HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Learning Semi-Supervised Anonymized Representations by Mutual Information

Abstract : This paper addresses the problem of removing from a set of data (here images) a given private information, while still allowing other utilities on the processed data. This is obtained by training concurrently a GAN-like discriminator and an autoencoder. The optimization of the resulting structure involves a novel surrogate of the misclassification probability of the information to remove. Several examples are given, demonstrating that a good level of privacy can be obtained on images at the cost of the introduction of very small artifacts.
Complete list of metadata

Contributor : Pierre Duhamel Connect in order to contact the contributor
Submitted on : Wednesday, May 13, 2020 - 3:39:30 PM
Last modification on : Friday, April 22, 2022 - 10:48:04 AM


Files produced by the author(s)



C. Feutry, P. Piantanida, Pierre Duhamel. Learning Semi-Supervised Anonymized Representations by Mutual Information. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), May 2020, Barcelone, Spain. pp.3467-3471, ⟨10.1109/ICASSP40776.2020.9053379⟩. ⟨hal-02572273⟩



Record views


Files downloads