  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.
