Face Anonymization by Manipulating Decoupled Identity Representation
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Privacy protection on human biological information has drawn increasing attention in recent years, among which face anonymization plays an importance role. We propose a novel approach which protects identity information of facial images from leakage with slightest modification. Specifically, we disentangle identity representation from other facial attributes leveraging the power of generative adversarial networks trained on a conditional multi-scale reconstruction (CMR) loss and an identity loss. We evaulate the disentangle ability of our model, and propose an effective method for identity anonymization, namely Anonymous Identity Generation (AIG), to reach the goal of face anonymization meanwhile maintaining similarity to the original image as much as possible. Quantitative and qualitative results demonstrate our method's superiority compared with the SOTAs on both visual quality and anonymization success rate.
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