The tremendous progress in deep learning has enabled to extract soft-biometric attributes from faces, which raises privacy concerns over images collected for face recognition. Advances toward attribute privacy have been able to conceal multiple attributes while preserving identity information but suffer from limitations: they 1) only consider a few soft-biometric attributes and 2) fail to support reversibility for attribute privacy preservation. To break these limitations, we design a reversible privacy-preserving scheme for various face attributes, called reversible attribute privacy preservation (RAPP). RAPP benefits from two modules: 1) The attribute obfuscator introduces a stream cipher to determine that special attributes have to be concealed with the user-defined password, which also supports recovering original attributes. 2) The attribute adversarial network is proposed to generate perturbed images that conceal various attributes while retaining the utility of face verification. In addition, when a wrong password is provided, the returned image with wrong attribute classification results still keeps realistic, which confuses an attacker to know whether the recovery is correct. Extensive experiments demonstrate that RAPP enables to conceal various attributes and recover original images while facilitating face verification.

中文翻译：

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RAPP：各种人脸属性的可逆隐私保护


深度学习的巨大进步使得能够从人脸中提取软生物特征属性，这引发了对为人脸识别而收集的图像的隐私问题。属性隐私的进步已经能够在保留身份信息的同时隐藏多个属性，但受到限制：它们 1) 仅考虑一些软生物特征属性，2) 无法支持属性隐私保存的可逆性。为了打破这些限制，我们针对各种人脸属性设计了一种可逆的隐私保护方案，称为可逆属性隐私保护（RAPP）。 RAPP受益于两个模块：1）属性混淆器引入了流密码来确定必须使用用户定义的密码隐藏特殊属性，并且还支持恢复原始属性。 2）提出属性对抗网络来生成隐藏各种属性的扰动图像，同时保留人脸验证的效用。此外，当提供错误的密码时，返回的属性分类结果错误的图像仍然保持真实，这使得攻击者无法知道恢复是否正确。大量实验表明，RAPP 能够隐藏各种属性并恢复原始图像，同时促进人脸验证。