Training face verification models from generated face identity data
Dennis Conway,
Loic Simon,
Alexis Lechervy
et al.
Abstract:Machine learning tools are becoming increasingly powerful and widely used. Unfortunately membership attacks, which seek to uncover information from data sets used in machine learning, have the potential to limit data sharing. In this paper we consider an approach to increase the privacy protection of data sets, as applied to face recognition. Using an auxiliary face recognition model, we build on the StyleGAN generative adversarial network and feed it with latent codes combining two distinct sub-codes, one enc… Show more
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