  The unprecedented capture and application of face images raise increasing
concerns on anonymization to fight against privacy disclosure. Most existing
methods may suffer from the problem of excessive change of the
identity-independent information or insufficient identity protection. In this
paper, we present a new face anonymization approach by distracting the
intrinsic and extrinsic identity attentions. On the one hand, we anonymize the
identity information in the feature space by distracting the intrinsic identity
attention. On the other, we anonymize the visual clues (i.e. appearance and
geometry structure) by distracting the extrinsic identity attention. Our
approach allows for flexible and intuitive manipulation of face appearance and
geometry structure to produce diverse results, and it can also be used to
instruct users to perform personalized anonymization. We conduct extensive
experiments on multiple datasets and demonstrate that our approach outperforms
state-of-the-art methods.
