Differentially Private Imaging via Latent Space Manipulation
- URL: http://arxiv.org/abs/2103.05472v1
- Date: Mon, 8 Mar 2021 17:32:08 GMT
- Title: Differentially Private Imaging via Latent Space Manipulation
- Authors: Tao Li, Chris Clifton
- Abstract summary: We present a novel approach for image obfuscation by manipulating latent spaces of an unconditionally trained generative model.
This is the first approach to image privacy that satisfies $varepsilon$-differential privacy emphfor the person.
- Score: 5.446368808660037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: There is growing concern about image privacy due to the popularity of social
media and photo devices, along with increasing use of face recognition systems.
However, established image de-identification techniques are either too subject
to re-identification, produce photos that are insufficiently realistic, or
both. To tackle this, we present a novel approach for image obfuscation by
manipulating latent spaces of an unconditionally trained generative model that
is able to synthesize photo-realistic facial images of high resolution. This
manipulation is done in a way that satisfies the formal privacy standard of
local differential privacy. To our knowledge, this is the first approach to
image privacy that satisfies $\varepsilon$-differential privacy \emph{for the
person.}
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