  In this work, we develop efficient disruptions of black-box image translation
deepfake generation systems. We are the first to demonstrate black-box deepfake
generation disruption by presenting image translation formulations of attacks
initially proposed for classification models. Nevertheless, a naive adaptation
of classification black-box attacks results in a prohibitive number of queries
for image translation systems in the real-world. We present a frustratingly
simple yet highly effective algorithm Leaking Universal Perturbations (LUP),
that significantly reduces the number of queries needed to attack an image. LUP
consists of two phases: (1) a short leaking phase where we attack the network
using traditional black-box attacks and gather information on successful
attacks on a small dataset and (2) and an exploitation phase where we leverage
said information to subsequently attack the network with improved efficiency.
Our attack reduces the total number of queries necessary to attack GANimation
and StarGAN by 30%.
