Abstract
With the identity information in face data more closely related to personal credit and property security, people pay increasing attention to the protection of face data privacy. In different tasks, people have various requirements for face de-identification (De-ID), so we propose a systematical solution compatible for these De-ID operations. Firstly, an attribute disentanglement and generative network is constructed to encode two parts of the face, which are the identity (facial features like mouth, nose and eyes) and expression (including expression, pose and illumination). Through face swapping, we can remove the original ID completely. Secondly, we add an adversarial vector mapping network to perturb the latent code of the face image, different from previous traditional adversarial methods. Through this, we can construct unrestricted adversarial image to decrease ID similarity recognized by model. Our method can flexibly de-identify the face data in various ways and the processed images have high image quality.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China 61772529, Beijing Natural Science Foundation under Grant 4192058, National Natural Science Foundation of China 61972395 and National Key Research and Development Program of China 2020AAA0140003.
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Yang, S., Wang, W., Cheng, Y., Dong, J. (2021). A Systematical Solution for Face De-identification. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_3
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DOI: https://doi.org/10.1007/978-3-030-86608-2_3
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