  Machine Learning (ML) already has been integrated into all kinds of systems,
helping developers to solve problems with even higher accuracy than human
beings. However, when integrating ML models into a system, developers may
accidentally take not enough care of the outputs of ML models, mainly because
of their unfamiliarity with ML and AI, resulting in severe consequences like
hurting data owners' privacy. In this work, we focus on understanding the risks
of abusing embeddings of ML models, an important and popular way of using ML.
To show the consequence, we reveal several kinds of channels in which
embeddings are accidentally leaked. As our study shows, a face verification
system deployed by a government organization leaking only distance to authentic
users allows an attacker to exactly recover the embedding of the verifier's
pre-installed photo. Further, as we discovered, with the leaked embedding,
attackers can easily recover the input photo with negligible quality losses,
indicating devastating consequences to users' privacy. This is achieved with
our devised GAN-like structure model, which showed 93.65% success rate on
popular face embedding model under black box assumption.
