With billions of personal images being generated from social media and
cameras of all sorts on a daily basis, security and privacy are unprecedentedly
challenged. Although extensive attempts have been made, existing face image
de-identification techniques are either insufficient in photo-reality or
incapable of balancing privacy and usability qualitatively and quantitatively,
i.e., they fail to answer counterfactual questions such as "is it private
now?", "how private is it?", and "can it be more private?" In this paper, we
propose a novel framework called AnonymousNet, with an effort to address these
issues systematically, balance usability, and enhance privacy in a natural and
measurable manner. The framework encompasses four stages: facial attribute
estimation, privacy-metric-oriented face obfuscation, directed natural image
synthesis, and adversarial perturbation. Not only do we achieve the
state-of-the-arts in terms of image quality and attribute prediction accuracy,
we are also the first to show that facial privacy is measurable, can be
factorized, and accordingly be manipulated in a photo-realistic fashion to
fulfill different requirements and application scenarios. Experiments further
demonstrate the effectiveness of the proposed framework.

本文提出了一种名为 AnonymousNet 的新框架，该框架包含四个阶段：面部属性估计、以隐私指标为导向的面部模糊处理、定向自然图像合成和对抗扰动。AnonymousNet 不仅在图像质量和属性预测准确性方面取得了最先进的成果，而且还首次展示了面部隐私的可衡量性和可分解性，以满足不同的要求和应用场景。实验进一步证明了该框架的有效性。