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Manipulable, reversible and diversified de-identification via face identity disentanglement

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Abstract

Face de-identification has always been a focal point in privacy-preserving research. Most existing de-identification methods focus only on the anonymization phase, neglecting the importance of deanonymization. Moreover, existing reversible de-identification methods are unsatisfactory in terms of diversity and manipulability. To overcome these limitations, we propose MRDD-FID, short for Manipulable, Reversible and Diversified De-identification via Face Identity Disentanglement. The framework realizes individual modification of identity representations while kee** non-identity representations unchanged through face identity disentanglement. Randomized passwords are used for identity modification, thus ensuring complete randomness in the modification process. By utilizing Generative Adversarial Networks (GANs) for training, we effectively enhance the realism and diversity of de-identification. Furthermore, MRDD-FID can precisely control the degree and direction of de-identification based on user-specified strengths and styles without compromising the image quality. Compared to existing methods, MRDD-FID offers higher flexibility and security. Extensive experiments demonstrate the effectiveness and superiority of our method in terms of anonymity, diversity, reversibility and manipulability.

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Data availability statement

Publicly available dataset was used in this study. The FFHQ and CelebA-HQ datasets can be found here: https://github.com/NVlabs/ffhq-dataset and https://github.com/tkarras/progressive_growing_of_gans.

Notes

  1. https://github.com/WuJie1010/Facial-Expression-Recognition.Pytorch

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Acknowledgements

The work was supported by the National Key R &D Program of China(Grant No. 2020YFB1805400), the National Natural Science Foundation of China (Grant No. 62072063) and the Project Supported by Graduate Student Research and Innovation Foundation of Chongqing, China (Grant No. CYB22063, CYB23045).

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  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Di **ao, **gdong **a, Min Li & Maolan Zhang

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  1. Di **ao
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  2. **gdong **a
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**ao, D., **a, J., Li, M. et al. Manipulable, reversible and diversified de-identification via face identity disentanglement. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18538-9

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