  As billions of personal data being shared through social media and network,
the data privacy and security have drawn an increasing attention. Several
attempts have been made to alleviate the leakage of identity information from
face photos, with the aid of, e.g., image obfuscation techniques. However, most
of the present results are either perceptually unsatisfactory or ineffective
against face recognition systems. Our goal in this paper is to develop a
technique that can encrypt the personal photos such that they can protect users
from unauthorized face recognition systems but remain visually identical to the
original version for human beings. To achieve this, we propose a targeted
identity-protection iterative method (TIP-IM) to generate adversarial identity
masks which can be overlaid on facial images, such that the original identities
can be concealed without sacrificing the visual quality. Extensive experiments
demonstrate that TIP-IM provides 95\%+ protection success rate against various
state-of-the-art face recognition models under practical test scenarios.
Besides, we also show the practical and effective applicability of our method
on a commercial API service.
