Abstract
The growing use of deep learning methods in various applications has raised concerns about privacy, as these methods heavily rely on large-scale datasets. To address this issue, anonymization methods have been proposed that usually use face detection models to remove the original face and regenerate a new one. Although these methods have achieved promising performance, alternative face detection models significantly increase the training cost and inference time. To solve these problems, we propose an end-to-end mobile anonymization method with a joint reconstruction and deidentification (JRD) framework. We introduce deidentification loss into a generative adversarial network to anonymize the original image and add reconstruction loss to reconstruct the background of the original image. By balancing these two losses, we can anonymize the identity without changing the background and no longer rely on additional face detection models. In addition, to ensure the essential difference between the generated and original images, we generated a random code for each original image. Further, we verified the effectiveness and superiority of JRD on the CelebA dataset. The experimental results show that JRD not only outperforms existing one-to-one methods but is also superior to many-to-one methods.
Similar content being viewed by others
Data Availability
All data included in this study are available upon request by contact with the corresponding author.
References
Li X, Hou Z, Liang J, Chen C (2020) Human action recognition based on 3D body mask and depth spatial-temporal maps. Multimed Tools Appl 79:35761–35778
Cao Y, Liu C, Huang Z, Sheng Y, Ju Y (2021) Skeleton-based action recognition with temporal action graph and temporal adaptive graph convolution structure. Multimed Tools Appl 80(19):29139–29162
Pang Z, Guo J, Ma Z, Sun W, Xiao Y (2021) Median stable clustering and global distance classification for cross-domain person re-identification. IEEE Trans Circ Sys Video Tech 32(5):3164–3177
Sheng W, Li X (2021) Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network. Pattern Recog 114:107868
Pang Z, Zhao L, Liu Q, Wang C (2022) Camera invariant feature learning for unsupervised person re-identification. IEEE Trans Multimed
Zhao H, Wang J, Li C, Liu P, Yang R (2023) Fabric defect detection via feature fusion and total variation regularized low-rank decomposition. Multimed Tools Appl 1–25
Li C, Li H, Gao G, Liu Z, Liu P (2023) An accelerating convolutional neural networks via a 2D entropy based-adaptive filter search method for image recognition. Appl Soft Comput 142:110326
Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2022) A Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorization
Lee D, Park N (2021) Blockchain based privacy preserving multimedia intelligent video surveillance using secure Merkle tree. Multimed Tools Appl 80(26):34517–34534
Zhang Y, Jiang Y, Qi L, Bhuiyan MZA, Qian P (2021) Epilepsy diagnosis using multi-view & multi-medoid entropy-based clustering with privacy protection. ACM Trans Internet Technol 21(2):1–21
Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, ... Poor HV (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans Inf Forensic Secur 15:3454–3469
Desiato D (2018) A Methodology for GDPR Compliant Data Processing. In SEBD
Harvey A, LaPlace J (2019) Megapixels: origins, ethics, and privacy implications of publicly available face recognition image datasets. Megapixels 1(2):6
Rodríguez-Triana MJ, Prieto LP, Holzer A, Gillet D (2020) Instruction, student engagement, and learning outcomes: a case study using anonymous social media in a face-to-face classroom. IEEE Trans Learn Technol 13(4):718–733
Newton EM, Sweeney L, Malin B (2005) Preserving privacy by de-identifying face images. IEEE Trans Knowl Data Eng 17(2):232–243
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, p 3182–3191
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, ... Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144
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, p 4401–4410
Maximov M, Elezi I, Leal-Taixé L (2020) Ciagan: Conditional identity anonymization generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, p 5447–5456
Ma T, Li D, Wang W, Dong J (2021) CFA-Net: controllable face anonymization network with identity representation manipulation. arXiv preprint. arXiv:2105.11137
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
Wu Y, Yang F, Xu Y, Ling H (2019) Privacy-protective-GAN for privacy preserving face de-identification. J Comput Sci Technol 34(1):47–60
Kumar A, Kaur A, Kumar M (2019) Face detection techniques: a review. Artif Intell Rev 52(2):927–948
Yang S, Luo P, Loy CC, Tang X (2016) Wider face: A face detection benchmark. In Proceedings of the IEEE conference on computer vision and pattern recognition, p 5525–5533
Newton EM, Sweeney L, Malin B (2005) Preserving privacy by de-identifying face images. IEEE Trans Knowl Data Eng 17(2):232–243
Sweeney L (2002) k-anonymity: A model for protecting privacy. Int J Uncertain Fuzziness Knowl-Based Syst 10(05):557–570
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, p 1125–1134
Guo J, Pang Z, Bai M, Xie P, Chen Y (2021) Dual generative adversarial active learning. Appl Intell 51(8):5953–5964
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, p 2794–2802
Pang Z, Guo J, Sun W, Xiao Y, Yu M (2022) Cross-domain person re-identification by hybrid supervised and unsupervised learning. Appl Intell 52(3):2987–3001
Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, p 3722–3731
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Adv Neural Inf Process Sys 29
Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, p 3730–3738
Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Sys 30
Bińkowski M, Sutherland DJ, Arbel M, Gretton A (2018) Demystifying mmd gans. arXiv preprint. arXiv:1801.01401
King DE (2009) Dlib-ml: A machine learning toolkit. J Mach Learn Res 10:1755–1758
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, p 815–823
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI conference on artificial intelligence
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, p 2223–2232
Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, p 212–220
Shan S, Wenger E, Zhang J, Li H, Zheng H, Zhao BY (2020) Fawkes: Protecting privacy against unauthorized deep learning models. In 29th USENIX security symposium (USENIX Security 20), p 1589–1604
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflicts of interest
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
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
Kim, H., Zhao, L., Pang, Z. et al. Joint reconstruction and deidentification for mobile identity anonymization. Multimed Tools Appl 83, 38313–38328 (2024). https://doi.org/10.1007/s11042-023-17107-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-17107-w