEANIR: controllable attribute-preserving natural identity remover. Appl Sci 10(3):1120
Zhan F, Zhu H, Lu S (2019) Spatial fusion gan for image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3653–3662
Kuang Z, Liu H, Yu J, Tian A, Wang L, Fan J, Babaguchi N (2021) Effective de-identification generative adversarial network for face anonymization. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3182–3191
Zhang X, Wang X, Shi C, Yan Z, Li X, Kong B, Mumtaz I (2022) De-gan: domain embedded gan for high quality face image inpainting. Pattern Recogn 124:108415
Li Y, Lu Q, Tao Q, Zhao X, Yu Y (2021) SF-GAN: face de-identification method without losing facial attribute information. IEEE Signal Process Lett 28:1345–1349
Mirza M, Osindero S (2014) Conditional generative adversarial nets. Preprint at arXiv:1411.1784
Murez Z, Kolouri S, Kriegman D, Ramamoorthi R, Kim K (2018) Image to image translation for domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4500–4509
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134
Demir U, Unal G (2018) Patch-based image inpainting with generative adversarial networks. Preprint at arXiv:1803.07422
Berthelot D, Schumm T, Metz L (2017) Began: Boundary equilibrium generative adversarial networks. Preprint at arXiv:1703.10717
Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232
Zhang Z, Luo P, Loy CC, Tang X (2014) Facial landmark detection by deep multi-task learning. European Conference on Computer Vision. Springer, Cham, pp 94–108
Nguyen TT, Nguyen QVH, Nguyen CM, Nguyen D, Nguyen DT, Nahavandi S (2019) Deep learning for deepfakes creation and detection: a survey. Preprint at arXiv:1909.11573.
Liu Y, Jourabloo A, Ren W, Liu X (2017) Dense face alignment. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1619–1628
King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758
Shu C, Ding X, Fang C (2011) Histogram of the oriented gradient for face recognition. Tsinghua Sci Technol 16(2):216–224
Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M (2012) A brief introduction to OpenCV. In: 2012 Proceedings of the 35th International Convention MIPRO. IEEE, pp. 1725–1730
Liu Z, Luo P, Wang X, Tang X (2018) Large-scale celebfaces attributes (celeba) dataset. Retrieved August 15(2018):11
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 234–241
Li J, Wang Y, Wang C, Tai Y, Qian J, Yang J, Huang F (2019) DSFD: dual shot face detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5060–5069
Acknowledgements
This paper was supported by Konkuk University Researcher Fund in 2021.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jang, Ss., Kim, Cj., Hwang, Sy. et al. L-GAN: landmark-based generative adversarial network for efficient face de-identification. J Supercomput 79, 7132–7159 (2023). https://doi.org/10.1007/s11227-022-04954-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-022-04954-x