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In this work, we develop efficient disruptions of black-box image translationdeepfake generation systems. We are the first to demonstrate black-box deepfakegeneration disruption by presenting image translation formulations of attacksinitially proposed for classification models. Nevertheless, a naive adaptationof classification black-box attacks results in a prohibitive number of queriesfor image translation systems in the real-world. We present a frustratinglysimple yet highly effective algorithm Leaking Universal Perturbations (LUP),that significantly reduces the number of queries needed to attack an image. LUPconsists of two phases: (1) a short leaking phase where we attack the networkusing traditional black-box attacks and gather information on successfulattacks on a small dataset and (2) and an exploitation phase where we leveragesaid information to subsequently attack the network with improved efficiency.Our attack reduces the total number of queries necessary to attack GANimationand StarGAN by 30%.

Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks