  The goal of unsupervised image-to-image translation is to map images from one
domain to another without the ground truth correspondence between the two
domains. State-of-art methods learn the correspondence using large numbers of
unpaired examples from both domains and are based on generative adversarial
networks. In order to preserve the semantics of the input image, the
adversarial objective is usually combined with a cycle-consistency loss that
penalizes incorrect reconstruction of the input image from the translated one.
However, if the target mapping is many-to-one, e.g. aerial photos to maps, such
a restriction forces the generator to hide information in low-amplitude
structured noise that is undetectable by human eye or by the discriminator. In
this paper, we show how such self-attacking behavior of unsupervised
translation methods affects their performance and provide two defense
techniques. We perform a quantitative evaluation of the proposed techniques and
show that making the translation model more robust to the self-adversarial
attack increases its generation quality and reconstruction reliability and
makes the model less sensitive to low-amplitude perturbations.
