Learnable Privacy-Preserving Anonymization for Pedestrian Images
- URL: http://arxiv.org/abs/2207.11677v1
- Date: Sun, 24 Jul 2022 07:04:16 GMT
- Title: Learnable Privacy-Preserving Anonymization for Pedestrian Images
- Authors: Junwu Zhang, Mang Ye, Yao Yang
- Abstract summary: This paper studies a novel privacy-preserving anonymization problem for pedestrian images.
It preserves personal identity information (PII) for authorized models and prevents PII from being recognized by third parties.
We propose a joint learning reversible anonymization framework, which can reversibly generate full-body anonymous images.
- Score: 27.178354411900127
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper studies a novel privacy-preserving anonymization problem for
pedestrian images, which preserves personal identity information (PII) for
authorized models and prevents PII from being recognized by third parties.
Conventional anonymization methods unavoidably cause semantic information loss,
leading to limited data utility. Besides, existing learned anonymization
techniques, while retaining various identity-irrelevant utilities, will change
the pedestrian identity, and thus are unsuitable for training robust
re-identification models. To explore the privacy-utility trade-off for
pedestrian images, we propose a joint learning reversible anonymization
framework, which can reversibly generate full-body anonymous images with little
performance drop on person re-identification tasks. The core idea is that we
adopt desensitized images generated by conventional methods as the initial
privacy-preserving supervision and jointly train an anonymization encoder with
a recovery decoder and an identity-invariant model. We further propose a
progressive training strategy to improve the performance, which iteratively
upgrades the initial anonymization supervision. Experiments further demonstrate
the effectiveness of our anonymized pedestrian images for privacy protection,
which boosts the re-identification performance while preserving privacy. Code
is available at \url{https://github.com/whuzjw/privacy-reid}.
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