Abstract
In recent years, with the widespread use of face recognition authentication technology, the phenomenon that a large number of face photos are stored on a third-party server is very common, and the problem of face privacy protection is very prominent. This paper presents a face privacy protection algorithm based on deep convolutional neural network (CNN), FBSR (Face Block Scrambling Recognition). The algorithm uses Arnold random scrambling to segment key face images and key parts. The server directly verifies scrambled face images through CNN model. The FBSR algorithm enables the server to save the original face template throughout the entire process, thus it achieves effective scrambling protection of the original face image. Experimental results show that the proposed algorithm has a recognition rate of 97.62% after CNN recognition, which strengthens face privacy protection to some extent.
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Acknowledgement
This research is supported by National Key R&D Program of China (No. 2016YFB0800201), National Natural Science Foundation of China (No. 61772162), Zhejiang Natural Science Foundation of China (No. LY16F020016).
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Shen, W., Wu, Z., Zhang, J. (2018). A Face Privacy Protection Algorithm Based on Block Scrambling and Deep Learning. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_33
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DOI: https://doi.org/10.1007/978-3-030-00012-7_33
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